Form Submitted

Thanks for your enquiry, we'll be in touch soon.

We appreciate you taking the time to get in touch. Your message has been received and will be reviewed by a member of the Trace team within 1–2 business days.

While you wait, see what we're all about. Learn more about how Trace works, what sets us apart, and the kinds of problems we help clients solve.

Latest news & Insights

Explore insights from
the Trace team.

Explore our insights to see how we approach real-world complexity with clarity, structure, and measurable outcomes.

Technology

Warehouse and Order Management Technology: What Australian Businesses Need to Get Right

Mathew Tolley
March 2026
Australian businesses are investing heavily in warehouse and order management technology — but too many are getting the selection and implementation wrong. Here's what to look for, what to avoid, and how to make the investment count.

There's a familiar arc to most warehouse technology projects. The vendor demos look sharp. The business case stacks up. The implementation timeline feels manageable. And then reality hits — the system doesn't handle your specific pick logic without expensive customisation, the integration with your ERP turns out to be twice as complex as anyone estimated, and six months after go-live, half the warehouse team is still using workarounds because the platform doesn't match how the operation actually runs.

It's not a niche problem. Industry data suggests roughly 60% of warehouse management system (WMS) projects experience budget overruns or schedule delays. Over 44% of companies report delayed return on investment due to drawn-out integration timelines and undercooked training programs. The global WMS market is forecast to grow from around USD 3.4 billion in 2025 to nearly USD 16 billion by 2033, and order management systems (OMS) are on a similar trajectory. Australian businesses are clearly investing. The question is whether they're investing well.

This article is for supply chain leaders, operations managers, and technology teams at Australian businesses who are either evaluating warehouse and order management technology for the first time or looking to get more out of what they've already got. We'll cover what these systems actually do, where organisations typically go wrong, and how to approach selection and implementation in a way that delivers lasting value.

What Are We Actually Talking About?

It's worth getting the definitions straight, because the lines between warehouse management systems and order management systems have blurred significantly in recent years.

A warehouse management system is software that manages and optimises the physical operations inside a warehouse or distribution centre — receiving, putaway, inventory tracking, picking, packing, shipping, and returns. A good WMS gives you real-time visibility of stock across locations, directs your workforce through optimised task sequences, and integrates with hardware like barcode scanners, conveyor systems, and sortation equipment.

An order management system sits a layer above. It manages the lifecycle of a customer order from the moment it's placed through to fulfilment and post-purchase service. In a multi-channel environment — which is most Australian retailers and wholesalers at this point — the OMS is what decides which fulfilment location should handle each order, manages inventory visibility across channels, coordinates ship-from-store and click-and-collect, and handles the increasingly complex world of returns and exchanges.

Many modern platforms blend both capabilities. Manhattan Associates, for instance, has pushed hard on unifying WMS and OMS into a single platform. At the other end of the market, you've got lightweight cloud-based solutions like CartonCloud or Cin7 that combine basic warehouse management with order and inventory management in a single subscription. The right answer for your business depends entirely on how complex your operation is — and that's where most organisations go wrong.

It's also worth noting that the distinction between WMS and OMS matters most for businesses operating across multiple fulfilment channels. If you're running a single warehouse shipping B2B orders, a solid WMS with basic order management may be all you need. But if you're a retailer fulfilling from DCs, stores, and third-party partners — managing click-and-collect, ship-from-store, marketplace orders, and returns across all of them — the OMS layer becomes critical. It's the orchestration engine that decides where each order gets fulfilled, based on inventory availability, proximity to the customer, fulfilment cost, and capacity. Without it, you're making those decisions manually or not making them at all.

Why This Matters More in Australia

Australia's geography creates supply chain challenges that don't exist in many other markets. Vast distances between major population centres, high domestic freight costs, and a relatively small consumer base spread across an enormous landmass all mean that warehouse and distribution efficiency matters disproportionately here.

Add to that the rapid growth of online retail. Australian e-commerce has grown consistently over the past five years, and consumer expectations around delivery speed, real-time tracking, and seamless returns have sharpened dramatically. For retailers operating across in-store and online channels, the operational complexity of fulfilling orders profitably from multiple locations — DCs, stores, third-party logistics providers — is a fundamentally different challenge than it was even five years ago.

For FMCG and manufacturing businesses, the pressures are different but no less acute. Higher SKU counts, shorter product lifecycles, and the shift toward more frequent, smaller deliveries are all stretching warehouse operations that were designed for a different era. Many Australian manufacturers are still running warehouse processes on spreadsheets, or on basic ERP modules that were never designed to handle the picking complexity, labour management, or automation interfaces that modern operations require.

The technology exists to solve these problems. The challenge is picking the right technology and implementing it properly.

Where Organisations Get It Wrong

Having worked across numerous warehouse technology projects, there are patterns that come up again and again. They're worth understanding because they're almost always avoidable.

Starting with the Technology, Not the Operation

The most consequential mistake happens before a single vendor has been shortlisted. Someone in the leadership team sees a compelling demo at a conference, or a vendor approaches the business with an attractive proposition, and the conversation jumps straight to features, modules, and pricing. What gets skipped is the foundational work of understanding what the operation actually needs.

A proper selection process starts with a thorough assessment of the current operation — how goods flow through the facility, where the bottlenecks sit, what workarounds the team relies on, where errors originate, and what the operation needs to look like in three to five years. This work produces the functional requirements specification that drives the entire selection. Without it, you're evaluating technology in a vacuum.

This is where having an independent advisor makes a significant difference. Vendors will always present their platform in the best light, and internal teams often lack the cross-industry perspective to know what "good" looks like. A firm like Trace Consultants that works across multiple sectors and has seen dozens of WMS and OMS implementations can bring a level of objectivity that's hard to replicate internally.

The Tier Mismatch

WMS and OMS platforms exist across a spectrum. At one end, you've got enterprise-grade platforms from the likes of Manhattan Associates, Blue Yonder, and SAP — capable of managing complex, multi-site, multi-channel operations with advanced optimisation, labour management, and automation interfaces. At the other end, you've got cloud-native solutions designed for simpler operations that can be up and running in weeks rather than months.

One of the most common selection errors is a tier mismatch. Over-specifying — selecting a Tier 1 enterprise WMS for a single-site operation that doesn't need or can't absorb that complexity — wastes money and overwhelms the team. Under-specifying — selecting a basic system for a complex multi-site distribution network — forces expensive customisation, workarounds, and eventually re-implementation.

Getting the tier right requires an honest, data-driven assessment of operational complexity. A business running straightforward pick-pack-ship out of a single DC has fundamentally different requirements from one managing mixed B2B and B2C fulfilment, cross-docking, value-added services, and automation interfaces across multiple sites.

Underestimating Integration Complexity

No WMS or OMS operates in isolation. It sits within an ecosystem of enterprise systems — ERP, e-commerce platforms, carrier management, point-of-sale, and potentially warehouse automation control systems. The quality of these integrations is what determines whether the technology delivers its promised value or becomes a source of ongoing operational friction.

Yet integration complexity is consistently underestimated. Organisations evaluate platforms based on standalone capabilities and assume the integration will be straightforward. It rarely is. Legacy ERP systems may lack the APIs or data standards needed for real-time integration. Data formats may be inconsistent across systems. Business rules embedded in existing platforms may conflict with the logic in the new WMS or OMS.

A proper selection process evaluates integration requirements with the same rigour as functional requirements. It maps the specific data flows between systems, the frequency and latency requirements, and the technical approach — whether through middleware, direct API connections, or file-based exchange. This work isn't glamorous, but it's where projects succeed or fail.

Treating It as a Technology Project Rather Than an Operational Transformation

This one is worth dwelling on because it's the root cause of most implementation failures. A WMS or OMS implementation isn't primarily a technology project — it's an operational transformation that happens to involve technology. The system will change how every person in the warehouse does their job. It will change processes, responsibilities, performance measurement, and daily workflows.

Organisations that treat implementation as a software deployment project — hand it to IT, configure the system, switch it on — consistently underperform. The ones that succeed invest heavily in change management, workforce training, process redesign, and the organisational effort required to make the technology stick.

What Good Looks Like

So if those are the common pitfalls, what does a well-executed warehouse and order management technology project look like?

Requirements-Led Selection

It starts with requirements, not vendors. A thorough current-state assessment of warehouse operations, order profiles, inventory characteristics, integration landscape, and future-state ambitions. This produces a structured requirements document that distinguishes between non-negotiable functional requirements, desirable capabilities, and integration needs. Only then do you go to market.

The selection process itself should be structured and transparent — a long list based on market scan, a shortlist based on requirements fit, detailed demonstrations scripted against your actual operational scenarios (not the vendor's standard demo), reference checks with comparable operations, and a total cost of ownership analysis that accounts for implementation, integration, training, and ongoing support.

Phased Implementation with Operational Focus

Rather than a big-bang go-live, best practice is a phased approach that sequences functionality based on operational priority and organisational readiness. Start with core inventory management and basic warehouse processes, stabilise, then layer on more advanced capabilities like labour management, wave planning optimisation, or automation interfaces.

Each phase should include thorough process redesign — not just configuring the system to replicate existing processes, but rethinking workflows to take advantage of what the technology enables. This is where experienced supply chain consultants add real value. They've seen enough implementations to know which process changes deliver the biggest gains, and which ones will trip up the warehouse team.

Workforce Readiness

The technology is only as good as the people using it. Training needs to go well beyond "how to use the screen." Warehouse teams need to understand why processes are changing, how the new system supports their work, and what the performance expectations look like. Supervisors and team leaders need deeper training on exception handling, system configuration, and performance analytics.

This ties directly into workforce planning — understanding how the new system will change labour requirements, shift patterns, and skill profiles. A WMS that introduces wave-based picking or directed putaway changes the nature of warehouse work. That needs to be planned for, not discovered on day one.

Measuring What Matters

Post-implementation, the organisations that extract the most value from their WMS and OMS investment are the ones that actively manage performance against a clear set of operational KPIs — order accuracy, pick rates, inventory accuracy, order-to-ship cycle time, and cost per order. The technology provides the data. The discipline of actually using that data to drive continuous improvement is an organisational capability, not a system feature.

This is an area where many Australian businesses leave significant value on the table. They invest heavily in the technology, stabilise the operation after go-live, and then move on to the next priority. But the real payoff comes from ongoing optimisation — refining pick paths as order profiles change, adjusting slotting strategies based on seasonal demand patterns, using labour management data to improve rostering and productivity, and leveraging order data to refine fulfilment allocation rules. A WMS or OMS that's been live for two years should be performing materially better than it did at go-live. If it isn't, that's a process and discipline issue, not a technology issue.

This is also where robust planning and operations processes make a tangible difference. If your demand planning is poor, your warehouse will always be reacting — expediting orders, dealing with stockouts, and absorbing the cost of unplanned activity. The technology can't compensate for bad inputs.

The Emerging Technology Landscape

It's worth touching on where the technology is heading, because decisions made today need to account for what's coming.

Cloud-based WMS adoption is accelerating. The flexibility, lower upfront costs, and faster deployment timelines of cloud platforms are making them the default choice for a growing number of Australian businesses. The global cloud WMS segment is growing at over 22% annually, and for good reason — cloud platforms are easier to update, scale, and integrate than traditional on-premise deployments.

Artificial intelligence and machine learning are starting to move from marketing buzzwords into practical warehouse applications. AI-powered demand forecasting that adjusts inventory positioning ahead of seasonal peaks. Machine learning algorithms that optimise picking routes based on historical order patterns. Predictive maintenance that flags equipment issues before they cause downtime. These capabilities are increasingly available as standard features in the leading WMS and OMS platforms, not as bolt-on modules that require separate implementation.

Warehouse automation — from goods-to-person robotics to automated storage and retrieval systems — is growing rapidly in Australia, with major retailers and logistics providers investing heavily. Woolworths' automated case picking distribution centres are a good example of what's coming — purpose-built facilities using shuttle technology and automated guided vehicles to process tens of thousands of products daily with near-perfect picking accuracy. The critical point for most businesses isn't whether to automate, but how to ensure their WMS can interface with and orchestrate the automation they're likely to adopt over the next five to ten years. Selecting a WMS that can't grow into automation is an expensive mistake.

For Australian businesses specifically, local carrier and integration ecosystem support is a practical consideration that's easy to overlook. Your WMS or OMS needs to integrate with Australia Post, StarTrack, Toll, Aramex, and multi-carrier platforms like Shippit or Starshipit. It needs to work with Xero or MYOB for accounting. And if you're in retail, it needs clean connections to Shopify, BigCommerce, Amazon AU, eBay, Catch, and whatever marketplace comes next. These aren't edge cases — they're table stakes for operating in the Australian market.

How Trace Consultants Can Help

Trace Consultants is an Australian supply chain consultancy that works with organisations across retail, FMCG and manufacturing, government and defence, and property, hospitality and services to get warehouse and order management technology right.

Our approach is vendor-independent and requirements-led. Our job is to make sure the technology you select is genuinely the best fit for your operation — not the best fit for the vendor's sales target.

Here's what we typically help with:

Operational assessment and requirements definition. Before any technology conversation, we map the current state of your warehouse and distribution operations — material flows, process bottlenecks, labour utilisation, inventory accuracy, and order profiles. We document functional and integration requirements, assess the gap between current state and target state, and build the business case for investment.

Technology selection. We run structured selection processes — market scans, vendor long-listing and shortlisting, scripted demonstrations against your operational scenarios, reference checks, and total cost of ownership analysis. We help you evaluate not just the technology, but the vendor's implementation capability, local support model, and product roadmap.

Implementation support. We work alongside your team and the vendor's implementation team to ensure the project stays focused on operational outcomes, not just system configuration. This includes process redesign, integration design and testing, data migration planning, training design, and go-live support.

Network and distribution strategy. Often, the right time to evaluate warehouse technology is alongside a broader review of your distribution network. Where should your DCs be? How many do you need? What's the right balance between centralised and decentralised fulfilment? These strategic questions shape the technology requirements, and we help clients work through both together.

Planning and operations improvement. Technology is one part of the equation. We also help clients improve the planning and operational processes that sit around the technology — demand planning, inventory management, procurement, and supplier management. A brilliant WMS bolted onto poor planning processes will underperform. We help clients address both.

If you're evaluating warehouse or order management technology — or if you've already implemented a system and aren't getting the value you expected — we'd welcome a conversation. You can reach us here.

The Bottom Line

Warehouse and order management technology, properly selected and implemented, transforms operations. It drives measurable improvements in productivity, accuracy, cost performance, and customer experience. The Australian WMS market alone is forecast to grow at over 20% annually through the end of the decade — this isn't a passing trend, it's a structural shift in how supply chains operate.

But the technology is only as good as the process that selects it and the organisation that implements it. Start with the operation, not the vendor. Be honest about your complexity. Invest in integration and change management with the same rigour you invest in the software licence. And measure what matters after go-live.

Get those things right, and the returns will follow.

Trace Consultants is an Australian supply chain and procurement consultancy specialising in strategy, operations, and technology. For more insights, visit our insights page or explore our technology advisory services.

BOH Logistics

Back-of-House (BOH) Design in Stadiums

Emma Woodberry
March 2026
Stadium success isn't just about sightlines and seating bowls. The back-of-house — docks, service corridors, waste rooms, goods lifts and storage areas — determines whether a venue can actually deliver on its promise to fans, operators and the community. Here's why BOH design deserves far more attention than it typically gets.

Walk into any major Australian stadium on game day and you'll notice the things you're supposed to notice. The sweep of the seating bowl. The quality of the turf. The screens, the lighting, the atmosphere. What you won't notice — if everything is working properly — is the back-of-house. The loading docks where food and beverage deliveries arrived at 5am. The service corridors where catering teams are wheeling stock to concourse outlets. The waste holding rooms where compactors are processing the remnants of 40,000 meals. The goods lifts quietly moving equipment between levels.

You won't notice any of it, and that's entirely the point. When back-of-house logistics work well, they're invisible to the fan. When they don't, the consequences are anything but invisible: long queues at food outlets because stock couldn't be replenished fast enough, overflowing bins in concourse areas because waste collection couldn't keep pace, delayed bump-ins for concerts because the dock schedule clashed with catering deliveries, and rising operational costs that eventually land on the ticket price.

The uncomfortable truth about stadium development in Australia is that back-of-house design is routinely treated as an afterthought. Architects and developers — understandably — focus their energy on the fan experience, the urban design interface, the commercial suites and the broadcast infrastructure. The BOH gets whatever space is left over. And then, for the next thirty or forty years, every operator who works in that building pays the price for decisions that were made in the design phase by people who never had to run a dock at 4am on a State of Origin morning.

This article makes the case that back-of-house design deserves to be treated as a first-order design consideration in stadium and event venue development — not as a logistics problem to be solved after the architect has locked in the floorplate, but as a fundamental input to the design process from concept stage onwards.

What Do We Actually Mean by Back-of-House?

Before going further, it's worth being specific about scope. In the context of a sporting or event stadium, back-of-house logistics encompasses every physical system and operational process that supports the delivery of goods, services and waste management behind the scenes. That includes dock and loading infrastructure (how goods physically enter and exit the building), service corridors and goods lifts (how goods move through the building once they're inside), storage and holding areas (where goods are staged before they reach their destination), waste infrastructure (how waste is collected, consolidated, compacted and removed), food and beverage supply chain (the logistics chain from supplier delivery through to point of sale), and the operating model that governs scheduling, access control and contractor coordination across all of these functions.

In a major stadium, these aren't peripheral concerns. A venue hosting 50,000 patrons for an AFL final or a Bledisloe Cup test match might process 15 to 20 tonnes of food and beverage product in a single event cycle. It might generate five to eight tonnes of waste. It might require 80 to 120 individual dock movements across a 24-hour bump-in, event and bump-out window. Every one of those movements needs physical infrastructure to support it — and if that infrastructure wasn't designed properly, the operation bends around the constraints in ways that cost money, create safety risks and degrade the experience.

Why BOH Design Fails: The Structural Problem

The reason back-of-house design so often falls short in stadium projects isn't that architects or developers don't care. It's structural. The problem sits in how these projects are typically delivered.

Stadium developments are complex, multi-stakeholder endeavours. The client might be a state government, a local council, a sporting code or a private developer. The design team involves architects, structural engineers, services engineers, landscape architects, urban designers and a host of specialist consultants. The commercial model involves naming rights partners, premium hospitality operators, food and beverage concessionaires and broadcast rights holders. Each of these stakeholders has legitimate priorities that compete for space, budget and design attention.

In this environment, the operational logistics of the building — how goods actually move through it, how waste is managed, how the dock functions on a busy day — rarely has a dedicated voice at the table during the early design phases. By the time an operations consultant or facilities manager is engaged, the floorplate is locked, the dock location is fixed, the service corridor widths are set and the goods lift provisions are determined. The operational team inherits a building and has to make it work.

This pattern repeats across Australian stadium and major venue projects. It's not unique to any single developer or architect. It's a systemic gap in how the industry approaches design, and it results in a predictable set of problems that show up in the first year of operations and persist for the life of the asset.

The Five Most Common BOH Design Failures in Stadiums

Having worked across major venue and precinct developments in Australia, the same design failures appear with remarkable consistency. They're worth examining in detail because each one is avoidable if addressed during the design phase — and extremely expensive to fix after construction.

Undersized Docks and Inadequate Scheduling Infrastructure

The loading dock is the single most constrained piece of infrastructure in a stadium's back-of-house. Every inbound delivery, every outbound waste collection, every equipment bump-in and bump-out passes through it. Yet docks are consistently undersized relative to peak-day demand.

The root cause is usually a design assumption based on average daily movements rather than peak-day movements. A stadium might process 30 dock movements on a quiet training day and 120 on a sold-out Saturday night with a post-match concert bump-in overlapping the catering breakdown. If the dock was designed for the average, it fails catastrophically on the peak.

Effective dock design requires detailed modelling of peak-day scenarios — not just the number of movements, but the dwell time of each vehicle, the sequencing constraints (refrigerated deliveries before dry goods, waste collection after catering breakdown), and the physical turning circles and manoeuvring requirements for the vehicle types that will actually use the dock. This is strategy and network design work applied to a single building, and it requires the same analytical rigour.

Service Corridors That Can't Handle Concurrent Flows

Service corridors in stadiums serve multiple functions simultaneously. They're goods movement routes for catering and retail stock. They're waste collection routes for bin runners. They're pedestrian routes for staff, contractors and sometimes athletes. And on event days, all of these functions peak at the same time.

The design failure here is usually one of width and intersection management. Corridors designed at 2.4 metres might be adequate for a single pallet jack moving in one direction. They're completely inadequate when a pallet jack, a waste bin runner and a group of catering staff need to pass each other at an intersection — which, on a game day, happens constantly.

The consequence is congestion, delays and safety incidents. In one Australian venue we assessed, service corridor congestion during peak pre-event stocking added an estimated 35% to the time required to replenish concourse food outlets, directly contributing to longer fan queues at first service.

Waste Rooms That Don't Match the Waste Profile

Waste management in stadiums is more complex than most designers appreciate. A single event generates multiple waste streams — general waste, mixed recyclables, organic waste, cooking oil, cardboard, glass (in premium areas) and sometimes biosecurity waste (for international events with catering from controlled food sources). Each stream has different containment, handling and collection requirements.

The common design failure is providing a single, undersized waste holding room and expecting the operator to sort it out. The result is mixed waste streams (destroying diversion rates and increasing disposal costs), overflow during peak events (creating hygiene and compliance risks) and inefficient collection (because the waste contractor can't access the right streams at the right time).

Effective waste infrastructure design starts with a waste generation model — how much of each stream, at what rate, across the event cycle — and works backwards to determine the holding capacity, compaction equipment, bin configuration and collection scheduling required. This is the kind of supply chain sustainability thinking that needs to be embedded in the design phase, not bolted on during operations.

Goods Lift Provision That Creates Bottlenecks

Multi-level stadiums rely on goods lifts to move stock between the dock level and the concourse, premium and corporate levels. The number, size, speed and location of goods lifts determines the throughput capacity of the entire vertical supply chain.

Under-provision of goods lifts is one of the most expensive BOH design failures because it's almost impossible to retrofit additional lift shafts after construction. The consequence is a permanent constraint on how quickly the building can be stocked, restocked and cleared — a constraint that the operator works around every single event day for the life of the building.

Goods lift provision needs to be modelled against peak-day vertical movement requirements, accounting for the mix of pallet, cage and bin movements, the cycle time of each lift (including loading and unloading), and the scheduling conflicts between competing users. A lift shared between food deliveries, waste collection and equipment moves will always be a bottleneck unless the capacity has been sized for concurrent demand.

No Separation Between Goods, Waste and Pedestrian Flows

The final common failure is the absence of clear flow separation between goods movements, waste movements and pedestrian movements within the BOH. When these flows share the same corridors, lifts and staging areas, the result is congestion, cross-contamination risk, safety incidents and scheduling complexity that drives up operating costs.

Flow separation doesn't necessarily mean duplicating every corridor. It means designing the circulation network so that goods, waste and people can move through the building without constantly crossing each other's paths — through dedicated routes where volumes justify it, time-based scheduling where they don't, and intersection design that manages conflict points safely.

The Economic Case for Getting BOH Right

The argument for investing in BOH design isn't just operational — it's financial. Poor back-of-house design creates costs that compound over the life of the asset, and the numbers are significant.

Labour is the largest operating cost in stadium logistics. When BOH infrastructure creates inefficiencies — longer replenishment times, manual handling workarounds, double-handling of waste, congestion-related delays — the cost shows up in the labour line. Across a 50-event season, even modest per-event inefficiencies compound into substantial annual costs.

Waste disposal is the second lever. A stadium that can't effectively segregate waste streams at source will pay significantly more per tonne for disposal than one with properly designed upstream sortation and compaction infrastructure. The difference in diversion rates between a well-designed and poorly designed waste system can be 30 percentage points or more — and with landfill levies rising across Australian states, that gap translates directly to the bottom line.

Then there's the revenue impact. If BOH constraints limit the speed at which concourse outlets can be restocked, the venue sells less food and beverage per patron. In a 50,000-seat stadium, even a small per-capita revenue uplift from faster service translates to material annual revenue.

And finally, there's the capital cost of retrofitting. Widening a service corridor, adding a goods lift shaft or reconfiguring a dock after construction is an order-of-magnitude more expensive than getting it right in the design phase. The cheapest time to fix a BOH problem is before the concrete is poured.

Sustainability and Compliance: The Growing Regulatory Dimension

Beyond economics, there's an increasingly important regulatory dimension to BOH design in Australian stadiums. State and local government sustainability requirements are tightening. Venues are being asked to demonstrate waste diversion performance, report on Scope 1 and 2 emissions, and align with Net Zero and ESG frameworks that require verifiable data on waste, energy and resource consumption.

A stadium with well-designed waste infrastructure — upstream sortation stations on every concourse level, dedicated organic waste processing, compaction equipment that reduces collection frequency and associated vehicle emissions — is structurally better positioned to meet these obligations than one that relies on back-end sorting at a materials recovery facility.

Biosecurity is another consideration that's growing in importance, particularly for venues that host international events. Quarantine and controlled waste requirements for food service at international sporting events require dedicated containment, chain-of-custody documentation and approved disposal pathways. If the physical infrastructure doesn't support these requirements, compliance becomes a manual, expensive workaround rather than an embedded system.

For stadium owners and operators navigating these obligations, having a clear resilience and risk management framework that extends to waste and logistics operations is no longer optional — it's a condition of operating in an increasingly regulated environment.

What Good Looks Like: Principles for BOH Design in Stadiums

Rather than prescribing a single solution (every venue is different), here are the principles that distinguish well-designed stadium BOH from the status quo.

The first principle is to design for peak, not average. Every piece of BOH infrastructure should be sized against the worst-case realistic scenario — the sold-out Saturday night final with a concert bump-in starting two hours after the final siren. If it works on that day, it works every day.

The second is to model before you build. Dock movements, corridor flows, lift utilisation and waste generation should be modelled quantitatively before the design is locked. This isn't guesswork — it's planning and operations analysis applied to building design.

Third, separate the streams. Goods, waste and people should have distinct circulation paths wherever volumes justify it. Where full separation isn't feasible, design the intersections to manage conflict safely and schedule the shared infrastructure to minimise concurrent demand.

Fourth, embed the operating model in the design. The building should be designed around an explicit operating model — who manages what, how scheduling works, how tenants interact with shared BOH infrastructure, how contractor performance is measured. The operating model shouldn't be invented after the building opens; it should be a design input.

Fifth, future-proof the infrastructure. Tenant mixes change. Event types evolve. Waste regulations tighten. The BOH should have enough flexibility — in space, services and access — to accommodate change without requiring structural modification.

And finally, bring logistics expertise into the design process early. Not after the schematic design is locked. Not during the detailed design phase when the floorplate is fixed. During the concept and masterplan phase, when the fundamental decisions about dock location, corridor width, lift provision and waste room sizing are being made. This is exactly the kind of engagement that a specialist BOH logistics consultancy brings to the table.

The Operator's Perspective: Living with Design Decisions

It's worth pausing to consider the perspective of the people who actually have to run these buildings. Stadium operations teams, catering managers, waste contractors and facilities managers don't get to redesign the dock. They work with what they're given.

In venues where the BOH was well-designed, operators describe their work in terms of systems and schedules. Deliveries arrive in designated windows. Stock moves through the building on predictable routes. Waste is collected on a rhythm that matches generation rates. Problems are exceptions, not the default state.

In venues where the BOH was poorly designed, operators describe their work in terms of workarounds and compromises. Deliveries stack up because the dock can't process them fast enough. Stock sits in corridors because there isn't enough staging space. Waste overflows because the holding rooms are too small. The team spends its energy managing constraints rather than managing the operation.

The difference between these two experiences is almost entirely determined by decisions made during the design phase — decisions that the operator had no input into. This is the core argument for integrating operational logistics expertise into the design process: the people who understand how a building operates should have a seat at the table when the building is being designed.

How Trace Consultants Can Help

Trace Consultants is an Australian supply chain and logistics consultancy with deep experience in back-of-house design for complex venues, including stadiums, integrated resorts, airports and large-scale mixed-use developments.

Our BOH Logistics practice works with developers, architects, project managers and operators to embed goods movement and waste management strategy into the masterplan and design phases of stadium and venue projects. We bring the operational lens that's typically missing from the design table — translating real-world logistics requirements into design specifications that the architect and engineer can work with.

Our approach typically spans several workstreams that align with the lifecycle of a stadium project.

During concept and masterplan, we develop goods and waste movement strategies, model peak-day dock demand, size waste infrastructure and define the circulation principles that shape the BOH floorplate. This is the highest-leverage phase — the point where a relatively small investment in logistics analysis prevents significant operational costs downstream.

During design development, we review and validate detailed BOH design against operational requirements. We assess corridor widths, goods lift provision, waste room sizing, dock geometry and flow separation. We identify design risks and work with the architect to resolve them before they're built into the structure.

During operational readiness, we develop the operating model, scheduling frameworks and contractor specifications that translate the physical infrastructure into a functioning operation. This includes procurement support for waste and logistics service providers, as well as workforce planning for the BOH operations team.

And during optimisation of existing venues, we assess current BOH performance, identify constraints and inefficiencies, and develop improvement programs that extract more value from the existing infrastructure — often delivering material cost savings without capital expenditure.

We work across the property, hospitality and services sector, and our team understands the specific challenges of venues that operate in high-volume, time-compressed, security-controlled environments. If you're involved in a stadium or major venue project and want to ensure the back-of-house gets the attention it deserves, get in touch with our team.

The Bottom Line

Stadium and event venue development in Australia is entering a period of significant investment. New builds, major refurbishments and precinct developments are underway or planned across multiple states. Each of these projects presents an opportunity to get the back-of-house right — or to repeat the mistakes that have burdened operators and inflated costs in existing venues for decades.

The back-of-house isn't glamorous. It doesn't feature in the artist's impression or the minister's media release. But it's the engine room of the venue — the infrastructure that determines whether the building can actually deliver on its commercial, operational and sustainability promises.

Getting it right requires bringing logistics thinking into the design process early, modelling the operation before locking the design, and treating BOH infrastructure as a strategic investment rather than a residual allocation. The cost of getting it wrong is measured in decades of operational inefficiency, missed sustainability targets and retrofit expenditure that dwarfs the original saving.

The best time to fix a back-of-house problem is before the building exists. The second-best time is now.

For more insights on supply chain strategy, logistics infrastructure and operational design, visit the Trace Consultants Insights page.

Technology

WMS and TMS Selection: Why Most Implementations Fail and How to Get Yours Right

Tim Fagan
March 2026
Approximately 60% of WMS projects experience budget overruns or schedule delays. Over 44% of companies report delayed ROI due to prolonged integration timelines and training challenges. Yet the global WMS market is forecast to grow from US$4.7 billion in 2024 to US$23.6 billion by 2033, and the TMS market from US$10.3 billion to over US$36 billion in the same period. Businesses are investing heavily in logistics technology — but too many are getting the selection and implementation wrong.

There's a particular kind of optimism that takes hold when an organisation decides to invest in a new warehouse management system or transport management system. The vendor demonstrations look impressive. The business case projects compelling returns. The implementation timeline seems manageable. And then reality arrives.

The warehouse team discovers the system doesn't handle their specific pick-and-pack processes without expensive customisation. The transport planners find the routing algorithms don't account for the access restrictions at half their delivery sites. The integration with the ERP turns out to be far more complex than anyone estimated. The go-live date slips. The budget expands. And six months after implementation, half the warehouse staff are still using workarounds because the system doesn't match how the operation actually runs.

This isn't a rare outcome. Industry data suggests that roughly 60% of WMS projects experience budget overruns or schedule delays, often because organisations underestimate the complexity of change management and system integration. Over 44% of companies report delayed return on investment due to prolonged integration timelines and training requirements. Average WMS implementation costs range from US$70,000 to US$500,000 depending on features and scale — before you account for the overruns.

The pattern for TMS implementations is similar. Organisations invest in sophisticated route optimisation and freight management capabilities, only to discover that the technology works brilliantly in the demonstration environment and poorly in the messy reality of their actual transport network, carrier relationships, and operational constraints.

None of this means the technology isn't valuable. WMS and TMS platforms, properly selected and implemented, transform warehouse and transport operations. They drive measurable improvements in productivity, accuracy, visibility, and cost performance. The problem isn't the technology itself — it's how organisations go about choosing and deploying it.

The selection problem: why organisations pick the wrong system

The most consequential mistake in any WMS or TMS project happens before a single line of code is configured — it happens during selection. And the root cause is almost always the same: the organisation starts with the technology rather than starting with the operation.

Vendor-led versus requirements-led selection

Most WMS and TMS selection processes begin when a vendor approaches the business, or when someone in the leadership team sees a compelling demonstration at a conference. The conversation immediately shifts to features, modules, and pricing tiers. What gets skipped is the hard, unglamorous work of understanding what the operation actually needs.

A requirements-led selection process starts differently. It starts with a thorough assessment of current warehouse and distribution operations — how goods flow through the facility, where the bottlenecks are, what workarounds the team relies on, where errors originate, and what the operation needs to look like in three to five years. It documents the non-negotiable functional requirements (the things the system absolutely must do on day one), the desirable capabilities (the things that would improve operations but aren't essential at launch), and the integration requirements (how the WMS or TMS needs to communicate with ERP, order management, carrier systems, and hardware like scanners, sorters, and printers).

This requirements work is what separates organisations that select a system well-matched to their operation from organisations that spend eighteen months and several hundred thousand dollars implementing a platform that was never the right fit.

The tier mismatch

WMS and TMS platforms exist across a spectrum from lightweight, cloud-based solutions designed for simpler operations through to enterprise-grade platforms capable of managing complex, multi-site, multi-channel operations with advanced optimisation, labour management, and automation interfaces.

One of the most common selection errors is a tier mismatch — either over-specifying (selecting a complex, expensive Tier 1 system for an operation that doesn't need or can't absorb that level of sophistication) or under-specifying (selecting a basic system that can't handle the operational complexity, forcing expensive customisation or an eventual re-implementation).

Getting the tier right requires an honest assessment of operational complexity — current and projected. A single-site operation running straightforward pick-pack-ship processes has fundamentally different requirements from a multi-site distribution network managing mixed B2B and B2C fulfilment, cross-docking, value-added services, and automation interfaces. The right system for one is the wrong system for the other.

Ignoring the integration reality

No WMS or TMS operates in isolation. It sits within an ecosystem of enterprise systems — ERP, order management, e-commerce platforms, carrier systems, yard management, and potentially warehouse automation control systems. The quality of these integrations determines whether the technology delivers its promised value or becomes a source of ongoing operational friction.

Yet integration complexity is consistently underestimated during selection. Organisations evaluate WMS and TMS platforms based on their standalone capabilities and assume the integration will be straightforward. It rarely is. Legacy ERP systems may lack the APIs or data standards needed for real-time integration. Data formats may be inconsistent. Business rules embedded in existing systems may conflict with the logic in the new platform.

A proper selection process evaluates integration requirements with the same rigour as functional requirements. It identifies the specific data flows between systems, the frequency and latency requirements for those flows, and the technical approach to integration — whether through middleware, direct API connections, or file-based exchange.

The implementation problem: where good selections go wrong

Even when an organisation selects the right system, implementation is where value is won or lost. The technology vendors have strong implementation methodologies. The challenge is that implementation is not primarily a technology project — it's an operational transformation project that happens to involve technology.

Process design before system configuration

The single most important principle in WMS and TMS implementation is this: design your processes before you configure your system, not the other way around.

Too many implementations begin with system configuration based on how the operation currently works. The team documents existing processes, configures the system to replicate them, and then wonders why the new technology hasn't delivered the step-change improvement they expected. The answer is obvious in hindsight — if you automate a bad process, you get a faster bad process.

Effective implementation starts with process redesign. Before the system is configured, the project team should define the target-state processes — how receiving, put-away, replenishment, picking, packing, shipping, cycle counting, and returns should work in the new environment. For TMS, this means defining how orders are consolidated, how loads are planned and optimised, how carriers are selected and tendered, how shipments are tracked, and how freight is audited and settled.

This process design work often reveals opportunities that the technology alone would never have surfaced — changes to warehouse layout, slotting strategy, pick methodology, wave planning logic, or transport routing that deliver benefits independent of the system. It also ensures that the system is configured to support the operation you want, not the operation you have.

Data readiness: the unsexy foundation

Every WMS and TMS implementation depends on clean, accurate master data — item masters with correct dimensions and weights, location masters with accurate capacity and constraint information, carrier rate tables, vehicle specifications, delivery windows, and customer shipping requirements. If this data is incomplete, inconsistent, or wrong, the system will produce incorrect put-away decisions, suboptimal pick paths, inaccurate load plans, and unreliable cost estimates.

Data migration and cleansing is consistently the least glamorous and most underestimated workstream in any implementation. It requires painstaking effort to audit existing data, identify and correct errors, fill gaps, and establish data governance processes to maintain quality post-go-live. Organisations that shortcut this work pay for it repeatedly in operational errors, workarounds, and lost confidence in the system.

Change management: the difference between go-live and adoption

A WMS or TMS goes live on a specific date. But going live and achieving adoption are very different things. Industry research indicates that 70% of software implementations fail to deliver expected results due to poor user adoption, and 63% of employees stop using technology if they don't see its relevance to their daily work.

In a warehouse environment, this translates directly to operational performance. If pickers revert to paper-based processes because the RF-directed workflow is confusing, you've spent hundreds of thousands of dollars on a system that isn't being used. If transport planners override the TMS optimisation because they don't trust it, you're paying for a planning tool that's functioning as an expensive spreadsheet.

Effective change management for WMS and TMS implementations requires investment in three areas. First, stakeholder engagement — involving warehouse supervisors, team leaders, and experienced operators in process design and system configuration so they understand and own the new way of working. Second, training — not generic vendor training, but role-specific training that shows each user exactly how the system supports their daily tasks, delivered close to go-live and reinforced in the weeks afterwards. Third, sustained support — recognising that the first four to eight weeks after go-live are when adoption is won or lost, and providing sufficient floor support, super-users, and rapid issue resolution to build confidence.

This is where project and change management capability makes the difference between a system that delivers its business case and a system that becomes an expensive source of operational frustration.

WMS and TMS: different systems, different challenges

While WMS and TMS share many of the same selection and implementation pitfalls, they present distinct challenges that warrant separate consideration.

WMS-specific considerations

Warehouse management systems are deeply operational — they direct the physical movement of goods through a facility, and their effectiveness is measured in real-time productivity metrics. Key considerations for Australian businesses include hardware dependency (WMS relies on scanners, mobile devices, label printers, and potentially automation interfaces — hardware selection and network infrastructure are implementation workstreams in their own right), warehouse layout and slotting (the system's performance depends on how well the facility is configured to support directed work — poor slotting or layout design will undermine even the best WMS), scalability for peak periods (Australian retail and FMCG operations experience significant seasonal peaks, and the WMS needs to handle peak volumes — including temporary labour using the system with minimal training — without degradation), and multi-channel complexity (organisations running B2B wholesale and B2C e-commerce from the same facility need a WMS that can manage fundamentally different fulfilment profiles without operational conflict).

The global WMS market is growing at roughly 19.5% CAGR, with cloud-based deployments gaining share rapidly. For Australian businesses, cloud WMS platforms offer faster deployment and lower upfront capital, but require careful evaluation of data sovereignty, network latency, and vendor support in Australian time zones.

TMS-specific considerations

Transport management systems operate across a broader ecosystem of carriers, routes, rates, and service levels, and their value depends heavily on the quality of data flowing in and out. Key considerations include carrier integration (a TMS is only as useful as its connectivity to your carrier base — Australian businesses often work with a mix of national carriers, regional operators, and specialist providers, and the system needs to accommodate this diversity), rate management complexity (Australian freight operates across a complicated rate structure involving zone-based pricing, weight breaks, fuel levies, accessorial charges, and contract-specific arrangements — the TMS must handle this granularity accurately or its cost calculations are meaningless), geographic and network challenges (Australia's vast distances, concentrated population centres, and thin regional freight networks create optimisation challenges that many TMS platforms — designed primarily for denser US or European markets — handle poorly without configuration), and visibility and exception management (the real value of a TMS often isn't in the initial load plan but in the ability to identify and manage exceptions — late pickups, missed deliveries, carrier capacity issues — in near real time).

The Australian context

Australian supply chain operations face specific challenges that influence both WMS and TMS selection. Our geographic distances make transport cost a larger proportion of total supply chain cost than in most comparable markets. Our concentrated population distribution — with the majority of demand in the eastern seaboard capital cities — creates particular network design dynamics. Our labour market — characterised by higher wages and increasing difficulty in recruiting warehouse and transport workers — makes the productivity improvements from well-implemented WMS and TMS especially valuable.

At the same time, the Australian market is smaller than the US or European markets that most WMS and TMS vendors are primarily designed for. This means that vendor presence, local support, implementation partner availability, and the depth of Australian reference sites all require careful evaluation. A system that works brilliantly for a 500,000-square-foot distribution centre in Ohio may not have the local support infrastructure to deliver the same experience for a 20,000-square-metre facility in western Sydney or Melbourne's south-east.

The sector matters too. FMCG and manufacturing operations have different WMS requirements from retail e-commerce fulfilment centres. Government and defence supply chains operate under compliance and security requirements that constrain technology choices. Health and human services organisations managing pharmaceutical or cold-chain distribution need specialised capabilities. A good selection process accounts for these sector-specific requirements rather than treating WMS and TMS as generic horizontal technologies.

What good looks like

Organisations that consistently deliver successful WMS and TMS implementations share several characteristics.

They invest in upfront requirements definition — spending eight to twelve weeks documenting operational requirements, integration needs, and future-state process designs before engaging with vendors. This investment feels slow at the start but dramatically reduces rework, scope creep, and misalignment later.

They run structured selection processes — evaluating a shortlist of vendors against weighted criteria, using scripted demonstrations based on their specific operational scenarios rather than the vendor's standard demo script, and checking references with comparable Australian operations. They include operational stakeholders in the evaluation, not just IT and procurement.

They treat implementation as an organisational change programme, not a technology installation. They appoint a business-side project lead with the authority and capacity to make decisions, assign experienced operators to the project team, and invest in change management from day one rather than bolting it on as an afterthought.

They plan realistic timelines and budgets — acknowledging that a typical WMS implementation for a mid-complexity operation takes six to twelve months, and a TMS implementation of similar complexity takes four to nine months, and that these timelines include process design, data preparation, integration development, testing, training, and post-go-live stabilisation. They budget for the full programme cost, not just the software licence and implementation services.

They stage their go-live — starting with core functionality, building confidence and competence, and then progressively activating more advanced features like labour management, slotting optimisation, or advanced transport planning. Trying to implement every feature simultaneously is how projects collapse under their own weight.

They also protect institutional knowledge during the transition. The experienced warehouse supervisor who knows every quirk of the operation is not an obstacle to the new system — they're the person who can tell you whether the configured processes will actually work on a Monday morning in peak season. Smart implementation teams capture that knowledge and build it into the system design.

And they define success in business terms — not "the system went live" but "pick productivity improved by X%, inventory accuracy reached Y%, transport cost per unit decreased by Z%." They track these metrics through go-live and hold the programme accountable for delivering the business case.

How Trace can help

At Trace Consultants, we help Australian organisations select and implement WMS and TMS platforms that actually deliver their business case. Our interest is in helping you choose the right system for your operation and getting the implementation right first time.

Our work in this space covers the full lifecycle. We start with operational assessment and requirements definition — working with your warehouse and transport teams to document how the operation runs today, where the pain points and opportunities are, and what the target-state operation needs to look like. This produces the functional and technical requirements specification that drives a rigorous selection process.

We then manage structured vendor evaluation — developing evaluation criteria, scripting vendor demonstrations around your operational scenarios, facilitating site reference visits, and conducting commercial analysis that goes beyond licence fees to evaluate total cost of ownership including implementation, integration, training, and ongoing support.

During implementation, we provide programme management, process design, and change management support — ensuring the project stays on track, the system is configured to support redesigned processes rather than replicate existing ones, and the organisation is ready to adopt the new way of working at go-live. We work alongside your team and the vendor's implementation consultants, providing the independent perspective that keeps the project focused on business outcomes.

We work across planning and operations, warehousing and distribution, procurement, and strategy and network design — which means we understand how WMS and TMS fit within the broader supply chain operating model, not just as standalone technology deployments.

We've supported WMS and TMS selections and implementations across FMCG and manufacturing, retail, resources and energy, health, and government — sectors where getting the technology right has direct, measurable impact on operational performance and cost.

If you're considering a WMS or TMS investment — or if you're partway through an implementation that isn't going to plan — get in touch. The difference between a technology investment that delivers and one that disappoints is almost always in the approach, not the software. We can help you get the approach right.

Trace Consultants is an Australian supply chain and procurement consulting firm. We help organisations select, implement, and optimise logistics technology — independently, practically, and with a relentless focus on operational outcomes. Visit our insights page for more on the challenges shaping Australian supply chains.

Resilience & Risk Management

China-Plus-One for Australian Businesses: How to Diversify Your Supplier Base Without Losing Quality, Reliability or Cost Competitiveness

Mathew Tolley
March 2026
China accounts for roughly 28% of Australia's total merchandise imports — over US$75 billion annually. For many Australian manufacturers, retailers, and distributors, that concentration represents a strategic vulnerability that boards and executive teams can no longer ignore. But diversification done poorly creates more problems than it solves. Here's how to approach it as a structured procurement and supply chain design challenge.

Every few years, the conversation about reducing dependence on Chinese manufacturing gets louder. The pandemic accelerated it. US-China trade tensions intensified it. Tariff escalations in 2025 — with effective duty rates on some Chinese-origin products exceeding 50% for US importers — brought it to a head. And while Australia hasn't imposed equivalent tariffs, the flow-on effects are reshaping global supply chains in ways that Australian businesses can't afford to ignore.

According to the United Nations COMTRADE database, Australia imported US$75.7 billion in goods from China in 2024. China remains Australia's largest source of manufactured imports by a significant margin, supplying everything from electronics and machinery to building materials, packaging, and consumer goods. For many Australian businesses, Chinese suppliers represent 60-90% of their imported product base. That kind of concentration — in any category, from any source — is a risk that prudent supply chain management should address.

But the China-Plus-One conversation often starts in the wrong place. It starts with a reaction — a geopolitical headline, a tariff announcement, a supply disruption — and jumps straight to "we need to find suppliers in Vietnam" without the structured evaluation that a decision of this magnitude demands. Supplier diversification is not just a procurement exercise. It's a supply chain design challenge that touches sourcing strategy, logistics network configuration, quality management, inventory policy, working capital, and risk management simultaneously.

This article is about how to approach that challenge methodically — evaluating alternatives honestly, understanding the trade-offs, and building a diversified supplier base that actually works in practice, not just on a strategy slide.

Why diversification matters now — and why it's harder than it looks

The case for diversification is straightforward. Concentration risk in any part of a supply chain creates vulnerability to disruption — whether from geopolitical tension, regulatory change, natural disaster, or pandemic. When 76% of European shippers reported supply chain disruptions in 2024, with a quarter experiencing more than 20 disruptive events, the data confirmed what most supply chain leaders already knew: single-source and single-country strategies carry real financial consequences.

For Australian businesses specifically, the calculus includes several additional factors. Australia's geographic remoteness means that any supply chain disruption from Asia has an amplified impact — longer shipping lanes mean longer recovery times. The diplomatic relationship between Australia and China, while stabilising, has demonstrated that trade can be used as a geopolitical lever. And the broader reconfiguration of global trade — with the US imposing escalating tariffs, companies pursuing regionalisation strategies, and new trade corridors emerging — is reshaping the competitive landscape for sourced goods.

But here's what the diversification advocates often understate: China's manufacturing ecosystem is genuinely difficult to replace. It's not just about cheap labour anymore — China's average manufacturing wages reached approximately US$770 per month in 2023, higher than many ASEAN alternatives. China's advantage lies in the depth and breadth of its supplier base, the speed and scale of its production capacity, and the quality of its industrial infrastructure. Chinese manufacturers can scale rapidly because they operate within dense supplier ecosystems where components, materials, and services are available locally. Trying to replicate that in a market where you need to import raw materials from China to manufacture in Vietnam somewhat undermines the diversification thesis.

This is why diversification needs to be approached as a structured strategic exercise, not a reactive scramble. Done well, it reduces risk, builds resilience, and can even improve total cost outcomes. Done poorly, it fragments your supply base, increases complexity, degrades quality, and costs more than staying concentrated.

The alternative markets: an honest assessment

Vietnam

Vietnam is the most commonly cited China-Plus-One destination, and for good reason. Its electronics exports reached US$72.6 billion in 2024, up 26.6% year on year, driven by major investments from Samsung, Apple suppliers, Intel, and others. The country has a young, increasingly skilled workforce, with average manufacturing wages roughly half of China's. Its government has been proactive in developing industrial zones, improving port infrastructure, and signing trade agreements.

For Australian importers, Vietnam offers strong trade agreement coverage. It's a signatory to the CPTPP, RCEP, and AANZFTA — giving Australian businesses multiple pathways to preferential tariff treatment. The country excels in electronics assembly, textiles and apparel, footwear, furniture, and increasingly in light industrial manufacturing.

The challenges are real, though. Vietnam's total population of approximately 98 million limits its scalable workforce compared to China or India. Infrastructure, while improving rapidly, is under pressure — ports and roads face congestion as trade volumes surge. And critically, many Vietnamese manufacturers still rely on Chinese-sourced raw materials and components, which means your supply chain diversification may be less complete than it appears on paper. If you're sourcing finished goods from Vietnam but those goods contain 60-70% Chinese inputs, you've moved assembly but not truly diversified your supply base.

India

India offers scale that few other markets can match — a population exceeding 1.4 billion, a large English-speaking workforce, and a government actively pursuing manufacturing investment through the "Make in India" initiative and Production-Linked Incentive schemes across electronics, pharmaceuticals, solar, textiles, and automotive.

Australia's trade relationship with India is strengthening. The Australia-India Economic Cooperation and Trade Agreement (AI-ECTA), which entered into force in December 2022, is reducing tariffs on a growing range of goods, with a more comprehensive agreement (AI-CECA) under negotiation. India has been described as the world's third most sought-after manufacturing destination, with the potential to export US$1 trillion worth of goods by 2030.

The reality on the ground, however, requires careful navigation. Infrastructure remains uneven — while major metro areas have improved power, ports, and highways, many industrial regions still suffer from logistics delays, power reliability issues, and congestion. Bureaucratic complexity is well-documented. Labour laws vary by state and can be restrictive. And India is not yet a member of the CPTPP, which limits some of the tariff advantages available through other ASEAN sourcing routes. For Australian businesses, India works best for categories where scale matters, lead times are manageable, and quality can be controlled through established supplier relationships — pharmaceuticals, textiles, automotive components, and increasingly electronics.

Indonesia

Indonesia is Southeast Asia's largest economy, with a population of over 270 million and a government that has committed to industrial development through its "Making Indonesia 4.0" programme. The country received approximately US$33 billion in greenfield manufacturing FDI in 2023, reflecting genuine investor confidence.

For Australian businesses, Indonesia offers several advantages. Geographic proximity means shorter shipping lanes than China for many routes. The Indonesia-Australia Comprehensive Economic Partnership Agreement (IA-CEPA), in force since July 2020, provides preferential market access. Indonesia is also a RCEP and AANZFTA member. The country has established manufacturing capabilities in textiles, automotive, palm oil derivatives, building materials, and is positioning itself as a hub for electric vehicle battery supply chains, leveraging its substantial nickel reserves.

Indonesia's challenges include infrastructure gaps across its archipelago, regulatory complexity that varies by region, and a manufacturing base that, while growing, lacks the density of China's supplier ecosystems. Scaling production can take longer, and quality control requires more active management than in more mature manufacturing markets.

Mexico

While geographically distant from Australia, Mexico deserves mention because the US-China tariff environment is redirecting significant manufacturing investment there. Companies serving North American markets are establishing Mexican production to avoid Chinese-origin tariffs. For Australian businesses with US-facing supply chains or those sourcing from global manufacturers with Mexican operations, this shift can create new procurement options. Mexico is also a CPTPP signatory, which provides a framework for preferential trade with Australia.

Australia's trade agreement advantage

One aspect of the diversification conversation that's often underappreciated is just how well-positioned Australia is from a trade agreement perspective. Australia has free trade agreements in force with virtually every significant alternative manufacturing country, including AANZFTA (covering all ten ASEAN nations), RCEP (covering China, Japan, Korea, and ASEAN), CPTPP (covering Vietnam, Malaysia, Mexico, and others), IA-CEPA (Indonesia), and AI-ECTA (India).

This means Australian businesses can access preferential tariff rates from multiple alternative sourcing countries. The AANZFTA upgrade, which entered into force in April 2025, further improves processes for traders to access existing tariff preferences. RCEP, as the world's largest free trade agreement by members' GDP, provides common rules of origin that can simplify multi-country sourcing strategies.

The practical implication is that Australian procurement teams should be actively evaluating the tariff and rules-of-origin implications of sourcing shifts — because in many categories, the trade agreement framework already supports diversification. The barrier isn't tariffs; it's the operational complexity of managing a diversified supplier base.

A structured approach to diversification

The organisations that diversify successfully treat it as a deliberate strategy and network design exercise, not an ad hoc reaction. Here's what that looks like in practice.

Start with a supplier concentration audit

Before evaluating alternatives, understand your current exposure. Map your supply base by country of origin — not just where your purchase orders go, but where the goods are actually manufactured and where the critical raw materials originate. Many Australian businesses discover that their apparent diversity (ordering from multiple trading companies) masks genuine concentration (all the product comes from the same region or even the same factory cluster in China).

Identify which categories have the highest concentration risk, the highest spend, and the greatest vulnerability to disruption. These are your priority categories for diversification. Don't try to diversify everything simultaneously — that's a recipe for the complexity explosion that makes diversification fail.

Define what you're optimising for

Diversification involves trade-offs. You may accept slightly higher unit costs in exchange for reduced concentration risk. You may accept longer lead times from a new market in exchange for tariff savings. You may accept lower initial quality from a developing supplier in exchange for long-term optionality.

Being explicit about these trade-offs — and quantifying them where possible — is essential. A total cost of ownership model that includes unit cost, freight, duties, quality costs, inventory carrying costs, lead time variability costs, and risk-adjusted disruption costs will give you a more honest comparison than unit price alone. In our experience, organisations that evaluate alternatives on unit price alone consistently underestimate the true cost of switching — and then either revert to Chinese suppliers or accept higher costs than they anticipated.

Evaluate suppliers on capability, not just cost

The most common failure mode in China-Plus-One strategies is selecting alternative suppliers primarily on price and then discovering that quality, reliability, or capacity doesn't meet requirements. A structured supplier evaluation should assess manufacturing capability and capacity (not just current output, but ability to scale), quality management systems and track record, raw material sourcing and sub-tier supply chain visibility, financial stability, logistics infrastructure and export capability, and compliance with Australian standards and regulations.

This evaluation takes time and resources. It typically involves site visits, sample production runs, and a qualification period before shifting meaningful volume. Organisations that shortcut this process — or rely solely on online supplier directories and trading company introductions — take on significant quality and reliability risk.

Plan for a transition, not a switch

Diversification is not about abandoning Chinese suppliers overnight. The most effective approach is to gradually build alternative sources while maintaining existing relationships. A common starting point is shifting 20-30% of volume in a target category to an alternative supplier, running dual supply for a qualification period, and then adjusting the allocation based on performance.

This parallel approach has several advantages. It maintains your relationship and leverage with existing Chinese suppliers. It allows you to test the alternative supplier at manageable volumes before committing. It gives your logistics and planning teams time to adapt to new lead times, shipping routes, and inventory patterns. And it provides a fallback if the alternative supplier doesn't perform as expected.

Redesign your inventory and logistics strategy

Adding supply sources from new countries changes your logistics network and inventory requirements. Different lead times, shipping frequencies, and reliability profiles from Vietnam or India versus China may require adjusting safety stock levels, reorder points, and warehouse configurations. If you're sourcing the same product from two countries, you need a planning process that can manage split allocations and variable lead times without creating either stockouts or excess inventory.

This is where diversification intersects with supply chain planning. Organisations that treat sourcing diversification as purely a procurement exercise — without adjusting their planning parameters, inventory policies, and logistics arrangements — often find that the operational disruption of dual-sourcing outweighs the risk reduction benefits.

Invest in supplier development and quality management

Alternative markets are called "alternative" for a reason — many suppliers in Vietnam, India, and Indonesia are less mature than their Chinese counterparts in areas like quality management, process standardisation, and export documentation. This doesn't mean they can't deliver quality product, but it does mean you may need to invest more in supplier development, quality assurance, and ongoing management than you do with established Chinese suppliers.

Demand for third-party inspections and audits in ASEAN countries has surged — up 36-62% year on year in some markets — as companies diversifying from China recognise the need for more active quality oversight during the transition period. This investment in quality management should be factored into the total cost of diversification, and it should be treated as a capability-building exercise, not just a policing exercise.

For Australian businesses, the practical implication is that your procurement team needs the capacity and capability to manage a more complex supplier base. If your team is already stretched managing existing Chinese suppliers, adding suppliers across two or three additional countries without additional resources or organisational design changes is likely to result in quality failures, delivery problems, and frustrated stakeholders. Diversification requires investment in people and processes, not just contracts.

What this means for Australian businesses

The China-Plus-One conversation is not going away. The structural forces driving it — geopolitical tension, tariff escalation, supply chain resilience imperatives, regulatory change, and the broader reconfiguration of global trade — are long-term trends, not temporary disruptions.

For Australian businesses, the question isn't whether to diversify, but how to do it in a way that actually improves your competitive position rather than just adding cost and complexity. It's worth noting that the original "China-Plus-One" concept is itself evolving. Leading organisations are now building multi-country sourcing networks — "China-Plus-Many" — with different countries serving different purposes in the portfolio. China for complex, high-volume production where ecosystem depth matters. Vietnam for mid-tier manufacturing with strong trade agreement access. India for categories where long-term scale and English-speaking business engagement provide advantages. Indonesia for resource-linked products and proximity benefits. The point isn't to find a single replacement for China — it's to build a portfolio of sourcing options that gives you flexibility and reduces your exposure to any single point of failure. The organisations getting this right share several characteristics: they approach diversification as a strategic programme rather than an ad hoc procurement exercise; they invest in structured supplier evaluation and qualification rather than chasing the cheapest quote; they adjust their supply chain operating model — planning, inventory, logistics — to accommodate a more complex supplier base; and they maintain strong relationships with Chinese suppliers while building alternatives, rather than treating it as an either-or decision.

Australia's trade agreement network provides a genuine advantage that many businesses aren't fully leveraging. With preferential access to ASEAN, India, and a growing list of other markets, the tariff barriers to diversification are lower than many procurement teams assume. The real barriers are operational — and overcoming them requires the kind of structured approach outlined above.

How Trace can help

At Trace, we help Australian organisations design and execute supplier diversification strategies that balance risk reduction with commercial pragmatism. We don't believe in diversification for its own sake — we believe in building supply chains that deliver reliable, cost-competitive outcomes across a range of scenarios.

Our work in this space typically starts with a supplier concentration and risk assessment: mapping your current supply base by country of origin, identifying single-source and single-country dependencies, and quantifying the financial exposure of concentration risk. From there, we help clients develop category strategies that identify which categories should be prioritised for diversification and which alternative markets offer the best fit, build total cost of ownership models that give an honest comparison of alternatives — not just unit prices but landed costs, quality costs, inventory implications, and risk-adjusted disruption costs, design sourcing strategies that structure the transition from concentrated to diversified supply bases without disrupting operations, adjust supply chain planning and inventory policies to accommodate new lead times, shipping routes, and supplier reliability profiles, and evaluate the trade agreement and tariff implications of sourcing shifts to ensure you're capturing available preferential treatment.

We work across FMCG and manufacturing, retail, government, health, and resources — sectors where supply chain concentration risk is both significant and actionable.

We're independent. We don't represent suppliers, we don't take commissions from sourcing agents, and we don't have preferred-country partnerships that bias our recommendations. Our interest is in helping you build a supply chain that's resilient, competitive, and aligned to your commercial strategy.

If you're evaluating your China exposure, exploring alternative sourcing markets, or navigating the trade agreement landscape — get in touch. Diversification done well is a source of competitive advantage. Diversification done poorly is just expensive complexity. We can help you get it right.

Trace Consultants is an Australian supply chain and procurement consulting firm. We help organisations build resilient, cost-competitive supply chains through structured strategy, not reactive switching. Visit our insights page for more on the challenges shaping Australian supply chains.

Technology

Supply Chain Transformation: Why the Technology Is the Easy Part

Tim Fagan
February 2026
The technology isn't the problem. The process redesign, organisational change, executive alignment, and capability building that surround the technology — that's where transformations succeed or die.

There's a pattern we see repeatedly in Australian organisations attempting to transform their supply chains. It starts with a compelling case for change — costs are too high, service levels are inconsistent, inventory is bloated, planning is reactive rather than proactive. Leadership agrees something needs to change. A project is initiated. And almost immediately, the conversation turns to technology.

Which planning system should we implement? Should we move to a cloud ERP? What AI tools are available? Who are the leading vendors? Can we see a demo?

This is understandable. Technology is tangible. It can be evaluated, costed, and procured. It has a vendor behind it who will show up with a team and a project plan. It feels like progress. And in a world where 82% of supply chain organisations report increasing their IT spending, the expectation that technology will solve the problem is reinforced at every conference, in every analyst report, and by every vendor pitch.

But here's the uncomfortable truth that the data keeps confirming: the technology is rarely why supply chain transformations fail. McKinsey estimates that more than 70% of digital transformations fail to deliver their expected results. Gartner puts ERP implementation failure rates above 75%. BCG's study of over 850 companies found that only 35% of digital transformations meet their value targets globally. These aren't technology failures — they're failures of everything that surrounds the technology. Process redesign. Organisational change. Executive alignment. Capability building. Governance. The human side of transformation.

This article is about that human side — and why the organisations that get it right consistently outperform those that don't, regardless of which technology they choose.

The technology illusion

The technology market for supply chain management has never been more capable. Advanced planning systems can run probabilistic demand forecasts across thousands of SKUs. Digital twins can simulate network configurations before committing capital. AI-powered analytics can identify patterns in procurement data that humans would miss. Real-time visibility platforms can track shipments across global supply chains. The tools exist.

And yet, McKinsey's 2025 survey of 100 global supply chain leaders confirms that the majority of companies understand their supply chain risks only up to tier one — and that this visibility has actually deteriorated since 2022 as pandemic urgency faded. PwC's 2025 Digital Trends in Operations Survey found that 82% of operations leaders face challenges balancing short-term needs with long-term strategic change. Ninety percent expect supplier and material costs to increase significantly. The technology is deployed, but the outcomes lag.

The reason is what we might call the technology illusion: the belief that implementing a new system will, by itself, change how the organisation operates. It won't. A new planning system layered over a broken S&OP process will produce better-formatted reports about the same bad decisions. A new ERP implemented without redesigning the underlying business processes will, as one observer put it, simply automate chaos faster. A visibility platform that nobody trusts or acts on is an expensive dashboard.

We see this in Australia regularly. An organisation invests $5-15 million in a new ERP or planning platform. The implementation partner delivers the system on time and on budget. The project is declared a success. Six months later, planners are running the same spreadsheets alongside the new system because they don't trust the outputs. Procurement is processing purchase orders through the new platform but hasn't changed any of its sourcing processes. Warehouse operations have reverted to manual workarounds because the system configuration doesn't match how the warehouse actually operates. The technology was successfully implemented. The transformation never happened.

The organisations that succeed treat technology as an enabler of transformation, not the transformation itself. They invest as much — often more — in the process redesign, organisational change, and capability building that allow the technology to deliver its potential. The ones that fail treat the technology implementation as the project, and then wonder why nothing actually changed.

The five things that actually determine whether a transformation succeeds

1. Process redesign before system design

The single most cited cause of ERP and supply chain technology implementation failure is poor business process mapping. Organisations jump into implementations without thoroughly understanding or redesigning their current processes. The technology then gets configured to replicate existing workflows — including all their inefficiencies, workarounds, and dysfunction.

Lidl's experience is a cautionary example at scale: the German retailer spent an estimated €500 million over seven years on a SAP implementation before abandoning the project entirely and reverting to its legacy system. The failure was attributed not to the software but to an inability to reconcile the company's operating processes with the system's design philosophy.

In our experience at Trace, the organisations that succeed in supply chain transformation invest significant effort — often three to six months — in process design before making technology decisions. This means mapping current-state processes end to end, identifying where value is created and where waste accumulates, defining the target-state operating model, and only then determining what technology is needed to enable it. The technology selection follows the process design, not the other way around.

This is particularly important for planning transformations. The difference between a functional S&OP process and a dysfunctional one isn't the planning software — it's whether demand and supply planners collaborate effectively, whether commercial teams contribute meaningful demand signals, whether the executive S&OP meeting makes real decisions or just reviews slides, and whether the planning outputs actually drive operational execution. These are process and behavioural challenges, not technology challenges. The best APS in the world cannot compensate for a planning process where commercial and operations teams don't trust each other's numbers.

2. Executive alignment and sustained sponsorship

Insufficient executive sponsorship kills more supply chain transformations than technical issues. This isn't just about a CEO signing off on a business case. It's about sustained, visible leadership commitment through the difficult middle phase of transformation — when the old ways no longer work, the new ways aren't yet embedded, and the organisation is tempted to revert.

Supply chain transformation is inherently cross-functional. It touches procurement, manufacturing, logistics, commercial, finance, and IT. Each of these functions has its own priorities, its own metrics, and its own definition of success. Without executive alignment on what the transformation is trying to achieve — and a willingness to resolve the trade-offs that inevitably emerge — the project fractures along functional lines.

PwC's survey found that 82% of operations and supply chain leaders face challenges balancing short-term needs with long-term strategic changes. This tension plays out in transformation programmes constantly: the supply chain director wants to transform planning processes, but the sales director won't release commercial staff for the S&OP redesign because they have quarterly targets to hit. The CFO approved the business case but now wants to cut the change management budget because Q3 results are soft. The IT department insists on a different technology platform than the one the supply chain team evaluated.

The organisations that navigate this successfully have a senior executive — ideally at C-suite level — who owns the transformation outcome (not just the project), who has the authority to resolve cross-functional conflicts, and who maintains visible engagement throughout the multi-year programme. In our experience, transformations without this level of sponsorship have a near-zero success rate, regardless of how good the technology or the implementation partner is.

3. Change management as a core workstream, not an afterthought

Deloitte has identified change management as the single biggest failure point for ERP and supply chain transformation projects, citing the critical people-related challenges that leaders face at every stage. Research suggests that 70% of all software implementations fail due to poor user adoption. Forty-five percent of employees say new software is introduced without adequate training, and 63% will stop using new technology if they don't see its relevance or receive help.

Yet in most supply chain transformation budgets, change management is a fraction of the investment — if it's budgeted explicitly at all. The technology licence, the implementation partner fees, and the data migration dominate the cost structure. Training is compressed into a few sessions near go-live. Communication is limited to project updates in the company newsletter. The organisational redesign that should accompany the new ways of working is deferred because it's "too disruptive."

Effective change management in supply chain transformation involves several elements. Stakeholder analysis and engagement from the outset — understanding who will be affected, what their concerns are, and how to bring them on the journey. Role and process redesign — defining explicitly how each role changes under the new operating model, and ensuring people understand and accept their new responsibilities. Capability building — not just system training (how to use the tool) but process training (how to work differently) and analytical training (how to interpret and act on the new information available). Communication that goes beyond project updates to address the "what's in it for me" question that every affected employee is asking. And sustained reinforcement after go-live — because the hardest phase of any transformation is the three to six months after implementation, when old habits reassert themselves and the system hasn't yet proven its value.

At Trace, we've seen organisations that invest 15-20% of total transformation spend on change management consistently outperform those that invest less than 5%. The delta isn't marginal — it's often the difference between a transformation that delivers its business case and one that becomes an expensive technology deployment with no measurable impact.

This is especially true in supply chain, where the people affected by the transformation are often the most operationally critical. Warehouse managers, demand planners, procurement specialists, logistics coordinators — these are the people who keep the supply chain running day to day. If they don't understand, accept, and adopt the new ways of working, no technology investment will deliver its promised returns. And unlike office-based knowledge workers who might adapt to a new CRM, supply chain professionals operate under real-time pressure where a clunky workaround today means a missed shipment tomorrow. The stakes of poor adoption are immediate and visible.

4. Data foundations that actually support decision-making

There's a common assumption that implementing a new system will fix data problems. It won't. If your master data is inconsistent, your demand history is unreliable, your inventory records are inaccurate, and your cost data doesn't reflect reality, a new system will simply process bad data faster and present it more attractively.

Panorama Consulting found that 23% of ERP projects exceed budgets, with half requiring additional technology that nobody planned for and 40% discovering organisational issues that should have been obvious from the start. Much of this unplanned effort relates to data: the migration, cleansing, and harmonisation work that nobody fully scoped because it's unglamorous and hard to estimate.

In supply chain transformation specifically, the data challenge has several dimensions. Master data harmonisation — ensuring consistent product hierarchies, supplier records, customer classifications, and location data across business units. Demand data quality — ensuring the demand history that will feed new planning algorithms actually reflects true demand rather than constrained shipments, one-off promotions, or data entry errors. Inventory accuracy — ensuring the system's view of inventory matches physical reality, which for many organisations means investing in cycle counting, warehouse management processes, and potentially warehouse technology before a planning system can be effective. Cost data integrity — ensuring that the cost models driving procurement, make-or-buy, and network design decisions reflect actual costs rather than averaged or allocated figures that obscure the true economics.

This data work isn't optional, and it can't be deferred until after the technology is implemented. It needs to run in parallel with — or ideally ahead of — the system implementation. Organisations that treat data readiness as a prerequisite rather than an afterthought dramatically reduce implementation risk and accelerate time to value.

In practice, this means appointing data owners for each critical data domain well before the implementation begins, establishing data governance processes that will persist after the project, investing in the often tedious work of data cleansing and standardisation, and testing the new system with real data — not sanitised test data — before go-live. The organisations that do this well often find that the data remediation effort alone delivers value, because clean, consistent data improves decision-making even in existing systems.

5. Organisational design that reflects the new operating model

Supply chain transformation changes how work gets done, which means it changes what skills are needed, how roles are defined, and how the organisation is structured. Yet many transformations leave the organisational design unchanged — implementing new technology and processes within the same reporting structures, role definitions, and capability profiles that existed before.

This creates a predictable set of problems. New planning processes require analytical skills that existing planners don't have — but there's no plan to recruit, develop, or supplement that capability. Cross-functional processes like IBP require regular collaboration between demand planners, supply planners, commercial managers, and finance — but the organisation still operates in functional silos with no shared forums or decision-making processes. The new operating model requires someone to own end-to-end supply chain performance — but accountability remains fragmented across procurement, manufacturing, logistics, and customer service.

McKinsey's research on successful enterprise platform transformations highlights that the organisations that deliver sustained value combine technology implementation with cross-functional working groups, capability building, and a continuous improvement culture. The technology is the platform, but the organisational design is what determines whether the platform generates value.

In Australia, this challenge is compounded by the structural talent shortage in supply chain and operations. With 90% of supply chain leaders globally reporting their companies lack the necessary talent and skills to achieve digitisation goals, the gap between what the new operating model requires and what the existing workforce can deliver is often substantial. Closing that gap requires a deliberate capability strategy — combining recruitment, development, process simplification, and strategic use of external expertise — that runs alongside the technology implementation rather than being addressed after it.

The failure patterns we see most often

Having worked across dozens of supply chain transformation programmes in Australian organisations, we see several recurring patterns that lead to disappointing outcomes.

The vendor-led transformation. The organisation selects a technology vendor or implementation partner and then allows the vendor's methodology to define the transformation scope, timeline, and approach. Because the vendor's incentive is to implement the software (and bill consulting hours), the non-technology workstreams — process redesign, organisational change, capability building — are under-scoped or treated as secondary. The system goes live on schedule, but the business outcomes don't materialise because the surrounding transformation was never adequately addressed.

The IT-owned transformation. Supply chain transformation is positioned as an IT project with a technology-focused project manager and governance structure. Business stakeholders are consulted but don't own the outcomes. Decisions default to what's easiest to implement technically rather than what delivers the most business value. The result is a technically sound system that doesn't match how the business needs to operate — leading to workarounds, shadow systems, and frustrated users.

The big-bang approach. The organisation attempts to transform everything simultaneously — new ERP, new planning system, new warehouse management system, new procurement platform, new organisational structure — in a single programme. The complexity overwhelms the organisation's capacity to absorb change. Testing is compressed because the timeline is aggressive. Training is superficial because there's too much to cover. Go-live is chaotic, and the organisation spends the next 12-18 months stabilising rather than realising benefits. Research consistently shows that incremental approaches have significantly lower failure rates than these large-scale overhauls.

The underfunded change programme. The business case includes $10 million for technology and implementation but $200,000 for change management — essentially a communications plan and a few training sessions. The system is technically functional but user adoption is poor, process compliance is low, and the organisation gradually reverts to old ways of working. Twelve months after go-live, an internal review finds that the transformation hasn't delivered its expected benefits, and the finger-pointing begins.

The transformation without a target operating model. The organisation implements new technology without first defining the target operating model — including governance, decision rights, process ownership, performance metrics, and organisational structure. The technology gets layered onto the existing operating model, which wasn't designed for it. The mismatch creates friction, workarounds, and frustration, and the promised benefits of integration and visibility are never realised because the organisational design doesn't support them.

Each of these patterns is avoidable. But avoiding them requires recognising that supply chain transformation is fundamentally an organisational change programme that happens to involve technology — not a technology project that happens to affect the organisation.

What good looks like in practice

The organisations we've seen succeed in supply chain transformation — whether they're implementing a new planning system, redesigning their network, restructuring their procurement function, or building end-to-end visibility — share several characteristics that transcend the specific technology or initiative.

They start with the business problem, not the technology solution. The transformation is driven by a clear articulation of what needs to change in business outcomes — service levels, cost performance, working capital, resilience, agility — and the technology is selected and configured to deliver those outcomes. When McKinsey documented a successful enterprise platform transformation that identified over 40 operational initiatives worth approximately $250 million, the key difference from the company's previous failed attempt was that the successful effort focused on achieving combined business and IT benefits rather than treating it as a technology project.

They invest in process design before system design. Target-state processes are defined, tested, and agreed before the technology is configured. This means the technology is built to support the way the organisation wants to work, not the way it works today.

They budget realistically for change. The total cost of transformation includes not just the technology and implementation but the process redesign, data remediation, organisational restructuring, capability building, and sustained change management that determine whether the investment delivers returns. The best organisations we work with allocate 30-40% of total programme spend to these non-technology elements.

They sequence deliberately. Rather than attempting a big-bang transformation across the entire supply chain, they identify the highest-value, lowest-risk starting points, build capability and confidence, and expand from there. This mirrors the broader finding that incremental approaches show significantly lower failure rates than large-scale overhauls, because they reduce complexity and allow the organisation to learn as it goes.

They measure what matters. Success metrics are defined at the outset in business terms — not in project management terms like "on time" and "on budget" but in operational terms like forecast accuracy improvement, inventory days reduction, service level uplift, or cost per unit shipped. These metrics are tracked from before the transformation through to steady state, creating accountability for actual business impact rather than just system delivery.

And they sustain the effort after go-live. The three to six months after a system goes live is the most critical period — and the period most likely to be under-resourced because the project team has moved on and the organisation assumes the transformation is "done." In reality, this is when new processes need to be reinforced, when users need the most support, when workarounds need to be identified and eliminated, and when the benefits start to materialise (or don't). Organisations that maintain dedicated transformation support through this period consistently outperform those that don't. Successful companies also conduct formal post-implementation reviews — not to assign blame, but to capture what worked, identify what didn't, and feed those lessons into the next phase of the transformation or the next initiative. Without this discipline, the same mistakes are repeated across the organisation, and institutional learning from expensive transformation experience is lost.

What this means for Australian supply chains

Australian organisations face specific dynamics that make the non-technology dimensions of transformation even more critical.

Geographic dispersion means that supply chain processes must work across multiple time zones, climates, and operating environments — from dense urban distribution networks in Melbourne and Sydney to remote operations in Western Australia and the Northern Territory. Technology that works in a single-site pilot may not scale without significant process adaptation.

The concentrated supplier market means that procurement and supply chain transformations must work within the reality of limited supplier optionality. Transformations designed for markets with deep supplier bases need to be adapted for categories where there may be two or three viable suppliers nationally.

The talent challenge means that transformation programmes can't assume access to the specialist skills they need — whether in planning, analytics, technology, or change management. Capability building and knowledge transfer must be embedded in the programme design, not treated as something that happens organically.

And the scale of most Australian organisations — smaller than US or European peers in most sectors — means that transformations need to deliver value faster and with lower total investment. This argues for pragmatic, phased approaches rather than multi-year, enterprise-wide programmes that defer benefits while accumulating cost.

The good news is that this same scale can be an advantage. Smaller organisations can move faster, with shorter decision-making chains and less organisational inertia. Cross-functional alignment is easier to achieve when the supply chain director and the commercial director sit in the same office. Process redesign can be tested and refined quickly when there are dozens of users rather than thousands. The key is to design the transformation approach for the Australian operating context rather than importing a methodology designed for Fortune 500 scale.

This also means being realistic about the role of emerging technologies like AI and generative AI. While these tools offer genuine potential — particularly in demand sensing, spend analytics, and exception management — the foundational work of process design, data quality, and organisational alignment must come first. Organisations that rush into AI before their core processes and data are sound will repeat the same failure patterns that have plagued ERP implementations for decades, just with newer technology.

How Trace can help

At Trace, we help Australian organisations design and deliver supply chain transformations that actually work — by focusing on the business outcomes, process redesign, organisational change, and capability building that determine whether a technology investment pays off.

Our approach begins with understanding the business problem, not the technology landscape. We work with clients to define what good looks like in operational terms, design the target-state processes and operating model, identify the technology requirements that follow from that design, and then support the implementation with the change management, capability building, and governance that sustains the transformation.

We work across planning and operations, procurement, warehousing and distribution, strategy and network design, organisational design, and project and change management. We serve clients across FMCG and manufacturing, retail, government, health, and resources.

We're independent — we don't sell software, we don't take commissions from technology vendors, and we don't have implementation partnerships that bias our recommendations. When we advise on technology, our only interest is what will deliver the best outcome for your organisation.

If you're planning a supply chain transformation — or recovering from one that didn't deliver — get in touch. The technology is the easy part. Let us help you get the hard part right.

Trace Consultants is an Australian supply chain and procurement consulting firm. We help organisations transform their supply chains by getting the fundamentals right — process design, organisational change, capability building, and governance — so that technology investments deliver real business impact. Visit our insights page for more on the challenges shaping Australian supply chains.

Procurement

Procurement Savings Are Getting Harder to Find — Here's Where the Next Wave Comes From

James Allt-Graham
February 2026
If your procurement function is delivering diminishing returns from the same sourcing events, supplier negotiations, and contract renewals, you're not alone. The traditional savings playbook has run its course — and the next wave of procurement value looks fundamentally different.

There's a conversation happening quietly inside most Australian procurement functions, and it goes something like this: the easy savings are gone.

The strategic sourcing waves that delivered 8-12% cost reductions five years ago now deliver 2-3%. The supplier consolidation that once created leverage has been done — and in some cases overdone, creating concentration risk. The competitive tenders that used to generate genuine competitive tension now feel performative, because there are only two credible suppliers left in the category. The annual negotiation cycle yields marginal gains that barely keep pace with inflation. And the procurement team — which once earned its seat at the table by delivering hard dollar savings — is now under pressure to justify its existence using increasingly creative definitions of "value."

This isn't an Australian problem alone, but it's acute here. The Hackett Group's 2026 Procurement Key Issues research shows procurement leaders facing an 8% increase in workload amid declining headcount and operating budgets. Deloitte's 2025 Global CPO Survey, covering more than 250 CPOs across 40 countries, found that 57% identify siloed working structures as the biggest barrier to delivering value. Competing priorities dilute focus for 46%, and 34% cite the talent gap. The traditional procurement model — run sourcing events, negotiate savings, report the number — is producing diminishing returns at the exact moment the organisation is asking procurement to do more.

This article is about where the next wave of procurement savings and value actually comes from, and why the organisations that find it will look fundamentally different from the ones still running the old playbook.

Why the traditional playbook has run its course

The procurement savings model that most Australian organisations still operate was built for a specific era. It was designed when globalisation was expanding the supply base, when switching suppliers was relatively easy, when commodity deflation was the baseline, and when procurement's primary job was to reduce the unit price of things the organisation was already buying.

That era is over.

Supply markets have consolidated. In many categories — particularly in Australia's relatively concentrated market — there are fewer credible suppliers than there were a decade ago. This means competitive tension is harder to create, switching costs are higher, and the "threat of substitution" that underpins traditional negotiation leverage is weaker. Running a tender with two suppliers who both know they're the only realistic options is theatre, not strategy.

Inflation has structurally changed the savings equation. After years of deflation-era thinking where procurement could reliably deliver year-on-year price reductions, many categories have experienced sustained cost increases driven by energy, labour, raw materials, and logistics. In this environment, cost avoidance — preventing prices from rising as much as they otherwise would — is genuinely valuable work, but it's hard to quantify and even harder to get credit for. Finance teams accustomed to seeing hard dollar savings struggle to recognise cost avoidance as equivalent value.

And globalisation is fragmenting rather than expanding. Tariff uncertainty, geopolitical risk, and the shift toward regionalisation mean that the cheapest source of supply is no longer always the smartest. Over 90% of manufacturers globally are now prioritising supply chain regionalisation, according to the World Economic Forum and Kearney. Nearly two-thirds are adopting "power of two" sourcing strategies where most direct spend is covered by two separate regions. These are strategically sound moves, but they often increase unit costs in the short term — precisely the opposite of what the traditional procurement savings model is designed to deliver.

The expectations placed on procurement have also expanded dramatically without a corresponding expansion in resources. The Hackett Group's 2024 CPO Agenda found that cost reduction had returned to the number one priority for procurement leaders, driven by economic uncertainty and executive pressure to reclaim margins eroded by inflation. But in the same breath, procurement is being asked to manage third-party risk, deliver on sustainability targets, support digital transformation, and serve as a strategic advisor to the business. Trying to deliver all of these with a team and operating model designed primarily for sourcing events creates the impossible equation that most procurement leaders are living with today.

The result is a procurement function that's working harder than ever but delivering less of what the organisation has historically measured. This isn't a failure of effort or capability — it's a structural shift that requires a fundamentally different approach to where procurement creates value.

The eight sources of next-wave procurement value

1. Spend visibility and leakage recovery

Before looking for new savings, most organisations should start by capturing the value they're already supposed to have.

The reality in most Australian organisations is that a significant portion of negotiated savings never reaches the bottom line. This happens through several mechanisms. Maverick spending — purchases made outside established procurement processes — costs organisations between 10-20% of their negotiated savings according to Ivalua's analysis. The Hackett Group's research puts the figure at 5-16% of targeted savings lost to maverick buying. The American Productivity and Quality Center found that maverick buying accounted for 1.8% of annual purchase value in organisations studied — which for a business with $1 billion in purchases means approximately $18 million in uncontrolled spend annually.

Contract leakage is equally significant. Prices paid don't match contracted rates — one analysis found systematic pricing leakage where actual payments exceeded contracted rates by 12%. Payment terms negotiated in contracts aren't reflected in actual payment practices. Volume commitments that unlock tiered pricing aren't tracked, so the organisation pays a higher rate than it's entitled to.

Tail spend — the long tail of low-value purchases that typically accounts for 80% of transactions but only 20% of spend — is where much of this leakage concentrates. These transactions involve hundreds of suppliers, often with no negotiated pricing, no consolidated demand, and no systematic management.

The first wave of next-generation procurement value comes from getting serious about spend visibility: consolidating procurement data across business units, identifying where actual spend diverges from contracted terms, quantifying the gap, and implementing the process and technology controls to close it. This isn't glamorous work, but in our experience at Trace, it routinely unlocks 3-7% of addressable spend — often more than the next sourcing event would deliver.

2. Demand management — buying less, not buying cheaper

The most powerful savings lever in procurement isn't negotiating a better price — it's reducing the volume of what gets purchased in the first place. Yet most procurement functions spend virtually all their time on supply-side levers (who to buy from, at what price, on what terms) and almost none on demand-side levers (whether to buy at all, how much to buy, whether the specification is right).

Demand management operates across several dimensions. Specification rationalisation involves reviewing whether the organisation is over-specifying its requirements — buying higher grades, tighter tolerances, or premium features that don't add value to the end customer. This is particularly prevalent in professional services, facilities management, and IT categories, where scope creep is endemic and requirements documents grow with each procurement cycle.

Volume challenge involves questioning whether the quantity being purchased is right. Are we ordering based on actual consumption data, or based on a budget allocation that hasn't been revisited? Are we carrying excess inventory because the min-max settings haven't been updated? Are we subscribing to software licenses we don't use? Gartner's research found that nearly 40% of SaaS spending goes unmonitored — a significant pool of value that procurement hasn't traditionally touched.

Substitution involves identifying whether a different product or service could meet the business need at lower cost without meaningful compromise. This requires procurement to understand the business need, not just the purchase requisition — which in turn requires the kind of stakeholder relationship that transactional procurement models don't build.

In our experience working with clients across FMCG, retail, resources, and government, demand management consistently delivers larger savings than the next round of supplier negotiations — but it requires procurement to be involved earlier in the buying cycle, to have credibility with business stakeholders, and to understand the operational context well enough to challenge requirements constructively.

3. Category strategy that goes beyond sourcing events

Traditional procurement treats categories as portfolios to be tendered on a cycle. Every 2-3 years, the contract comes up, procurement runs a go-to-market process, negotiates new terms, and reports the savings. Between cycles, the category is largely unmanaged.

Mature category management looks fundamentally different. It involves continuous market intelligence — understanding supply market dynamics, cost drivers, and supplier economics in enough depth to identify value creation opportunities outside the tender cycle. It involves supplier relationship management that goes beyond performance scorecards to genuine collaboration on cost reduction, innovation, and process improvement. It involves total cost of ownership analysis that considers not just the purchase price but the full lifecycle cost — including quality costs, logistics costs, inventory carrying costs, administrative costs, and disposal costs.

Deloitte's 2025 Global CPO Survey found a stark performance divide. Organisations they classify as "Digital Masters" — characterised by higher investment in technology and talent — met or exceeded their savings plans 96% of the time, compared to 80% for followers. These leading organisations aren't just negotiating harder. They're investing in the analytical capability and supplier relationships that unlock value sources that purely transactional procurement can't reach.

The Hackett Group's Digital World Class research reinforces this: top-performing procurement organisations deliver 2.6 times greater ROI than peers, while operating with 31% fewer full-time employees and at 19% lower cost as a percentage of spend. They achieve this not by doing more sourcing events but by investing in better analytics, deeper category expertise, and stronger supplier collaboration — and by spending 1.8 times more on procurement technology to enable it.

4. Supplier collaboration and value engineering

Once you've exhausted the savings available from competitive tension, the next wave comes from working with suppliers rather than against them. This sounds counterintuitive to procurement functions raised on adversarial negotiation, but the logic is straightforward: your suppliers understand their own cost structures better than you do, and in a mature supply relationship, there are often opportunities to reduce costs jointly that neither party can capture alone.

Value engineering involves working with suppliers to redesign products, processes, or service delivery models to reduce cost without reducing value. This might mean changing a packaging format to reduce material cost and freight cost simultaneously. It might mean adjusting delivery schedules to optimise the supplier's production runs, reducing their manufacturing costs in exchange for a share of the savings. It might mean co-investing in automation that reduces the supplier's labour cost on your account.

Joint process improvement involves eliminating waste in the transaction and relationship itself — simplifying ordering processes, reducing unnecessary quality inspections, consolidating deliveries, standardising specifications, or integrating planning processes to reduce buffer inventory on both sides of the relationship.

Innovation capture involves positioning procurement as a channel through which supplier innovations reach the organisation — new materials, new processes, new technologies that can improve performance or reduce cost. This only works if the supplier sees the relationship as worth investing in, which in turn only works if procurement has moved beyond treating every interaction as a price negotiation.

None of this is possible with a purely transactional procurement model. It requires procurement professionals with deep category knowledge, relationship management skills, and the commercial judgement to structure collaborative arrangements that create value for both parties. It also requires a procurement operating model that gives category managers the time and mandate to do this work — which means automating or eliminating the low-value transactional activities that currently consume most of their time.

5. Working capital and commercial terms

Procurement's impact on the balance sheet is often larger than its impact on the P&L, but most procurement functions don't measure it, don't manage it, and don't get credit for it.

Payment terms directly affect working capital. Extending payment terms from 30 to 60 days on $100 million of annual spend frees up approximately $8 million in working capital — real cash that the organisation can deploy elsewhere. Procurement is uniquely positioned to negotiate these terms because it controls the commercial relationship.

Inventory optimisation is equally significant. Procurement decisions about order quantities, delivery frequencies, and safety stock levels directly determine how much working capital is tied up in inventory. The Hackett Group's 2025 U.S. Working Capital Survey identified $1.7 trillion in trapped liquidity across the companies studied; the European equivalent found €1.4 trillion. Much of this is locked in inventory and payables that procurement can directly influence.

Supply chain financing and dynamic discounting programmes create additional value by allowing buyers to offer early payment to suppliers (at a discount) when the buyer has excess cash, or to extend payment terms when they don't. These programmes generate returns that typically exceed the organisation's cost of capital, creating genuine value rather than just transferring it between buyer and supplier.

For procurement functions that have traditionally been measured only on cost savings, expanding the value framework to include working capital improvements often reveals a pool of value that's larger than the remaining savings opportunity on the P&L.

6. Risk reduction as value creation

Every supply disruption has a cost. When a supplier fails and the organisation has to source material at spot market prices, the premium paid is a cost that procurement could have prevented. When a quality failure causes a product recall, the cost dwarfs the savings that were achieved by choosing the cheapest supplier. When a single-source supplier raises prices by 15% because they know you have no alternative, the cost of that dependency is a procurement failure, even if the original sourcing decision looked sound.

Deloitte's CPO Survey found that the most effective risk mitigation strategies among leading CPOs are maintaining active alternative sources (74%), enabling greater visibility into the supply chain (64%), and enhancing supplier information sharing and collaboration (61%). A CDP study of over 8,000 businesses estimated that climate-related supply disruptions alone will cost $120 billion by 2026, with manufacturing and food industries most affected.

Procurement organisations that quantify the cost of risk — and that treat risk reduction as a measurable value contribution — can unlock investment in resilience measures that would otherwise be seen as pure cost. This includes supplier diversification, qualification of alternative sources, inventory buffering for critical materials, and supply chain mapping to identify and mitigate hidden dependencies.

In Australia's concentrated supply markets, this is particularly important. Many categories have limited domestic supplier options, making the cost of supplier failure disproportionately high. Procurement functions that can quantify this risk and present it alongside traditional savings metrics are more likely to secure the investment needed to build genuine supply resilience — and to get credit for the value that resilience creates.

7. Sustainability as a procurement value driver

Sustainability has moved from a reporting obligation to a genuine source of procurement value — though most organisations haven't yet made that connection operationally.

The regulatory trajectory is clear. In Australia, the Australian Sustainability Reporting Standards (ASRS) are progressively requiring large entities to report on climate-related financial risks, including Scope 3 emissions — which for most organisations are dominated by the supply chain. European regulations including the Corporate Sustainability Due Diligence Directive (CSDDD) are creating extraterritorial compliance obligations for Australian companies with European operations or customers. And the Australian Government's Environmentally Sustainable Procurement Policy is requiring businesses bidding for government work to demonstrate specific sustainability outcomes.

But the value opportunity extends beyond compliance. Sustainable procurement practices often reduce cost in parallel: reducing packaging reduces both waste disposal costs and freight costs; energy-efficient specifications reduce operating costs for the buyer; circular economy approaches to end-of-life management can convert disposal costs into recovered value. Supplier sustainability assessments frequently reveal operational risks — poor environmental management often correlates with poor quality management, financial instability, or regulatory non-compliance.

Procurement functions that integrate sustainability criteria into category strategies, supplier assessments, and total cost of ownership analysis can deliver measurable value across multiple dimensions simultaneously — cost, risk, compliance, and brand. Those that treat sustainability as a separate workstream divorced from commercial procurement will find it adds cost and complexity without delivering proportional value.

8. Procurement operating model redesign

This is the meta-lever — the one that enables all the others. Most Australian procurement functions are structured to run sourcing events and manage contracts. Their operating models, capabilities, technology, and performance metrics all reflect this transactional orientation. The next wave of value requires a fundamentally different operating model.

The Hackett Group's 2026 research found that 76% of organisations now report AI-driven improvements of 25% or more in key performance metrics as adoption scales. Deloitte found that Digital Master procurement organisations — those allocating up to 24% of their budgets to technology — achieved a 3.2 times return on their generative AI investments. But the technology alone isn't what drives the performance. It's the combination of technology and the operating model redesign that allows people to work differently.

An operating model redesign for next-wave procurement value typically involves several elements. First, automating transactional procurement — purchase order processing, invoice matching, catalogue management, routine supplier queries — to free up human capacity for strategic work. Second, investing in analytics capability — spend analytics, market intelligence, cost modelling, risk analytics — to provide the insights that drive better decisions. Third, restructuring the organisation around categories rather than around processes — so that category managers have end-to-end accountability for value creation, not just for running tenders. Fourth, redesigning the performance framework to measure total value (savings, cost avoidance, working capital, risk reduction, sustainability, innovation, stakeholder satisfaction) rather than just cost savings. Fifth, building the capability pipeline — through recruitment, development, and strategic use of external expertise — to close the skills gap that 34% of CPOs identify as a barrier to value delivery.

What this means for Australian organisations

Australian procurement functions face a particular version of this challenge shaped by the characteristics of this market.

The domestic supply market is smaller and more concentrated than in the US or Europe, which limits competitive tension in many categories. Industries like construction materials, facilities services, packaging, and professional services have seen significant supplier consolidation over the past decade. In some categories, there are effectively two or three viable suppliers nationally — which makes adversarial negotiation not just unproductive but counterproductive, since the suppliers know their position as well as procurement does.

The tyranny of distance adds logistics cost and complexity that procurement must factor into total cost decisions. A supplier in Melbourne serving sites in Perth faces fundamentally different cost economics than a supplier serving sites in Sydney. Network design and distribution configuration directly affect the supply base available to procurement — a connection that's often invisible when procurement and supply chain planning operate in silos.

Tariff and trade policy shifts — including the flow-on effects of US tariff volatility on global supply chains — are reshaping sourcing economics in real time. Australian organisations that source components, materials, or finished goods from China, Southeast Asia, or the US are navigating a tariff environment that changes faster than contract terms can adapt. This requires procurement to develop scenario planning capability and supplier optionality that the traditional annual sourcing cycle doesn't provide.

And the talent shortage in procurement and supply chain is structural, not cyclical. Hays reports that category managers, sourcing specialists, and procurement professionals with technology and sustainability expertise are among the hardest roles to fill in Australia. Strategic sourcing managers in Perth and Melbourne can command salaries up to $210,000, reflecting the scarcity of qualified professionals. This means procurement functions can't simply hire their way to better performance — they need operating models and technology that amplify the capability of the people they have.

Against this backdrop, procurement functions that continue to measure themselves primarily on negotiated savings will find themselves on a treadmill — working harder to deliver diminishing results while the organisation's expectations grow. The procurement functions that thrive will be the ones that redefine what value means, invest in the capabilities to deliver it, and build the credibility with the business to be measured on it.

This requires honest conversations with CFOs and CEOs about what procurement can and should deliver. It requires investment in technology, analytics, and people — not as cost to be minimised but as capability to be built. And it requires a procurement leadership that sees the function's role not as reducing the cost of what the organisation buys, but as optimising the total value of how the organisation engages with its supply markets.

Where to start

For procurement leaders reading this and recognising their own organisation, the question is where to begin. The answer depends on context, but there are several practical starting points that apply broadly.

Conduct a savings leakage audit. Before investing in new savings initiatives, quantify how much of your existing negotiated value is reaching the bottom line. Map contracted terms against actual prices paid, identify maverick spend by category, and quantify the gap. This exercise typically takes four to six weeks and almost always reveals more value than expected — value that requires process improvement and governance rather than new sourcing activity.

Redefine the value framework with finance. Have the conversation with your CFO about what procurement value means beyond cost savings. Build a value framework that includes cost avoidance, working capital improvement, risk reduction, and sustainability outcomes. Agree on how each will be measured and reported. This isn't a cosmetic exercise — it fundamentally changes what procurement focuses on and how it allocates its limited resources.

Identify the categories where traditional levers are exhausted. Not every category needs a new approach. Some categories still have competitive supply bases and genuine switching optionality — traditional sourcing approaches work fine there. Focus the new playbook on the categories where traditional approaches have plateaued: sole or limited source categories, long-standing supplier relationships with no recent competitive test, categories where specification drift has increased costs without corresponding value, and categories where risk concentration is high.

Invest in analytics before investing in technology. The most common mistake is buying a procurement technology platform before understanding what questions you need the data to answer. Start with spend analytics — getting clean, classified spend data that tells you where your money goes, who you're buying from, and how that compares to what you contracted. This foundation supports every other initiative in the value framework.

Build category management capability. The shift from transactional sourcing to strategic category management is the single most impactful organisational change most procurement functions can make. It requires dedicated category managers with market knowledge, analytical skills, and stakeholder credibility — not generalist buyers who run tenders across unrelated categories. If you can't hire these people (and in today's market, you may not be able to), consider supplementing with external category expertise while you build internal capability.

How Trace can help

At Trace, we work with Australian organisations to diagnose where procurement value is being lost, redesign procurement operating models for the next wave of value creation, build category strategies that go beyond sourcing events, and implement the governance, processes, and performance frameworks that make it sustainable.

Our procurement advisory spans strategic sourcing and go-to-market, category management, spend analytics, procurement operating model design, supplier relationship management, and procurement technology strategy. We work across government, health, FMCG and manufacturing, retail, and resources sectors — each with distinct procurement challenges and value opportunities.

We're independent — we don't sell procurement software, and we don't have commercial relationships with technology vendors. Our recommendations are driven by what will create the most value for your organisation, not by what generates licence revenue for a platform provider.

If your procurement function is working harder to deliver less, the answer isn't more effort on the same playbook. It's a different playbook entirely. Get in touch to discuss how we can help you find the next wave.

Trace Consultants is an Australian supply chain and procurement consulting firm. We help organisations move procurement from cost centre to value driver — with deep category expertise, rigorous analytics, and practical operating model design grounded in the realities of Australian supply markets. Visit our insights page for more on the challenges shaping Australian procurement and supply chain.

Organisational Design

What a Good Supply Chain Operating Model Actually Looks Like

David Carroll
February 2026
An operating model is the difference between a supply chain that reacts to every crisis and one that consistently delivers. Most organisations have never deliberately designed theirs — and the cost of that gap is larger than they realise.

Ask most supply chain leaders what their operating model is, and you'll get one of two responses. The first is a blank look followed by something about the org chart. The second is a detailed description of their technology stack. Neither answer is right. An operating model is not an organisational structure, and it's not a set of systems. It's the blueprint for how a supply chain actually works — how decisions get made, where accountabilities sit, how processes connect across functions, what gets measured, and how people and technology work together to deliver outcomes.

Most supply chain operating models aren't designed. They're inherited. They evolve through a succession of reorganisations, system implementations, acquisitions, and responses to crises. The demand planning process works one way because that's how it was set up when SAP went in eight years ago. The procurement team reports to finance because of a restructure three CEOs ago. The warehouse operates semi-independently because it was an acquisition that was never fully integrated. Each individual decision may have been reasonable at the time. But the cumulative effect is an operating model that nobody designed, that doesn't align with the current business strategy, and that creates friction, duplication, and gaps that cost real money and slow everything down.

The McKinsey Global Supply Chain Leader Survey has consistently found that nine in ten organisations report supply chain challenges in a given year. Gartner's Future of Supply Chain 2025 report reveals that only 29% of supply chain organisations have embraced at least three of the five key characteristics that define future readiness. These numbers aren't just about external disruption. A significant portion of what organisations experience as supply chain "problems" are actually symptoms of an operating model that doesn't work — unclear decision rights, disconnected planning processes, misaligned metrics, and structural gaps between functions that should be working in lockstep.

This article is about what a well-designed supply chain operating model actually looks like, why it matters, and how Australian organisations can close the gap between what they have and what they need.

What an operating model actually is

A supply chain operating model answers a set of foundational questions that most organisations have never explicitly addressed.

The first is scope: where does the supply chain function begin and end? Does it include procurement, or does procurement sit elsewhere? Does it extend to customer fulfilment and last-mile delivery? Does it encompass manufacturing operations, or only planning and logistics? The answer varies by organisation, but what matters is that it's deliberate — that someone has consciously decided where the boundaries sit and how the interfaces across those boundaries work.

The second is governance: how do decisions get made? Who has authority over inventory policy? Who resolves the tension between customer service levels and cost? Who decides when to accept a customer order that the supply chain can't currently fulfil? Who owns the trade-off between holding safety stock and risking stockouts? In organisations without a clear operating model, these decisions either get escalated to people who don't have enough context to make them well, or they get made implicitly by whoever happens to be in the room — which leads to inconsistency, finger-pointing, and suboptimal outcomes.

The third is process: what are the core end-to-end processes, how do they connect, and who owns each one? A supply chain has a relatively small number of critical processes — demand planning, supply planning, procurement, order management, warehousing and distribution, and the integrating process that sits above all of them (typically S&OP or IBP). In a well-designed operating model, each process has clear ownership, defined inputs and outputs, established cadences, and explicit connections to the processes it feeds and that feed it. In most organisations, these processes exist but aren't connected — demand planning runs on a different cycle to supply planning, procurement operates independently of both, and the S&OP meeting is a monthly ritual where people share information but don't actually make decisions.

The fourth is organisation: how are people structured, and what capabilities does the function need? This is the piece most people jump to first — the org chart. But the org chart should be the output of the operating model design, not the starting point. Structure follows strategy, and in the supply chain, structure should follow the answers to the previous three questions. Get the scope, governance, and process design right, and the right organisational structure usually becomes obvious. Start with the org chart, and you're likely to design something that perpetuates the existing problems.

The fifth is technology: what systems and data are needed to enable the operating model? Again, technology should serve the model, not define it. The right technology configuration depends on what the operating model requires — which decisions need real-time data, which processes need to be automated, where manual intervention adds value, and how information needs to flow across functions and between the organisation and its suppliers, customers, and logistics partners.

And the sixth is performance: how does the organisation know whether the operating model is working? What gets measured, how frequently, and by whom? How are trade-offs between competing objectives — cost versus service, speed versus efficiency, risk versus cost — quantified and managed? Performance management sounds straightforward, but in practice it's where most operating models fall apart, because organisations measure activity rather than outcomes, or measure the right things but at the wrong level, or measure everything and therefore nothing.

Why it matters now

Australian supply chain functions are under more pressure than at any point in recent memory. The combination of tariff volatility, geopolitical complexity, rising customer expectations, sustainability reporting requirements, and persistent cost pressure means that the supply chain is being asked to deliver on multiple, often competing objectives simultaneously.

Gartner's research shows that 60% of C-level executives view the external environment as unfavourable to business performance, with reduced demand and accelerated inflation identified as the most significant pressures. CSCOs are being asked to shift from reactive cost-cutting to proactive cost leadership — which requires governance, transparency, and accountability that most supply chain operating models simply don't provide.

At the same time, the talent market is tight and getting tighter. Jobs and Skills Australia's 2025 Occupation Shortage List shows that 29% of assessed occupations are in national shortage. The Supply Chain and Logistics Association of Australia has highlighted an emerging "skills cliff" — not just a shortage of warehouse labour and truck drivers (which persists), but an accelerating gap in data analytics, automation, systems engineering, and the hybrid technical-operational roles that modern supply chains require. The World Economic Forum's 2025 Future of Jobs Report reinforces ongoing global shortages in supply chain skills. Companies that can't attract or retain talent in a tight market need an operating model that makes the most of the people they have — one that puts the right people in the right roles, eliminates waste in how people spend their time, and uses technology to amplify human capability rather than create additional administrative burden.

The organisations getting ahead are treating their operating model as a strategic asset, not an administrative inheritance. Gartner's research categorises the most successful supply chain organisations as following a "Design" pathway — proactively investing in long-term capability and deliberate strategy rather than reacting to short-term pressures. Those organisations have embedded agility, resilience, and integration as structural features of how they operate, not just aspirational goals.

The six elements of a well-designed operating model

1. A planning architecture that actually integrates

The heart of a supply chain operating model is its planning architecture — the set of interconnected processes that translate business strategy and customer demand into operational execution. In most organisations, this architecture has three layers: strategic planning (network design, capacity planning, long-range sourcing — typically 1-5 year horizons), tactical planning (S&OP or IBP, demand planning, supply planning — typically 1-18 month horizons), and operational execution (scheduling, order management, warehouse operations, transport — daily to weekly horizons).

The critical word is "interconnected." Each layer should feed and constrain the layers below it — strategic decisions about network configuration should shape the parameters within which tactical planning operates; tactical plans should set the guardrails for operational execution. And information should flow upward — execution reality should inform tactical plans, which should inform strategic reviews.

In practice, these layers are usually disconnected. Strategic network reviews happen once every few years (if at all). The S&OP process runs monthly but doesn't connect to the financial planning cycle. Operational execution proceeds largely independently, reacting to whatever arrives in the order book. The result is a supply chain that's perpetually misaligned — strategic intent doesn't translate into operational reality, and operational reality doesn't inform strategic direction.

The most mature organisations have evolved their planning architecture toward Integrated Business Planning, where demand and supply planning connects directly to financial planning and strategic objectives. McKinsey's research on mature IBP practitioners shows they enjoy meaningfully better performance on metrics including forecast accuracy, inventory turns, customer service, and margin. But achieving this requires more than implementing planning software — it requires redesigning how planning decisions actually get made, who's in the room when they're made, and what information those people have access to.

2. Clear governance and decision rights

The most common symptom of a dysfunctional operating model is that nobody knows who decides. Not in theory — most organisations have RACI matrices filed somewhere. In practice: when the sales team accepts a large order that exceeds available capacity, who decides whether to expedite production (at cost), delay other orders (with service impact), or push back on the customer? When inventory levels exceed target, who decides whether to adjust production, run promotions, or write down stock? When a key supplier signals a potential disruption, who decides how to respond?

In well-designed operating models, these decisions are pre-allocated. Decision rights are defined not just at the process level but at the decision level — and they distinguish between decisions that should be made locally (by the person closest to the situation), decisions that need cross-functional alignment (because they involve trade-offs between competing objectives), and decisions that need executive escalation (because they're material or precedent-setting).

This governance architecture also includes the forums where cross-functional decisions get made — S&OP meetings, supply reviews, risk committees — with clear terms of reference, decision authority, escalation pathways, and, critically, accountability for outcomes. Too many S&OP processes are information-sharing sessions, not decision-making forums. The difference between a functional and dysfunctional S&OP is not the quality of the data deck — it's whether real decisions get made in the room and whether the people making them have the authority and information to make them well.

3. End-to-end process integration

A supply chain operating model must define the core end-to-end processes and, more importantly, the interfaces between them. The processes themselves are well-understood: plan-to-produce, source-to-pay, order-to-deliver, forecast-to-fulfil. The SCOR model provides a useful reference framework, organising supply chain processes across five areas — plan, source, make, deliver, return — with standardised metrics at each level.

But knowing what the processes are is not the same as having them work together. The most common breakdowns occur at the handoffs: between demand planning and supply planning (where forecast assumptions don't translate into executable production plans), between procurement and operations (where sourcing decisions are made without understanding manufacturing constraints, or vice versa), between planning and execution (where the plan says one thing but the warehouse or transport team does something different because they lack visibility or capability to execute the plan), and between supply chain and commercial functions (where customer commitments are made without understanding supply implications).

Process integration means that each process has defined inputs, outputs, owners, and cadences — and that the outputs of one process reliably become the inputs of the next. It means that the interfaces between processes are explicit, governed, and measured. And it means that when processes change (because the business changes, or technology changes, or the external environment changes), the interfaces are deliberately redesigned rather than left to break.

4. An organisational structure that follows the model

Once scope, governance, and processes are defined, the organisational structure should reflect them. There are three broad archetypes for supply chain organisation, and the right choice depends on the business context.

A centralised model works best when products across business units are similar, when geographic concentration allows shared services, and when leverage and consistency are the primary objectives. Centralisation enables standardised processes, leveraged procurement spend, unified planning, and consistent performance management. But it can be slow to respond to local market conditions and can feel distant from the operational reality of individual business units.

A decentralised model works best when business units are genuinely independent — different products, different markets, different supply chain characteristics. Decentralisation enables responsiveness, local accountability, and deep category expertise. But it sacrifices leverage, creates duplication, and makes it difficult to share best practices or maintain consistent performance standards.

The model that works best for most multi-business-unit organisations in Australia is what practitioners call a centre-led or federated model — where a central supply chain function sets strategy, defines standards, manages enterprise-level processes (like S&OP and strategic sourcing), and governs performance, while business unit or regional supply chain teams execute within those frameworks with appropriate autonomy. Gartner's research reinforces this, recommending that CSCOs consider which activities need enterprise-level integration and which should be differentiated at the business unit level.

The centre-led model requires only a small central team — the authors of the CLAN (Centre-Led Action Network) concept estimated 3-4 people per functional discipline — but it requires that team to be highly capable, credible with the business units, and focused on value creation rather than compliance. The central team's job is to make the business units better, not to control them.

Regardless of archetype, the organisational structure must address capability. What skills does the function need? Where are the gaps? How will those gaps be closed — through recruitment, development, automation, or outsourcing? Given that supply chain skills remain in shortage in Australia (SEEK lists over 3,000 supply chain data analyst roles alone), the capability plan needs to be realistic about the talent market and creative about how to build capability in ways that don't depend solely on hiring people who don't exist in sufficient numbers.

5. Technology that enables rather than constrains

A supply chain operating model should define what technology is needed, not start with what technology is available. In practice, most organisations do it backwards — they implement an ERP system, a WMS, a TMS, and various planning tools, and then try to build an operating model around whatever those systems can do. The result is processes shaped by system limitations rather than business needs, workarounds in spreadsheets for what the system can't handle, and technology investments that don't deliver the intended value because the operating model wasn't designed to use them effectively.

The technology component of the operating model should address several questions. What data is needed to support the decision-making architecture? Where does that data come from, and how is its quality maintained? What processes should be automated (because they're high-volume, rules-based, and don't benefit from human judgement) versus technology-assisted (where technology provides information and recommendations but humans make the final call)? Where does end-to-end visibility need to extend — within the four walls, across the domestic supply chain, across the international supply chain, into the supplier base?

Gartner reports that 82% of CSCOs expect to increase investment in supply chain technology over the next two years, and 72% of supply chain organisations have deployed generative AI in some form. But most are experiencing middling results — because the technology is being layered on top of an operating model that wasn't designed for it. Investments in AI-driven demand sensing, control towers, or digital twins deliver their full value only when the underlying operating model defines how those tools integrate with decision-making processes, who acts on the insights they generate, and how their output connects to execution.

6. Performance management that drives behaviour

The final element is performance management — the metrics, targets, reviews, and incentives that tell people what matters and drive behaviour accordingly. This sounds simple but is routinely done poorly.

The most common mistake is measuring too many things. A supply chain dashboard with fifty KPIs measures nothing, because no one can manage fifty metrics simultaneously. A well-designed performance framework has a small number of top-level metrics (typically five to eight) that capture the essential trade-offs the supply chain must manage: cost efficiency (supply chain cost as a percentage of revenue, not an absolute dollar figure — Gartner's research emphasises this distinction), service performance (order fill rates, on-time delivery, customer satisfaction), asset efficiency (inventory turns, working capital, capacity utilisation), risk and resilience (supplier concentration, disruption response time, plan adherence), and increasingly, sustainability (Scope 3 emissions, waste reduction, circular economy metrics — with 67% of CSCOs now accountable for environmental and social sustainability KPIs according to Gartner).

Below these top-level metrics, more detailed operational KPIs cascade to individual process owners. But the cascade should be deliberate — each lower-level metric should connect logically to a top-level outcome, and people should understand the connection between what they're measured on and what the supply chain is trying to achieve.

Equally important is how performance is reviewed. The operating model should define the cadence and forums for performance review — from daily operational huddles to weekly tactical reviews to monthly S&OP meetings to quarterly business reviews. Each forum should have a clear purpose, the right attendees, the right data, and the authority to make decisions. And the performance framework should include explicit recognition that some objectives conflict — that pursuing cost reduction may reduce service, that increasing resilience may increase inventory, that sustainability investments may not have short-term financial returns. The operating model should define how those trade-offs are managed, not leave them to individual interpretation.

The common patterns we see in Australian organisations

Having described what good looks like, it's worth naming the patterns that most Australian organisations exhibit — not to be critical, but because recognising the pattern is the first step toward changing it.

The inherited model. The operating model was never deliberately designed. It evolved through successive reorganisations, system implementations, and leadership changes. Individual components may work well, but they don't connect into a coherent whole. Planning, procurement, logistics, and operations each optimise within their silo, and the end-to-end result is suboptimal.

The ERP-shaped model. The operating model is defined by what the ERP system does. Processes follow system workflows rather than business logic. Planning tools are used for transaction management rather than decision support. The system dictates the operating cadence rather than the business need determining what the system should do.

The person-dependent model. The operating model works because of specific individuals who bridge the structural gaps through personal relationships, institutional knowledge, and sheer effort. When those individuals leave — which in a tight talent market, they increasingly do — the gaps they were papering over become immediately visible.

The strategy-execution gap. The organisation has a supply chain strategy, often articulated in a well-crafted presentation. But the operating model — the processes, governance, structure, and capabilities that would need to exist to deliver that strategy — hasn't been designed. The strategy says "demand-driven," but the planning process is still forecast-push. The strategy says "customer-centric," but service levels are managed by product rather than by customer segment. The strategy says "integrated," but procurement, operations, and logistics still report to different executives with different incentives.

The perpetual firefighting model. Teams spend most of their time reacting to urgent operational issues — stockouts, delivery failures, supplier problems, quality issues — and have no bandwidth for the strategic and tactical work that would prevent those issues from recurring. This pattern is self-reinforcing: because there's no time to fix the root causes, the fires keep burning, which consumes more time, which leaves even less capacity for improvement. Breaking this cycle requires a deliberate redesign of how the function spends its time — which is fundamentally an operating model question.

How to get from here to there

Designing or redesigning a supply chain operating model is a significant undertaking. McKinsey estimates that a typical supply chain transformation is an 18-24 month effort requiring motivated leadership, careful sequencing, and substantial investment in change management. It shouldn't be approached lightly — but it shouldn't be deferred indefinitely either, because the cost of operating with a dysfunctional model accumulates every day.

The practical approach involves several phases.

Diagnose the current state honestly. Map the existing operating model — not how it's documented, but how it actually works. Where do decisions actually get made? What processes actually run, on what cadence, with what inputs? Where are the disconnects, duplications, and gaps? Where do people spend their time, and how much of that time is on value-adding work versus firefighting and administrative overhead? This diagnostic phase requires honesty and a willingness to hear uncomfortable truths from the people who live within the current model every day.

Define what the operating model needs to deliver. Start with the business strategy and the supply chain strategy, and translate those into specific requirements for the operating model. If the strategy requires demand-driven responsiveness, the operating model needs short-cycle planning, real-time demand sensing, and flexible execution capability. If the strategy requires cost leadership, the operating model needs rigorous performance management, leveraged procurement, and process standardisation. If the strategy requires resilience, the operating model needs risk visibility, scenario planning capability, and pre-defined response protocols.

Design the target model. Work through the six elements — scope, governance, process, organisation, technology, performance — in sequence. Design the model from the outside in: start with what external outcomes the supply chain needs to deliver (customer service, cost position, working capital, risk profile, sustainability), then design the processes that deliver those outcomes, then the governance that ensures those processes work, then the organisation that staffs them, then the technology that enables them, then the performance framework that manages them.

Plan and execute the transition. A new operating model can't be implemented overnight. It requires a phased approach — prioritising the changes that will have the greatest impact, sequencing structural and process changes so they reinforce each other, investing in training and capability development to ensure people can operate effectively in the new model, and managing the change explicitly through communication, engagement, and support. This is where most operating model redesigns fail — not in the design, but in the execution. The design is intellectually satisfying; the change management is hard, human work.

Embed and continuously improve. An operating model isn't a one-time exercise. It should be reviewed periodically — particularly when the business strategy changes, when significant external changes occur (new markets, acquisitions, regulatory shifts), or when performance data indicates the model isn't delivering as intended. The best operating models include feedback loops that allow for continuous adjustment, not just periodic redesign.

How Trace can help

At Trace, we work with organisations across FMCG and manufacturing, retail and consumer, resources and energy, health and human services, and government and defence to design and implement supply chain operating models that actually work. Our engagements span operating model diagnostics, planning and operations process design (including S&OP and IBP), organisational design and capability assessment, governance framework design, technology strategy and selection, performance framework development, and the change management required to make it all stick.

We're independent — we don't implement ERP systems, sell planning software, or have commercial relationships with technology vendors. That means our operating model recommendations are driven by what your business needs, not by what a particular software platform can do.

We've written previously about why supply chain AI projects fail — and one of the core reasons is that organisations layer technology onto broken operating models and wonder why they don't get results. We've also explored the real cost of reshoring and the most common supply chain problems in Australia. In each case, the operating model is a central factor in whether organisations navigate these challenges well or poorly.

If your supply chain operating model was inherited rather than designed — or if it was designed for a business that no longer exists — it's worth the investment to get it right. Get in touch to discuss how we can help.

Trace Consultants is an Australian supply chain and procurement consulting firm. We help organisations design and deliver supply chain operating models that connect strategy to execution — grounded in deep operational experience and independent of any technology vendor or platform. Visit our insights page for more on the challenges shaping Australian supply chains.

People & Perspectives

The Real Cost of Reshoring: What Australian Businesses Get Wrong About Bringing Supply Chains Closer to Home

James Allt-Graham
February 2026
Everyone's talking about reshoring. Far fewer are doing the maths properly. For Australian businesses, the decision to bring supply chains closer to home involves trade-offs that are routinely underestimated — and the right answer is rarely all-or-nothing.

There's a powerful narrative running through Australian boardrooms right now, and it goes something like this: global supply chains are broken, China is too risky, tariffs are making imports expensive, the government is offering incentives, and we should be making more things here — or at least closer to here. It's a narrative with genuine merit. But it's also a narrative that glosses over the economics, underestimates the complexity, and risks leading organisations into decisions they'll regret.

The reshoring conversation in Australia has intensified significantly since 2020. The pandemic exposed just how dependent the country had become on extended global supply chains — for PPE, for pharmaceuticals, for basic manufactured goods. Then came the geopolitical tensions with China, the Red Sea shipping disruptions, and the tariff shocks of 2025. Each new disruption reinforced the same message: distance is risk, and dependence on a single source is dangerous.

The Australian government has responded with policy settings that explicitly encourage domestic manufacturing. The Future Made in Australia Act, introduced in the 2024 federal budget, allocates approximately $22.7 billion over ten years to support clean energy and advanced manufacturing — including production tax credits for critical minerals processing and renewable hydrogen. The National Reconstruction Fund provides $15 billion in financing for projects that diversify and transform Australian industry, though it has faced criticism for slow deployment. A $5 billion Net Zero Fund, announced in September 2025 and delivered through the NRF, targets industrial decarbonisation.

Meanwhile, the Ai Group's 2025 Trade and Supply Chain Survey shows that 44% of Australian manufacturers intend to increase supply chain investment in 2026, with digital technologies and AI a focus. HKTDC's sourcing analysis confirms that many Australian businesses are adopting a China-plus-one strategy, diversifying their supplier base to reduce single-source reliance, with some considering reshoring operations and investing in local suppliers.

All of this creates momentum. And momentum, without rigorous analysis, is where expensive mistakes get made.

The unit cost trap

The most common mistake in reshoring analysis is comparing the wrong numbers. Specifically, comparing the unit manufacturing cost of a product made in Australia (or Vietnam, or Indonesia) against the unit cost from China — and concluding that the gap is too large to justify a move.

This comparison is misleading in both directions.

It understates the true cost of offshore sourcing by ignoring the hidden costs that accumulate across an extended supply chain: international freight and insurance, customs duties and tariff exposure, inventory carrying costs driven by long lead times, quality inspection and rework costs, travel and communication costs for supplier management, intellectual property risk, compliance management across jurisdictions, and the opportunity cost of slow responsiveness to market changes.

The Reshoring Initiative in the United States has documented this consistently. Their research shows that making sourcing decisions based solely on unit price typically results in a 20-30% understatement of true offshoring costs. When organisations conduct a proper total cost of ownership analysis — accounting for freight, duty, inventory, quality, risk and overhead — roughly 25% of what is currently sourced offshore would be more profitably reshored, even without tariff protection.

But the unit cost comparison can also overstate the case for reshoring by ignoring the genuine cost disadvantages of domestic production. Australian manufacturing labour costs are among the highest in the world. Energy costs have been rising. The domestic supplier base for many components and materials is thin or non-existent. Regulatory compliance costs are significant. Scale economies that exist in Chinese or Southeast Asian manufacturing clusters are difficult to replicate in a market of 26 million people. The Productivity Commission has cautioned that supporting industries without long-term competitive advantage can lead to ongoing costs — a warning that deserves attention in the current policy environment.

The honest answer, for most Australian businesses, sits somewhere in the uncomfortable middle: reshoring makes sense for some products, in some categories, under some conditions — and not for others. Getting that assessment right requires analytical rigour, not enthusiasm.

What total cost of ownership actually means in the Australian context

A proper TCO analysis for an Australian business considering reshoring or nearshoring should account for at least the following cost categories, each of which plays out differently depending on the product, the source market, and the supply chain configuration.

Manufacturing cost differential. The starting point, but only the starting point. Australian manufacturing wages are significantly higher than alternatives in Southeast Asia. But productivity differences, automation potential, and quality costs can narrow the gap substantially. For highly automated production where labour content is low, the wage differential matters less. For labour-intensive production, it matters enormously.

Logistics and freight. Australia is an island continent. Everything that arrives from offshore comes by sea or air, through a relatively small number of ports and airports. Freight costs from China to Australia have been volatile — spiking during the pandemic, normalising, then spiking again during Red Sea disruptions. But the baseline cost of shipping from Southeast Asia is actually lower than many assume for standard containerised goods. The logistics cost advantage of domestic production is real but often overstated for shelf-stable, non-perishable products. It's most significant for bulky, low-value items where freight is a large percentage of landed cost, and for products requiring temperature control or other special handling.

Inventory carrying costs. This is where the economics get interesting, and where many organisations undercount the cost of offshore sourcing. When your supply chain involves 4-6 month lead times from Asia (common for many Australian importers), you need to carry substantially more inventory than if you were sourcing domestically or from a nearby region. At current capital costs — the Reserve Bank cash rate has been elevated, and weighted average cost of capital for most Australian businesses sits between 8-12% — carrying excess inventory is expensive. A business with $50 million in inventory is spending $4-6 million per year just on the cost of capital tied up in stock. If domestic or nearshore sourcing could reduce that inventory by 30% through shorter lead times and more frequent ordering, that's $1.5-1.8 million in annual savings to offset against any unit cost premium.

Tariff and trade policy exposure. The tariff landscape has been volatile. Australian exporters paid approximately A$1.4 billion in additional duties following US tariff impositions, and the full effects of trade policy changes are still flowing through. The February 2026 US Supreme Court ruling that the use of IEEPA for tariffs was unlawful, and the subsequent replacement with 15% Section 122 tariffs, illustrates the unpredictability. For Australian importers, tariff exposure works differently — but the principle is the same: trade policy can change the economics of your supply chain overnight, and concentrated sourcing from a single geography amplifies that risk.

Quality and compliance costs. Managing quality across distance is expensive. Inspection regimes, factory audits, sample testing, rework and returns all cost more when your supplier is thousands of kilometres away. For products subject to TGA regulation, food safety requirements, or other Australian compliance standards, the cost of ensuring offshore suppliers meet requirements — and demonstrating that compliance to regulators — adds meaningfully to the total cost. Modern slavery reporting requirements add another layer of due diligence cost for extended supply chains.

Responsiveness and speed to market. This is harder to quantify but often strategically decisive. When your supply chain has 4-6 month lead times, you're making production decisions based on forecasts that are inherently uncertain. When demand shifts — a product takes off unexpectedly, a competitor launches, a promotion overperforms — you can't respond quickly. You either miss the opportunity or you've overcommitted to stock you can't sell. Domestic or nearshore sourcing with shorter lead times allows smaller, more frequent orders, better matching of supply to actual demand, and faster response to market changes. For businesses in categories with short product lifecycles, seasonal patterns, or promotional intensity, this responsiveness has real financial value.

Risk and resilience. The cost of supply chain disruption is notoriously difficult to quantify in advance but very real when it happens. The Ai Group found that 81% of Australian businesses that experienced supply chain disruptions reported increased costs, and 44% said disruptions constrained their growth. A diversified supply base — which might include some domestic or nearshore sourcing — reduces concentration risk. The question is whether the premium for that diversification is worth paying as a form of insurance, and the answer depends on how much disruption would actually cost your business.

The geography that matters: why "reshoring" for Australia is really "nearshoring to Southeast Asia"

When American companies talk about reshoring, they often mean bringing production back to the United States — a market of 330 million people with deep manufacturing capability, an extensive domestic supplier base, and massive government incentives. When Australian companies talk about reshoring, the reality is more nuanced.

Australia's manufacturing sector accounts for roughly 5.7% of GDP. The domestic supplier base for many manufactured components is thin. ABS data shows the sector employed approximately 902,000 people at June 2024, and while Industry Value Added grew modestly to $134.8 billion, EBITDA actually declined by $3.6 billion — highlighting the profitability pressures that make cost competitiveness a genuine challenge.

For most Australian businesses, the practical supply chain reconfiguration opportunity is not "bring everything back to Australia" — it's a combination of strategic decisions about what to make domestically, what to source from nearby alternative markets in the Asia-Pacific region, and what to continue sourcing from established offshore suppliers with better risk management.

The alternative markets have different profiles, and understanding those differences matters.

Vietnam has become the most common first alternative to China for Australian importers. It offers competitive labour costs, a growing industrial base, government support for export manufacturing, and improving infrastructure. It's also a member of both CPTPP and RCEP, providing preferential trade access. But capacity constraints are real — particularly for complex products — and quality consistency can be variable. Shipping times to Australia are somewhat shorter than from China, but not dramatically so for most trade lanes.

Indonesia is closer to Australia than any other major manufacturing economy, which creates genuine freight cost and lead time advantages. It has a large domestic market, competitive labour costs, and government interest in developing export manufacturing capability. But the manufacturing base is concentrated in certain categories, the regulatory environment requires careful navigation, and infrastructure outside Java can be challenging. The proximity advantage is most significant for bulky, low-value goods where freight cost is decisive.

India offers enormous scale and a strong engineering base, particularly for pharmaceuticals, chemicals, and IT-enabled services. But inland logistics infrastructure remains challenging, bureaucratic complexity is real, and quality consistency across suppliers varies widely. It's typically a stronger option for businesses with the resources to invest in supplier development and ongoing relationship management.

Domestic Australian manufacturing makes the strongest case in specific circumstances: where quality control and IP protection are paramount (defence, medical devices, specialised industrial products); where speed to market is a competitive differentiator; where the product is bulky and low-value relative to freight cost; where government procurement requirements mandate or incentivise local content; or where automation can offset labour cost disadvantage. The National Reconstruction Fund and Future Made in Australia incentives can shift the economics for certain product categories — particularly in clean energy, critical minerals processing, battery manufacturing, and medical technology.

The right answer for most Australian businesses is a deliberate portfolio approach: different products and components sourced from different locations based on a rigorous assessment of total cost, risk, responsiveness, and strategic importance. Not a wholesale shift in any one direction, but a conscious diversification of the supply base.

The five mistakes organisations make

Having set out the analytical framework, it's worth naming the specific mistakes we see most often when Australian organisations approach reshoring or supply chain reconfiguration.

Mistake one: comparing unit costs instead of total cost of ownership. We've covered this, but it bears repeating because it's so common. The unit price from the factory gate is the starting point of the analysis, not the answer. Organisations that make sourcing decisions on unit cost alone consistently underestimate the true cost of offshore supply by 20-30% — and overestimate the premium for domestic or nearshore alternatives by a corresponding margin.

Mistake two: underestimating the transition cost and timeline. Qualifying a new supplier — whether domestic, nearshore, or in an alternative offshore market — takes time. For simple products, six months might be sufficient. For complex manufactured goods, medical devices, food products, or anything with regulatory requirements, twelve to eighteen months is more realistic. And that's just qualification — building genuine dual-source capability, where the alternative supplier can reliably deliver at volume and quality, takes longer still.

During the transition, you're typically carrying cost in both the old and new supply chains: dual tooling, parallel testing, additional inventory buffers, project management overhead, travel costs. These transition costs are routinely underestimated and can be significant — enough to eliminate the projected savings from the switch for the first two to three years. Organisations that don't budget for the transition realistically either abandon the project partway through or deliver it over budget and behind schedule.

Mistake three: treating reshoring as an all-or-nothing decision. Some products should be reshored or nearshored. Others shouldn't. Treating the entire product portfolio the same way — either moving everything or moving nothing — misses the opportunity for a smarter, more nuanced approach. The most effective supply chain strategies segment the portfolio: critical components, high-risk categories, and products where responsiveness matters most get prioritised for diversification; commodity inputs where cost is the primary driver and supply risk is manageable stay with established low-cost sources.

Mistake four: ignoring the procurement capability required. Managing a diversified supply base across multiple countries is harder than managing concentrated sourcing from a single market. It requires more sophisticated procurement capability — in supplier identification and qualification, contract management, quality assurance, logistics coordination, trade compliance, and supplier relationship management. Many Australian organisations have procurement teams that are already stretched managing their existing supplier base. Adding new suppliers in new markets, with different languages, different regulatory frameworks, different quality systems and different commercial practices, requires capability investment that needs to happen concurrently with the sourcing transition.

Mistake five: making the decision reactively rather than strategically. The worst time to diversify your supply base is during a crisis — when you're scrambling for alternatives, your negotiating position is weak, and you don't have time for proper due diligence. The best time is before you need to, when you can assess alternatives methodically, qualify suppliers properly, negotiate from a position of strength, and build optionality into your supply chain before it's urgently needed.

The organisations that are best positioned right now began their diversification programs in 2022 or 2023, giving alternative suppliers time to qualify, build capacity, and prove their reliability before disruption hit. Organisations starting now are already behind — but starting late is better than not starting at all, because the forces driving supply chain reconfiguration (geopolitical tension, tariff volatility, Scope 3 reporting requirements, regulatory complexity) are structural, not cyclical. They're not going away.

What the policy environment means for Australian supply chain decisions

The Australian government's industrial policy settings are the most interventionist in a generation, and they create real incentives that can shift the economics for specific categories.

The Future Made in Australia Act commits approximately $22.7 billion over ten years. This includes production tax credits for critical minerals processing and renewable hydrogen, substantial funding through ARENA for clean energy technology manufacturing (batteries, electrolysers, solar components), and a $5 billion Net Zero Fund targeting industrial decarbonisation. The NRF's recent investments — including $75 million in Alpha HPA's alumina facility in Gladstone and $20 million in Diraq's quantum technology — signal the types of projects being supported.

For businesses operating in the targeted sectors — critical minerals, clean energy, battery manufacturing, medical technology, defence — these incentives can genuinely change the reshoring calculus. A production tax credit that covers a material portion of the cost disadvantage versus offshore production can make domestic manufacturing viable where it otherwise wouldn't be.

But there are important caveats. The incentives are concentrated in specific sectors. If your business makes consumer electronics, textiles, general industrial components, or other products outside the priority areas, the government support is limited. The NRF has faced criticism for slow deployment — stakeholders have noted that much of the funding remains unutilised. And the Productivity Commission has warned about the risks of supporting industries that don't have long-term competitive advantage, cautioning that the era of getting the most competitively priced goods on the global market is coming to a close, with consumers and businesses paying more as supply chains duplicate and fragment.

For supply chain leaders, the policy environment is a factor in the analysis, not the answer. Government incentives can shift the economics at the margin, and they should be factored into any TCO assessment. But they don't eliminate the underlying cost disadvantage of Australian manufacturing for most product categories, and they introduce their own risks — policy settings change with governments, sunset clauses expire, and eligibility criteria evolve. A reshoring decision that only works with a government subsidy is a decision that carries political risk alongside the commercial risk.

The Scope 3 connection

One dimension that's increasingly relevant but rarely integrated into reshoring analysis is the interaction with Scope 3 emissions reporting. Under AASB S2, large Australian organisations will be required to report Scope 3 emissions from their second reporting year — which for the first tranche (revenue exceeding $500 million) means mid-2026.

This changes the reshoring conversation in a subtle but important way. Transport emissions are a significant component of Scope 3 for most importers. Sourcing from Southeast Asian alternatives — particularly Indonesia, which is closer to Australia than China — reduces transport emissions compared to longer trade lanes. Domestic sourcing eliminates international transport emissions almost entirely.

At the same time, the emissions profile of manufacturing itself varies enormously by country. Manufacturing in a country with a coal-heavy energy grid may generate higher production emissions than manufacturing in Australia (which has a growing renewable energy share) or in a country with a cleaner energy mix. A proper Scope 3 analysis needs to consider both transport and production emissions — not just proximity.

The practical implication is that Scope 3 reporting requirements are adding another variable to the total cost of ownership calculation. Organisations subject to mandatory reporting will need visibility into the emissions profile of their supply chain, and sourcing decisions will increasingly need to account for emissions alongside cost, quality, risk and responsiveness. This doesn't automatically favour reshoring — but it does favour supply chain transparency, supplier engagement, and the kind of rigorous analysis that most organisations haven't yet invested in.

What a good reshoring assessment looks like

If your organisation is considering supply chain reconfiguration — whether that's reshoring to Australia, nearshoring to Southeast Asia, or diversifying away from concentrated offshore sourcing — the assessment should follow a structured approach.

Start with supply chain mapping. Before you can assess alternatives, you need to understand your current supply chain in detail. Where are your tier-one suppliers located? What about tier-two and tier-three? Where are the concentration risks — multiple products or components sourced from a single supplier, a single region, or a single trade lane? What percentage of your spend goes through your top ten suppliers? How many critical items are single-sourced? Many organisations can't answer these questions without significant data gathering across fragmented procurement systems and business units.

Segment your portfolio. Not every product or component needs the same supply chain strategy. Segment based on a combination of supply risk (concentration, geopolitical exposure, lead time vulnerability, supplier financial health), strategic importance (revenue impact, customer impact, substitutability), and value characteristics (unit cost, freight sensitivity, shelf life, quality criticality). High-risk, high-importance categories are the priority for diversification. Low-risk commodity inputs with well-functioning competitive supply markets are not.

Conduct total cost of ownership analysis for priority categories. For the categories you've identified as priorities, model the true total cost across alternative sourcing scenarios — current state, nearshore alternative, domestic alternative, diversified portfolio. Include all the cost categories discussed earlier: manufacturing cost, logistics, inventory carrying costs, tariff exposure, quality and compliance, responsiveness value, and risk premium. Be honest about the transition costs and timeline.

Assess alternative markets and suppliers. For each priority category, identify potential alternative suppliers in target markets. Assess them against capability, capacity, quality, financial stability, ESG compliance, and willingness to invest in the relationship. This is where deep procurement expertise matters — identifying and qualifying suppliers in unfamiliar markets requires knowledge of those markets, established networks, and structured evaluation frameworks.

Develop a phased transition plan. Don't try to move everything at once. Prioritise based on the combination of risk urgency, financial impact, and readiness (both your readiness and the alternative supplier's readiness). Plan for dual sourcing periods where you're running both old and new supply chains in parallel. Budget for the transition costs realistically. Set clear milestones and decision gates.

Integrate with broader supply chain strategy. Reshoring and nearshoring decisions don't sit in isolation. They connect to your network design (where are your distribution centres relative to where you're now sourcing?), your planning processes (shorter lead times from nearshore sourcing change inventory parameters and planning horizons), your warehousing and distribution configuration, your Scope 3 reporting obligations, and your resilience strategy. The best outcomes come from treating supply chain reconfiguration as an integrated strategic exercise, not a standalone procurement project.

The honest conclusion

Reshoring and nearshoring are real, strategically important options for Australian businesses. The forces driving supply chain reconfiguration — geopolitical volatility, tariff uncertainty, regulatory complexity, Scope 3 requirements, and the painful lessons of recent disruptions — are structural and enduring.

But the decision to reconfigure your supply chain should be based on rigorous analysis, not narrative momentum. The economics are more complex than most commentary suggests. The transition is harder, slower, and more expensive than most business cases assume. The right answer is almost always nuanced — a portfolio approach that puts different products and components in different places based on a clear-eyed assessment of total cost, risk, responsiveness, and strategic importance.

The organisations getting this right are treating it as a multi-year strategic program, not a reactive response to the latest disruption. They're investing in the analytical capability to make good decisions, the procurement capability to execute them, and the supply chain design thinking to integrate sourcing decisions with network, planning, and distribution strategy.

How Trace can help

At Trace, we work with Australian organisations across FMCG and manufacturing, retail and consumer, resources and energy, health and human services, and government and defence on exactly these decisions. Our work spans supply chain risk assessment and exposure mapping, total cost of ownership analysis across alternative sourcing scenarios, market analysis and alternative supplier identification, procurement strategy that integrates diversification with cost and service objectives, supplier qualification and evaluation frameworks, transition planning and project management, and network design that reflects the new sourcing configuration.

We're independent — we don't have commercial relationships with suppliers, logistics providers, or technology vendors that would influence our recommendations. Our advice is based entirely on what's right for your organisation.

We've written previously about how tariff disruption is affecting Australian businesses and about the broader challenges shaping Australian supply chains in 2026. If you're considering supply chain reconfiguration and want a rigorous, independent assessment of what makes sense for your business, get in touch.

Trace Consultants is an Australian supply chain and procurement consulting firm. We help organisations make better supply chain decisions — grounded in data, informed by deep operational experience, and independent of any vendor or supplier interest. Visit our insights page for more on the challenges shaping Australian supply chains.

Technology

Why Most Supply Chain AI Projects Fail — And What Australian Organisations Should Do Differently

Mathew Tolley
February 2026
A 2025 MIT study found 95% of enterprise AI pilots deliver no measurable return. Four in five organisations see no bottom-line impact, and only 16% have scaled AI in supply chain. AI is mature, platforms exist, budgets are approved—yet most projects still fail. This article explains why, through the lens of Australian supply chain and operations teams.

Let's get the uncomfortable truth out of the way first.

The supply chain AI market is growing rapidly. Investment is accelerating. According to a Deloitte survey, 85% of leaders increased their AI investment over the past year. The Australian Industry Group reports that 27% of industrial businesses rank AI-powered solutions as a top investment priority for 2026. Platforms like o9 Solutions, Kinaxis, Blue Yonder and SAP IBP are competing aggressively for enterprise adoption. The technology itself — for demand forecasting, inventory optimisation, logistics routing, supplier risk, warehouse automation — is genuinely capable.

And yet, the results are overwhelmingly disappointing.

An MIT study published in mid-2025 found that 95% of enterprise generative AI pilots delivered zero measurable return on investment. Forbes reports that upwards of 85% of AI initiatives don't make it into production, and of those that do, a significant portion fail to deliver the expected value. McKinsey's 2025 State of AI survey found that while 88% of organisations use AI in at least one function, only about a third have managed to scale it beyond experiments or pilots — and only 39% report any improvement to EBIT at all, with most of those seeing less than 5% impact.

For supply chain specifically, the picture is equally stark. McKinsey research found that only 16% of companies have successfully scaled AI in supply chain operations, despite 68% claiming to use it. PwC's 2025 Digital Trends in Operations Survey reported that 92% of operations and supply chain leaders cite at least one reason why their technology investments haven't fully delivered expected results, with integration complexity (47%) and data issues (44%) the most common culprits.

These numbers should give every Australian supply chain leader pause — not because AI doesn't work, but because something between the investment decision and the operational outcome is consistently going wrong. Understanding what that something is, and how to avoid it, is worth more than any vendor demonstration or conference keynote.

The gap isn't technology. It's everything around the technology.

The most important thing to understand about supply chain AI failure is that the technology itself is almost never the problem. The algorithms work. Machine learning can genuinely improve forecast accuracy. Optimisation engines can identify inventory savings that humans miss. Predictive models can flag supplier risks earlier than manual monitoring. Computer vision can improve quality control. Natural language processing can accelerate document review and contract analysis.

The failures happen in the space between the technology's capabilities and the organisation's ability to use them. As one analysis put it, the high failure rate observed today reflects implementation challenges, not inherent limitations of AI. Companies that mistake these struggles for permanent constraints risk missing the window to build a competitive advantage.

This is a critical distinction. When an AI project fails, the instinct is often to blame the tool — the vendor oversold it, the model wasn't accurate enough, the platform was too complex. Sometimes those things are true. But far more often, the project failed because the organisation wasn't ready for it — and nobody assessed that readiness honestly before committing budget.

Let's look at the specific patterns.

Pattern one: starting with the technology instead of the problem

This is the most common and most expensive mistake. An organisation sees a compelling vendor demonstration, reads about what a competitor is doing, or faces board pressure to "do something with AI." So they select a platform, launch a pilot, and then try to find a problem for it to solve.

It happens more than you'd think. Gartner found that 37% of leaders in low-maturity AI organisations identified "finding the right use case" as one of their top three implementation barriers. That statistic should be alarming — it means more than a third of organisations are investing in AI without being clear about what they're trying to achieve.

In supply chain, the technology-first approach typically looks like this: a team selects an AI-powered demand planning tool, runs it alongside their existing forecast for a few months, gets results that look promising in a controlled test environment, and then struggles to integrate those results into their actual planning process. The forecast might be better in theory, but if the planning process doesn't change, if planners don't trust the output, if the data feeding the model isn't maintained to the standard the model requires, the theoretical improvement never translates into operational results.

The organisations that succeed with AI start from the other end. They identify the operational decisions that matter most and are currently made poorly — where is the most value being lost? — and then ask whether AI can improve those decisions. Sometimes the answer is yes. Sometimes the answer is that better data, cleaner processes, or more disciplined governance would improve the decision without any AI at all. Either way, the improvement is grounded in a real operational problem rather than a technology aspiration.

Pattern two: the data foundation isn't there

If there is one theme that dominates every credible analysis of AI failure in supply chains, it's data. Not as an abstract concept, but as a practical, operational blocker.

PwC's survey identified data issues as the second most common reason technology investments fail to deliver (44% of respondents). BCG research found that 61% of supply chain leaders cite poor data quality and system integration as the top barriers to successful AI implementation. Gartner's research confirmed that data availability and quality remain among the top challenges regardless of AI maturity — identified by 34% of leaders in low-maturity and 29% in high-maturity organisations.

In Australian supply chains, the data challenge has several dimensions that are worth spelling out because they're often underestimated.

Fragmented systems. Most organisations of any scale run multiple systems across their supply chain — an ERP (sometimes more than one, particularly after acquisitions), a WMS, a TMS, procurement systems, demand planning tools, spreadsheets that bridge the gaps, and manual processes that bridge the gaps between the spreadsheets. Each system holds part of the picture. AI models need the full picture. Getting data out of these systems, aligning it, cleaning it and maintaining it at the quality the model requires is not a small task. It's a foundational infrastructure project — and most organisations underinvest in it dramatically relative to what they spend on the AI tool itself.

Master data quality. Inaccurate lead times, incorrect unit conversions, outdated supplier records, inconsistent product hierarchies, duplicated customer records — these are mundane, unglamorous problems. But they directly determine whether an AI model produces useful output or garbage. When a demand forecasting model is trained on historical data where product hierarchies changed mid-period, where promotions weren't flagged consistently, where returns were recorded inconsistently, or where intercompany transfers contaminated demand signals — the forecast will be wrong in ways that are hard to diagnose and expensive to fix.

Historical data gaps. Machine learning models learn from patterns in historical data. If you've only been tracking a particular metric for twelve months, or if your data from two years ago is unreliable because it predates a system migration, or if a pandemic scrambled your demand patterns so thoroughly that three years of history are essentially unusable — the model has less to work with than you think. This is particularly relevant for Australian businesses with seasonal demand patterns that differ from Northern Hemisphere norms, or those with relatively short data histories in newer product categories.

Integration costs. The ASCLA highlighted that data quality across fragmented systems remains a persistent challenge for logistics operations, noting that when AI tries to optimise across disconnected sources, decisions can be dangerously flawed. ABI Research's 2025 supply chain survey recommended that organisations expect integration and change costs to exceed computational spend by a factor of 1.2 to 2.5 times. That's not the AI platform licence — that's the unglamorous plumbing work required to make the platform actually function.

The practical implication is straightforward but frequently ignored: the data foundation work needs to happen before the AI investment, or at minimum concurrently with a realistic assessment of how long it will take. Organisations that treat data quality as a problem to be solved during implementation rather than a prerequisite for implementation consistently overshoot timelines and budgets.

Pattern three: bolting AI onto broken processes

This one is subtle and therefore dangerous. An organisation has a demand planning process that doesn't work particularly well — forecasts are inaccurate, planners override the statistical baseline without discipline, the S&OP process is more presentation than decision-making, and inventory parameters haven't been reviewed in two years. They invest in an AI-powered planning platform expecting it to fix the problem.

It doesn't. The AI tool generates a better statistical baseline, but the same planners override it with the same undisciplined adjustments. The improved forecast feeds into the same S&OP process that doesn't connect demand to supply to finance. The inventory parameters are still wrong, so the system optimises around incorrect targets. The result: an expensive new platform producing results that aren't materially better than what came before.

KPMG identified this explicitly in their analysis of AI in supply chains: true scalability requires clean data, standardised processes, and disciplined governance. The technology layer sits on top of a process layer, which sits on top of a data layer. If any layer is broken, the layers above it can't function properly.

This is where the process design and transformation work becomes essential — not as a separate initiative from AI adoption, but as an integral part of it. The organisations that get value from AI in demand planning, for example, typically redesign their planning process concurrently: clarifying who owns the forecast, establishing governance for overrides, connecting the demand plan to supply and financial plans, and defining how the AI output is used in decision-making. The technology and the process change together.

Pattern four: the pilot trap

The data on this is unambiguous. Organisations are launching AI pilots at scale, but very few of those pilots reach production.

McKinsey's 2025 survey found that the majority of organisations are still in experimenting or piloting stages. BCG reported that 90% of vertical, function-specific AI use cases remain stuck in pilot mode. The MIT study's finding that 95% of enterprise AI pilots deliver zero measurable return isn't about pilot failure in the technical sense — many pilots "work" — it's about the gap between a successful pilot and a scaled deployment that delivers measurable business value.

The pilot trap works like this. An organisation identifies a use case, selects a vendor or builds something internally, runs a proof of concept in a controlled environment, and demonstrates that the model can produce better results than the current approach. The pilot is declared a success. Then the project stalls.

It stalls because the pilot environment was curated — clean data, engaged users, limited scope. Production is messy — inconsistent data quality, users who weren't involved in the pilot, integration with live systems, edge cases the pilot didn't encounter, organisational resistance from people whose workflows are being changed. The gap between pilot and production is not a small step — it's often a larger, more expensive, and more complex undertaking than the pilot itself.

The SupplyChainBrain analysis noted this directly: buying AI tools from specialised vendors succeeds about 67% of the time, compared to about one-third for internal builds. But even for vendor solutions, success depends on choosing the right product, configuring it for the business, and adopting it strategically — starting narrow and scaling from there rather than building something generic.

For Australian organisations, this has a practical implication. Don't conflate a successful pilot with a successful implementation. Budget and plan for the pilot-to-production journey explicitly, including data integration, process redesign, change management, user training, and ongoing model maintenance. If the business case only works at production scale, ensure there's a realistic plan and budget to get from pilot to production — or don't start the pilot.

Pattern five: underinvesting in people and change

Gartner predicts that by 2028, 60% of supply chain digital adoption efforts will fail to deliver promised value due to insufficient investment in learning and development. Their survey of 579 supply chain practitioners found that 58% identified rapid tech advancement as a major future challenge, while 40% believed hyperautomation was evolving skills requirements faster than they could adapt.

This is not a soft problem. It's the difference between success and failure.

When an AI tool changes how a planner does their job — from manually building a forecast in a spreadsheet to reviewing, adjusting and approving an AI-generated forecast — that's not a small change. It requires the planner to develop new skills: understanding what the model is doing well enough to know when to trust it and when to override it, interpreting confidence intervals and scenario outputs, managing by exception rather than rebuilding from scratch. If the planner doesn't develop those skills, they'll either ignore the AI output (making the investment worthless) or accept it blindly (introducing new risks).

The same applies across warehouse operations, procurement, logistics planning and supplier management. AI changes the nature of work in each of these functions. People need new skills, new workflows, and enough confidence in the technology to use it effectively. That requires deliberate investment in training, ongoing support, and an organisational culture that embraces experimentation and learning.

McKinsey's research confirmed that high-performing AI organisations are nearly three times more likely than others to fundamentally redesign their workflows. They don't just add AI to existing processes — they rethink how work is done. And high performers are more likely to have defined processes to determine how and when model outputs need human validation. The human-in-the-loop design is intentional, not an afterthought.

In the Australian context, where supply chain talent is already constrained — the ASCLA reports ongoing shortages of skilled drivers, warehouse staff and logistics professionals — the workforce dimension of AI adoption deserves even more attention. Organisations aren't just competing for AI-literate talent; they're asking existing teams with full workloads to adopt new ways of working while maintaining operational performance. That requires investment in capability building and organisational design that goes well beyond a vendor training session.

Pattern six: no clear success metrics

A surprisingly common contributor to AI "failure" is that nobody defined what success looks like before the project started.

Gartner's research found that mature AI organisations were more likely to define performance metrics early in the ideation phase of every use case. PwC identified unclear objectives and weak business rationale as one of the least-selected reasons for technology investment failure — which they interpret not as evidence that objectives are clear, but as a blind spot. Organisations don't realise their objectives are unclear until the project is underway and nobody can agree on whether it's working.

In supply chain, this plays out in several ways. A demand forecasting project might improve forecast accuracy at a national level but not at the SKU-location level where inventory decisions are actually made. An inventory optimisation project might reduce total stockholding but increase stockouts on critical lines. A logistics optimisation project might reduce total transport cost but create service problems for premium customers. Whether any of these outcomes counts as "success" depends entirely on what was defined upfront.

The most effective approach is brutally specific. Before committing to an AI project, define the operational metric you're trying to improve, the baseline you're measuring from, the target improvement, and the timeframe. If you can't do that — if the best you can say is "we want to use AI to improve our supply chain" — you're not ready to invest. You need a diagnostic first.

Pattern seven: trying to do too much at once

The final pattern is ambition that exceeds organisational capacity. An organisation commits to an enterprise-wide AI transformation — new demand planning, new inventory optimisation, new logistics routing, new supplier risk monitoring, all at once. The scope is overwhelming, the change burden on the organisation is unsustainable, and the project collapses under its own weight.

The evidence strongly favours a focused, sequential approach. The SupplyChainBrain analysis recommended starting with a single AI application and then adding more over time, building a solid foundation before expanding capabilities. Infios' leadership team noted that most AI initiatives fail because companies chase tools rather than solving specific problems. Supply chain AI trends research from Dataiku emphasised that organisations should start narrow, prove value, then scale — not attempt broad transformation from day one.

For Australian supply chain teams, the sequencing question is particularly important. Resources are constrained. Operational demands are relentless. Tariff disruption, Scope 3 reporting deadlines, and cost pressures are all consuming management bandwidth simultaneously. Trying to run a multi-workstream AI transformation on top of these priorities is a recipe for failure.

A phased approach works better. Start with one use case where the business case is clear, the data is relatively clean, and the process is well understood. Deliver measurable results. Build internal confidence and capability. Use the lessons from the first deployment to inform the second. This is slower than the ambition suggests, but it's how value actually gets captured.

What the organisations that succeed are doing differently

The patterns above describe how AI projects fail. The inverse — what successful organisations do — is equally instructive, and the evidence is remarkably consistent.

They start with operational problems, not technology. The organisations capturing value from AI identified specific decisions that were being made poorly, quantified the cost of those poor decisions, and then evaluated whether AI could improve them. The technology selection followed the problem definition, not the other way around.

They invest heavily in data foundations. Not as a separate initiative, but as an integral part of AI deployment. They clean master data, build integration pipelines, establish data governance, and create processes to maintain data quality over time. They treat this work as ongoing investment, not a one-time project.

They redesign processes concurrently. McKinsey's high performers are three times more likely to fundamentally redesign workflows. They don't bolt AI onto broken processes. They redesign the process to take advantage of AI capabilities — establishing new roles, new governance, new decision rights, and new ways of working.

They invest in their people. Training, change management, capability building, and ongoing support. They recognise that AI changes the nature of supply chain work and invest in helping their teams make that transition. They deploy human-in-the-loop controls deliberately, building trust in the technology over time.

They define success metrics upfront. Specific, measurable, operationally grounded. They know what they're trying to achieve before they start, and they measure progress against those targets relentlessly.

They start narrow and scale deliberately. One use case, well-executed, delivering measurable results. Then a second, informed by lessons from the first. This isn't lack of ambition — it's recognition that sustainable AI value comes from building organisational capability alongside technical capability.

They have senior leadership commitment. McKinsey found that high performers tend to have senior leaders who demonstrate strong ownership and commitment to AI initiatives. AI adoption is not delegated to IT or an innovation team — it's owned by the supply chain leadership as an operational priority.

What this means for Australian supply chain leaders

The Australian supply chain landscape creates both specific challenges and specific opportunities for AI adoption.

The challenges are real. Fragmented systems from years of organic growth and acquisition. Data quality issues that predate the AI conversation by a decade. Talent constraints that make it hard to build internal AI capability. Operational complexity driven by Australia's geography — vast distances, concentrated coastal populations, long inbound lead times, and relatively small domestic markets compared to the infrastructure required to serve them. Regulatory complexity, from Scope 3 reporting to modern slavery to product safety, consuming management attention that might otherwise go to technology transformation.

But there are opportunities too. Australian labour costs are among the highest in the world, which means AI tools that improve productivity have a shorter payback period than in lower-cost markets. The country's geographic isolation creates structural supply chain complexity — long lead times, concentrated shipping lanes, vulnerability to disruption — that AI-powered planning and risk management tools are well-suited to address. And the relatively concentrated nature of many Australian industries means that organisations that get AI right can establish meaningful competitive advantages quickly.

The practical question for most Australian supply chain leaders is not whether to invest in AI — it's how to invest in a way that actually delivers results rather than adding to the 85% failure rate.

Based on everything we've seen — in the research, in client engagements, in the pattern of successes and failures across the market — here's what we'd recommend.

Start with a diagnostic. Before committing to any AI investment, understand where your supply chain is actually losing value. Map your end-to-end supply chain costs, processes and capabilities. Identify the decisions that matter most and are currently made most poorly. Quantify the opportunity. This creates the fact base for prioritising AI investments — and it often reveals that the highest-value improvements don't require AI at all. Better data, cleaner processes, more disciplined governance, and stronger procurement practices can deliver substantial value without any algorithms.

Assess your readiness honestly. Data quality, system integration, process maturity, workforce capability — evaluate these honestly, not aspirationally. If your master data is unreliable, your first investment should be in fixing it. If your planning process doesn't work, redesign the process before layering AI on top of it. If your team doesn't have the skills to work with AI tools, invest in capability building before you invest in the tools.

Choose one use case to start. Not the most ambitious one — the one with the clearest business case, the best data foundation, and the most receptive team. In most Australian supply chains, demand forecasting or inventory optimisation are strong starting points because the data typically exists (even if it needs cleaning), the business case is quantifiable (working capital, service levels, waste reduction), and the technology is relatively mature.

Plan for the full journey. Budget and plan not just for the pilot, but for the pilot-to-production transition, the integration work, the process redesign, the change management, and the ongoing model maintenance. If the total cost of that journey exceeds what the organisation can support, reduce the scope rather than reducing the investment in the things that determine success.

Get independent advice on technology selection. The supply chain technology market is crowded, vendor claims are aggressive, and the difference between a platform that's right for your organisation and one that isn't can be the difference between success and a multi-year, multi-million-dollar disappointment. Independent advisory — from a firm that doesn't sell software and doesn't have vendor partnerships that create conflicts — is worth the investment. Trace is independent of all technology vendors, which means our recommendations are based entirely on what's right for your organisation.

Connect AI to your broader supply chain strategy. AI isn't a standalone initiative. It connects to your network design, your procurement strategy, your planning processes, your warehousing and distribution operations, your resilience planning, and your workforce strategy. The organisations that get the most value from AI are those that integrate it into a coherent supply chain improvement program rather than treating it as a separate technology project.

The role of advisory in AI adoption

There's an irony in the supply chain AI market that's worth naming. The same organisations that are underinvesting in data foundations, process design and change management — the things that determine whether AI projects succeed — are overinvesting in technology licences and vendor engagements. They're spending millions on platforms and relatively little on the advisory work that determines whether those platforms deliver value.

This isn't an argument for hiring consultants instead of buying technology. It's an argument for getting the sequence right. A well-run diagnostic, a clear readiness assessment, a disciplined use case prioritisation, a structured technology selection process, a thoughtful process redesign, and a proper change management program — these are the things that determine whether an AI investment succeeds or fails.

At Trace, this is exactly the work we do. We're a specialist supply chain and procurement consulting firm — it's all we do, which gives us the operational depth that generalist firms and technology vendors can't match. We're independent of all technology vendors, which means we can assess platforms, evaluate vendors and make recommendations based entirely on what's right for your organisation. And we understand how AI fits into the broader supply chain context — because we work across strategy and network design, procurement, planning and operations, warehousing and distribution, organisational design, technology advisory, and resilience.

We've written previously about how to adopt AI practically in your supply chain and about the seven things that need to be right before the technology matters. This article adds the failure patterns — because understanding what goes wrong is often more instructive than understanding what to aim for.

The bottom line

The supply chain AI market is not going away. The technology is getting better. The use cases are getting clearer. The competitive pressure to adopt is intensifying. Gartner predicts that 70% of large organisations will adopt AI-based supply chain forecasting by 2030. BCG reports that agentic AI systems already account for 17% of total AI value and are projected to reach 29% by 2028.

But the gap between AI investment and AI results is a real and present problem. Four in five organisations are seeing no tangible bottom-line impact from their AI initiatives. Ninety-five percent of pilots deliver zero measurable return. The organisations that are capturing value are doing fundamentally different things — not buying better technology, but building better foundations, redesigning processes, investing in people, and approaching AI with the same operational discipline they apply to every other aspect of supply chain management.

The window for getting this right is narrowing. Organisations that build the foundations now — clean data, sound processes, capable people, clear strategies — will be positioned to capture genuine value from AI as the technology continues to mature. Organisations that keep launching pilots without addressing the foundations will keep getting the same disappointing results, while falling further behind competitors who've figured out that AI success is an operational challenge, not a technology one.

If you're an Australian supply chain or operations leader thinking about your AI strategy — or wondering why your current AI investments aren't delivering the results you expected — we'd welcome the conversation. Get in touch and let's talk about what's really going on and what to do about it.

Trace Consultants is an Australian supply chain and procurement consulting firm working across FMCG and manufacturing, retail and consumer, resources and energy, health and human services, and government and defence. We help organisations navigate the intersection of supply chain strategy, technology adoption and operational improvement — with the independence and depth that comes from doing nothing else. Visit our insights page for more articles on the challenges shaping Australian supply chains.

Strategy & Network Design

The 12 Most Searched Supply Chain Problems in Australia Right Now — And What to Do About Each One

Shanaka Jayasinghe
February 2026
We track what Australian supply chain and procurement leaders are searching for. In early 2026, the same twelve problems keep appearing — driven by a combination of regulatory deadlines, trade disruption, cost pressure, technology change and workforce challenges. Some are perennial. Some are new. All of them are urgent. This guide covers every one: what's driving it, why it matters, what good looks like, and where to start.

We spend a lot of time talking to supply chain and procurement leaders across Australian organisations — in FMCG, retail, manufacturing, resources, healthcare, government and defence. The conversations vary, but the underlying problems are remarkably consistent. The same challenges keep surfacing, in boardrooms, in planning meetings, in procurement reviews, and in the search queries that bring people to our website.

In early 2026, the Australian supply chain landscape is shaped by a convergence of forces: mandatory climate reporting deadlines that are turning Scope 3 emissions into an operational emergency, a global trade environment still reverberating from tariff disruption, persistent cost pressure against thin margins, technology investment decisions that carry significant risk, labour shortages that refuse to resolve, and the ever-present question of how to make supply chains more resilient without making them more expensive.

These forces aren't theoretical. They're showing up as real, urgent problems that organisations need practical help with — not more frameworks, not more thought leadership that describes the problem without solving it, but concrete guidance on what to do, in what order, with what resources.

This article covers the twelve supply chain problems that Australian organisations are searching for answers to right now. For each one, we explain what's driving it, why it matters in the Australian context specifically, what good practice looks like, and how Trace Consultants can help. It's long — deliberately so — because each of these problems deserves serious treatment, not a paragraph and a platitude.

Before we get into the specifics, a few things are worth noting about the Australian supply chain landscape in 2026.

Business confidence remains fragile — the NAB Business Confidence Index slipped to just 1 in November 2025, the weakest reading since April, even as consumer sentiment improved. Businesses are operating near full capacity (utilisation at 83.6%, the strongest in 18 months) but with thin margins and limited room for error. This creates a paradox: organisations know they need to invest in supply chain capability, but they need those investments to deliver returns quickly and with high certainty.

The ASCLA (formerly SCLAA) identifies three priorities for 2026: leveraging technology and automation, building resilience and managing risk, and embedding sustainability into strategy. Manhattan Associates research shows 81% of Australian supply chain leaders expect new technologies to reduce freight costs and improve efficiency. FedEx's B2B Trends 2026 report describes AI adoption as "table stakes" rather than experimental, with IoT-enabled sensors detecting more than 60% of potential supply chain disruptions earlier.

Against this backdrop, here are the twelve problems that are generating the most search activity, the most client conversations, and — in our assessment — the most genuine need for practical advisory support.

1. Scope 3 Emissions Reporting and Supply Chain Decarbonisation

If there's one topic that has moved from "nice to have" to "board-level emergency" faster than any other, it's Scope 3 emissions reporting. And it's fundamentally a supply chain problem — even if many organisations are still treating it as an accounting exercise.

What's driving it. Australia's mandatory climate reporting regime under the Australian Sustainability Reporting Standards (ASRS) is now live. AASB S2 requires companies to report on climate-related risks, opportunities and greenhouse gas emissions. For the largest companies — those with revenue above $500 million — reporting started from 1 January 2025. Critically, while Scope 3 emissions disclosure is not required in the first reporting year, it becomes mandatory from the second year onwards. For the first tranche of companies, that means Scope 3 reporting is required from mid-2026.

The second tranche — companies with revenue above $200 million — faces comprehensive emissions reporting from 2026, including Scope 3. Smaller companies earning above $50 million join the mandatory framework in 2027. By that point, virtually every significant business in Australia will need to understand and report on its supply chain emissions.

The implications cascade through supply chains. Even organisations that aren't directly in scope of the reporting requirements will feel the effects. As ASIC has noted, many small businesses form part of the supply chains of larger businesses, which means they may need to engage with climate reporting considerations over time — because the Scope 3 emissions of a large reporting entity include the emissions of its suppliers. In practice, this means larger organisations will begin requesting emissions data from their suppliers, creating a ripple effect that extends the reporting requirement well beyond the entities directly in scope.

The penalties for non-compliance mirror those for financial reporting under the Corporations Act, including civil penalties for directors. This isn't soft regulation — it has teeth.

Why it's a supply chain problem. Scope 3 emissions — the indirect emissions that occur across an organisation's value chain, both upstream and downstream — typically represent 65-95% of a company's total carbon footprint, according to PwC's analysis. They include emissions from purchased goods and services, transportation and distribution, business travel, waste generated in operations, and the use and end-of-life treatment of sold products.

Measuring these emissions requires detailed understanding of the supply chain: who supplies what, from where, using what processes, with what transport modes, over what distances. It requires data from suppliers — many of whom don't yet measure their own emissions — and methodologies for estimating where direct data isn't available. The Greenhouse Gas Protocol allows the use of industry averages, proxies and spend-based methodologies, but the direction of travel is clearly toward more granular, supplier-specific data.

PwC's experience suggests that as much as 80% of an organisation's supply chain emissions can come from as few as one-fifth of its purchases. One public-sector agency found that just 20 suppliers were responsible for 94% of its Scope 3 supply chain emissions. This concentration means that targeted supplier engagement — rather than boiling the ocean — can be a practical path to both measurement and reduction.

What good looks like. Organisations that are handling Scope 3 well are treating it as a supply chain transformation opportunity, not just a compliance obligation. They're mapping their supply chains to identify emission hotspots, engaging with key suppliers on data collection and reduction targets, building internal capability to measure and manage supply chain emissions on an ongoing basis, and connecting Scope 3 insights to procurement decisions — factoring emissions into supplier selection, contract negotiation and category strategy.

They're also being honest about data limitations. The Australian standards acknowledge that Scope 3 data will be imperfect, particularly in early years, and include transitional provisions for "reasonable endeavours" in data collection. The key is to start, establish a baseline, and improve over time — not wait for perfect data before acting.

The procurement connection. One of the most practical ways to address Scope 3 emissions is through procurement. If up to 90% of a company's carbon emissions sit in its supply chain — as research from The Carbon Trust suggests — then procurement decisions are climate decisions. Supplier selection criteria should include emissions performance. Contract negotiations should address emissions reduction targets. Category strategies should identify decarbonisation opportunities within each spend category. And supplier performance management should track emissions alongside cost, quality and service.

This doesn't require a complete overhaul of procurement processes. It requires adding an emissions lens to existing processes — and having the supply chain data to support it. Organisations that already have strong procurement disciplines — structured category management, rigorous go-to-market processes, active contract governance — are well-positioned to integrate emissions management. Those that don't will struggle with Scope 3 regardless of how much they invest in emissions measurement tools.

The assurance dimension. It's worth noting that assurance requirements are being introduced gradually under the ASRS. In early years, limited assurance is required; by year four, reasonable assurance kicks in across all categories. This means companies need clear records, consistent methodologies and reliable data trails — not just a number at the end. The governance framework for Scope 3 reporting needs to be robust enough to withstand external scrutiny, which in turn requires clear ownership, documented assumptions and transparent limitations.

How Trace Consultants can help. At Trace, we approach Scope 3 as a supply chain mapping and procurement challenge. We help organisations map their supply chain emission hotspots, identify the suppliers and categories that account for the majority of Scope 3 emissions, develop data collection frameworks and supplier engagement strategies, integrate emissions considerations into procurement processes and category strategies, design the governance and organisational frameworks to sustain Scope 3 management beyond the first reporting cycle, and connect Scope 3 work with broader supply chain resilience and cost optimisation — because decarbonisation, when done well, often reduces cost and risk as well. Our independence from emissions software vendors means our advice is focused on what works operationally — not what creates the most subscription revenue.

2. Tariff Impact, Trade Disruption and Supply Chain Reconfiguration

The global trade environment has been turbulent for years, but 2025-2026 has brought a new level of complexity for Australian businesses. The search volume around tariff impact, sourcing diversification and supply chain reconfiguration is at levels we haven't seen since the pandemic.

What's driving it. The U.S. tariff regime introduced in 2025 imposed a 10% baseline tariff on Australian exports, 25% on steel and aluminium, and up to 54% on Chinese goods. Australian exporters have paid approximately A$1.4 billion in additional duties since the measures were introduced, according to industry estimates. In February 2026, the U.S. Supreme Court ruled that the IEEPA tariffs were unlawful — only for the administration to replace them with a 15% Section 122 tariff that roughly approximates the previous regime.

The Australian Industry Group reports that 47% of Australian industrial businesses are now experiencing supply chain disruptions, with the rate still rising as tariff effects work through supply chains. Among affected businesses, 81% report increased costs as the leading impact. Critically, the Ai Group notes that the full impact of already-in-force tariffs won't become apparent until early 2026, with further tariffs lagging after that.

Beyond the direct U.S.-Australia trade relationship, the secondary effects are significant. U.S. tariffs on Chinese goods are diverting Chinese exports to alternative markets — including Australia — creating competitive pressure for domestic manufacturers. Meanwhile, the broader trend toward "secure trade" rather than liberalised trade is reshaping how governments and businesses think about supply chain design.

The Australian context. Australia's position as an island continent heavily reliant on maritime and air freight for both imports and exports makes it particularly vulnerable to trade disruption. Long inbound supply chains, concentrated shipping lanes and limited domestic manufacturing capacity mean that tariff-driven reconfiguration of global supply chains has outsized impacts here.

At the same time, Australia has opportunities. The CPTPP and RCEP agreements provide rules-based access to Indo-Pacific markets with preferential tariff treatment. Australia's critical minerals endowment — lithium, rare earths, antimony — positions it as a strategic supplier in the global energy transition and defence supply chains.

What good looks like. Organisations that are navigating trade disruption effectively are mapping their tariff exposure across the full supply chain — not just direct imports but the embedded tariff costs in components sourced by their suppliers. They're modelling alternative sourcing scenarios using network design tools, diversifying supplier bases with a deliberate "China-plus-one" or multi-source strategy, leveraging free trade agreements for preferential tariff treatment, and building the agility to shift sourcing and logistics patterns as trade policy evolves.

The organisations that have coped best aren't the ones that predicted exactly what would happen. They're the ones that built optionality into their supply chains before the disruption hit.

The EU-Australia FTA dimension. Adding further complexity, the EU-Australia Free Trade Agreement — which launched negotiations in 2018 — may be finalised in early 2026. As one procurement commentator noted, this "will mean structural change flowing through to sourcing strategies, pricing structures, and long-term supply chain risk planning." The EU is Australia's third-largest two-way trading partner and second-largest source of foreign investment. For procurement and supply chain professionals, this creates both opportunity (lower tariffs on EU goods, new sourcing options) and urgency (limited runway to prepare).

Meanwhile, Australia's existing FTA network — CPTPP, RCEP, the bilateral agreements with the UK, India, UAE and others — provides a web of preferential trade arrangements that sophisticated sourcing strategies can leverage. But taking advantage of these agreements requires understanding rules of origin, tariff schedules and compliance requirements that many procurement teams are not yet equipped to navigate.

The practical response. For most Australian organisations, the response to trade disruption shouldn't be a wholesale restructuring of the supply chain. It should be a structured assessment of exposure and a pragmatic plan to build resilience. This means starting with visibility — mapping where your goods and materials actually come from, through all tiers of the supply chain, and identifying where tariff exposure and concentration risk sit. Then modelling the financial impact of different scenarios — what happens to your landed cost if tariffs on Chinese goods increase by another 10%? What if the EU FTA is finalised and you can shift sourcing to European suppliers? What if a key shipping corridor is disrupted for 60 days?

With that analysis in hand, the strategy becomes clearer: which sourcing shifts are worth pursuing now, which should be planned as options, and which are better managed through other mechanisms (inventory buffers, contractual protections, pricing pass-through).

How Trace Consultants can help. Trace has written extensively about tariff implications for Australian businesses, and we work with organisations on export and risk assessments, n-tier supply chain mapping to identify embedded tariff exposure, scenario modelling for alternative sourcing strategies, procurement strategy for supplier diversification, FTA utilisation analysis, and network design to optimise the trade-offs between cost, risk, service and resilience under different trade scenarios.

3. Supply Chain Cost Reduction

Cost reduction never goes out of fashion in supply chains, but the current environment has made it existential for many Australian organisations. More than a third of supply chain leaders surveyed by TMX Transform identify cost as their single biggest challenge — and margins are being squeezed from multiple directions simultaneously.

What's driving it. The cost pressure facing Australian supply chains in 2026 is multi-dimensional. Logistics costs remain volatile, driven by fuel price uncertainty, tariff effects and Red Sea shipping disruptions that continue to affect global freight rates. Labour costs in warehousing, transport and logistics have risen sharply due to persistent shortages — the ASCLA reports ongoing difficulty finding skilled truck drivers, warehouse staff and logistics professionals. Interest rates, while beginning to moderate, have made working capital more expensive, amplifying the cost of carrying excess inventory. Meanwhile, new compliance requirements — Scope 3 reporting, modern slavery due diligence, product safety standards — add operational cost even as they deliver other benefits.

The challenge is that traditional cost-reduction approaches — renegotiating supplier rates, reducing headcount, consolidating shipments — have largely been exhausted. Organisations that have already gone through multiple rounds of procurement savings are finding diminishing returns from the same playbook. The costs that remain are often structural: embedded in network design, process inefficiency, specification creep, contract structures and technology limitations.

Where costs actually hide. In our experience working across Australian supply chains, the biggest cost reduction opportunities typically sit in five areas: network and logistics costs (sub-optimal DC locations, inefficient transport routes, excessive nodes in the distribution network, carrier contracts that haven't been market-tested), inventory carrying costs (excess safety stock, slow-moving and obsolete inventory, poor demand forecasting driving overstock, misaligned replenishment parameters), procurement leakage (specifications that haven't been challenged, contracts that auto-renew without renegotiation, fragmented buying across categories, poor compliance with negotiated agreements), operational inefficiency (manual processes that should be automated, warehouse layouts that create wasted movement, planning processes that consume analyst time without improving decision quality), and total cost of ownership blind spots (evaluating purchases on unit price rather than total cost, ignoring transition costs, underweighting quality and service costs).

What good looks like. Effective cost reduction in 2026 requires an end-to-end view — connecting procurement savings with network design optimisation, inventory planning improvements and warehouse operational efficiency. The organisations capturing the most value are the ones that look across these dimensions simultaneously, rather than optimising each in isolation.

The best cost reduction programs also distinguish between quick wins (contract renegotiations, specification reviews, process fixes that can deliver savings within 3-6 months), structural improvements (network redesign, planning process transformation, technology implementation that deliver sustained savings over 1-3 years), and strategic shifts (sourcing strategy changes, make-or-buy decisions, operating model redesign that reshape the cost base fundamentally).

Starting with quick wins builds credibility and funds the longer-term work. But if the program stops at quick wins, the structural costs remain — and the same exercise needs to be repeated every two years, with diminishing returns each time.

The working capital dimension. One cost category that deserves special mention is inventory and working capital. With interest rates still elevated relative to the near-zero environment of 2020-2021, the cost of carrying inventory is materially higher. For a business carrying $50 million in inventory at a weighted average cost of capital of 8%, the annual carrying cost is $4 million — before you add warehousing, insurance, obsolescence and shrinkage. Reducing inventory by 20% — through better demand forecasting, optimised safety stock parameters, improved supplier lead times and disciplined slow-moving management — can release $10 million in working capital and save $800,000 or more annually in carrying costs alone.

This is where the connection between planning and cost reduction becomes critical. Inventory is a symptom — of forecast error, supply variability, long lead times, poor parameter management and inadequate governance. Attacking the symptom (writing off slow movers, cutting stock targets) without addressing the causes (improving forecast accuracy, reducing lead times, optimising replenishment parameters) just creates service problems.

How Trace Consultants can help. Cost reduction runs through everything we do at Trace. Our approach starts with understanding where costs actually sit — through data analysis, process observation and benchmarking — and then identifying the structural changes that will deliver sustainable reduction rather than one-off savings. We work across procurement (strategic sourcing, category strategy, go-to-market processes, contract management), planning and operations (demand planning, inventory optimisation, S&OP, replenishment), warehousing and distribution (network design, warehouse efficiency, logistics optimisation, 3PL management), and organisational design (operating model, capability, governance). Our end-to-end perspective means we identify the cross-functional opportunities that siloed cost reduction exercises miss — like the procurement decision that's optimising unit price at the expense of total logistics cost, or the inventory policy that's reducing stockholding at the expense of production efficiency.

4. Warehouse and Distribution Network Optimisation

As organisations grapple with cost pressure, service expectations and resilience requirements, distribution network design is getting serious strategic attention. The question isn't just "where should our warehouses be?" — it's "what's the right number of nodes, in what locations, with what capabilities, serving what channels, at what service level, at what cost?"

What's driving it. Several forces are converging to make network design a priority. E-commerce growth continues to reshape fulfilment requirements — consumers expect faster delivery, accurate tracking and flexible fulfilment options. The TMX Transform State of Supply Chain report identifies the tension between cost management and customer experience as the dominant theme for 2026. Climate events — flooding and extreme weather — have exposed the fragility of networks built around single distribution hubs, driving a shift toward more distributed, resilient network models. Industrial property costs in major Australian markets have risen significantly, making warehouse space more expensive and forcing harder decisions about where to invest. And the ASCLA forecasts that some congestion and bottlenecks will ease as companies expand regional distribution centres and diversify transport modes — but advises building multi-modal backup plans rather than assuming steady improvement.

The Australian challenge. Australia's geography creates unique network design challenges. The population is concentrated in a handful of coastal cities separated by vast distances. Interstate freight is dominated by road and rail corridors that are vulnerable to disruption. Port infrastructure constraints limit the capacity to handle volume fluctuations. And the economics of last-mile delivery in a dispersed suburban landscape are fundamentally different from the dense urban environments that most global logistics models are designed for.

For organisations serving national markets, the trade-off between centralised efficiency (fewer, larger DCs) and decentralised service (more DCs closer to customers) is a genuine strategic decision — not an obvious answer. The right answer depends on product characteristics, demand patterns, service level requirements, channel mix, transport cost structures and tolerance for inventory investment.

What good looks like. Strong network design combines quantitative modelling with strategic judgement. The modelling piece uses actual demand data, transport cost structures, warehouse operating costs and service level requirements to test different network configurations — how many DCs, where, what size, what product range, what replenishment frequency. The strategic judgement piece incorporates factors that models struggle with: future demand growth patterns, channel evolution, resilience requirements, lease flexibility and the practical realities of warehouse labour markets in different locations.

Organisations that get this right typically review their network design every 3-5 years or when triggered by significant changes in demand patterns, channel mix, customer locations, cost structures or service requirements. They don't treat network design as a one-time project but as a strategic capability that informs ongoing decisions about where to invest, what leases to renew, and how to allocate inventory.

The automation question. Distribution network decisions increasingly intersect with automation investment decisions. The level of automation appropriate for a DC depends on throughput volume, SKU range, order profile, labour availability and cost, and the expected life of the facility. A network design that recommends consolidating two manual DCs into one larger facility may be predicated on automation enabling the throughput that manual operations couldn't achieve. Conversely, a network design that distributes inventory across multiple smaller facilities may not generate the volume to justify automation at any single site.

These decisions need to be made together, not sequentially. The optimal network design depends on the automation strategy, and vice versa. Organisations that separate these decisions — running a network design project first and then an automation feasibility study — often find that the conclusions of one invalidate the assumptions of the other.

Multi-channel complexity. For organisations serving both retail and direct-to-consumer channels, network design has become significantly more complex. Retail replenishment typically involves pallet and carton movement in predictable patterns, while e-commerce fulfilment involves individual item picking with high variability and tight delivery windows. These channels have different optimal network configurations — which creates the question of whether to serve both from the same facilities, operate parallel networks, or use hybrid models with shared infrastructure and separate fulfilment areas.

The answer depends on the relative volume of each channel, the degree of SKU overlap, the service level requirements, and the cost structure. There's no universal right answer, which is why modelling with real data matters more than adopting a generic "best practice" model.

How Trace Consultants can help. Network design is one of Trace's core capabilities. We help organisations model and evaluate distribution network alternatives using scenario analysis, quantifying the cost, service and risk trade-offs of different configurations. We support DC site selection, 3PL evaluation, automation feasibility assessment, and the transition planning required to move from current to target state. Our work spans FMCG and manufacturing, retail and consumer, health and human services and government and defence — each with distinct network design requirements.

5. S&OP and Integrated Business Planning Transformation

Sales and operations planning remains one of the most searched — and most frustrating — topics in supply chain management. Almost every organisation has some form of S&OP process. Very few are happy with it.

What's driving it. The challenge with S&OP isn't a lack of awareness — it's a gap between what S&OP is supposed to deliver (cross-functional alignment, better demand-supply balancing, improved decision-making) and what most organisations actually experience (a monthly meeting cycle that consumes enormous time, produces consensus forecasts that nobody fully trusts, and fails to connect operational planning with financial outcomes).

A recent MHD Supply Chain Solutions feature on an ANZ-based manufacturer's planning transformation highlights the typical starting point: fragmented planning processes across regions, manual reconciliation, inconsistent data structures, and planning outcomes that varied depending on local tools and planning maturity. The manufacturer had average inbound lead times of four months but an order book that extended only a few weeks — creating permanent tension between inventory levels, service performance and responsiveness.

The article highlighted what many organisations discover: that technology alone won't resolve planning challenges without disciplined data and process foundations. Master data quality, consistent planning parameters, standard processes and clear governance are prerequisites — not afterthoughts.

The evolution to IBP. The leading organisations are moving beyond traditional S&OP toward integrated business planning (IBP) — a cross-functional planning operating model that connects demand planning, supply planning, inventory planning and financial reconciliation into a single process. IBP doesn't just balance demand and supply; it aligns operational plans with business strategy and financial targets.

This evolution requires several things that many organisations lack: a single version of the truth for demand (rather than competing forecasts from sales, marketing and supply chain), planning processes that operate at the right level of granularity for each decision (strategic vs tactical vs operational), financial integration that translates volume plans into P&L and cash flow impacts, and governance that ensures planning decisions are made by the right people at the right time.

What good looks like. Effective S&OP/IBP is characterised by a demand planning process that produces a realistic, unbiased baseline forecast — ideally with statistical or AI-generated foundations — that's then enriched by commercial intelligence. A supply planning process that translates demand into production, procurement and logistics requirements, identifying constraints and trade-offs. An inventory planning process that sets parameters based on service level targets, demand variability and supply lead times — not gut feel. A financial reconciliation step that ensures plans are achievable and aligned with budget. And an executive S&OP meeting that focuses on decisions, not data review.

The data foundation. One theme that surfaces consistently in planning transformations is the criticality of master data quality. Consistent item hierarchies, lead times, sourcing rules and planning parameters are prerequisites for any planning system or process to produce reliable outputs. This applies whether you're using spreadsheets, a mid-range planning tool, or a full APS implementation. Without disciplined data foundations, the planning engine — however sophisticated — will produce unreliable outputs that planners learn to ignore.

This data work needs to run in parallel with process design and system implementation. It's tempting to treat it as a precursor that can be "completed" before the main project starts. In reality, data readiness is an ongoing discipline that needs governance, ownership and continuous attention. The organisations that succeed at planning transformation are the ones that invest in master data governance as a permanent capability, not a one-off project.

The Australian context. For Australian supply chains specifically, long inbound lead times (4-6 months is common for imported goods) create a structural tension in planning. The demand signal changes faster than the supply chain can respond. This makes forecast accuracy particularly valuable — every percentage point of improvement in forecast accuracy translates directly to reduced safety stock requirements and improved service levels. It also makes S&OP governance more important: when the supply chain is slow, the quality of decisions made months in advance determines operational outcomes today.

Seasonal patterns (reversed from the Northern Hemisphere, creating alignment challenges for global businesses), promotional intensity (particularly in FMCG and retail), and concentrated customer bases (where a handful of major retailers represent a disproportionate share of demand) all create planning complexity that generic global S&OP models don't always address.

How Trace Consultants can help. Trace helps organisations design and implement planning and operations processes that actually work — from demand planning through S&OP to execution. We've also written a detailed guide to Advanced Planning Systems and their role in supporting planning process maturity. Our approach starts with understanding the current process, identifying where value is being lost, and designing a target operating model that's realistic for the organisation's maturity level — then helping implement it with the right blend of process design, technology enablement, data foundation work and change management. We're sector-agnostic on planning — the principles are consistent across FMCG, retail, resources and government — but the specific design needs to reflect each sector's demand patterns, supply chain characteristics and decision-making rhythms.

6. 3PL Selection, Outsourcing and Transition Management

The decision to outsource logistics — or to retender an existing 3PL arrangement — is one of the most consequential supply chain decisions an organisation can make. And in the current cost environment, many Australian organisations are revisiting these arrangements.

What's driving it. Several factors are pushing organisations to reassess their logistics outsourcing. Contract renewal cycles — many 3PL contracts established during or after the pandemic are coming up for renewal, and organisations want to ensure they're getting market-competitive pricing and service. Cost pressure — the gap between 3PL costs and in-house alternatives is narrowing in some categories as warehouse automation becomes more accessible. Service dissatisfaction — organisations that outsourced logistics to reduce cost are finding that service levels, flexibility and responsiveness aren't meeting expectations. Scale changes — growth, channel evolution or geographic expansion may mean the current 3PL arrangement no longer fits. And consolidation in the Australian 3PL market is changing the competitive landscape, creating opportunities for organisations that go to market at the right time.

The complexity. 3PL selection is more complex than most procurement exercises because you're buying an operating capability, not just a product or service. The 3PL will run your warehouse, handle your inventory, pick and pack your orders, manage your transport, and interact with your customers. Their performance directly affects your service levels, your cost structure and your brand reputation. Getting it wrong — or managing the transition poorly — can cause significant operational disruption.

The evaluation needs to go well beyond price. It should assess operational capability (can they actually run the operation at the required volume, complexity and service level?), technology and systems (is their WMS compatible with your systems? do they offer the visibility and reporting you need?), cultural fit (will they be responsive, transparent and collaborative?), flexibility (can they scale up and down as your volumes change?), and financial stability (will they be around for the term of the contract?).

The make-or-buy question. Before going to market for a 3PL, organisations should honestly assess whether outsourcing is the right model. The case for 3PL outsourcing is typically strongest when the operation requires specialist capability the organisation doesn't have internally (cold chain, dangerous goods, high-volume e-commerce fulfilment), when volume variability is high and the organisation needs to avoid fixed cost (seasonal peaks, promotional surges), when the organisation wants to focus management attention on core activities rather than logistics operations, and when the 3PL can leverage shared infrastructure across clients to deliver cost advantages.

The case for in-house operations is typically strongest when logistics is a core competitive differentiator (customer experience, speed, quality control), when volume is stable enough to utilise fixed assets efficiently, when integration between logistics and other functions (manufacturing, customer service, merchandising) is critical, and when the organisation has strong operational management capability.

Many organisations end up with hybrid models — some operations in-house, others outsourced — based on the specific characteristics of each operation. The important thing is that the decision is deliberate and evidence-based, not a legacy of historical choices that nobody has revisited.

The contract structure. 3PL contracts deserve more attention than they typically receive. The commercial structure — open book vs closed book, fixed vs variable pricing, gain-share mechanisms, KPI regimes, indexation provisions — determines the incentive alignment between client and provider. A poorly structured contract can create perverse incentives: a 3PL paid per unit handled has no incentive to reduce handling; a 3PL on a fixed fee has no incentive to invest in efficiency improvements that benefit the client.

The best 3PL contracts balance risk and reward, include meaningful performance metrics with financial consequences, provide transparency into the cost base, and include clear mechanisms for managing change — because the operation will change over the contract term, and the contract needs to accommodate that without triggering renegotiation every time.

What good looks like. A well-run 3PL selection process starts with a clear understanding of what you need — documented in sufficient detail for 3PL providers to price accurately and for the evaluation to be meaningful. It includes a market scan to identify the right providers, a structured RFx process that gives providers enough information to respond thoughtfully, an evaluation methodology that weights operational capability alongside price, site visits and reference checks, and detailed transition planning that manages the handover risk.

The transition is where most 3PL engagements succeed or fail. The best organisations invest significant time in transition planning — including parallel running, phased handover, clear escalation paths and performance monitoring during the stabilisation period. A typical 3PL transition takes 3-6 months from contract signing to steady-state operations, with the first 8-12 weeks being the highest risk period. Having dedicated project management, clear workstream ownership and agreed milestones is essential.

Post-transition, the ongoing governance model matters enormously. The initial contract sets the terms, but the day-to-day management determines whether the relationship delivers value. This means regular performance reviews against agreed KPIs, structured business improvement processes, open book cost transparency (where the commercial model supports it), and a clear process for managing scope changes and resolving disputes.

The current market. The Australian 3PL market is consolidating, with larger providers acquiring regional operators and investing in technology and automation. This creates opportunities for organisations going to market — there's active competition for quality accounts. But it also means that the due diligence needs to be thorough: understanding the integration risks when a 3PL has recently acquired another business, assessing whether the local management team (not just the corporate pitch team) has the capability to run your operation, and ensuring that the technology platform is stable and fit for purpose.

How Trace Consultants can help. Trace supports organisations through the full 3PL lifecycle — from the initial make-or-buy decision through go-to-market strategy, RFx development and management, evaluation and shortlisting, commercial negotiation, and transition planning and execution. Our warehousing and distribution expertise means we understand the operational realities of what we're asking 3PL providers to deliver — which makes for better requirements, better evaluation and better outcomes.

7. Procurement Transformation and Operating Model Design

Procurement has undergone a fundamental repositioning in recent years — from back-office cost centre to strategic function. But for many Australian organisations, the operating model hasn't kept pace with the ambition.

What's driving it. The 2026 ProcureCon Australia agenda captures it well: procurement is expected to "elevate its influence, align more closely with business priorities, and lead transformative initiatives." BCG's Inverto procurement trends report identifies four strategic imperatives for 2026: scaling AI across procurement, driving innovation through procurement partnerships, building geopolitical resilience, and developing AI-ready procurement talent.

But the reality on the ground in many Australian organisations is more prosaic. Procurement teams are stretched — expected to deliver more savings, more governance, more risk management, more sustainability compliance — with headcount that hasn't grown since pre-COVID. The workload arrives in surges (contract renewals clustering, major sourcing programs coinciding), and the operating model isn't designed to handle the peaks. Category management exists in theory but not in practice. Contract management is reactive rather than strategic. Supplier relationship management is a spreadsheet, not a discipline. And the data quality and technology infrastructure needed to support a modern procurement function often isn't there.

What good looks like. A well-designed procurement operating model addresses several dimensions: organisational structure (how is the team organised — by category, by business unit, by process? where does procurement sit in the organisation? what's the reporting line?), capability and roles (what roles exist, what skills are required, where are the gaps? how do you attract and retain procurement talent in a competitive market?), process and governance (how are sourcing decisions made? what approvals are required? how are contracts managed after award? how is supplier performance measured?), technology and data (what systems support procurement processes? is spend data visible and trusted? can the team access market intelligence and analytics?), and performance measurement (how is procurement's contribution measured? beyond savings — what about risk reduction, service improvement, sustainability outcomes, working capital impact?).

The concept of Procurement as a Service (PraaS) is gaining traction in Australia — a managed, scalable procurement capability that can be dialled up or down based on workload. This isn't outsourcing procurement; it's augmenting internal capability with structured external support during peak periods or for specialist categories.

Done well, PraaS provides several things that internal teams often struggle with: surge capacity when major sourcing programs cluster, specialist category expertise for complex or infrequent procurement (facilities management, IT managed services, logistics outsourcing), structured methodology and tools that may not exist internally, and market intelligence and benchmarking from working across multiple organisations and categories.

The key to effective PraaS is structure. It needs standard ways of working, clear governance, defined roles and responsibilities, and integration with the client's internal procurement team — not a parallel function that operates independently.

The capability gap. Deloitte's latest CPO survey highlights digital literacy and AI fluency as core skills for tomorrow's procurement teams. The BCG/Inverto trends report emphasises that winning procurement organisations invest in workforce readiness, operating model redesign and cross-functional change management to ensure AI becomes embedded in everyday decision-making. New roles are emerging: procurement data translators who turn analytics into action, automation and AI product owners for source-to-pay workflows, and supplier risk and ESG specialists embedded in category teams.

For many Australian procurement teams, this represents a significant capability gap. The current team may be excellent at running sourcing events and managing supplier relationships, but lack the data skills, technology fluency and strategic capabilities that the evolving role demands. Bridging this gap requires deliberate investment in recruitment, training, role design and career development — not just an expectation that existing staff will "upskill" in their spare time.

How Trace Consultants can help. Procurement transformation is core to what Trace does. We help organisations design and implement procurement operating models that deliver strategic value — from organisational design and capability assessment through process redesign, technology enablement and performance framework development. We also provide Procurement as a Service for organisations that need scalable procurement capacity, including go-to-market management, contract management support, category strategy execution and strategic workforce planning for the procurement function itself.

8. Supply Chain Resilience and Risk Management

Resilience has been a buzzword in supply chains since the pandemic. But in 2026, it's moving from concept to operational discipline — driven by regulatory requirements, board-level accountability and the lived experience of repeated disruptions.

What's driving it. The Australian Industry Group's trade and supply chain report provides a stark picture: 47% of industrial businesses are experiencing supply chain disruptions, up from pre-pandemic levels. The rate is expected to continue rising as tariff effects work through supply chains. Among affected businesses, 81% report increased costs, 44% report constrained growth, and significant proportions report reduced productivity and customer service impacts.

Beyond tariffs, the disruption landscape includes climate events (the ASCLA identifies extreme weather as having exposed the fragility of single-hub networks), geopolitical risk (U.S.-China tensions, Red Sea shipping disruptions, critical mineral export restrictions), cyber threats (increasingly targeting supply chain systems and data), regulatory risk (new compliance requirements creating supply chain complexity), and supplier financial risk (cost pressures creating viability concerns for smaller suppliers).

The regulatory environment is also pushing resilience higher on the agenda. CPS 230 requires banks and other APRA-regulated entities to identify critical operations, maintain tolerance levels for disruption, and extend oversight to third and fourth parties — effectively mandating supply chain resilience. The standard came into force on 1 July 2025, with material service provider registers due to APRA by 1 October 2025 and pre-existing contracts required to comply by the earlier of renewal or 1 July 2026.

CPS 230 is significant because it treats operational resilience as fundamentally a supply chain problem. It requires organisations to map their operational supply chains — not just direct service providers but the suppliers' suppliers (fourth parties) — with the same rigour that supply-chain-intensive industries have applied for decades. It requires identifying concentration risks (where multiple critical operations depend on the same provider — for example, a single cloud infrastructure provider), establishing tolerance levels for disruption (how long can each critical operation be disrupted before the impact becomes unacceptable?), and maintaining business continuity plans that are regularly tested.

While CPS 230 is specific to financial services, the principles are spreading. Boards across sectors are asking harder questions about supply chain vulnerability, driven by the lived experience of pandemic disruption, the ongoing tariff volatility, climate events and cyber incidents. Organisations that build resilience capability now — regardless of their regulatory obligations — will be better positioned when broader resilience requirements inevitably emerge.

What good looks like. Effective supply chain resilience combines several capabilities: risk identification and assessment (understanding which supply chain nodes, suppliers and dependencies create the greatest vulnerability), scenario planning and stress testing (modelling the impact of specific disruption scenarios — not generic risk matrices but specific, quantified analysis of what happens if a key supplier fails, a transport corridor is disrupted, or a critical material becomes unavailable), mitigation planning (for identified risks — dual sourcing, safety stock positioning, alternative logistics routes, contractual protections, insurance), monitoring and early warning (tracking leading indicators that suggest a disruption is emerging — supplier financial health, geopolitical developments, weather patterns, demand anomalies), and response capability (the organisational muscle to respond quickly when disruption occurs — clear roles, pre-defined playbooks, communication protocols, escalation paths).

The best organisations treat resilience as a design principle, not a bolt-on. They build optionality into their supply chain design — multiple suppliers, flexible contracts, distributed inventory, multi-modal logistics — so that when disruption occurs, they have options.

The cost of resilience — and how to manage it. One of the most common objections to resilience investment is cost. Dual sourcing costs more than single sourcing. Safety stock costs more than just-in-time. Distributed networks cost more than centralised ones. These trade-offs are real — but they're often overstated, and they need to be weighed against the cost of disruption.

The Ai Group's data shows that 81% of disrupted businesses report increased costs as a consequence. Those costs — expedited freight, emergency sourcing, lost sales, production downtime, customer penalties, reputational damage — are often invisible in normal cost reporting because they're spread across multiple budget lines. When you quantify the actual cost of recent disruptions and compare it to the incremental cost of resilience measures, the business case often changes dramatically.

The smartest organisations are finding ways to build resilience that don't simply add cost. Supplier diversification, for example, can increase competitive tension and improve commercial outcomes. Better demand planning can reduce safety stock requirements while improving service reliability. Network redundancy can be achieved through flexible 3PL arrangements rather than owned assets. The key is creative design, not simply spending more.

Resilience as competitive advantage. There's also an offensive dimension to resilience that's often overlooked. When disruption hits an industry, the organisations that maintain supply gain market share from those that don't. Customers who switch suppliers during a disruption often don't switch back. Being the reliable supplier in an unreliable market is a powerful competitive advantage — one that can justify the investment in resilience many times over.

How Trace Consultants can help. Trace helps organisations build resilience into their supply chain design and operations. This includes supply chain risk assessment and n-tier mapping, scenario modelling and stress testing, network design for resilience (balancing cost efficiency with redundancy and flexibility), supplier risk assessment and mitigation planning, business continuity planning for supply chain operations, CPS 230 compliance support for APRA-regulated entities, and integration of resilience considerations into procurement strategy and supplier selection.

9. WMS, TMS and Supply Chain Technology Selection

Technology selection is one of the highest-stakes decisions in supply chain management. Get it right and you unlock years of operational improvement. Get it wrong and you're stuck with a system that doesn't fit your operation, at a cost that wasn't in the business case.

What's driving it. The Australian supply chain management software market reached USD $647 million in 2025 and is growing at nearly 10% CAGR, according to IMARC Group data. Industry surveys show 68% of Australian enterprises plan to increase spending on supply chain software and analytics over the next two years. Cloud-based platform adoption among mid-sized enterprises increased 22% year-on-year in 2025.

The drivers are familiar: legacy systems that can't support modern requirements (real-time visibility, AI-enabled planning, omnichannel fulfilment), integration challenges between fragmented systems, the need for better data and analytics, and vendor pressure as major platforms (SAP, Oracle, Blue Yonder, Manhattan, Kinaxis) invest heavily in next-generation capabilities.

The categories getting the most attention in Australia right now are warehouse management systems (driven by e-commerce complexity, labour cost pressure and automation investment), transport management systems (driven by logistics cost pressure and the need for route optimisation and carrier management), and advanced planning systems (driven by the desire for AI-enhanced forecasting, inventory optimisation and scenario planning — a topic we've covered in detail in our guide to APS selection).

What organisations get wrong. The most common mistakes in supply chain technology selection are starting with the vendor rather than the problem (letting vendor shortlists drive requirements rather than the other way around), evaluating against feature checklists (counting features rather than testing whether the system produces better decisions with your data in your process), accepting canned demos (every demo looks impressive when the vendor controls the data and the scenario), underweighting implementation (the implementation partner, approach and change management typically determine 70% of the outcome — yet receive 30% of the evaluation focus), treating cost as licensing cost (implementation, integration, data migration, training and change management typically represent 60-70% of total cost of ownership), and selecting technology without redesigning processes (new technology in old processes produces old results — just more expensively).

What good looks like. A well-run technology selection process starts with a clear definition of the operational problem to be solved, not a technology specification. It includes a structured go-to-market process with clearly defined requirements, scenario-based demonstrations using representative data, evaluation criteria that weight implementation approach and team alongside software capability, total cost of ownership analysis, and thorough reference checking and site visits.

The integration question. One of the biggest decisions in supply chain technology is architecture: should you pursue an integrated suite from a single vendor (simpler integration, single relationship, potential compromises on functionality) or a best-of-breed approach with specialist tools connected via APIs (best functionality for each domain, more complex integration, multiple vendor relationships)?

The trend in 2026 is clearly toward composable, API-first ecosystems — an ERP backbone surrounded by specialist tools for planning, warehousing, transport, procurement and analytics. The "rip and replace" mega-suite approach is losing ground as organisations recognise that no single vendor is best-in-class across all domains, and that API technology has made integration far more feasible than it was a decade ago.

But integration still requires investment, governance and ongoing maintenance. Every API connection is a potential point of failure. Data consistency across systems needs active management. And the total cost of a multi-vendor ecosystem — including integration development, data orchestration and vendor management overhead — can exceed a single-vendor approach if not managed carefully.

The right answer depends on the organisation's complexity, internal technical capability, budget, and the specific functional gaps that need to be addressed. There's no universal "right" architecture — which is why independent advisory that starts from business requirements rather than technology preference is valuable.

The AI dimension. Increasingly, supply chain technology selection involves evaluating AI and machine learning capabilities — whether embedded in APS platforms (AI-enhanced demand forecasting, inventory optimisation), WMS systems (predictive slotting, demand-driven labour scheduling), or standalone AI tools. We've covered this topic extensively in our guide to AI adoption in supply chain and operations, including the readiness assessment, process design and data foundations that determine whether AI technology delivers value.

How Trace Consultants can help. Technology advisory is a core Trace capability. We help organisations navigate the supply chain technology landscape with an independent perspective — evaluating WMS, TMS, APS and other platforms against specific operational requirements through structured procurement processes. We support the full selection lifecycle: requirements definition, market scan, RFx development, vendor evaluation, commercial negotiation and implementation readiness. We're independent of technology vendors, which means our recommendations are based on what will deliver the best outcome for the client, not what generates vendor commission.

10. Labour, Workforce Planning and Automation in Supply Chain

Labour challenges in Australian supply chains are structural, not cyclical. They're not going away, and they're reshaping how organisations think about workforce planning, automation investment and operating model design.

What's driving it. The ASCLA reports ongoing shortages of skilled truck drivers, warehouse staff and logistics professionals across Australia. These shortages have pushed up labour costs, slowed throughput and contributed to the broader cost pressure facing supply chains. The workforce transformation lag is real — adapting to automation and robotics requires training investment, even as many companies struggle to attract and retain workers.

At the same time, automation and robotics are becoming more accessible and more proven in Australian supply chain operations. Dematic reports having implemented over 25 automated meat production storage and distribution systems in ANZ, enabling red meat companies to ship products internationally up to one day faster. Robotics investment in food processing is delivering 50% operational efficiency gains for some SMEs, according to data cited in the National Robotics Strategy. The Australian Government's National Robotics Strategy targets AUD $170-600 billion in annual GDP contribution by 2030 from robotics and automation. And the National Reconstruction Fund's $15 billion allocation supports automation deployment across manufacturing and logistics.

The investment case is supported by government incentives, but the practical challenges remain significant. Automation requires capital investment, lead time for implementation (typically 12-24 months for significant warehouse automation projects), technical skills for operation and maintenance, and process redesign to capture the benefits. Infrastructure limitations — including electricity supply at new sites — create practical constraints. And the human factor remains critical: supply chain operations need people who can work alongside technology, manage exceptions, make judgement calls and maintain relationships with customers and suppliers.

The workforce planning challenge. For most Australian supply chain operations, the question isn't "automate everything" or "keep doing things manually." It's "which tasks should be automated, in what sequence, with what timeline, and how do we manage the workforce transition?" This requires a structured approach to strategic workforce planning that considers current and forecast labour requirements by role and skill, automation opportunities ranked by business case and feasibility, transition planning for affected roles (redeployment, retraining, natural attrition), new roles and skills required to operate and maintain automated systems, and workforce sourcing strategy (permanent, contract, agency, outsourced) for different role types.

What good looks like. The organisations managing this transition well are taking a phased approach — starting with automation in high-volume, repetitive tasks where the business case is clearest, building internal capability to operate and maintain automated systems, and using the efficiency gains to fund the next wave of investment. They're also investing in their existing workforce — upskilling operators to work with new technology rather than treating automation as purely a headcount reduction exercise.

The economics of automation in Australia. The business case for automation in Australian warehousing and logistics is often stronger than in other markets because of the labour cost structure. Australian warehouse labour costs are among the highest globally, which means the payback period for automation investment can be shorter than in lower-wage markets. A goods-to-person picking system that costs $5 million to implement but reduces picking labour by 60% can pay back in 2-3 years in an Australian warehouse — compared to 5-7 years in some Asian markets.

However, the economics are sensitive to volume. Automation assets need throughput to justify their cost. Seasonal businesses, or operations with significant volume variability, need to model carefully whether the average throughput justifies the investment — or whether the automation will be underutilised for much of the year while the peaks still require manual labour to supplement.

Infrastructure readiness is also a factor. As industry commentators have noted, electricity supply can be challenging at new sites in Australia, which affects the feasibility of automation deployment. Site selection for automated facilities needs to consider power availability, three-phase supply, and the capacity to support peak loads alongside normal building services.

The hybrid model. For most Australian supply chain operations, the foreseeable future is a hybrid model — automation handling the predictable, high-volume tasks while human workers manage exceptions, complex decisions and tasks requiring physical dexterity or judgement. Designing for this hybrid model is an organisational design challenge as much as a technology challenge: how do workflows integrate automated and manual tasks? How are handoffs managed? How are performance metrics designed to reflect the different characteristics of automated and manual processes?

How Trace Consultants can help. Trace helps organisations plan the workforce transition in supply chain and operations through strategic workforce planning (forecasting future skill requirements and developing pathways), automation opportunity assessment and business case development (quantifying the case and identifying the right starting point), organisational design for automated and hybrid operations (redesigning roles, workflows and governance), and project and change management for technology-driven workforce transitions (managing the human side of automation rollouts). We work across sectors where automation is gaining traction — including FMCG, retail, health and government — bringing cross-sector perspective on what works in practice.

11. Supplier Diversification and China-Plus-One Sourcing Strategy

The geopolitical landscape has made supplier concentration risk a strategic issue — not just a procurement consideration. "China-plus-one" has become shorthand for a broader rethinking of how Australian organisations structure their global supply bases.

What's driving it. The convergence of U.S. tariffs on Chinese goods, China's own export restrictions on critical materials (including the antimony export ban implemented in December 2025), and broader geopolitical tensions between major economies has forced Australian businesses to reconsider supply chains that are heavily concentrated in China.

The HKTDC reports that many Australian businesses are adopting a China-plus-one strategy, diversifying their supplier base to reduce reliance on a single source. Manufacturers are considering reshoring operations and investing in local suppliers to enhance supply chain resilience. The most commonly cited alternative sourcing locations are Vietnam, India, Indonesia and Mexico — each with different cost structures, capability profiles, infrastructure maturity and risk characteristics.

The complexity of diversification. Supplier diversification sounds simple in principle — just add more suppliers in more locations. In practice, it's a significant undertaking that involves identifying and qualifying new suppliers in unfamiliar markets (assessing capability, quality, capacity, financial stability, ESG compliance), managing the cost premium that diversification often introduces (alternative suppliers may not offer the same cost as established Chinese suppliers, at least initially), maintaining quality and consistency across a broader supplier base, building logistics and customs capability for new trade lanes, and managing the relationship with existing Chinese suppliers (who may still be the best option for certain products or components).

The risk of doing it badly — rushing into alternative suppliers without proper qualification, or diversifying on paper without building genuine dual-source capability — can be worse than maintaining the status quo.

Evaluating alternative markets. Each alternative sourcing market has a distinct profile that procurement teams need to understand before committing.

Vietnam has emerged as the most common first alternative for organisations diversifying from China, particularly for textiles, electronics assembly and light manufacturing. It offers lower labour costs, improving infrastructure and a growing industrial base. But capacity constraints in high-demand categories, quality variability, and less developed supply chain infrastructure compared to China mean that transitioning isn't straightforward.

India offers enormous scale potential, a strong engineering and pharmaceutical manufacturing base, and growing government support for manufacturing investment. However, infrastructure challenges (particularly inland logistics), bureaucratic complexity and quality consistency concerns remain barriers for many categories.

Indonesia has a large domestic market, competitive labour costs and proximity to Australia (important for freight cost and lead time). But manufacturing capability is concentrated in certain categories, and the regulatory environment requires careful navigation.

For Australian organisations specifically, Southeast Asian alternatives offer the additional advantage of shorter shipping lanes compared to China — potentially reducing both transit time and freight cost, while also lowering Scope 3 transport emissions.

The local manufacturing option. For some categories, diversification doesn't necessarily mean shifting to another Asian market — it means exploring Australian or New Zealand manufacturing. The National Reconstruction Fund's $15 billion allocation supports domestic manufacturing capability. For products where quality control, IP protection, speed to market or supply chain security are paramount, local manufacturing may justify a cost premium.

This is particularly relevant for defence and government procurement, where sovereign capability requirements are increasingly explicit, and for categories where supply chain risk cost — the potential cost of disruption — is high enough to change the total cost of ownership calculation.

What good looks like. Effective supplier diversification is driven by a clear understanding of which products and components are most exposed to concentration risk, a structured assessment of alternative markets and suppliers, a phased transition plan that manages risk (dual sourcing before switching, rather than abrupt changes), investment in supplier development and qualification, and ongoing monitoring of the diversified supply base for quality, cost and risk performance.

It should also be connected to broader procurement strategy — not treated as a standalone project. Category strategies should reflect diversification objectives, and sourcing decisions should be evaluated against total cost of ownership (including risk cost), not just unit price.

The timeline reality. Supplier diversification is not a quick fix. Qualifying a new supplier in a new market typically takes 6-18 months, depending on the complexity of the product, the regulatory requirements (particularly for food, pharmaceutical and medical device categories), and the maturity of the supplier. Building the new supplier to the point where they can reliably serve as a genuine alternative — not just a name on the approved supplier list — takes longer still.

This means organisations that are only now starting to think about diversification in response to the 2025-2026 tariff disruptions are already behind. The organisations that were best prepared had begun diversification work in 2022-2023, giving their alternative suppliers time to qualify, build capacity and prove their reliability before the disruption hit.

For organisations starting now, the implication is clear: begin the qualification process immediately, even if the current supply base is adequate. Build optionality before you need it, because by the time you need it, it's too late to build. Treat diversification as a multi-year strategic program, not a reactive response to the latest tariff headline.

The data challenge. Effective supplier diversification also requires visibility into the current supply base that many organisations lack. How concentrated is your supplier base? What percentage of spend flows through your top 10 suppliers? What's the geographic distribution? How many critical components have a single source? These questions sound basic, but many organisations can't answer them confidently — because their procurement data is fragmented across systems, business units and categories.

How Trace Consultants can help. Trace supports organisations through supplier diversification with supply chain risk assessment and exposure mapping, market analysis and alternative supplier identification, procurement strategy that integrates diversification with cost and service objectives, supplier qualification and evaluation frameworks, transition planning and project management, and ongoing supplier performance management. We work across sectors including FMCG and manufacturing, resources and energy and government and defence — each with distinct diversification requirements and considerations.

12. Healthcare and Aged Care Supply Chain Transformation

Healthcare supply chain is a category that's generating increasing search volume — driven by sector growth, operational complexity and growing recognition that supply chain efficiency directly impacts patient outcomes and financial sustainability.

What's driving it. Healthcare is Australia's largest employer, with more than 2.23 million workers. The sector is expanding rapidly, driven by an ageing population, increased demand for aged care and community services, growth in home health and specialty care, and the ongoing rollout of telehealth and digital health services. Healthcare expenditure is among the fastest-growing categories in the Australian economy — and supply chain costs represent a significant proportion of operating budgets.

At the same time, healthcare supply chains face unique challenges: product complexity (thousands of SKUs across medical devices, pharmaceuticals, surgical supplies, personal protective equipment, food and nutrition, linen and laundry, and general consumables), regulatory requirements (TGA compliance, cold chain management, traceability requirements, product recall capability), demand variability (driven by patient acuity, seasonal illness patterns, pandemic preparedness requirements and elective surgery scheduling), and fragmented procurement (clinical preference items, physician-preferred devices, and the tension between clinical autonomy and procurement standardisation).

For aged care providers specifically, the Royal Commission's recommendations have created additional expectations around care quality, workforce capability and financial transparency — all of which have supply chain and procurement implications. Food services, clinical supplies, equipment management and facility services all need to be managed more professionally and with greater visibility.

The cost opportunity. Healthcare supply chain costs in Australia are significant — and often poorly understood. Many healthcare organisations don't have clear visibility of total supply chain cost, because spending is fragmented across clinical departments, facilities management, food services, linen, and general operations. Procurement decisions are made by clinicians, administrators, facilities managers and finance teams — often without coordination and sometimes without the commercial rigour that would be applied in other sectors.

In our experience, healthcare organisations that undertake a thorough supply chain and procurement diagnostic typically identify cost reduction opportunities of 10-20% in non-clinical categories and 5-15% in clinical categories — without compromising care quality. The savings come from standardisation (reducing product variation where clinical equivalence exists), consolidation (leveraging volume across sites and categories), process improvement (reducing waste, improving inventory management, streamlining logistics), and commercial improvement (better-structured contracts, more competitive go-to-market processes, stronger supplier performance management).

The clinical product challenge. Clinical preference items — the products that clinicians specify based on their training, experience and personal familiarity — represent one of the most complex procurement challenges in healthcare. Surgeons have strong preferences for specific implants, instruments and consumables. These preferences are often clinically justified, but they can also create cost variability that's difficult to manage.

The most effective approach isn't to override clinical preference — it's to engage clinicians in evidence-based product evaluation that considers clinical outcomes, safety, quality and cost together. This requires procurement teams with the credibility and clinical literacy to have those conversations, and governance structures that bring clinical and commercial perspectives together in a constructive way.

Digital health implications. The growth of telehealth, remote monitoring, hospital-in-the-home programs and digital therapeutics is creating new supply chain requirements that traditional healthcare logistics models aren't designed for. Medical devices, monitoring equipment and pharmaceutical supplies need to be delivered to patients' homes — not just to hospital loading docks. This creates last-mile logistics challenges, reverse logistics for equipment return and refurbishment, and inventory management across a dispersed network of care locations.

Where the opportunities sit. In our experience, the biggest improvement opportunities in healthcare supply chain typically include procurement consolidation and standardisation (reducing product variation, leveraging volume across sites, challenging clinical preference where clinically appropriate alternatives exist at lower cost), inventory management (reducing stockholding without compromising availability, improving demand visibility, implementing automated replenishment for high-volume consumables), distribution and logistics (optimising delivery frequency, consolidating suppliers, improving receiving and put-away processes, developing home delivery capability for hospital-in-the-home programs), contract management (establishing proper governance for supplier contracts, monitoring compliance, managing price escalation mechanisms), and workforce and capability (building procurement and supply chain capability in organisations where these functions have historically been under-resourced).

What good looks like. Healthcare organisations that are leading in supply chain performance are treating supply chain as a clinical support function — not just a cost centre. They're investing in data and visibility (understanding what they spend, with whom, on what), standardising where possible while maintaining clinical flexibility where necessary, building procurement and supply chain capability as a professional discipline, and connecting supply chain performance to patient outcomes and financial sustainability.

They're also learning from other sectors. Many of the supply chain disciplines that are well-established in FMCG, retail and manufacturing — demand planning, inventory optimisation, structured go-to-market processes, supplier performance management, network design — are directly applicable to healthcare, with appropriate adaptation for the clinical context. The organisations that are making the fastest progress are the ones that bring supply chain expertise into healthcare, rather than trying to develop it entirely from within.

The aged care dimension. Aged care deserves specific mention because the supply chain challenges are distinct from acute healthcare. Aged care facilities are typically smaller, more geographically dispersed, and more focused on consumables (food, personal care products, cleaning supplies, linen) than high-value medical devices. The procurement challenge is often about consolidation and leverage — aggregating demand across multiple sites to achieve better commercial outcomes — while the logistics challenge is about efficient delivery to a large number of small-volume locations. Many aged care providers have grown through acquisition and haven't integrated their supply chain operations, resulting in inconsistent practices, fragmented supplier relationships and limited visibility of total spend.

How Trace Consultants can help. Trace works with healthcare and aged care organisations on procurement strategy and execution (including clinical product standardisation and category strategy), supply chain operating model design, inventory management and replenishment optimisation, distribution network and logistics efficiency, and organisational design and capability building for healthcare supply chain functions. Our work in the health and human services sector means we understand the unique dynamics of healthcare supply chains — the clinical context, the regulatory environment, the stakeholder complexity and the imperative to balance cost efficiency with care quality.

The common thread: operational discipline beats silver bullets

Across all twelve of these problems, there's a consistent pattern. The organisations that are solving them most effectively aren't the ones with the biggest budgets, the most advanced technology, or the most innovative strategies. They're the ones with the strongest operational discipline: clear processes, good data, capable people, honest measurement and the willingness to do the unglamorous foundational work that makes everything else possible.

Technology matters — but technology deployed on poor foundations delivers poor results. Strategy matters — but strategy without execution is a slide deck. Innovation matters — but innovation without governance is experimentation.

What we see working in the Australian market, across every one of these twelve challenges, is a combination of clear-eyed diagnosis (understanding the problem as it actually is, not as you wish it were), practical strategy (solutions that work in the Australian context, with Australian constraints, at Australian scale), disciplined execution (structured approaches that deliver results on time and on budget), and sustainable capability (building the internal skills and processes that ensure improvements stick after the consultants leave).

The interconnections matter. One thing that's easy to miss when looking at these twelve problems individually is how interconnected they are. Scope 3 reporting requires supply chain mapping — which is also the foundation for resilience planning and supplier diversification. Cost reduction requires better planning — which requires better data — which requires technology investment — which requires a structured selection process. Procurement transformation underpins better go-to-market processes — which deliver better 3PL contracts — which improve warehouse and logistics performance — which reduces cost and improves service.

These connections mean that siloed approaches to individual problems often underperform. An organisation that runs a Scope 3 reporting project, a cost reduction program, and a technology selection process as three separate initiatives — with three different teams, three different consultants, and three different timelines — will miss the synergies and create inefficiencies. The best results come from an integrated approach that recognises the connections and designs solutions that work across multiple challenges simultaneously.

The sequencing matters too. Not all twelve problems need to be tackled at once. For most organisations, the right approach is to prioritise based on urgency (what has a regulatory deadline or a board mandate?), impact (what will deliver the most financial value?), readiness (where does the organisation have the data, people and processes to succeed?), and dependencies (what needs to happen first to enable other initiatives?).

In our experience, the most common and effective starting points are a supply chain diagnostic that maps cost, capability and risk across the end-to-end chain — creating the fact base for prioritisation, a procurement improvement program that delivers measurable savings in 3-6 months while building the commercial capability for more strategic work, a planning process redesign that improves forecast accuracy and inventory management — creating the data and process foundation for AI adoption, and a network review that evaluates whether the current distribution footprint is fit for purpose — particularly for organisations that haven't reviewed their network in the past 3-5 years.

From these foundations, more ambitious initiatives — AI adoption, technology transformation, Scope 3 integration, resilience capability — can be built on solid ground.

How Trace Consultants can help

At Trace Consultants, we work with Australian organisations across all twelve of these challenge areas. We're a specialist supply chain and procurement consulting firm — it's all we do, which means we bring depth and practical experience that generalist firms can't match.

Our approach is grounded in a few principles that our clients tell us matter:

Independence. We don't sell software, run warehouses, or operate logistics networks. We don't have vendor partnerships that influence our recommendations. Our advice is based entirely on what will deliver the best outcome for your organisation. This matters particularly for technology selection, where many advisory firms have commercial relationships with vendors that create conflicts of interest.

Practicality. We focus on solutions that work in the real world — not theoretical frameworks that look good in presentations but don't survive contact with operational reality. Every recommendation we make is designed to be implementable, affordable and sustainable. We've seen too many consulting engagements that produce beautiful strategy documents and then leave the organisation to figure out how to actually do it. That's not how we work.

Depth. We've worked across every sector that matters in Australia — FMCG and manufacturing, retail and consumer, resources and energy, health and human services, and government and defence. We understand how supply chain challenges play out in each context — the economics, the constraints, the stakeholders and the politics. A supply chain problem in a hospital looks very different from the same problem in a fast-moving consumer goods company, even if the underlying principles are similar.

End-to-end capability. From strategy and network design through procurement, planning and operations, warehousing and distribution, organisational design, technology, resilience, strategic workforce planning, and project and change management — we cover the full supply chain, which means we see the connections between problems that siloed approaches miss. When we help an organisation with procurement, we understand the implications for planning. When we redesign a distribution network, we understand the workforce and technology implications. When we assess AI readiness, we understand the operational processes that AI needs to serve.

Commitment to results. We measure our success by the results our clients achieve — not by the reports we produce. Every engagement is designed to deliver measurable improvement: cost reduction, service improvement, risk mitigation, capability uplift, or strategic clarity. We stay close to the work, we take accountability for outcomes, and we build the internal capability to ensure improvements are sustained long after our engagement ends.

If any of these twelve challenges are keeping you up at night — or if you're not sure which ones should be your priority — we'd welcome the conversation.

Technology

AI in Supply Chain and Operations: The Seven Things Australian Organisations Need to Get Right Before the Technology Matters

Shanaka Jayasinghe
February 2026
Australian organisations are under mounting pressure to deploy AI across their supply chains and operations. The technology is ready. The vendor pitches are compelling. The board is asking questions. But the organisations that are actually capturing value from AI aren't the ones with the biggest budgets or the most ambitious transformation programs. They're the ones that got seven foundational things right — things that have nothing to do with algorithms and everything to do with operational discipline. This guide covers all seven, in the order they need to happen.

There's a widening gap in Australian supply chains right now, and it's not the one you'd expect. It's not between organisations that have AI and those that don't. It's between organisations that have deployed AI and are getting genuine operational value from it, and organisations that have deployed AI and are quietly wondering why the results don't match the business case.

The Australian Government's National AI Centre tracks AI adoption across the economy, and its Q1 2025 data tells an instructive story. When asked whether AI had helped with supply chain and supplier management, only 14% of surveyed businesses said "definitely." Another 46% said "possibly" — the kind of answer you give when something is technically in place but the results aren't clear enough to point to. The remaining 40% said "unlikely." Meanwhile, industry data from the Supply Chain and Logistics Association of Australia suggests nearly 40% of supply chain leaders report measurable improvements from AI implementation — which means 60% haven't captured those benefits yet.

These numbers don't reflect a technology failure. The AI tools available today — for demand forecasting, inventory optimisation, logistics routing, predictive maintenance, warehouse automation and supply risk management — are genuinely capable. The algorithms work. The platforms are maturing. The cloud infrastructure to run them is accessible and affordable.

What these numbers reflect is an execution failure. Organisations are adopting AI without adequate preparation in the areas that actually determine whether it works: understanding their own readiness, selecting the right use cases, redesigning processes to consume AI outputs, choosing the right technology for their context, running pilots that test the full operating model, building the data foundations that AI depends on, and developing the human capability to work alongside AI tools effectively.

These seven disciplines aren't optional extras that you bolt on after the technology is deployed. They're the work that determines whether your AI investment delivers a 15% improvement in forecast accuracy and a $30 million reduction in inventory — or becomes a line item that finance questions at every budget review.

This article covers all seven, in the order they need to happen, with practical guidance for Australian supply chain and operations leaders who want to get this right.

1. Supply Chain AI Readiness Assessment

Every AI journey should start with an honest answer to a question most organisations skip: are we actually ready for this?

Readiness isn't about whether you can buy an AI tool. Of course you can — the market is flooded with options. Readiness is about whether your organisation has the foundations in place to deploy AI in a way that produces reliable outputs, integrates into operational decision-making, and sustains value over time.

A genuine AI readiness assessment evaluates five dimensions.

Data maturity. AI is only as good as the data it learns from and operates on. For demand forecasting, that means clean, granular historical transaction data at the right level of detail — typically SKU-location-week — with promotional activity, pricing changes, new product introductions and other demand-shaping events accurately captured. For inventory optimisation, it means reliable lead time data, supplier performance history and demand variability metrics. For logistics optimisation, it means accurate delivery windows, vehicle constraints, cost structures and geographic data.

Most Australian organisations have this data somewhere in their systems. The question is whether it's accessible, consistent, integrated and trustworthy. Data quality issues, system fragmentation, inconsistent master data and manual workarounds are the norm. A readiness assessment quantifies where you actually stand — not where you think you stand — against the data requirements of specific AI use cases.

Process maturity. AI tools produce recommendations, forecasts, optimisation outputs and alerts. Those outputs only create value if there's a business process that consumes them. If your S&OP process is dysfunctional, a better demand forecast won't fix it. If your warehouse has no standard operating procedures for pick path management, an AI-optimised slotting recommendation won't translate to throughput improvement. If your procurement team doesn't have a structured approach to supplier risk management, an AI early warning system will generate alerts that nobody acts on.

Process maturity assessment looks at the planning and operations processes that would need to consume AI outputs: demand planning, supply planning, inventory management, S&OP, logistics planning, warehouse management, procurement execution. For each, it evaluates whether the process is defined, followed, measured and governed — because these are the prerequisites for AI integration.

Technology landscape. What systems are currently in place? What's the ERP platform? Is there a warehouse management system? A transport management system? An existing planning tool? What are the integration points and constraints? What's the cloud posture? These questions determine what's technically feasible, what integration work is required, and whether AI tools can plug into the existing architecture or need a parallel infrastructure.

Organisational capability. Do your planners, analysts and operations managers have the skills to work with AI tools? Not to build models — that's a different skill set — but to interpret AI outputs, manage exceptions, configure parameters, and know when to trust the model versus override it. The readiness assessment should evaluate current capability levels across the roles that will interact with AI, identify gaps, and flag the training and development investment required.

Governance and culture. Does the organisation have a framework for managing AI responsibly? This includes data governance (who owns data quality, who authorises data use), model governance (who monitors performance, who recalibrates, who decides when a model should be retired), decision governance (who is accountable when AI-informed decisions go wrong), and cultural readiness (is the organisation open to changing established ways of working based on AI-generated insights, or will there be resistance?).

The Australian Government's 2025 Guidance for AI Adoption sets out six essential practices for responsible AI governance, reflecting a maturing regulatory environment that organisations need to align with. The readiness assessment should evaluate current governance maturity against these expectations.

What the output looks like. A well-executed readiness assessment produces a clear picture of where the organisation stands across all five dimensions, with specific gaps identified and prioritised. It should include a heat map of readiness by AI use case — showing which applications are ready to pursue now, which need foundation work first, and which should be deferred until earlier initiatives have matured. This becomes the basis for a sequenced AI roadmap that's grounded in reality rather than aspiration.

The organisations that skip this step — that jump straight to technology selection or pilot execution — almost always find themselves circling back to readiness issues 6-12 months later, having spent budget and credibility on initiatives that underperformed because the foundations weren't in place.

2. AI Use Case Identification and Business Case Development

With a clear readiness picture in hand, the next step is identifying which AI applications to pursue and building the business cases to justify the investment.

This sounds straightforward. It rarely is. The challenge isn't a shortage of potential use cases — it's an overabundance. Every AI vendor, every conference presentation, every industry report suggests dozens of ways AI could be applied across the supply chain. The risk is either trying to do too many things at once (spreading resources thin and delivering nothing well) or picking the wrong things to start with (choosing technically interesting applications that don't address the organisation's most material operational problems).

Starting with the operational problem, not the technology. The most reliable approach to use case identification starts with a simple question: which operational decisions are currently made most poorly, and which have the largest financial impact?

In supply chain and operations, the decisions that typically offer the highest leverage for AI-assisted improvement include demand forecasting (where machine learning can materially improve accuracy for products with complex demand patterns driven by promotions, seasonality, weather or external signals), inventory optimisation (where AI-driven parameter setting can improve service levels while reducing working capital — particularly valuable in the current interest rate environment), logistics and route optimisation (where AI can reduce transport costs by 5-15% through better vehicle allocation and delivery sequencing — particularly impactful given Australian distances), supply risk and disruption management (where AI can monitor external data sources to provide early warning of disruptions), warehouse operations (from predictive slotting through to demand-driven labour scheduling), and predictive maintenance (where AI can reduce unplanned downtime by identifying equipment failure patterns).

Each of these is a broad category. The use case identification process should drill into the specific version of each that's relevant to the organisation: which product segments have the worst forecast accuracy? Which inventory positions are structurally wrong? Which logistics lanes have the most inefficiency? Where are the biggest maintenance cost drivers?

Quantifying the opportunity. For each candidate use case, the business case needs to quantify the current cost of the problem (excess inventory, lost sales from stockouts, transport cost premium, unplanned downtime cost), the realistic improvement AI can deliver (based on benchmarks, published research and the organisation's specific data characteristics — not vendor claims), the investment required (technology, implementation, data preparation, process redesign, training, ongoing support), and the payback period and return profile.

This is analytical work that requires deep understanding of supply chain economics — understanding total cost of ownership for inventory, the service-level-to-stock-level trade-off, the relationship between forecast accuracy and safety stock requirements, the cost structure of logistics operations. It's strategy work as much as technology work.

Prioritisation. With quantified business cases for multiple use cases, prioritisation should consider both financial attractiveness and readiness. The best starting point is a use case that has high financial impact and where the organisation's data, process and capability foundations are strong enough to support successful deployment. Starting with a lower-impact but higher-readiness use case can also make sense if it builds confidence and capability that enables more ambitious applications later.

The output should be a prioritised portfolio of 3-5 use cases with detailed business cases, clear sequencing, identified dependencies, and a realistic timeline. This becomes the investment case for the AI program — and the accountability framework against which results are measured.

3. AI-Enabled Planning and Operations Process Design

This is where most AI initiatives fall down, and it's the area that gets the least attention. Organisations invest heavily in selecting and implementing AI technology, then try to insert it into planning and operations processes that were designed for a pre-AI world. The result is predictable: the AI tool produces outputs that don't fit the existing workflow, planners don't know what to do with them, and the system either gets ignored or creates more work rather than less.

AI-enabled process design means redesigning the planning and operations processes — not just adding AI as an input — so that the entire workflow takes advantage of what AI makes possible.

What changes when AI enters the planning process. Consider demand planning as an example. In a traditional process, planners spend the majority of their time generating the statistical baseline forecast, then adjusting it based on market intelligence, promotional plans, sales team input and other qualitative factors. The S&OP cycle revolves around reviewing and reaching consensus on the forecast.

When AI handles the statistical forecasting — and does it better, faster and more granularly than manual methods — the planner's role fundamentally changes. They shift from forecast generation to forecast management: reviewing AI outputs, focusing attention on the exceptions and outliers where human judgement adds value, incorporating market intelligence that the model can't access, and making the cross-functional trade-off decisions that require business context.

This is a better use of skilled planners' time. But it requires a redesigned process that defines how AI forecasts are generated and reviewed, what exception thresholds trigger human intervention, how the planner's adjusted forecast feeds into the S&OP cycle, what governance ensures the AI model stays calibrated, and how forecast accuracy is measured and reported.

The same principle applies across other domains. AI-optimised inventory parameters need a process for planner review and override. AI-generated route plans need a process for driver exceptions and real-time adjustment. AI supply risk alerts need an escalation and response framework.

Designing the target operating model. For each AI use case, the process design work should define the end-to-end workflow (how does the AI-enabled process work, step by step, from data input through to operational decision?), the role of the planner or operator (what does the human do that the AI doesn't, and how do they interact with the system?), the exception management framework (what triggers human intervention, and what's the process for handling exceptions?), the governance rhythm (how often are AI outputs reviewed, who is accountable for model performance, what are the escalation paths?), and the performance measurement framework (how do we know the AI-enabled process is delivering better outcomes than the previous approach?).

This is organisational design work applied to AI-enabled operations. It requires understanding both the technical capabilities of the AI tool and the operational realities of the planning or operations environment — how planners actually work, what information they need, where they add value, and what frustrates them about current processes.

The S&OP connection. For organisations with a formal S&OP or integrated business planning process, AI-enabled planning has significant implications. AI can accelerate the demand planning phase, improve the quality of scenario modelling, provide more granular inventory trade-off analysis, and enable faster response to demand or supply changes between formal planning cycles. But these benefits only materialise if the S&OP process is redesigned to take advantage of them — if the meeting cadence, the information packs, the decision frameworks and the accountability structures all reflect the new AI-enabled capability.

Trace has written extensively about S&OP and planning process design — organisations looking at this intersection should also review our thinking on planning and operations process maturity and our guide to Advanced Planning Systems which covers how APS capabilities integrate with planning processes.

4. AI Technology Selection and Vendor Advisory

With clear use cases, quantified business cases and a target process design, the organisation is ready to evaluate technology options. Not before.

The sequence matters because technology selection should be driven by defined requirements — not the other way around. Too many organisations start with a technology shortlist and then try to find use cases that justify the purchase. This leads to solutions looking for problems, features that don't map to actual decision-making needs, and implementations that are technically impressive but operationally irrelevant.

The AI technology landscape for supply chain and operations. The options fall into four broad categories.

Embedded AI within existing platforms. Your ERP vendor's demand planning module may now include ML-based forecasting. Your WMS might offer AI-powered slotting optimisation. Your TMS may have added AI routing capabilities. These embedded capabilities are typically the easiest to deploy (no new integration required) but may be less sophisticated than specialist tools.

Best-of-breed AI point solutions. Standalone platforms focused on specific use cases — demand sensing, inventory optimisation, logistics route optimisation, predictive maintenance, supply risk monitoring. These typically offer deeper functionality for their specific domain but create integration requirements and add another vendor relationship to manage.

Advanced Planning Systems with native AI. The dedicated supply chain planning platforms — Kinaxis, Blue Yonder, o9 Solutions, SAP IBP, RELEX, Logility and others — increasingly embed AI and ML capabilities across their planning modules. For organisations considering a broader planning transformation, these platforms offer comprehensive capability but require significant implementation investment.

General-purpose AI and analytics platforms. Cloud ML platforms (AWS SageMaker, Google Cloud AI, Azure ML) and business intelligence tools with predictive capabilities can be configured for supply chain use cases. These offer maximum flexibility but require more internal technical capability to deploy and maintain.

How to evaluate. The evaluation approach should mirror the structured RFx process we recommend for any significant technology selection, but with specific adaptations for AI.

First, evaluate against defined use cases, not feature lists. The question isn't "does this platform support demand forecasting?" (they all do). It's "does this platform's forecasting capability handle our specific demand patterns — high promotional variability, long-tail SKUs, new product introductions with no history — better than alternatives?"

Second, insist on demonstrations with representative data. Canned demos tell you nothing about how the system will perform in your environment. Provide shortlisted vendors with a sample dataset that reflects your real data characteristics and ask them to demonstrate their system's outputs against defined scenarios.

Third, evaluate the implementation approach and partner ecosystem as heavily as the software. For AI tools specifically, the quality of model configuration, data engineering and calibration during implementation determines the majority of the outcome. A superior algorithm badly implemented will underperform an adequate algorithm well implemented.

Fourth, assess total cost of ownership, not just licensing. Implementation services, data engineering, integration development, training, change management and ongoing model monitoring and recalibration typically represent 60-70% of the total investment.

Fifth, evaluate local capability. For Australian organisations, the vendor's or partner's presence and experience in the ANZ market matters. Long inbound supply chains, Australian seasonal patterns, concentrated retail customers and specific regulatory requirements create a context that global vendors may not have deep experience with.

The procurement disciplines of structured evaluation, commercial benchmarking and negotiation are just as important for AI technology selection as for any other significant purchase — perhaps more so, given the complexity and the risk of vendor lock-in.

5. AI Pilot Design and Execution Support

Before committing to full-scale deployment, most organisations benefit from a structured pilot that tests whether the AI tool, configured for their data and integrated into their process, actually produces better operational outcomes than the current approach.

The key word is "structured." An AI pilot isn't a sandbox experiment where data scientists play with models to see what's possible. It's a controlled operational test with defined scope, clear success metrics, baseline measurement, and a rigorous evaluation framework that determines whether to scale, adjust, or stop.

Designing the pilot. A well-designed AI pilot defines several critical elements.

Scope. Which products, which locations, which planning horizon, which operational process? The scope should be large enough to be representative but small enough to be manageable. For a demand forecasting pilot, this might mean a specific product category across a defined set of locations over a 12-16 week period. For an inventory optimisation pilot, it might mean a subset of SKUs in a defined part of the distribution network.

Baseline. What does "current performance" look like, measured rigorously? If you're testing AI-driven demand forecasting, you need a clean baseline of current forecast accuracy by SKU, by location, by time horizon — measured consistently over a period long enough to account for normal variability. Without a solid baseline, you can't measure improvement.

Success metrics. What does "better" look like, quantified? A 10% improvement in forecast accuracy at SKU-week level? A 15% reduction in safety stock without service degradation? A 7% reduction in transport cost per delivery? The metrics should be defined before the pilot starts, agreed with stakeholders, and directly traceable to the business case.

Operating model for the pilot period. How will planners or operators interact with the AI tool during the pilot? Will they use AI outputs as their primary input (replacing the current approach) or run in parallel (comparing AI outputs against current methods)? Parallel running is safer but less realistic — planners who know they have a fallback behave differently than planners who are depending on the new tool. The pilot design should specify the operating model clearly.

Duration. The pilot needs to run long enough to test performance across different demand conditions — not just steady-state weeks but promotional periods, seasonal transitions, supply disruptions and other real-world variability. For most supply chain use cases, 12-16 weeks is a reasonable minimum.

Evaluation framework. How will the pilot be evaluated? Who reviews the results? What threshold of improvement justifies scaling? What happens if results are mixed — some metrics improve, others don't? The evaluation framework should be defined upfront, not designed retrospectively to fit the results.

Testing the full operating model. The most important — and most frequently missed — aspect of pilot design is testing the full operating model, not just the technology. This means evaluating whether planners are actually using the AI outputs in their decision-making, whether the process changes are working as designed, whether exceptions are being managed appropriately, and whether the results are genuinely better than the current approach across the full range of real-world conditions.

A pilot that demonstrates impressive algorithmic accuracy in a test environment but doesn't test whether planners trust and use the outputs is answering the wrong question. The question isn't "can this model forecast well?" It's "does this model, used by our planners, in our process, with our data, produce better operational decisions than what we do today?"

The scale decision. At the end of the pilot, the organisation faces a clear decision: scale, adjust, or stop. If the pilot demonstrates clear, measurable improvement against the defined success metrics — and the operating model is working — the case for scaling is strong. If results are mixed, the pilot data should inform what needs to change before scaling (data quality issues? process gaps? model configuration? planner training?). If results are poor despite good execution, that's also valuable information — it means this particular use case, with this particular tool, in this particular context, doesn't deliver the expected value.

Honest evaluation at this stage — rather than confirmation bias that justifies the investment already made — is what separates organisations that deploy AI effectively from those that scale failures.

6. Data Readiness and Foundation Work

If there's one section of this article that deserves to be read twice, it's this one. Data readiness is the single largest determinant of AI success in supply chain and operations, and it's the area where organisations most consistently underinvest.

Every AI model — whether it's forecasting demand, optimising inventory, routing deliveries or predicting equipment failures — depends on data. The quality, completeness, granularity and accessibility of that data determines the ceiling of what AI can achieve. No algorithm, however sophisticated, can overcome fundamentally poor data.

What "data ready" actually means. For supply chain AI applications, data readiness typically requires several things.

Clean historical transaction data. For demand forecasting, this means order or shipment history at the right level of granularity (SKU-location-day or week), with anomalies identified and addressed (one-off bulk orders, data entry errors, system migration artefacts), and sufficient history to train models effectively (typically 2-3 years minimum, more for seasonal products).

Accurate demand-shaping event data. Promotions, pricing changes, new product introductions, range deletions, competitor activity — the events that cause demand to deviate from underlying patterns. AI models can learn from these events, but only if they're captured accurately in the data. Most organisations have promotional calendars, but the linkage between promotions and transaction data is often incomplete or inaccurate.

Reliable master data. Product hierarchies, location hierarchies, supplier data, customer segmentation, lead times, minimum order quantities, shelf life constraints — the reference data that AI models use to structure their analysis. Master data quality is a pervasive challenge in Australian organisations, and it directly impacts AI model performance.

Integration and accessibility. Data often sits in multiple systems — ERP, WMS, TMS, point of sale, CRM, external data sources — and needs to be brought together in a form that AI tools can consume. This requires data integration pipelines, potentially a data warehouse or data lake, and APIs or connectors to the AI platform.

Timeliness. Some AI applications require near-real-time data (logistics optimisation, warehouse operations), while others work on daily or weekly data cycles (demand planning, inventory optimisation). The data infrastructure needs to support the refresh frequency that each use case requires.

The foundation work. Getting data ready is unglamorous, time-consuming work. It includes data profiling and quality assessment (understanding what you have, where the gaps are, and how severe the quality issues are), data cleansing and enrichment (fixing historical anomalies, filling gaps, enriching transaction data with event data), master data governance (establishing ownership, standards, processes and tools for maintaining data quality on an ongoing basis — because data quality degrades continuously without active management), integration development (building the pipelines that bring data together from multiple sources into a form AI tools can consume), and data architecture design (determining where data lives, how it flows, and what infrastructure supports it).

This work should begin early — ideally during the readiness assessment phase — and continue in parallel with use case development and technology selection. It's the longest-lead-time activity in most AI programs, and it's the one most commonly underestimated.

The ongoing challenge. Data readiness isn't a one-time project. It's a continuous discipline. Data quality degrades as products change, systems are updated, processes evolve and people make mistakes. AI models trained on historical data gradually lose accuracy as the underlying patterns shift. Master data requires ongoing maintenance as the business changes.

This is why data governance — the ongoing organisational capability to maintain data quality — matters as much as the initial data cleansing effort. Without it, the AI investment delivers diminishing returns over time as the data foundations erode.

For organisations across FMCG and manufacturing, retail and consumer, resources and energy and government and defence, data readiness challenges take different forms — but the fundamental principle is the same: invest in the data foundation before investing in the AI tool, and establish the governance to sustain it.

7. AI-Focused Capability Building and Training

The final discipline — and the one that determines whether AI delivers value in year one only or compounds value over time — is building the human capability to work effectively alongside AI tools.

This isn't about turning planners into data scientists. It's about developing a set of practical skills that enable supply chain and operations professionals to get the most from AI-enabled tools and processes.

What AI-literate supply chain professionals need to know. The capability requirements fall into several layers.

Interpreting AI outputs. Understanding what a demand forecast from an ML model represents — including its confidence intervals, its assumptions, and its known limitations. Understanding what an inventory optimisation recommendation means — why the model is suggesting a particular safety stock level, what inputs are driving it, and what would change if assumptions shifted. This isn't deep technical knowledge; it's practical literacy that allows professionals to use AI outputs intelligently rather than either blindly following them or reflexively ignoring them.

Managing exceptions. Knowing when and how to override AI recommendations. AI models work well for the majority of routine decisions but struggle with genuine exceptions — unprecedented events, data quality issues, business context the model can't see. Building the judgement to recognise these situations — and the confidence to intervene — is a critical skill that comes from training, practice and organisational support.

Configuring and monitoring. Understanding how to adjust parameters that the AI tool exposes — service level targets, demand segmentation rules, exception thresholds, scenario assumptions. Knowing how to monitor whether the model is performing as expected and recognising the signs of degradation (declining accuracy, increasing exceptions, outputs that don't align with business reality).

Asking better questions. Perhaps the most valuable capability shift is moving from "generating the answer" (which AI now handles) to "asking better questions." What scenarios should we test? What assumptions should we challenge? What trade-offs should we explore? What risks aren't reflected in the data? This is where experienced supply chain professionals add the most value in an AI-enabled world — and it's a capability that needs to be actively developed, not assumed.

How to build it. Capability building for AI-enabled operations typically involves several components.

Assessment. Understanding the current capability baseline across the roles that will interact with AI tools — planners, analysts, operations managers, supply chain leaders. What's the current level of data literacy? Analytical skill? Comfort with technology? Openness to changing established ways of working?

Training programs. Structured learning that covers the practical skills outlined above — tailored to specific roles and specific AI tools. This isn't generic "AI awareness" training; it's hands-on instruction using the actual systems and processes the team will work with. It should include real data, real scenarios and real decision-making practice.

Playbooks and reference material. Documentation that supports ongoing performance — standard operating procedures for AI-enabled processes, exception management guides, parameter configuration guides, troubleshooting resources. These should be living documents that evolve as the organisation's AI capability matures.

Coaching and support. Particularly in the early stages of AI adoption, planners and operators benefit from accessible support — someone who can help when the model produces an output they don't understand, when they're unsure whether to override a recommendation, or when they encounter a situation the training didn't cover.

Communities of practice. As AI adoption scales across the organisation, connecting practitioners — planners in different sites, analysts in different categories, operators in different facilities — creates a peer learning network that accelerates capability development and shares best practice.

The leadership dimension. Capability building isn't just about frontline users. Supply chain leaders need their own capability development — not in how to use AI tools, but in how to lead AI-enabled organisations. This includes understanding what AI can and can't do (to set realistic expectations), how to interpret AI-related performance metrics (to make informed governance decisions), how to allocate resources between AI investment and other priorities (to make sound trade-offs), and how to create a culture that embraces AI-enabled ways of working while maintaining appropriate scepticism and human oversight.

Strategic workforce planning for AI-enabled supply chains should address this leadership dimension alongside the frontline capability requirements — because leadership buy-in and capability is what sustains AI adoption beyond the initial implementation.

The partnership model: where to build and where to partner

One of the most important strategic decisions for organisations adopting AI in supply chain and operations is what to build internally versus what to source from external partners.

In our view, the answer is clear for most Australian organisations. The capabilities that should be built internally — because they're core to operational performance and need to be sustained over time — are AI literacy across the planning and operations team, process design and governance for AI-enabled operations, data governance and quality management, and performance monitoring and continuous improvement.

The capabilities that are typically better sourced from external partners — because they require specialist skills that aren't needed on a full-time basis — are AI model development and engineering (the deep technical work of building, training and deploying models), technology platform implementation and integration, advanced data engineering for initial setup and migration, and strategic advisory on AI roadmap, technology selection and organisational readiness.

This is where the distinction between AI engineering firms and supply chain advisory firms matters. AI engineering firms bring deep technical capability in building and deploying AI systems — custom model development, agentic AI frameworks, large language model integration, deployment infrastructure. They're the right partner for the model development, platform engineering and technical deployment work. Supply chain advisory firms bring the domain expertise to ensure AI is applied to the right problems, embedded in the right processes, and supported by the right organisational capability.

The most effective AI programs use both: domain experts who understand the operational context and define what needs to happen, partnered with technical experts who know how to make it happen. Neither alone is sufficient. An AI engineering firm without supply chain domain expertise will build technically impressive solutions that don't address the most valuable operational problems. A supply chain advisory firm without AI engineering capability can design the right solution but can't build the models. The partnership model brings both together.

For Australian organisations specifically, this model has a practical advantage. The local talent market for deep AI engineering skills is tight and expensive. Building a permanent internal team of ML engineers, data scientists and AI architects is feasible for the largest organisations but impractical for most. A partnership model lets organisations access world-class AI engineering capability on a project basis while building the internal domain expertise and operational capability that sustains value over time.

How Trace Consultants can help

At Trace Consultants, we sit firmly on the domain expertise side of this partnership model. We don't build AI models. We make sure the AI investments you make actually work in your operations.

Our role in AI adoption spans all seven of the disciplines covered in this article:

AI readiness assessment. We assess your data maturity, process maturity, technology landscape, organisational capability and governance readiness — producing a clear, honest picture of where you stand and what needs to happen before AI can deliver value. This is strategy work grounded in practical supply chain experience.

Use case identification and business case development. We identify the highest-value AI applications for your specific operation, quantify the opportunity with rigour, and build business cases that stand up to scrutiny. Our deep understanding of supply chain economics — from inventory optimisation to logistics cost structures to procurement spend analysis — ensures use cases are anchored in real operational value.

AI-enabled process design. We redesign planning and operations processes to take advantage of what AI makes possible — defining workflows, roles, exception management, governance and performance measurement for AI-enabled operations. This is organisational design work that bridges the gap between technology capability and operational reality.

Technology selection and vendor advisory. We help you navigate the AI technology landscape with an independent, informed perspective — evaluating embedded capabilities, best-of-breed tools and APS platforms against your specific requirements through structured procurement processes. Our technology advisory ensures you select the right tool for your context, not the most impressive demo.

Pilot design and execution support. We design structured AI pilots with clear scope, success metrics, baseline measurement and evaluation frameworks — ensuring you test the full operating model, not just the technology. Our project and change management capability ensures pilots run smoothly and produce the information needed for sound scaling decisions.

Data readiness and foundation work. We support the data quality, master data governance, integration planning and data architecture work that determines whether AI tools produce reliable outputs. This isn't glamorous work — but it's the work that protects your AI investment.

Capability building and training. We develop and deliver practical training programs that build AI literacy across supply chain and operations teams — from frontline planners to senior leaders. Our strategic workforce planning expertise ensures capability development is structured, role-specific and sustainable.

Our independence matters. We don't sell AI software. We don't build AI models. We don't have partnership arrangements with AI vendors that influence our recommendations. Our advice is based entirely on what will deliver the best operational outcome for your organisation — which technology, which use cases, which sequencing, which partnerships.

We work across FMCG and manufacturing, retail and consumer, resources and energy, health and human services, and government and defence — bringing cross-sector perspective on what works in practice, not just in theory.

Getting started

AI in supply chain and operations isn't a future state. It's happening now, in Australian organisations across every sector. But the organisations capturing value aren't the ones with the most ambitious AI strategies or the largest technology budgets. They're the ones that got the foundations right: honest readiness assessment, disciplined use case selection, thoughtful process design, structured technology evaluation, rigorous pilots, serious data work, and genuine investment in human capability.

Every one of those seven disciplines is within reach of any Australian organisation with the commitment to do them properly. The technology will follow — and when it does, it will land on foundations that actually support it.

If your organisation is ready to move beyond AI aspiration and start building the operational capability that makes AI work, we'd welcome the conversation.

People & Perspectives

Adopting AI in Supply Chain and Operations: A Practical Guide for Australian Organisations That Want Results, Not Science Projects

Shanaka Jayasinghe
February 2026
The gap between AI enthusiasm and AI results in Australian supply chains is widening. Industry data shows nearly 40% of supply chain leaders report measurable improvements from AI implementation — which means 60% haven't captured those benefits yet.

Every supply chain and operations leader in Australia has been told, repeatedly, that artificial intelligence will transform their function. The conference presentations, the vendor pitches, the board-level questions about "what we're doing with AI" — the pressure to adopt is relentless. And it's not wrong. AI is genuinely changing how demand is forecast, how inventory is positioned, how logistics networks are optimised and how operational decisions are made.

But there's an enormous gap between recognising that AI matters and actually deploying it in ways that deliver measurable operational improvement. The Australian Government's AI Adoption Tracker for Q1 2025 found that while awareness and interest in AI continues to grow, challenges like rapid technological change, skills gaps and funding constraints remain significant barriers. Only 14% of surveyed businesses said AI had definitely helped with supply chain and supplier management — though another 46% said it possibly had, suggesting many organisations are still in early stages where benefits are emerging but not yet clear.

The pattern we see across our client base tells a similar story. Most organisations aren't struggling with whether to adopt AI. They're struggling with where to start, how to prioritise, what's genuinely ready for production versus what's still experimental, and how to build the foundations — data, process, people — that determine whether AI tools actually work in their operational context.

This article is a practical guide to navigating that challenge. Not a survey of what's theoretically possible, but a framework for how Australian supply chain and operations teams can adopt AI in ways that generate real, sustainable value.

Start with the problem, not the technology

The single most common mistake in AI adoption — across every sector and every function — is starting with the technology. "We should use machine learning for demand forecasting." "We need a generative AI copilot for our planning team." "Let's build a predictive model for supplier risk."

These aren't bad ideas. But they're solutions looking for problems. The organisations that extract genuine value from AI start from the other direction: they identify the operational decisions that matter most and are currently made poorly, then ask whether AI can improve them.

In supply chain and operations, the decisions that typically offer the highest leverage for AI-assisted improvement fall into several categories.

Demand forecasting and planning. Most Australian organisations still forecast demand using a combination of statistical models (often basic ones embedded in their ERP), manual adjustments based on sales team input, and spreadsheet-based reconciliation. The result is forecast accuracy that hovers around 50-70% at SKU level for many businesses — adequate for rough planning but costly in terms of excess inventory, emergency orders and missed service targets. Machine learning can genuinely improve forecast accuracy, particularly for products with complex demand patterns driven by promotions, seasonality, weather, competitive activity or other external signals. But the improvement is contingent on having clean, granular historical data and a planning process that actually uses the improved forecast.

Inventory optimisation. Setting safety stock levels and reorder parameters is one of the most consequential decisions in supply chain management — and one of the most commonly done by formula or rule of thumb. AI-driven inventory optimisation can improve service levels while reducing working capital by dynamically adjusting parameters based on demand variability, lead time variability, supplier reliability and service level targets. In the current interest rate environment, the working capital benefit alone often justifies the investment. But it requires integration with your ERP, clean master data, and a governance process for managing exceptions.

Logistics and route optimisation. AI-powered routing and scheduling can reduce transport costs by 5-15% by optimising vehicle allocation, delivery sequences and consolidation opportunities — particularly for organisations with complex, multi-drop delivery networks. Australian geography makes this particularly valuable: the distances involved mean that even modest efficiency improvements translate to material cost savings.

Supply risk and disruption management. AI can monitor external data sources — news feeds, shipping data, weather forecasts, financial indicators — to provide early warning of supply chain disruptions. For organisations managing complex, global supply chains with long lead times, this capability shifts the response model from reactive to proactive. But the value depends on having clear escalation processes and decision frameworks that translate alerts into action.

Warehouse operations. From predictive slotting and pick path optimisation through to demand-driven labour scheduling, AI can improve warehouse throughput and reduce cost per unit handled. For organisations managing large warehousing and distribution operations, the compounding effect of small efficiency improvements across millions of transactions is significant.

Quality and maintenance prediction. In manufacturing and processing environments, AI-powered predictive maintenance can reduce unplanned downtime by identifying equipment failure patterns before they cause outages. Similarly, AI-driven quality prediction can reduce waste by identifying process drift earlier in the production cycle.

The point isn't that every organisation should pursue all of these simultaneously. It's that the starting point should be a clear-eyed assessment of which operational decisions are currently weakest, which have the largest financial impact, and which have the data and process foundations to support AI-assisted improvement.

The three foundations that determine whether AI works

Every failed AI initiative we've seen shares a common root cause: the technology was deployed on top of inadequate foundations. Specifically, one or more of three prerequisites was missing.

Foundation one: data readiness

AI is only as good as the data it learns from and operates on. This isn't a platitude — it's a practical reality that determines whether a machine learning model produces useful outputs or garbage.

For demand forecasting, you need clean, granular historical transaction data at the right level of detail (SKU-location-week, typically), with promotional activity, pricing changes and other demand-shaping events accurately captured. For inventory optimisation, you need reliable lead time data, supplier performance history and demand variability metrics. For logistics optimisation, you need accurate delivery windows, vehicle constraints and cost structures.

Most Australian organisations have this data somewhere in their systems — but not necessarily in a form that's accessible, consistent or trustworthy. Data quality issues, system fragmentation, inconsistent master data and manual workarounds are the norm, not the exception.

Investing in data readiness — cleaning historical data, establishing data governance, building integration pipelines, fixing master data — isn't exciting. It doesn't feature in conference keynotes. But it's the work that determines whether your AI investment delivers 15% forecast accuracy improvement or becomes an expensive experiment that nobody trusts.

Foundation two: process maturity

AI tools produce recommendations, forecasts, optimisation outputs and alerts. Those outputs only create value if there's a business process that consumes them — a planning cycle that reviews the forecast, a replenishment process that acts on inventory recommendations, a logistics process that implements optimised routes, a management rhythm that responds to risk alerts.

If your S&OP process is dysfunctional, a better demand forecast won't fix it — the improved forecast will just be ignored or overridden the same way the current one is. If your warehouse has no standard operating procedures for pick path management, an AI-optimised slotting recommendation won't translate to throughput improvement.

This is why process design and AI adoption need to be pursued together, not sequentially. The organisations that get the most from AI are the ones that redesign their planning and operations processes to take advantage of what AI makes possible — faster exception identification, better scenario modelling, more granular segmentation — rather than trying to bolt AI onto processes designed for a pre-AI world.

Foundation three: people and capability

AI doesn't replace planners, analysts or operations managers. It changes what they do. Instead of spending 80% of their time gathering, cleaning and reconciling data and 20% making decisions, AI should flip that ratio — handling the routine analytical work so that people can focus on the exceptions, the judgement calls and the cross-functional coordination that create value.

But this shift requires new capabilities. Planners need to understand what the AI model is doing well enough to know when to trust it and when to override it. Analysts need enough data literacy to configure, monitor and troubleshoot AI tools. Managers need the confidence to change established ways of working based on AI-generated insights.

Strategic workforce planning for AI adoption isn't about hiring data scientists (though some organisations will need them). It's about upskilling existing operational teams to work effectively with AI tools — understanding the outputs, managing the exceptions, and knowing when human judgement should prevail.

A practical adoption framework

With those foundations in mind, here's a framework for sequencing AI adoption in supply chain and operations that we've seen work consistently in Australian organisations.

Stage one: assess and prioritise (4-8 weeks)

Map the operational decisions where AI could add value against two axes: business impact (cost, service, working capital, risk) and readiness (data availability, process maturity, organisational appetite). The intersection of high impact and high readiness is where you start.

This assessment should be grounded in data — actual forecast accuracy metrics, inventory performance, logistics costs, service level achievement — not assumptions or vendor claims. It should also include an honest evaluation of data readiness: is the data that an AI model would need actually available, accurate and accessible?

The output is a prioritised roadmap with clear sequencing: what to tackle first, what requires foundation work before AI can be effective, and what to defer until earlier initiatives have proven the model.

This is strategy and network design work applied to the AI adoption question — ensuring investments are directed where they'll deliver the most value rather than where the technology is most exciting.

Stage two: build foundations (8-16 weeks, concurrent with stage three)

For the prioritised use cases, address the data, process and capability gaps identified in stage one. This might include data cleansing and enrichment for historical demand data, establishing master data governance for products, locations and suppliers, building data integration pipelines between source systems and AI tools, redesigning planning or operational processes to incorporate AI outputs, and developing training programs for the teams who will use AI tools.

This work isn't glamorous, but it's where the ROI is protected. Skipping it — or rushing it — is the most reliable way to ensure your AI initiative disappoints.

Stage three: prove value with targeted pilots (8-12 weeks)

Start with one or two well-defined use cases in controlled environments. A demand forecasting pilot for a specific product category in a specific market. An inventory optimisation trial for a subset of SKUs in a defined part of the network. A route optimisation test for a specific distribution operation.

The pilot should have clear success metrics defined upfront: forecast accuracy improvement measured against the current baseline, inventory reduction without service degradation, cost per delivery reduction, or whatever metric is relevant to the use case. It should run long enough to demonstrate performance across different demand conditions — not just the easy weeks.

Critically, the pilot should test the full operating model, not just the technology. Are planners using the AI outputs? Are the process changes working? Are exceptions being managed appropriately? Are the results genuinely better than the current approach, measured rigorously?

Stage four: scale what works (ongoing)

When a pilot demonstrates clear value, scale it — but deliberately. Scaling means extending the AI capability across more products, more locations, more operations, while simultaneously extending the process changes and capability development that make it work.

Scaling also means establishing the governance and operating rhythm for ongoing management: who monitors model performance? Who recalibrates when accuracy degrades? Who decides when the model should be overridden? Who owns the data quality that feeds it? These aren't implementation details — they're the mechanisms that determine whether AI delivers value in year one only or sustains it over time.

For organisations with complex, multi-site operations — whether in FMCG and manufacturing, retail and consumer, resources and energy, or government and defence — the scaling phase is where project and change management capability becomes critical. Rolling out new tools and processes across multiple sites, teams and geographies requires structured change management, not just technical deployment.

Navigating the vendor landscape

One of the most confusing aspects of AI adoption for supply chain leaders is the vendor landscape. It includes embedded AI capabilities within existing platforms (your ERP vendor's demand planning module, your WMS vendor's slotting optimisation), specialised best-of-breed AI tools for specific use cases (standalone demand sensing, logistics optimisation, or predictive maintenance platforms), Advanced Planning Systems with native AI and ML capabilities (the dedicated supply chain planning platforms covered in Trace's APS guide), and general-purpose AI and analytics platforms that can be configured for supply chain use cases (cloud ML platforms, business intelligence tools with predictive capabilities).

Each approach has trade-offs. Embedded capabilities are easier to deploy but may be less sophisticated. Best-of-breed tools may deliver superior results for specific use cases but create integration complexity. APS platforms provide comprehensive planning capability but require significant implementation investment. General-purpose platforms offer flexibility but require more internal technical capability to deploy.

The right answer depends on your specific use cases, your existing technology landscape, your internal capability, and your appetite for integration complexity. A structured evaluation — similar to the procurement RFx approach we recommend for APS selection — ensures you're comparing options on the dimensions that actually matter rather than being swayed by demos and marketing.

The governance dimension

As AI becomes more embedded in operational decisions, governance becomes a practical necessity, not just a compliance consideration. The Australian Government's 2025 Guidance for AI Adoption sets out six essential practices for responsible AI governance, reflecting a maturing regulatory environment.

For supply chain and operations specifically, the governance questions that matter most are practical ones. How do you ensure AI-driven decisions are explainable — that a planner can understand why the system is recommending a particular forecast or inventory level? How do you manage bias — ensuring that AI models don't systematically over-serve some customers or under-invest in some regions? How do you handle data privacy — particularly when AI tools process customer demand data, supplier information or employee productivity metrics? How do you maintain accountability — ensuring that humans remain responsible for decisions, even when those decisions are informed by AI?

These aren't theoretical concerns. They're design choices that need to be made during implementation and embedded in the operating model. Organisational design for AI-enabled operations includes defining who owns model performance, who governs data quality, who authorises changes to AI parameters, and who escalates when AI recommendations don't align with business judgement.

What to avoid

A few patterns reliably lead to disappointing outcomes in supply chain AI adoption:

The moonshot. Starting with an ambitious, multi-year AI transformation program that tries to reimagine the entire supply chain. These programs generate impressive slide decks and burn significant budget before delivering any operational value. Start small, prove value, scale what works.

The technology-first approach. Selecting an AI platform before clearly defining the operational problems it needs to solve. This leads to solutions searching for problems and features that don't map to your actual decision-making needs.

The data will sort itself out. Assuming that data quality issues will be resolved as part of implementation rather than addressing them upfront. They won't. And the AI model trained on poor data will produce poor recommendations that planners quickly learn to ignore.

The standalone pilot. Running an AI pilot in isolation from the business processes and people who need to use it. A model that sits on a data scientist's laptop producing impressive accuracy metrics but never integrating into the planning cycle is a science project, not an operational capability.

Ignoring change management. Deploying AI tools without investing in the training, process redesign and stakeholder engagement needed for adoption. If planners don't trust the tool, they'll route around it — and your investment will deliver a fraction of its potential value.

How Trace Consultants can help

At Trace Consultants, we help Australian organisations adopt AI in supply chain and operations from a position of deep operational expertise. We're not an AI vendor or a technology implementation firm. We're supply chain and operations consultants who understand where AI creates genuine value — and where it doesn't.

AI readiness assessment and roadmap. We assess your current operations, data landscape, process maturity and organisational capability to identify where AI will deliver the highest return — and what foundation work is needed to support it. This is strategy work grounded in operational reality.

Use case definition and prioritisation. We help you cut through the noise — identifying the specific operational decisions that AI can improve in your context, quantifying the potential value, and sequencing adoption based on impact and readiness.

Data and process readiness. We support the foundation work that determines whether AI delivers: data quality improvement, process redesign, master data governance, and integration planning. Our planning and operations expertise ensures that AI tools are embedded in processes that actually work.

Technology evaluation and selection. We help you navigate the vendor landscape with an independent, informed perspective — evaluating embedded capabilities, best-of-breed tools and APS platforms against your specific requirements. Our procurement and technology expertise ensures you select the right tool, not just the most impressive demo.

Pilot design and execution. We design and support AI pilots that test the full operating model — technology, process and people — with clear success metrics and a realistic path to scale.

Scaling and change management. We help organisations scale proven AI capabilities across sites, teams and geographies — managing the project and change dimensions that determine whether AI becomes embedded in how the organisation operates or remains a one-off experiment.

Organisational design and capability building. We help design the organisational structures, roles and governance mechanisms that sustain AI-enabled operations — and build the internal capability to manage, tune and extend AI tools over time through workforce planning and development.

The practical path forward

AI in supply chain and operations isn't a future state. It's happening now, in Australian organisations across every sector, in applications ranging from demand forecasting through to warehouse optimisation and logistics routing. The organisations capturing value aren't the ones with the biggest AI budgets or the most ambitious transformation programs. They're the ones that started with clear operational problems, built the foundations to support AI, proved value in controlled environments, and scaled deliberately.

If your organisation is ready to move beyond the question of whether to adopt AI and start answering the questions of where, how and in what sequence, we'd welcome the conversation.

Technology

Demand Planning and Advanced Planning Systems: How to Run an RFx Process That Doesn't End in Regret

James Allt-Graham
February 2026
The market for Advanced Planning Systems has never been more crowded, more confusing, or more consequential. Vendors are racing to bolt on AI, rebrand legacy platforms, and claim they do everything from demand sensing to autonomous planning.

Choosing an Advanced Planning System is one of those decisions that looks straightforward on the surface and turns out to be fiendishly difficult in practice. The vendor landscape is broad. The functionality overlaps are significant. Every platform claims AI-powered demand forecasting, scenario modelling, inventory optimisation and seamless integration with your ERP. The demos are polished. The reference customers are carefully curated.

And yet, a significant proportion of APS implementations either fail outright, deliver far less value than the business case projected, or land in a state where the organisation is technically "live" but planners are still running their actual decisions through spreadsheets because the system doesn't work the way they need it to.

The root cause, in most cases, isn't bad technology. It's a selection process that failed to answer the right questions. The RFx was structured as a technology procurement exercise — feature lists, architecture requirements, integration specifications, pricing schedules — when it should have been structured as a planning capability assessment: does this system, configured for our data, our products, our network and our planning process, actually produce better plans than what we're doing today?

That distinction — between buying a technology product and selecting a planning capability — is what separates organisations that get genuine value from their APS investment from those that spend 18 months implementing a system nobody trusts.

Why APS selection is different from other technology procurement

Before diving into how to structure the RFx process, it's worth understanding why demand planning and APS selection doesn't follow the same playbook as procuring an ERP, a WMS, a TMS or most other enterprise technology.

The problem you're solving is harder to define. When you select a warehouse management system, the functional requirements are relatively concrete: receive stock, put it away, pick it, pack it, ship it, count it. The processes are physical, observable and well-understood. When you select a demand planning or supply chain planning system, the requirements are inherently more abstract. You're trying to improve the quality of decisions about uncertain futures — how much to make, where to hold stock, when to order, how to allocate constrained supply. The "right answer" changes daily. The value is in the system's ability to help planners navigate ambiguity, not execute a defined process.

Vendor capabilities genuinely vary in ways that are hard to assess. The Gartner Magic Quadrant for Supply Chain Planning Solutions now includes more than 20 vendors. The Leaders — Kinaxis, Blue Yonder, o9 Solutions, Logility and others — all claim comprehensive functionality across demand planning, supply planning, inventory optimisation, S&OP and production scheduling. But underneath those claims, the actual capabilities differ substantially. Some platforms are genuinely strong in statistical and machine-learning forecasting but weaker in constraint-based supply planning. Others excel at interactive scenario modelling but struggle with the algorithmic optimisation that drives inventory decisions. Some are purpose-built for specific industries — retail, FMCG, process manufacturing — while others are horizontal platforms that require significant configuration to work well in any specific context.

A standard RFx feature checklist — "does the system support promotional uplift modelling? Yes/No" — doesn't surface these differences. Every vendor will tick the box. The question isn't whether the feature exists but whether it works well enough, in your context, with your data, to make materially better decisions than your current approach.

The implementation matters as much as the software. APS aren't systems you install and switch on. They're systems you configure, calibrate and continuously tune — and the quality of that work determines whether the system produces plans worth following. The same platform can deliver transformative results in one organisation and be a $2 million shelf ornament in another, depending on how it was implemented, how the data was structured, how the planning processes were designed, and whether planners were genuinely brought along through the change.

This means the RFx process needs to evaluate not just the software but the implementation approach, the partner ecosystem, the support model and the vendor's track record in environments comparable to yours.

The six mistakes that derail most APS selection processes

Having supported organisations across FMCG and manufacturing, retail and consumer and other sectors through APS selection and implementation, we see the same patterns of failure repeatedly. Understanding them is the first step to designing a process that avoids them.

Mistake one: starting with the vendor shortlist instead of the planning problem

Too many APS selection processes begin with a list of vendors and a request for information rather than a clear articulation of what the organisation actually needs from its planning capability. What are the specific planning decisions that need to improve? Where is forecast accuracy weakest and why? Which inventory positions are structurally wrong — too much in some places, not enough in others? Where are supply and demand decisions disconnected? What does the S&OP process need that it doesn't currently have?

Without clear answers to these questions — grounded in data, not assumptions — the RFx process has no anchor. It becomes a general-purpose technology evaluation rather than a targeted assessment of which system best addresses the organisation's specific planning challenges.

This upfront work — what we'd describe as planning maturity assessment and requirements definition — is the most important phase of the entire selection process. It determines everything that follows: the evaluation criteria, the weighting, the scenarios for demonstration, and ultimately the commercial justification for the investment.

Mistake two: evaluating against feature checklists

The traditional approach to technology RFx involves compiling a requirements matrix — sometimes running to hundreds of rows — with each requirement scored on a scale (e.g. "fully met, partially met, not met, requires customisation"). Vendors complete the matrix, an evaluation panel scores them, and the highest-scoring platform is identified.

This approach works reasonably well for transactional systems with well-defined functional requirements. It works poorly for APS, for two reasons. First, the requirements that matter most in planning — forecast accuracy, scenario modelling quality, user experience for planners, speed of exception management — are qualitative and contextual, not binary. Second, vendors are skilled at completing these matrices in ways that maximise their score without necessarily reflecting how the system will perform in your environment. A planning system might technically "support" promotional uplift modelling, but if it requires three months of custom configuration and a data science team to maintain, that's a very different proposition from one where it's a native, well-tested capability.

Mistake three: accepting canned demos

Every APS vendor has a demonstration environment loaded with sample data and pre-configured scenarios designed to make the system look spectacular. These demos are useful for understanding the user interface and general approach, but they tell you almost nothing about how the system will perform with your data, your products, your demand patterns and your network complexity.

The organisations that make the best APS selection decisions insist on scripted demonstrations using representative samples of their own data. This doesn't mean a full proof-of-concept at the RFx stage — that comes later — but it does mean providing shortlisted vendors with a defined dataset and a set of planning scenarios that reflect your real-world challenges, then evaluating how each system handles them. The difference in insight between a canned demo and a data-driven demonstration is enormous.

Mistake four: underweighting implementation and change

Most RFx evaluation frameworks allocate the majority of their weighting to software functionality and price. Implementation approach, partner capability, change management methodology and ongoing support model might get 10-15% of the total score. This is backwards for APS, where the implementation and change journey typically determines 60-70% of the eventual outcome.

A system with excellent algorithmic capabilities but a weak implementation partner, a rigid configuration approach, or no credible change management methodology is a worse bet than a system with good-enough algorithms backed by a team that understands your industry, your data challenges, and how to get planners actually using the system to make decisions.

Mistake five: treating cost as licensing cost

APS pricing models vary enormously — from per-user licensing to volume-based pricing to flat annual subscriptions with modular add-ons. But the licensing cost is typically 30-40% of the total cost of ownership. Implementation services, data engineering, integration development, testing, training, change management, and ongoing support and optimisation make up the rest.

RFx processes that evaluate cost on licensing alone — or that allow vendors to quote implementation at unrealistically low levels to win the deal — set the organisation up for budget overruns and scope compromises during implementation. The RFx should require vendors and implementation partners to provide a realistic total cost of ownership estimate across a defined horizon (typically five years), including all the components that actually drive spend.

Mistake six: ignoring the planning process and people dimension

An APS is a tool. Its value depends entirely on the planning process it supports and the people who use it. Yet most RFx processes focus almost exclusively on the tool and pay limited attention to whether the organisation is ready to use it effectively.

Questions about planning and operations process design — how will the S&OP process change? What will the planner's daily workflow look like? How will exceptions be managed? What governance will ensure the system stays calibrated? — are rarely part of the vendor evaluation. Neither are questions about strategic workforce planning — do we have the right planning roles? Do our planners have the analytical skills to use an advanced system effectively? What training and development is needed?

These aren't implementation details to be figured out later. They're fundamental to whether the investment will deliver returns, and they should inform the selection decision.

How to structure an APS RFx process that works

With those pitfalls in mind, here's a practical framework for running an APS selection process that actually surfaces the information needed to make a good decision.

Phase one: planning capability assessment (4-6 weeks)

Before engaging the market, invest in understanding your own planning capability — where it's strong, where it's weak, and where the biggest opportunities for improvement sit.

This means conducting a structured assessment of current planning processes: demand planning, supply planning, inventory management, S&OP, production scheduling. It means analysing forecast accuracy by product segment, customer and time horizon. It means quantifying the inventory opportunity — how much working capital is tied up that shouldn't be, and where service levels are being missed despite high stock levels. It means mapping the data landscape: what's available, what's reliable, what's missing, and what integration is needed.

The output of this phase is a planning capability baseline and a set of prioritised requirements — not a generic feature list, but a specific articulation of the planning decisions that need to improve, the data and analytical capabilities required to improve them, and the process and organisational changes that will be needed alongside the technology.

This is strategy and network design work as much as it is technology work. It ensures the RFx is anchored in business value rather than vendor marketing.

Phase two: market scan and shortlisting (2-3 weeks)

With clear requirements in hand, conduct a structured market scan to identify the vendors and implementation partners most likely to be a good fit. This should consider the vendor's strength in your specific industry (FMCG, manufacturing, retail, resources), the maturity of their demand planning and forecasting capabilities relative to your needs, their track record in the ANZ market — including local implementation partners and support, their architecture and integration approach relative to your ERP and data landscape, and their pricing model and how it scales with your business.

The goal is to shortlist three to four vendors for detailed evaluation — enough for genuine competitive tension, not so many that the evaluation becomes unmanageable. Gartner's Magic Quadrant and Critical Capabilities reports are useful inputs here, but they should be one data point among several, not the sole basis for shortlisting. As independent analysis has noted, the Magic Quadrant format tends to reward vendors with broad market presence and comprehensive feature claims without necessarily distinguishing between deep capability and surface-level functionality.

Phase three: RFx design and issue (3-4 weeks)

The RFx document itself should be designed to generate the specific information needed for a high-quality evaluation — not to demonstrate procurement rigour through volume of documentation.

The most effective APS RFx documents we've seen include several key components. First, a business context section that gives vendors genuine insight into the organisation's planning challenges, data environment and improvement priorities — because better-informed vendors produce better responses. Second, a focused set of functional requirements organised around planning capability areas (demand planning, supply planning, inventory optimisation, S&OP, analytics) rather than exhaustive feature checklists. Third, a set of planning scenarios that vendors will be asked to demonstrate during evaluation — specific, data-driven scenarios that reflect the organisation's real challenges, such as "forecast a product with high promotional variability," "optimise safety stocks across a three-tier distribution network," or "model the impact of a supply constraint on customer service." Fourth, clear requirements for implementation approach, team composition, methodology, timeline and change management. Fifth, a total cost of ownership template that requires vendors to itemise licensing, implementation, integration, data engineering, training, change management and ongoing support costs over a defined horizon.

The scenarios are critical. They're what transform the evaluation from a paper exercise into a practical test of capability. Providing a representative dataset to shortlisted vendors — anonymised if necessary — and requiring them to demonstrate their system against defined scenarios reveals more in a two-hour session than a 200-page written response ever will.

Phase four: evaluation (4-6 weeks)

The evaluation phase should combine three streams of assessment.

Written response evaluation against the RFx criteria, focused on approach, methodology, team capability and total cost rather than feature compliance. This provides the baseline comparative view.

Scripted demonstrations where each shortlisted vendor works through the defined planning scenarios using the provided dataset. These sessions should be attended by planners, not just IT and procurement, because planners are the ones who will recognise whether the system handles their real-world challenges effectively. Evaluation should focus on the quality of outputs (does the forecast look sensible? Does the inventory recommendation align with service targets?), the usability of the planner interface (can a planner understand and act on what the system produces?), and the transparency of the system's logic (can planners see why the system is recommending what it's recommending, and override it when they have better information?).

Reference checks and site visits with comparable organisations that have implemented the same platform. These should go beyond the vendor's curated reference list — ask for references in your industry, your geography, your scale, and at a similar stage of planning maturity. The questions that matter most are: how long did implementation actually take versus plan? What was the real total cost versus budget? How quickly did planners adopt the system? What worked well and what would they do differently?

The evaluation criteria should weight practical demonstration performance, implementation credibility and total cost of ownership at least as heavily as written response quality. A common weighting structure might allocate 30% to demonstrated planning capability, 25% to implementation approach and team, 20% to total cost of ownership, 15% to architecture, integration and scalability, and 10% to vendor viability and support model.

Phase five: proof of concept (4-8 weeks, optional but recommended)

For high-value APS investments, a proof of concept with the preferred vendor — or in some cases, two finalists — provides a final layer of validation before committing. The PoC should use a defined subset of the organisation's actual data and test a specific set of planning capabilities in realistic conditions.

This isn't a full implementation. It's a structured test that answers the question: does this system, with our data, produce materially better planning outputs than our current approach? If the answer is yes — demonstrably, measurably — the business case for investment is validated. If the answer is equivocal, that's critical information to have before signing a multi-year contract.

Phase six: commercial negotiation and contracting (3-4 weeks)

With a preferred vendor identified and validated through demonstration and potentially PoC, the commercial negotiation phase should address the full scope of the relationship — not just licensing terms.

Key negotiation areas include licensing structure and scalability (what happens as user numbers or data volumes grow?), implementation pricing and risk sharing (fixed fee for defined scope versus time-and-materials), performance commitments (are there contractual commitments to system performance, forecast accuracy improvement, or implementation timeline?), data ownership and portability (if you leave, can you take your configuration, models and data?), support model and SLAs (response times, availability commitments, upgrade frequency), and contract term and exit provisions.

This is procurement work that benefits from experience with technology contracts specifically — understanding the commercial levers that matter in SaaS agreements, the risks that need to be allocated, and the governance mechanisms that protect the organisation's interests over a multi-year relationship.

The Australian and New Zealand context

For organisations operating in Australia and New Zealand, several characteristics of the local environment make APS selection particularly consequential.

Long inbound supply chains with variable lead times — particularly for imported products coming through congested port infrastructure — mean that demand planning accuracy and inventory optimisation have a disproportionate impact on both working capital and service levels. The buffer you need across a 12-week ocean lane from Asia is fundamentally different from what a European business needs across a 3-day road lane from a neighbouring country.

Seasonal and promotional demand patterns in Australian markets — particularly in FMCG, retail and agriculture — create concentrated demand peaks that expose the limitations of basic forecasting approaches. An APS that handles promotional uplift well can be worth millions in reduced waste and improved on-shelf availability.

The relatively small scale of the ANZ market means that some global APS vendors have limited local presence — fewer implementation consultants, smaller support teams, longer response times. The RFx process should explicitly evaluate local capability, not just global credentials. An outstanding platform with no credible ANZ implementation partner is a risk, not an asset.

Working capital pressure in the current interest rate environment makes inventory optimisation — one of the core APS capabilities — directly material to the balance sheet. Every dollar of unnecessary inventory carries a real financing cost that didn't matter as much when rates were near zero.

These factors reinforce why the RFx process needs to be tailored to ANZ operating conditions rather than run as a generic global technology evaluation. The scenarios used in evaluation should reflect Australian supply chain realities: long lanes, seasonal peaks, promotional volatility, concentrated retail customers and distributed geographic networks.

For more on how APS capabilities map to ANZ supply chain challenges, Trace has published a detailed guide on Advanced Planning Systems and how they transform supply chain planning in Australia and New Zealand.

Process readiness matters as much as system selection

One of the most important — and most frequently overlooked — aspects of APS selection is the question of process readiness. The best demand planning system in the world won't deliver value if the organisation doesn't have a functioning S&OP process, if planners don't have time to work with the system because they're buried in manual data manipulation, or if there's no governance rhythm for reviewing and acting on system outputs.

The RFx process should include an honest assessment of organisational readiness: do we have the planning processes, the roles, the data discipline and the executive sponsorship to implement and sustain an APS effectively? If the answer is "not yet," that doesn't mean abandoning the investment — but it does mean sequencing process improvement and capability building alongside (or ahead of) the technology implementation, and reflecting that reality in the implementation plan and timeline.

The organisations that get the most from their APS investment typically spend as much effort on project and change management — redesigning planning processes, upskilling planners, building governance mechanisms, securing stakeholder buy-in — as they do on configuring and deploying the technology. The RFx process should evaluate vendors and implementation partners on their ability to support this change dimension, not just their technical capability.

How Trace Consultants can help

At Trace Consultants, we help Australian organisations navigate APS selection and implementation from a position of genuine planning expertise — not just technology procurement capability.

We work across the full selection lifecycle:

Planning capability assessment. We assess your current demand planning, supply planning, inventory management and S&OP processes against best practice, quantify the improvement opportunity, and define the specific requirements that should drive your APS selection. This is planning and operations consulting grounded in practical supply chain experience.

Market scan and shortlisting. We help you navigate the vendor landscape with an informed, independent view — identifying which platforms are genuinely strong for your industry, your scale and your planning maturity, and which are better suited to different contexts.

RFx design and management. We design and manage the RFx process end to end — from document development through evaluation, demonstration management, reference checking and recommendation. Our approach emphasises scenario-based evaluation over feature checklists, ensuring the selection is based on demonstrated capability rather than written claims.

Commercial negotiation. We support procurement negotiations with APS vendors and implementation partners, bringing benchmarks and experience with technology contract structures to ensure the commercial terms protect your interests and reflect realistic expectations.

Implementation readiness. We help you prepare for implementation — designing the target planning processes, defining the organisational structure and roles, building the data readiness plan, and establishing the change management approach that will determine whether planners adopt the system or revert to spreadsheets.

Ongoing optimisation. Post-implementation, we support organisations in tuning and optimising their APS — recalibrating forecasting models, refining inventory parameters, improving S&OP process integration, and building internal capability to sustain and extend the system's value over time.

Our independence from any APS vendor means our recommendation is based entirely on which platform best fits your requirements, your data, your organisation and your budget — not on partnership arrangements or reseller margins. We've worked with organisations implementing platforms across the spectrum, from the major Leaders through to focused niche players, and our advice reflects that breadth of experience.

Getting this decision right

An APS investment is typically a multi-year, multi-million dollar commitment. The system you select will shape how your organisation plans demand, manages inventory, allocates supply and makes trade-off decisions for years to come. The planners who use it daily will either be empowered to make better decisions faster — or frustrated by a tool that doesn't fit their reality.

The RFx process is where you create the conditions for one of those outcomes or the other. A process that's rigorous about the right things — planning capability, practical demonstration, implementation credibility, total cost, change readiness — will consistently lead to better decisions than one that's rigorous about the wrong things: feature checklists, compliance documentation and licensing price.

If your organisation is considering an APS investment and you want to make sure the selection process is designed to deliver a system your planners will actually use, we'd welcome the conversation.

Procurement

Procurement Go-to-Market Process: How to Maximise Value and Stop Leaving Money on the Table

Joe Bryant
February 2026
Your procurement go-to-market process is probably costing you more than you think. Not because the templates are wrong or the evaluation panel isn't qualified — but because the strategic choices that determine 80% of the commercial outcome are being made (or not made) before a single supplier receives an invitation to respond.

There's a pattern that plays out in procurement teams across Australia with depressing regularity. A contract is approaching expiry. Someone raises the flag — usually later than ideal. A decision is made to "go to market." Templates are pulled from the last process. Requirements are gathered in a rush, often by copying the existing scope of work with a few tweaks. The RFx goes out. Responses come back. An evaluation panel scores them against criteria that may or may not reflect what the organisation actually needs. A preferred supplier is selected. Terms are negotiated — usually under time pressure because the existing contract is about to lapse. A new agreement is signed.

Everyone breathes a sigh of relief. The procurement team moves on to the next fire.

Joe Bryant, Senior Consultant

And somewhere in that process — usually in several places — the organisation left a significant amount of commercial value on the table. Not because anyone did anything wrong, exactly, but because the process was designed around compliance rather than value creation.

This is the fundamental challenge with procurement go-to-market processes in most Australian organisations, whether government or private sector. The mechanics of tendering — the RFx documents, the evaluation matrices, the probity frameworks — are well understood and generally well executed. What's far less consistent is the strategic thinking that should sit around those mechanics: the choices about when to go to market, how to structure the approach, what to test with suppliers before formalising requirements, how to design evaluation criteria that surface genuine capability differences, how to negotiate beyond price, and how to transition from procurement process to contract performance without losing the commitments that were made during the tender.

Each of those choices — made well or poorly — has a material impact on the commercial outcome. And collectively, they're where the difference sits between a GTM process that delivers 5% savings on the incumbent's rates and one that delivers 15-25% total cost improvement through a combination of scope redesign, service model innovation, better commercial structures and genuine competitive tension.

Why go-to-market processes underperform

Before getting into what good looks like, it's worth understanding why so many GTM processes deliver underwhelming results. In our experience working across procurement programs in both government and commercial organisations, the same failure patterns come up repeatedly.

Starting too late

The single biggest driver of value leakage in procurement is compressed timelines. When an organisation starts its GTM process six months before contract expiry, it's already behind. Complex service categories — facilities management, logistics, IT managed services, labour hire, professional services — need 12 to 18 months of lead time to do properly. That includes time for spend analysis, requirements definition, market sounding, document preparation, response period, evaluation, negotiation, contract execution and transition.

When timelines are compressed, the first things to get squeezed are the activities that create the most value: market research, requirements challenge, commercial baseline development, and negotiation. The process defaults to "get compliant responses and pick the best one" rather than "shape the market to deliver the outcome we actually need."

The Australian National Audit Office has repeatedly identified this as a systemic issue in Commonwealth procurement, noting that procurements need to be well planned and with sufficient time to give decision-makers a genuine choice. The same dynamic plays out in state government and the private sector.

Copying the existing scope instead of challenging it

Most go-to-market processes start by documenting what the organisation currently buys — the existing scope, volumes, service levels and specifications. This feels like a sensible starting point. The problem is that it anchors the entire process to the status quo.

If the organisation has been buying a service the same way for five years, there's a reasonable chance the scope has drifted, service levels have become misaligned with actual need, specifications include requirements that made sense when they were written but no longer do, and the commercial model reflects the last negotiation rather than what the market can actually deliver.

A well-run GTM process challenges the scope before going to market. It asks: are we buying the right things? Are the service levels calibrated to business value, or have they become an untouched artefact of the last contract? Could a different service model — different bundling, different risk allocation, different technology — deliver a better outcome? These are the questions that unlock genuine value, and they need to be answered before the RFx documents are drafted, not discovered during evaluation when it's too late to restructure.

This is fundamentally a strategy exercise, not an administrative one. It requires understanding what the organisation actually needs, what the market is capable of delivering, and where the gap between the two creates an opportunity for a different commercial conversation.

Treating evaluation as a scoring exercise

The evaluation stage of most GTM processes is built around weighted criteria and scoring matrices. Panel members read responses, assign scores against predetermined criteria, and the highest-scoring respondent is identified as the preferred supplier.

This approach has strengths — it's transparent, defensible and consistent. But it has a significant weakness: it tends to reward well-written responses rather than genuinely differentiated capability. A supplier with an excellent bid writer can score well on methodology, resourcing, innovation and risk management criteria without any of it translating into superior delivery. Meanwhile, a supplier with genuinely different capability but a less polished response may score lower.

The best GTM processes supplement scoring with structured evaluation activities that test capability more directly: reference checks that go beyond the names provided by the respondent, site visits to see operations first-hand, scenario-based sessions where shortlisted suppliers work through realistic problems, and commercial modelling that stress-tests pricing structures against volume variations, scope changes and escalation mechanisms.

These activities take more time. They also produce dramatically better information for making what is often a multi-million dollar decision.

Negotiating too narrowly

Perhaps the most common point of value leakage in procurement is the negotiation phase. In many organisations, "negotiation" effectively means pressing the preferred supplier on rates — asking them to sharpen their pricing, remove margins, or match the lowest price from the competitive field.

This is the least valuable form of negotiation. Suppliers know it's coming, and they price accordingly — building headroom into their initial offer specifically to accommodate the expected rate discussion. The result is a predictable dance where the supplier concedes 3-5% on rates, the procurement team claims a saving, and the actual commercial structure remains largely unchanged.

Genuine value in negotiation comes from a different set of levers entirely: scope definition (what's in, what's out, what's optional), risk allocation (who bears volume risk, who absorbs cost escalation, who owns transition), commercial mechanism design (fixed fee versus cost-plus versus gain-share versus outcome-based), indexation and escalation (CPI, WPI, bespoke indices, cap and collar structures), performance and remedies (KPIs that actually drive behaviour, meaningful service credits, improvement mechanisms), change control (how scope changes are priced, who initiates, what the governance looks like), and exit and transition (how the contract ends, what the obligations are, how IP and data are handled).

Each of these levers typically represents more commercial value than the rate negotiation that gets all the attention. A well-structured negotiation strategy identifies which levers matter most for the specific category and pursues them systematically.

Losing value at contract transition

The final failure pattern is the handover between procurement and contract management. In many organisations, these are different teams with different priorities and limited overlap. The procurement team manages the GTM process, negotiates the contract, gets it signed, and moves on. The contract management team inherits an agreement they weren't closely involved in shaping, with commitments they may not fully understand and performance expectations they may not have the tools or authority to enforce.

The ANAO has documented cases where Commonwealth contracts were managed by 19 different people over a 14-year period, with few having experience managing large-scale contracts or formal contract management training, and none involved in the original tender or negotiation. It's an extreme example, but the underlying dynamic — disconnection between procurement and contract management — is extremely common.

This is where the value negotiated during the GTM process either gets realised or evaporates. Without deliberate transition planning, performance baselines, and contract governance structures, the gap between what was agreed and what gets delivered can widen quickly.

What a high-performing GTM process looks like

The organisations that consistently maximise value from their go-to-market processes share a set of common practices. None of them are revolutionary — but the discipline of executing all of them, consistently, for every significant procurement, is what separates good outcomes from mediocre ones.

Phase one: strategic preparation

The GTM process should begin with a clear commercial hypothesis, not a set of tender documents. This means understanding the current state comprehensively: what are we spending, with whom, on what scope, at what service levels, under what commercial terms? It means analysing that baseline against market benchmarks and identifying where the gaps, inefficiencies and opportunities sit. And it means developing a go-to-market strategy that's tailored to the specific category — not a generic "run an RFT" decision, but a deliberate choice about the right approach for this market, this scope, this set of objectives.

For a mature, competitive market with clear specifications, a single-stage open tender may be appropriate. For a complex, evolving category where innovation matters, a multi-stage process starting with an expression of interest or request for proposal may deliver better results. For a category with limited competition or high switching costs, a structured negotiation with the incumbent — backed by genuine market intelligence — may be more effective than a full tender that attracts marginal competitors.

This strategic preparation phase should also include market sounding — structured conversations with potential suppliers (conducted within appropriate probity boundaries) to test assumptions about scope, pricing, capability and appetite. Market sounding is one of the most underutilised tools in Australian procurement. It costs relatively little time, and the intelligence it provides can fundamentally reshape the GTM strategy.

The Victorian Government's procurement guidance explicitly encourages market sounding as a means to "seek feedback from the market and encourage market interest in contesting the project." NSW Government's procurement framework positions open requests for proposal as particularly useful "when you're unsure of pricing or don't know which supplier can best satisfy your agency's procurement needs." Both recognise that preparation and market intelligence are prerequisites for value, not optional extras.

Phase two: requirements and document design

With a clear strategy and market intelligence in hand, the requirements definition and RFx document design phase can begin. The key principle here is that the documents should be designed to generate the information you need to make a good decision — not just to satisfy a compliance checklist.

This means writing requirements that describe outcomes rather than prescribing methods wherever possible. It means designing evaluation criteria that weight the factors that actually differentiate value — capability, approach, risk, innovation, total cost — rather than defaulting to a standard 60/40 or 70/30 split between technical and commercial scores. It means structuring the commercial response format so that pricing is comparable across respondents, transparent in its assumptions, and amenable to modelling under different scenarios.

Poorly designed documents are one of the most common causes of value leakage — not because they fail to attract responses, but because they attract responses that are difficult to compare, hard to evaluate meaningfully, and don't surface the information needed for effective negotiation.

For government organisations in particular, where probity constraints limit the ability to iterate with suppliers during evaluation, the quality of the documents issued at the start of the process has an outsized impact on the quality of the outcome.

Phase three: market engagement and response management

How an organisation engages with the market during the GTM process matters as much as what's in the documents. Pre-tender briefings, Q&A processes, site visits and industry engagement events all serve to improve the quality of responses by ensuring suppliers understand the context, the priorities and the genuine areas of flexibility.

Organisations that treat the response period as a black box — issue documents, answer formal questions, wait for submissions — typically receive less innovative and less commercially compelling responses than those that invest in genuine engagement. Suppliers respond to signals. When they see an organisation that's invested in understanding the market, has clear priorities, and is open to creative commercial structures, they invest more effort in their response.

Managing the response period effectively also means being responsive to questions, providing consistent information to all participants, and maintaining competitive tension. The goal is to create an environment where every shortlisted supplier believes they can win — because when suppliers believe the outcome is predetermined, the quality of their offer reflects it.

Phase four: evaluation and shortlisting

Evaluation should be multi-dimensional, combining scored assessment of written responses with practical verification activities for shortlisted suppliers. The evaluation plan should be designed before the process starts — not retrofitted once responses are in — and should align directly with the strategic objectives identified during preparation.

For complex categories, interactive evaluation sessions with shortlisted suppliers can be enormously valuable. These sessions — sometimes called "competitive dialogue" or "interactive tender process" — allow the evaluation panel to probe claims made in written responses, test capability through realistic scenarios, and identify genuine differentiators that don't emerge from scored documents.

The Victorian Government's procurement framework explicitly supports interactive tender processes and structured negotiations as tools for resolving issues "under competitive tension." These mechanisms are particularly valuable for service categories where the way a supplier approaches problems, manages exceptions and adapts to changing requirements is as important as their baseline capability.

Phase five: negotiation

Negotiation should be treated as a distinct phase with its own strategy, preparation and skilled execution — not as an afterthought bolted onto the end of evaluation.

A strong negotiation strategy identifies the key commercial levers for the specific category, establishes a clear BATNA (best alternative to negotiated agreement), sets target and walkaway positions for each lever, and sequences the negotiation to build toward agreement on the most valuable terms.

In our experience, the levers that create the most value are rarely the ones that get the most attention. Rate reductions are visible and easy to measure, but scope optimisation, risk reallocation, indexation design, and performance mechanism calibration typically deliver 2-3 times more commercial value over the life of a contract. The challenge is that these levers require deeper category knowledge and more sophisticated commercial modelling to pursue effectively.

This is one of the areas where external procurement expertise makes the most tangible difference — bringing category benchmarks, negotiation experience across comparable deals, and the commercial modelling capability to quantify the value of different structural options.

Phase six: contract transition and governance

The final phase — and the one most often neglected — is the transition from signed contract to operational performance. This requires a deliberate handover from the procurement team to the contract management or operational team, including detailed briefing on the commitments made, the rationale behind key commercial terms, the performance framework and how it's intended to work, and the escalation and governance mechanisms.

It also requires establishing the operational rhythm for contract governance: regular performance reviews, supplier relationship meetings, commercial reviews, and mechanisms for managing scope changes and continuous improvement. Without these structures, the commercial value negotiated during the GTM process erodes over the first 12-18 months as scope creeps, service levels blur, and the relationship defaults to informal arrangements that may not reflect the contract.

For organisations with complex, multi-site operations — whether in FMCG and manufacturing, resources and energy, or health and human services — this transition is particularly critical because the gap between head office contract terms and operational site-level behaviour can be substantial.

Common categories where GTM value gets lost

While these principles apply across all procurement categories, certain categories are particularly prone to value leakage during the GTM process.

Facilities management and property services. Complex scope, multiple service lines, significant site variation, and long contract terms create enormous potential for scope ambiguity and cost drift. The difference between a well-structured FM contract and a poorly scoped one can be 20-30% of total spend over the contract life.

Logistics, transport and warehousing. Categories where volume variability, geographic complexity and operational interdependencies mean that headline rates tell a small fraction of the commercial story. Understanding total cost — including indirect costs like inventory impact, service failures and administrative overhead — is essential for meaningful evaluation. Trace's warehousing and distribution and BOH logistics expertise is particularly relevant here.

IT managed services and technology. Categories characterised by rapid change, high switching costs, and commercial models that can range from input-based (people and hours) to outcome-based (service levels and availability). The scope and commercial structure choices in technology procurement often lock in more value — or destroy more — than the rate negotiation.

Labour hire and contingent workforce. Categories where margin transparency, markup structures, and the interplay between procurement terms and workforce planning and operations create significant complexity. A well-designed GTM process considers not just the rates paid but the workforce model that drives demand.

Professional and consulting services. Categories where the evaluation challenge is acute — distinguishing between suppliers who write well about capability and suppliers who actually deliver. Panel arrangements in particular can become stale, with work flowing to incumbents regardless of the competitive framework.

How Trace Consultants can help

At Trace Consultants, we help Australian organisations design and execute procurement go-to-market processes that deliver genuine commercial outcomes — not just compliant processes.

Our approach starts with the strategic preparation that most GTM processes skip: spend analysis, baseline development, market intelligence, requirements challenge and commercial hypothesis development. We do this work before a single tender document is drafted, because it's where the largest opportunities are identified and the GTM strategy is shaped.

Go-to-market strategy and design. We help organisations determine the right approach for each category — single or multi-stage, open or selective, competitive or negotiated — based on market conditions, organisational objectives and risk appetite. Our strategy and network design capability ensures the GTM approach reflects the broader supply chain and operational context.

Requirements definition and scope challenge. We work with stakeholders to define requirements that reflect actual business need rather than inherited scope, and structure them in ways that encourage innovative responses and enable meaningful comparison.

RFx document development and market engagement. We draft tender documentation that's clear, commercially intelligent and designed to generate the information needed for effective evaluation and negotiation. We support market sounding, pre-tender briefings and supplier engagement activities.

Evaluation design and support. We design evaluation frameworks that surface genuine capability differences, and support evaluation panels with commercial analysis, reference checking and scenario-based assessment activities.

Negotiation strategy and execution. We develop and execute negotiation strategies that go beyond rate reduction to address the full range of commercial levers — scope, risk, indexation, performance, change control and exit. Our procurement team brings benchmarks and deal experience across the categories where Australian organisations spend the most.

Contract transition and governance. We help organisations bridge the gap between procurement and contract management — establishing performance baselines, governance rhythms and management frameworks that protect the value negotiated during the GTM process. Our project and change management capability supports smooth transition from procurement to operational delivery.

Capability uplift. We help procurement functions build the internal capability to run effective GTM processes independently — through training, playbooks, templates and coaching that embed better practices into the organisation's standard way of working. Our organisational design expertise ensures procurement capability sits within a structure that supports sustainable performance.

Getting it right matters more than ever

Australian organisations — in both the public and private sectors — are operating in an environment where cost pressures are intensifying, supply markets are tightening in several critical categories, compliance and transparency expectations are increasing, and the consequences of getting procurement wrong are more visible than ever.

In that context, the go-to-market process is one of the highest-leverage activities any procurement function undertakes. The difference between a mediocre GTM process and an excellent one is measured in millions of dollars for large organisations — not over many years, but on each significant contract.

The organisations that invest in doing it properly — that give themselves enough time, challenge their own assumptions, engage the market intelligently, evaluate with rigour, negotiate with skill and transition with discipline — consistently outperform those that treat procurement as a transactional compliance exercise.

If your organisation is preparing to go to market on a significant contract and you want to make sure the process delivers its full commercial potential, we'd welcome the conversation.

Resilience & Risk Management

CPS 230: What This Means for Banks and Why Operational Resilience Is a Supply Chain Problem

David Carroll
February 2026
CPS 230 came into effect on 1 July 2025 and it's the most significant shift in operational risk regulation Australian banks have seen in a decade. It consolidates five previous standards into one, expands the definition of who matters in your supply chain, and holds boards directly accountable for operational resilience. The banks that treat this as a compliance exercise will tick boxes.

On 1 July 2025, APRA's Prudential Standard CPS 230 Operational Risk Management came into force across Australia's banking, insurance and superannuation sectors. It replaced five existing standards — including the separate outsourcing and business continuity frameworks that had governed operational risk for years — and introduced a fundamentally different set of expectations about how regulated entities identify, manage and recover from operational disruptions.

For banks specifically, CPS 230 represents the most consequential change to operational risk regulation in over a decade. Not because the individual requirements are radically unfamiliar — most banks already had business continuity plans, vendor management frameworks and risk registers — but because CPS 230 raises the bar on what "good" looks like across all of them simultaneously, and connects them into a single, coherent framework with board-level accountability and APRA oversight that has real teeth.

APRA Member Therese McCarthy Hockey put it plainly when the standard commenced: in an environment where one crashed server or ransomware attack could leave millions without access to essential banking services, effective operational risk management is vital for financial stability and community wellbeing. CPS 230, she said, requires entities to have "an entirely new mindset about where the boundaries of responsibility sit."

That phrase — where the boundaries of responsibility sit — is the key to understanding what CPS 230 actually demands. Because for most banks, the boundaries of their operational capability extend far beyond their own walls. They run through cloud providers, payment processors, technology platforms, data centres, telecommunications networks, printing and distribution services, physical security contractors, facilities management firms, and dozens of other third and fourth parties whose performance directly determines whether critical banking services stay available.

This is, at its core, a supply chain problem. And it needs supply chain thinking to solve properly.

What CPS 230 actually requires

Before getting into the supply chain implications, it's worth grounding the discussion in what the standard actually requires. CPS 230 is built around three pillars:

Operational risk management. Banks must maintain a comprehensive framework for identifying, assessing, managing and monitoring operational risks — including those arising from inadequate or failed internal processes, systems, people, or external events. This isn't new in concept, but CPS 230 is more prescriptive about expectations: risk appetite must be clearly defined, internal controls must be in place and tested, risk incidents must be reported to APRA within 72 hours if they're likely to have a material impact, and the board must actively oversee and challenge the entity's operational risk profile.

Business continuity. Banks must identify their critical operations — those essential functions that, if disrupted, could materially affect customers, financial markets or the broader economy — and set tolerance levels for how much disruption is acceptable. Business continuity plans must be maintained, tested through scenario exercises, and designed to ensure the bank can continue to deliver critical operations within those tolerance levels even during severe disruptions. The shift here is important: CPS 230 moves beyond recovery (how quickly can you get back to normal?) to resilience (can you keep operating through the disruption?).

Service provider management. Banks must identify their material service providers — any third party whose failure or disruption could affect a critical operation or expose the bank to material operational risk. For each material service provider, the bank must conduct due diligence, establish formal agreements with clear performance and resilience requirements, maintain ongoing monitoring, and develop contingency plans for service failure. Critically, CPS 230 extends this requirement down the supply chain to fourth parties — organisations engaged by the bank's service providers — requiring banks to seek assurance that their providers are managing these downstream risks appropriately.

Banks must submit a register of material service providers to APRA by 1 October 2025. Pre-existing contracts have a transitional period, with CPS 230 requirements applying from the earlier of the next contract renewal or 1 July 2026.

Why this is fundamentally a supply chain challenge

The language of CPS 230 is regulatory — risk frameworks, tolerance levels, prudential obligations. But the substance of what it requires banks to do is operational, and much of it maps directly onto supply chain management disciplines.

Consider what a bank actually needs to deliver to comply:

End-to-end mapping of critical operations. CPS 230 requires banks to have a complete understanding of their critical operations and the resources — people, processes, technology, facilities and third parties — that support them. This is, in effect, a supply chain mapping exercise. It requires the bank to trace every critical service back through its dependencies, identifying which systems, which teams, which locations and which external parties are involved in delivering it. Most banks have this information scattered across IT asset registers, vendor management systems, business continuity plans and procurement databases — but few have it integrated into a single, end-to-end view that shows how disruption in one node propagates through to customer-facing services.

Third and fourth party risk visibility. The expansion from "outsourcing" (under the old CPS 231) to "material service providers" (under CPS 230) is a deliberate broadening of scope. It recognises that modern banks depend on a complex web of service relationships — not just the traditional outsourced functions, but cloud infrastructure, software platforms, data feeds, payment networks, identity verification services, and dozens of other inputs that aren't "outsourcing" in the traditional sense but are every bit as critical.

Managing this web of dependencies — understanding who provides what, where the concentration risks are, what happens if a provider fails, and how fourth parties further down the chain might create cascading disruptions — is a supply chain management problem. It requires the same disciplines of supplier mapping, risk assessment, performance monitoring, and contingency planning that any complex, mission-critical supply chain demands.

Resilience testing and scenario analysis. CPS 230 requires banks to test their business continuity plans through scenario exercises — including scenarios involving cyberattacks, major third-party failures, data breaches and supply chain disruptions. These exercises must assess whether critical operations can actually continue within tolerance levels, not just whether recovery plans exist on paper.

This is operationally analogous to supply chain stress testing — modelling what happens when a key supplier fails, when a distribution node goes offline, or when a logistics corridor is disrupted. It requires understanding interdependencies, identifying single points of failure, and validating that contingency arrangements actually work under realistic conditions.

For government and defence agencies and other organisations that manage critical infrastructure, this kind of supply chain resilience thinking is well established. For banks, CPS 230 is bringing it into the regulatory mainstream.

Where banks are finding this hardest

Now that CPS 230 is in force, the practical challenges are becoming clearer. Several areas are proving particularly difficult for banks — and they're the areas where supply chain expertise is most relevant.

Material service provider identification and tiering

Many banks have hundreds or thousands of third-party relationships. Determining which ones are "material" under CPS 230 requires a structured assessment of each provider's role relative to critical operations — not just their contract value or the function they nominally support. A small technology firm providing a niche data feed that underpins a critical payment process may be more "material" than a large outsourced function that supports a non-critical activity.

This requires cross-functional collaboration between procurement, IT, operations, risk and business units — and a methodology for assessing materiality that's consistent, defensible and practical to apply at scale. Many banks have found their existing vendor management systems inadequate for this purpose, because they were designed around procurement categories and contract management rather than operational dependency mapping.

This is where procurement disciplines intersect with operational risk management. Getting the material service provider register right isn't just a compliance exercise — it's the foundation for all subsequent third-party risk management activity under CPS 230.

Fourth-party visibility

CPS 230 requires banks to look beyond their direct service providers to the organisations that those providers depend on — the fourth parties. In practice, this means understanding, for example, which cloud platform your technology vendor runs on, which sub-processors your data analytics provider uses, or which telecommunications carrier your managed services provider relies on for network connectivity.

Most banks have limited visibility at this level. Their contracts with direct providers may not include adequate information rights, and many service providers are reluctant to disclose their own supply chain arrangements in detail. Yet CPS 230 requires banks to seek assurance that material service providers have the capability to manage these downstream risks.

Building fourth-party visibility is a supply chain transparency challenge — one that requires contractual provisions, information sharing frameworks, and ongoing monitoring mechanisms. It's the same kind of supply chain mapping and tier-n visibility work that manufacturing and defence supply chains have been tackling for years, applied to the services supply chain that underpins banking operations.

Business continuity that goes beyond recovery

The traditional approach to business continuity in banking has been largely focused on recovery — how quickly can systems be restored, data recovered, and operations resumed after an incident? CPS 230 shifts the emphasis toward continuity through disruption — maintaining critical operations within tolerance levels even while the disruption is ongoing.

This requires a different kind of planning. It's not enough to have a disaster recovery site and a call tree. Banks need to understand which services are truly critical (and which can be temporarily degraded or suspended), what the minimum viable operating model looks like for each critical service, how dependencies between services create cascading failure risks, and what manual or alternative processes can sustain operations while primary systems are unavailable.

This is planning and operations work — designing operating models that are resilient by design rather than reliant on recovery after failure. It requires the same kind of scenario modelling, capacity planning and contingency design that applies to any supply chain where continuity of service is non-negotiable.

Governance and board accountability

CPS 230 places explicit obligations on bank boards: approving tolerance levels for disruptions to critical operations, reviewing risk and performance reporting on material service providers, and considering operational risk implications before making strategic decisions such as mergers, acquisitions or technology platform changes.

For boards to discharge these obligations meaningfully, they need clear, concise reporting that translates operational complexity into decision-relevant information. This means dashboards and reports that show the current state of critical operation resilience, material service provider performance against agreed standards, concentration risks across the third-party portfolio, results and insights from scenario testing exercises, and emerging risks from the service provider landscape.

Building the reporting and governance infrastructure to support board oversight is an organisational design challenge — one that requires clarity about roles, responsibilities, information flows and decision rights across the three lines of defence.

Concentration risk: the elephant in the room

One of the most significant systemic risks that CPS 230 surfaces is concentration — the degree to which multiple banks (and other financial institutions) depend on the same small number of critical service providers.

The most obvious example is cloud infrastructure. A small number of global hyperscale providers — AWS, Microsoft Azure, Google Cloud — underpin a significant and growing proportion of banking technology. If a single cloud provider experienced a sustained outage affecting its Australian region, the impact could cascade across multiple banks simultaneously, affecting payment systems, internet banking, lending platforms and customer communications.

CPS 230 doesn't prohibit this concentration, but it does require each bank to understand its implications, set tolerance levels for disruption, and have contingency plans. In practice, this means banks need to assess whether their critical operations would survive a prolonged outage of their primary cloud provider, whether multi-cloud or hybrid strategies provide genuine resilience (rather than just architectural complexity), and how concentration in other service categories — payment networks, telecommunications, identity verification — creates correlated failure risks.

This is a supply chain resilience challenge that parallels concentration risk in physical supply chains — where dependence on a single supplier, single geography or single transport corridor creates vulnerability to correlated disruption.

What this means for banks' service providers

CPS 230 doesn't directly regulate service providers — but it profoundly affects them. Banks are now contractually required to impose resilience expectations on their material service providers, including requirements around business continuity capabilities, incident reporting and notification timelines, sub-contracting and fourth-party transparency, audit and information rights, and exit and transition planning.

For organisations that provide services to banks — whether in technology, operations, facilities, logistics or professional services — CPS 230 effectively makes them part of the regulatory ecosystem. Failure to meet the resilience expectations embedded in bank contracts can have commercial and reputational consequences.

Service providers that proactively demonstrate their operational resilience — through mature business continuity planning, transparent supply chain practices, and robust incident management — will be better positioned commercially than those that treat CPS 230 requirements as an unwelcome contractual burden.

Beyond compliance: the operational opportunity

The banks that approach CPS 230 purely as a compliance obligation will produce policy documents, populate registers, and run tick-box scenario exercises. They'll meet the letter of the standard without fundamentally improving their operational resilience.

The banks that approach it as an operational transformation opportunity will do something more valuable: they'll build a genuinely integrated understanding of how their operational supply chain works — from internal processes through to third and fourth parties — and use that understanding to make better decisions about where to invest in resilience, where to reduce concentration risk, and where to simplify complexity that creates unnecessary vulnerability.

This is the difference between compliance and capability. CPS 230 sets a regulatory floor. The operational benefits of doing it well — reduced incidents, faster response, lower insurance costs, stronger customer trust, better commercial outcomes with service providers — extend far beyond that floor.

How Trace Consultants can help

At Trace Consultants, we bring supply chain expertise to operational challenges — including the service provider management, operational mapping, resilience planning and governance requirements that CPS 230 demands of banks.

We work at the intersection of supply chain strategy, procurement, operations and risk — which is exactly where CPS 230 compliance lives in practice.

Critical operation and supply chain mapping. We help banks map their critical operations end-to-end, identifying the people, processes, technology, facilities and third parties that support each service. This is strategy and network design work applied to the operational supply chain — creating the visibility foundation that everything else in CPS 230 depends on.

Material service provider assessment and tiering. We design and apply structured methodologies for assessing which service providers are material, how they should be tiered, and what management requirements apply to each tier. Our procurement expertise ensures that these assessments reflect operational dependency, not just contract value.

Third and fourth party risk management frameworks. We help banks design the policies, processes and systems for ongoing monitoring of material service providers — including fourth-party visibility, performance measurement, and escalation procedures. This draws on our experience managing complex, multi-tier supply chains across government and private sector contexts.

Business continuity and resilience planning. We support banks in designing business continuity plans that go beyond recovery to genuine operational resilience — including minimum viable operating models, scenario analysis, and contingency arrangements for critical service provider failure. Our planning and operations teams bring practical experience in designing operations that can absorb disruption, not just recover from it.

Governance, reporting and organisational design. We help banks design the organisational structures, reporting frameworks and governance arrangements that support board oversight of operational resilience — ensuring that information flows are clear, decision rights are defined, and the three lines of defence work as intended.

Service provider contract and exit strategy. We support banks in designing service provider agreements that embed CPS 230 requirements, and in developing exit and transition plans for material arrangements that reduce lock-in risk and ensure continuity. Our project and change management capability supports execution of complex service transitions.

Technology enablement. We help banks select and implement technology platforms for operational risk management, service provider monitoring and resilience reporting — ensuring that tools support rather than substitute for sound operational processes.

The clock is ticking

CPS 230 is live. The material service provider register is due to APRA by 1 October 2025. Pre-existing service provider contracts must comply by the earlier of their next renewal date or 1 July 2026. APRA has made clear that it expects regulated entities to demonstrate genuine capability, not just documentation.

For banks still working through implementation, the priority now is to close the gaps between policy and operational reality — to move from frameworks on paper to systems, processes and capabilities that actually work when tested. For banks that have met the initial compliance deadlines, the challenge is sustaining and maturing those capabilities over time, as the operational landscape continues to evolve.

Either way, the banks that treat CPS 230 as a catalyst for building genuine operational supply chain resilience — not just a regulatory hurdle to clear — will be the ones best positioned for whatever disruption comes next.

If your organisation is navigating CPS 230 implementation or looking to strengthen the operational supply chain that underpins your critical banking services, we'd welcome the conversation.

Planning, Forecasting, S&OP and IBP

Inventory Optimisation for FMCG: Why the Biggest Lever Most Australian Manufacturers Aren't Pulling Is Sitting in Their Warehouse

Tim Fagan
February 2026
Most Australian FMCG businesses know their inventory is too high. Fewer know exactly why — or what to do about it without putting service levels at risk. The answer isn't a single technology fix or a blanket safety stock reduction. It's a structured approach across range, network, manufacturing, procurement and planning that treats inventory as a strategic lever, not just a warehouse problem.

There's a conversation that happens in almost every FMCG boardroom in Australia at some point during the financial year. Someone from finance points at the balance sheet and asks why there's so much cash tied up in stock. Someone from supply chain explains that it's needed to maintain service levels. Someone from sales mentions a promotion that required a build. And the conversation loops back to where it started, with everyone agreeing that inventory is too high and nobody quite agreeing on what to do about it.

This isn't a failing of any particular function. It's a symptom of how most FMCG businesses manage inventory — reactively, in silos, and without the analytical frameworks needed to make genuinely informed trade-offs between service, cost and cash.

The Australian Food and Grocery Council has identified inventory optimisation as a priority for FMCG businesses, and for good reason. End-to-end inventory in Australian FMCG supply chains regularly extends to six months or more. In a sector worth over $130 billion annually, that represents an enormous volume of working capital sitting on warehouse floors and in production pipelines — capital that's costing more to carry than it has in a decade, with the RBA cash rate having risen sharply from its pandemic-era lows and holding at levels that make every dollar of unnecessary stock materially more expensive.

At the same time, the cost of not having stock — the stockout — is equally punishing. Research consistently shows that around half of consumers will switch products or retailers when they encounter an out-of-stock. In a market where private label is surging and brand loyalty is under pressure from value-driven consumption, losing a sale at shelf isn't just a missed transaction. It's an invitation for the customer to discover they're perfectly happy with the alternative.

So the challenge is real on both sides: too much inventory costs money, and too little costs customers. The solution isn't to pick a side. It's to get structurally better at deciding what to hold, where to hold it, how much to hold, and why.

That's what inventory optimisation actually means. And it's harder — and more valuable — than most people think.

Why FMCG inventory is different

Before getting into the levers, it's worth understanding why inventory optimisation in FMCG is a particular kind of challenge.

Product proliferation is relentless. The number of SKUs in a typical Australian FMCG manufacturer's range has grown steadily for years, driven by retailer demands for variety, category extensions, seasonal and promotional variants, different pack sizes for different channels, and the never-ending cycle of new product development. Every new SKU adds complexity to forecasting, production scheduling, warehousing and distribution — and every SKU needs safety stock, which means every SKU adds inventory. The long tail of the range — the bottom 20 or 30 per cent of SKUs that contribute a tiny fraction of revenue — often accounts for a disproportionate share of total inventory investment and an even larger share of obsolescence write-offs.

Promotional volatility distorts demand signals. Australian FMCG is heavily promoted. When a significant portion of volume moves through promotional events — price discounts, feature displays, bundled offers — the underlying demand pattern becomes extremely lumpy. Promotional builds require inventory investment weeks or months before the event. If the promotion over-delivers, you've cleared stock but potentially cannibalised future sales. If it under-delivers, you're sitting on excess stock that may need to be worked through at reduced margin. Either way, the demand signal that planning systems see is spiky, unpredictable and difficult to forecast with precision.

Shelf life creates a hard boundary. Unlike durable goods, most FMCG products have a finite life — and more importantly, they have retailer-imposed minimum remaining shelf life requirements that are typically tighter than the actual expiry date. A product with 12 months of shelf life might need 9 months remaining to be accepted by a major retailer. That means the window for holding and distributing inventory is shorter than it appears, and the cost of getting it wrong isn't just a markdown — it's a write-off.

The Australian geography adds cost and complexity. Serving customers across a continent from a limited number of production sites and distribution centres means that lead times, transport costs and network design all influence how much inventory is needed and where. A manufacturer with a single production site in Melbourne and customers in Perth, Darwin and Far North Queensland faces fundamentally different inventory positioning challenges than one serving a compact European market.

Retail power concentrates risk. With the Australian grocery market dominated by two major retailers, the dynamics of customer service are concentrated. A single poor delivery performance week to Woolworths or Coles can have an outsized impact on scorecards, ranging decisions and commercial negotiations. This creates a natural bias toward holding more inventory than might be optimal — because the perceived cost of a stockout to a key customer feels larger than the carrying cost of excess stock.

These characteristics don't make inventory optimisation impossible. But they do mean that generic approaches — blanket safety stock reductions, one-size-fits-all inventory policies, or technology implementations without underlying process change — tend to either fail outright or deliver short-lived improvements that erode as the business drifts back to old habits.

The levers that actually matter

Effective inventory optimisation in FMCG requires pulling multiple levers simultaneously, because inventory is the output of decisions made across the entire supply chain — from product range to production scheduling to procurement to network design to demand planning. Optimising any one of these in isolation will deliver a fraction of the potential benefit. Optimising them together, as part of a coherent strategy, is where the real value lies.

Range rationalisation

This is the lever that most businesses know they should pull but find politically difficult. Every SKU in the range has an internal champion — a sales manager who promised it to a customer, a marketing manager who believes in the brand extension, a product developer who spent a year on it. But the inventory reality is unforgiving: SKUs with low and intermittent demand require disproportionate safety stock relative to their revenue contribution, generate the highest forecast error rates, create production inefficiency through short runs and frequent changeovers, and carry the highest obsolescence risk.

A disciplined range review process — one that evaluates every SKU against clear criteria including volume, margin, strategic importance, inventory intensity and forecast accuracy — is often the single highest-impact lever for reducing inventory while simultaneously reducing complexity across the supply chain. This doesn't mean eliminating innovation or customer choice. It means being honest about which SKUs are earning their place in the range and which are consuming resources without adequate return.

Network and inventory positioning

Where you hold inventory matters as much as how much you hold. The question of centralisation versus decentralisation — how much stock sits at factory, how much at regional distribution centres, how much at forward locations — has a direct and quantifiable impact on total inventory investment.

Centralising inventory reduces total safety stock (because demand variability aggregates and smooths at a central point) but increases lead time to customers and transport cost. Decentralising inventory improves responsiveness and reduces last-mile cost but requires more total stock to achieve the same service level. The optimal answer depends on product characteristics, demand patterns, customer service requirements, transport economics and network infrastructure — and it's almost certainly different for different segments of the range.

This is a strategy and network design question that requires analytical modelling, not intuition. The right answer for a high-volume, nationally distributed ambient product is different from the right answer for a short-shelf-life chilled product sold primarily through one channel in eastern seaboard markets.

Manufacturing flexibility

Production scheduling and manufacturing constraints are often the hidden drivers of excess inventory. Long production runs reduce unit cost but create large batches that take weeks to sell through. Infrequent changeovers mean products are made in big quantities at longer intervals, pushing up cycle stock. Minimum order quantities from co-manufacturers force inventory builds that exceed near-term demand.

Improving manufacturing flexibility — through faster changeovers, smaller batch sizes, better production sequencing, and more responsive scheduling — directly reduces the cycle stock component of inventory. The trade-off is typically between production efficiency (cost per unit) and inventory efficiency (total working capital). Most FMCG businesses have optimised hard for production cost and under-invested in the flexibility that would allow them to make smaller quantities more frequently. Rebalancing this trade-off often unlocks significant inventory reduction without meaningful cost penalty, particularly when the carrying cost of inventory is properly accounted for.

Demand planning and forecast accuracy

Inventory exists, in large part, to buffer against forecast error. The less accurate your forecast, the more safety stock you need to maintain a given service level. Improving forecast accuracy — even modestly — directly reduces the inventory required.

In practice, this means investing in structured demand planning processes with clear accountability, incorporating external demand signals — retailer sell-through data, promotional calendars, weather patterns, market intelligence — rather than relying solely on historical shipments, distinguishing between base demand and promotional or event-driven demand, and handling new product introductions and end-of-life transitions with dedicated planning attention rather than treating them as business-as-usual.

Advanced planning systems and statistical forecasting tools help, but they're not a substitute for strong process discipline and skilled planners. The best technology in the world won't fix a demand plan that's been overridden by optimistic sales forecasts or that doesn't accurately reflect known promotional activity.

This connects directly to planning and operations maturity. Businesses with strong Sales and Operations Planning (S&OP) or Integrated Business Planning (IBP) processes consistently carry less inventory at higher service levels than those without — because they make better, more aligned decisions about demand, supply and inventory trade-offs on a regular cadence.

Safety stock policy and segmentation

Not all SKUs should be treated the same. A high-volume, stable-demand, strategically critical product needs a very different safety stock policy than a low-volume, intermittent, long-tail SKU. Yet many FMCG businesses apply a single safety stock methodology — or worse, a flat days-of-cover target — across the entire range.

Effective safety stock policy requires segmenting the range by demand variability, volume, strategic importance and supply reliability, then applying differentiated service targets and safety stock calculations to each segment. An A-class product with consistent demand and reliable supply might warrant a 99 per cent service target with relatively modest safety stock. A C-class product with sporadic demand and long import lead times might be better managed with a lower service target, a make-to-order approach, or removal from the range altogether.

This segmentation also needs to account for supply-side variability — not just demand. If a key ingredient has unreliable supply, or a co-manufacturer has a history of late delivery, that variability needs to be reflected in the safety stock calculation. Many businesses buffer only for demand uncertainty and ignore supply uncertainty, which means their safety stock levels are structurally wrong.

Procurement and supplier management

Procurement decisions directly influence inventory levels, often in ways that aren't immediately visible. Minimum order quantities imposed by suppliers drive batch sizes above what demand requires. Long supplier lead times increase pipeline stock and require larger safety buffers. Infrequent ordering windows (monthly rather than weekly, for example) create sawtooth inventory patterns that inflate average stock levels.

Negotiating more favourable MOQs, shorter lead times, more frequent delivery windows, or consignment arrangements with key suppliers can reduce inventory without touching a single safety stock parameter. These are procurement levers, not planning levers — and they're often underutilised because procurement teams optimise for unit cost rather than total supply chain cost.

Technology as enabler, not solution

It's tempting to look at the inventory challenge and reach for a technology answer — an advanced planning system, an inventory optimisation module, an AI-driven forecasting engine. These tools have genuine value, and the planning technology landscape has improved markedly in recent years.

But technology only works when it's deployed on top of sound processes, clean data, and clear governance. An APS that's fed inaccurate demand plans will produce optimised but wrong inventory targets. A forecasting algorithm trained on dirty historical data will replicate the errors of the past with greater confidence. A dashboard that nobody reviews or acts on is expensive wallpaper.

The sequence matters: get the processes and policies right first, then implement technology to automate, scale and sustain the improvements. Businesses that lead with technology and hope it will fix underlying process gaps almost always end up disappointed.

The S&OP connection

Inventory optimisation doesn't happen in isolation — it happens inside a governance rhythm that aligns demand, supply, inventory and financial plans on a regular cadence. That rhythm is Sales and Operations Planning, or its more mature cousin, Integrated Business Planning.

Effective S&OP creates the forum where range decisions are reviewed, demand plans are challenged and agreed, supply constraints are surfaced, inventory targets are set and monitored, and trade-offs between service, cost and cash are made explicitly rather than implicitly.

Without S&OP, inventory decisions are made by default — by whichever function shouts loudest or whichever system parameter was set three years ago and never reviewed. With S&OP, they're made by design — with visibility, accountability and a clear link to business objectives.

For FMCG and manufacturing businesses in Australia, S&OP maturity is often the single best predictor of inventory performance. Not because the process itself reduces inventory, but because it creates the discipline and alignment needed for all the other levers to work.

Measuring what matters

Inventory optimisation needs a measurement framework that goes beyond total inventory value or aggregate days of cover. Useful metrics include service level by customer and product segment (because aggregate service can mask poor performance on critical lines), forecast accuracy by SKU segment (because this drives safety stock requirements), inventory turns by category and product lifecycle stage (because a new product launch and a mature stable product should have very different turn profiles), obsolescence and write-off rates (because these are the clearest signal of inventory that shouldn't have been there), and working capital intensity relative to revenue (because this is the metric that finance and the board actually care about).

These metrics need to be reviewed regularly — monthly at minimum — and tied to accountability. Inventory performance that nobody owns doesn't improve.

How Trace Consultants can help

At Trace Consultants, we work with FMCG manufacturers and distributors across Australia and New Zealand to design and implement inventory optimisation strategies that deliver measurable reductions in working capital while maintaining or improving customer service.

We don't sell software. We bring independent supply chain expertise to the analytical, strategic and operational decisions that determine inventory performance.

Inventory diagnostic and opportunity assessment. We start by understanding where inventory sits across the end-to-end supply chain, what's driving it, and where the opportunities are. This includes segmentation analysis, safety stock benchmarking, demand variability profiling, and identification of the highest-impact levers for your specific business context.

Range complexity and rationalisation. We help businesses evaluate their product range through an inventory lens — identifying tail SKUs that consume disproportionate resources, quantifying the inventory cost of complexity, and designing range governance processes that maintain commercial flexibility while managing working capital. Our organisational design work ensures that range decisions have clear ownership and accountability.

Network and inventory positioning strategy. We model the optimal distribution of inventory across your network — how much to hold centrally versus regionally, where to position safety stock, and how to design replenishment policies that balance service and efficiency. This is core strategy and network design work, grounded in quantitative analysis of demand patterns, transport economics and service requirements.

Demand planning and S&OP process design. We design and embed planning and operations processes — from statistical forecasting to consensus demand planning to S&OP and IBP — that improve forecast accuracy and create the governance rhythm for ongoing inventory management. We build capability in your team, not dependency on ours.

Safety stock policy and parameter setting. We design segmented safety stock policies that reflect the actual demand, supply and service characteristics of your range — replacing blanket rules with evidence-based parameters that your planning systems can execute.

Procurement strategy for inventory reduction. We work with procurement teams to negotiate supplier terms — MOQs, lead times, delivery frequency, consignment arrangements — that directly reduce inventory requirements. This is often one of the fastest levers to pull, with benefits visible within a single procurement cycle.

Technology selection and implementation support. When technology investment is warranted, we help organisations select, configure and implement planning and inventory optimisation tools that fit their maturity level, data quality and operating model — ensuring the investment delivers lasting value.

Warehouse and distribution optimisation. Inventory management intersects with physical operations. We bring warehousing and distribution expertise to ensure that storage, handling and replenishment processes support rather than undermine inventory targets.

The opportunity is real

For most Australian FMCG manufacturers, the opportunity to reduce inventory by 15 to 30 per cent without compromising service is real and achievable — provided the approach is structured, holistic and sustained. That's not a small number. On a $50 million inventory base, a 20 per cent reduction releases $10 million in working capital. At current interest rates, the carrying cost saving alone is material — before you account for reduced warehousing costs, lower obsolescence, and improved cash flow.

The businesses that capture this opportunity are the ones that treat inventory as a strategic management discipline, not a warehouse problem or a finance complaint. They invest in the processes, skills and analytical frameworks that allow them to make deliberate, informed trade-offs — and they sustain those improvements through governance, measurement and continuous review.

If your business is carrying more inventory than it should, or if service performance suggests you're not carrying the right inventory in the right places, we'd welcome the conversation.

Strategy & Network Design

Reverse Logistics of Electrified Products: Why Australia's Supply Chains Aren't Ready for What's Coming Back

Shanaka Jayasinghe
February 2026
Australia sold millions of lithium-ion battery products last year. Most of them don't have a viable path back. E-bikes, e-scooters, power tools, cordless vacuums, electric ride-on toys — the electrification of everyday products has outpaced the reverse logistics systems meant to handle them at end of life. The result: fires in garbage trucks, overwhelmed councils, hazardous stockpiles, and a growing regulatory scramble. Here's what needs to change.

Walk through any Australian suburb on hard rubbish day and you'll see it: a dead e-scooter propped against a bin, a cordless vacuum with a swollen battery pack, a child's electric ride-on that stopped holding charge. None of these things should be sitting on the kerb. All of them contain lithium-ion batteries that, if crushed in the back of a compactor truck or punctured at a waste transfer station, can ignite a fire that's fast, toxic and extraordinarily difficult to extinguish.

This isn't a theoretical risk. It's happening every day across Australia.

The waste and recycling industry estimates that lithium-ion batteries are responsible for between 10,000 and 12,000 fires per year in Australia's waste and recycling streams. In the first half of 2025, the South Australian Metropolitan Fire Service responded to more than half of the previous year's total battery-related incidents in just six months — with roughly one in four linked to e-mobility devices. In New South Wales, about one in every 40 fires attended by Fire and Rescue NSW now involves a lithium-ion battery or battery-powered device. Queensland Fire Department reported more than 200 lithium-ion battery fires in the first eleven months of 2025 alone.

The Australian Capital Territory's main recycling facility was destroyed by a battery-related fire in 2022. The cost to replace a destroyed recycling facility runs to around $60 million. A single damaged waste collection truck costs between $250,000 and $500,000 to replace.

And yet, according to B-cycle's 2025 annual report, Australia is still only recycling around 18.5 per cent of available batteries. Half of all Australian households are still throwing batteries into general waste or recycling bins. Just three per cent of e-bike companies participate in battery stewardship schemes.

This is a supply chain problem. A big one. And it's getting bigger with every e-bike, e-scooter, cordless drill, robot vacuum and electric garden tool that rolls off a container ship and into Australian homes, workplaces and public hire fleets.

The electrification wave nobody planned the return journey for

The forward supply chain for electrified consumer products works well enough. Manufacturers design. Importers bring product in. Retailers sell. Consumers buy. Logistics networks deliver. The product reaches the customer with relative efficiency.

The reverse supply chain — what happens when that product reaches end of life, or when the battery degrades, swells, or fails — barely exists for most product categories.

This isn't because nobody cares. It's because the volume, diversity and hazard profile of electrified products has grown faster than the collection, transport, processing and recycling infrastructure needed to handle them safely. A decade ago, the battery challenge was largely confined to mobile phones, laptops and the occasional power tool. Today, lithium-ion batteries are embedded in everything from children's toys and Bluetooth speakers to ride-on mowers, electric wheelchairs, home energy storage systems, and — most visibly — the booming e-bike and e-scooter market.

The scale is striking. Global demand for batteries is expected to increase by 500 per cent by 2050. Lithium-ion battery waste in Australia could exceed 100,000 tonnes by 2036. Every one of those products will, at some point, need to come back through a reverse logistics system that currently can't cope with what's already in circulation.

For government and defence agencies, councils, waste management operators and the businesses that import and sell these products, this isn't a future problem. It's a now problem.

Why reverse logistics for electrified products is so different

Reverse logistics is never as simple as forward logistics in reverse. Products come back in unpredictable volumes, in variable condition, through fragmented channels, with inconsistent information about what's inside them. That's true for any product category.

But electrified products — specifically those containing lithium-ion batteries — add layers of complexity that make them fundamentally different from conventional reverse logistics flows.

They're hazardous goods. A lithium-ion battery that has been damaged, degraded, overcharged, improperly stored or exposed to heat or moisture can undergo thermal runaway — an uncontrollable self-heating reaction that produces toxic, flammable gases and can result in fire or explosion. This isn't a defect in the battery; it's a characteristic of the chemistry. It means that collection, transport, storage and processing all need to comply with dangerous goods regulations, including the Australian Dangerous Goods Code. That adds cost, requires specialist handling, limits which vehicles and facilities can be used, and creates liability for everyone in the chain.

They come in wildly different form factors. A button battery from a hearing aid, a removable battery pack from an e-bike, and an embedded battery in a cordless vacuum are all "lithium-ion batteries," but they require completely different collection containers, handling procedures, transport packaging and processing methods. A reverse logistics system that handles one category well may be entirely unsuitable for another.

Many batteries are embedded and non-removable. This is one of the most significant practical barriers. A growing proportion of consumer electronics and appliances have batteries that are glued, soldered or structurally integrated into the product — deliberately designed to be non-removable by the consumer. That means the entire product needs to enter the reverse logistics stream, not just the battery. Collection points, transport systems and processing facilities all need to accommodate whole products, which are bulkier, heavier and more complex to sort than standalone batteries.

Condition on return is unknown and variable. A battery that arrives at a collection point might be fully charged, partially discharged, physically damaged, water-affected, or already in the early stages of thermal instability. There's no reliable way to assess condition remotely. This uncertainty drives conservative handling requirements and creates genuine safety risks for workers at every point in the chain — from the council worker emptying a community recycling bin to the operator at a processing facility.

The economics are challenging. Unlike some material streams where the recovered commodity value offsets collection and processing costs, battery recycling is expensive. Transport of hazardous goods is costly. Processing requires specialist equipment. The value of recovered materials — lithium, cobalt, nickel — fluctuates with commodity markets and doesn't always cover the cost of safe handling and processing. Without stewardship levies, government subsidies or regulatory mandates, the commercial incentive to invest in reverse logistics infrastructure is marginal at best.

These characteristics mean that the reverse supply chain for electrified products can't simply be bolted onto existing waste management systems. It requires purpose-designed collection networks, specialist transport, dedicated storage and processing facilities, trained workforce, regulatory compliance frameworks and — critically — supply chain coordination across multiple parties who don't traditionally work together.

This is exactly the kind of challenge that benefits from rigorous strategy and network design thinking.

The e-bike and e-scooter headache

No product category illustrates the reverse logistics gap quite like e-bikes and e-scooters.

Sales have surged in Australia. E-bikes are now one of the fastest-growing segments of the cycling market, and shared e-scooter schemes operate in multiple capital cities. The batteries in these devices are large — typically 36V to 52V packs weighing several kilograms — and store enough energy to power a vehicle at speed for 40 to 80 kilometres. When they fail, they fail dramatically.

The fire risk is well documented. South Australia's Premier launched a joint emergency services campaign in mid-2025 specifically targeting lithium-ion battery fires from e-mobility devices, following a spike in residential fires linked to e-scooters and e-bikes being charged on incompatible chargers or modified by users to boost performance. NSW introduced mandatory safety standards for e-micromobility devices from February 2025, with testing, certification and marking requirements taking effect from February 2026 — including requirements for safe disposal information at point of sale. Victoria's Energy Safe regulator published a consultation paper in August 2025 proposing to declare e-transport devices as controlled electrical equipment, driven by the growing number of fire incidents.

But regulation at point of sale only addresses part of the problem. What happens when a three-year-old e-bike battery reaches end of life and the owner wants to get rid of it?

Right now, the answer is messy. Some users drop batteries at B-cycle collection points, which accept removable batteries up to 5 kilograms — but most e-bike batteries exceed that threshold. Some take them to council hazardous waste events, which happen periodically rather than continuously. Some leave them in garages, sheds or on balconies indefinitely. And some — too many — put them in the general waste bin or leave them on the kerb, where they enter a waste stream that's not designed to handle them.

For councils and government agencies managing waste services, this creates a compounding problem: rising fire risk in collection vehicles and transfer stations, growing volumes of hazardous material at facilities not rated for dangerous goods storage, increasing insurance costs and liability exposure, community expectation for convenient disposal options, and no clear funding mechanism to pay for purpose-built collection and processing infrastructure.

South Australia's government established four dedicated collection points in late 2025 specifically for embedded battery products — a welcome step, but one that highlights how far behind infrastructure has fallen relative to the products already in circulation.

The broader electrified product challenge

E-bikes and e-scooters are the most visible part of the problem, but they're far from the only one. Cordless power tools — drills, angle grinders, lawn mowers — run on substantial lithium-ion packs, and just 65 per cent of power tool companies participate in the B-cycle stewardship scheme. Electric mobility devices serve vulnerable users who may struggle to transport heavy batteries to collection points. Home energy storage systems will create a future wave of large-format battery returns that current residential waste systems are entirely unequipped for. And then there's the long tail: vapes, electric toys, robotic vacuums, portable speakers, fitness devices — each with different battery configurations, different disassembly requirements, and different end-of-life pathways, all converging on the same constrained recycling infrastructure.

The common thread: the forward supply chain works smoothly while the reverse supply chain ranges from inadequate to non-existent.

What a functioning reverse logistics system actually needs

Solving the reverse logistics challenge for electrified products requires thinking about it as a supply chain — with all the design, planning, infrastructure and governance disciplines that implies.

Collection network design. The most fundamental requirement is getting products and batteries out of homes, workplaces and public spaces and into a controlled stream. This means permanent, accessible collection points — not just periodic hazardous waste events. It means collection infrastructure that's designed for the actual products being returned (including large, heavy items like e-bike batteries and power tool packs, not just AA cells). It means geographic coverage that reflects where the products are sold and used, including regional and remote areas. And it means collection systems that are safe for the people operating them, with proper containment, labelling and handling procedures.

The network design challenge here is analogous to designing any distribution network — but in reverse. Where should collection points be located? How many are needed? What capacity do they require? How frequently do they need to be serviced? What's the catchment area for each point? These are strategy and network design questions that need to be answered with the same analytical rigour that would be applied to a retail distribution network or a warehousing and distribution strategy.

Transport, consolidation and storage. Moving collected batteries from dispersed collection points to processing facilities is a dangerous goods logistics challenge — vehicles need to be rated, packaging must comply with the Australian Dangerous Goods Code, drivers need to be trained, and the economics of collection transport (particularly in regional areas) need to work or the system won't scale. Between collection and processing, there's typically a consolidation step at transfer facilities that need to be purpose-designed for hazardous materials, with fire suppression, ventilation, spill containment and appropriate separation distances. Many existing waste transfer stations don't meet these requirements, which is why battery fires at these facilities are so common.

Processing and recycling infrastructure. Australia's domestic battery recycling capacity is growing but remains limited relative to the volume of batteries entering the market. B-cycle recycled over 3.3 million kilograms of batteries in its most recent reporting year through partners like Ecobatt, but this represents less than a fifth of available batteries. Scaling processing capacity requires capital investment, skilled workforce, environmental approvals and — critically — a stable feedstock supply that justifies the investment. That feedstock supply depends on the collection and transport systems upstream being effective.

Data and traceability. A functioning reverse logistics system needs visibility across the chain — what's been collected, where, in what condition, and where it's going. Without data, you can't optimise collection routes, manage inventory at consolidation points, forecast processing demand, or report on diversion rates and compliance. Technology systems that provide chain-of-custody tracking, condition assessment and performance reporting are essential infrastructure, not optional extras.

Governance and stakeholder coordination. A reverse logistics system for electrified products involves manufacturers, importers, retailers, consumers, councils, state and federal agencies, waste collectors, dangerous goods operators, storage facilities, recyclers and stewardship bodies. No single party controls the whole chain. Making it work requires governance structures that define roles, responsibilities, funding mechanisms and performance standards.

This is where procurement frameworks, organisational design and multi-stakeholder governance become essential. The supply chain won't self-organise.

The regulatory landscape is shifting — fast

Australian governments are moving on this, though not always in a coordinated way. NSW passed the Product Lifecycle Responsibility Bill in March 2025 — the first mandatory product stewardship legislation for batteries in Australia, requiring battery brand owners to participate in approved stewardship schemes. Victoria's Energy Safe regulator is consulting on declaring e-transport devices as controlled electrical equipment. Queensland, NSW and Victoria have been tasked by environment ministers to lead national reform on battery product stewardship. South Australia has introduced dedicated collection infrastructure. And the federal government has committed to national battery stewardship reform as part of its circular economy agenda.

For businesses importing and selling electrified products, these regulatory shifts create new compliance obligations around disposal information, stewardship participation and potentially design-for-disassembly requirements. For councils and government agencies, they create both obligations and opportunities to redesign collection systems and access stewardship funding. For waste operators, they create market signals about future volumes that may justify infrastructure investment.

But regulation alone doesn't build a supply chain. Legislation sets the rules. Someone still has to design, build and operate the physical network — the collection points, the transport routes, the consolidation facilities, the processing infrastructure — that actually moves material safely from consumer to recycler.

The role of product stewardship in funding reverse logistics

B-cycle, administered by the Battery Stewardship Council, is Australia's primary battery stewardship scheme. It operates over 5,400 collection points, has recycled millions of kilograms of batteries since launch, and is preparing its next phase — B-cycle 2.0 — to address current limitations and prepare for mandatory regulation.

Stewardship schemes work by placing a levy on products at point of import or sale, then using that revenue to fund collection, transport, processing and education. In principle, this closes the economic gap that makes reverse logistics commercially unviable without intervention. In practice, effectiveness depends on participation rates, levy levels being sufficient to fund safe collection and processing, collection infrastructure being convenient enough to achieve meaningful diversion rates, and processing capacity being available domestically at scale.

For organisations involved in the electrified product supply chain — whether as importers, retailers, councils or waste operators — understanding stewardship obligations and designing operational systems to meet them is becoming a core business requirement, not a peripheral compliance exercise.

The workforce dimension

Collecting, handling, transporting and processing lithium-ion batteries safely requires training that most waste and recycling workers don't currently have. Dangerous goods handling, thermal runaway recognition, emergency response procedures, high-voltage safety protocols — these are specialist skills that need structured training programs, not just toolbox talks. At the processing end, battery recycling is a technical discipline requiring mechanical, chemical and electrical engineering knowledge that's in short supply. And at the governance level, councils and government agencies need people who understand both supply chain management and hazardous materials regulation — a combination that's rare in the current workforce.

This is a strategic workforce planning challenge that intersects with training policy, industry development and organisational capability building.

How Trace Consultants can help

At Trace Consultants, we work with government agencies, councils and industry on supply chain challenges that sit at the intersection of infrastructure, logistics, procurement and operations — exactly where the reverse logistics challenge for electrified products lives.

We don't sell batteries, collection bins or recycling equipment. We bring independent supply chain expertise to the strategic and operational decisions that determine whether reverse logistics systems actually work.

Reverse logistics network design. We help organisations design collection, consolidation and processing networks optimised for coverage, cost and safety — including location analysis, capacity modelling, transport route optimisation and facility design. Our strategy and network design approach ensures infrastructure investment is directed where it will have the greatest impact.

Procurement strategy for stewardship and waste services. As mandatory stewardship obligations expand, councils and agencies need to procure collection, transport and processing services that comply with new requirements while managing cost. We design procurement strategies with clear service specifications, appropriate risk allocation and built-in performance measurement.

Warehouse and facility design. Consolidation facilities for returned electrified products need to meet dangerous goods requirements while operating efficiently. We bring warehousing and distribution and back-of-house logistics expertise to facility design — including layout, workflow, inventory management and safety systems.

Operating model and organisational design. For councils and agencies standing up new collection programs, we design the organisational structures, governance frameworks and planning and operations processes needed to manage a service line involving hazardous materials and multi-stakeholder coordination.

Workforce planning and change management. We support workforce planning and change management programs that build the skills, procedures and safety culture needed to operate reverse logistics systems safely.

Technology, data and resilience. Visibility across the reverse supply chain is essential for optimisation and compliance. We help organisations select technology solutions for traceability and performance monitoring, and design resilience frameworks that ensure continuity of service for safety-critical operations.

The window for planning is now

The electrification of consumer products isn't slowing down. Battery volumes entering Australia are growing year on year. Mandatory stewardship obligations are being legislated. Fire risk in waste systems is escalating. And the infrastructure gap between what's needed and what exists is widening.

Organisations that start designing their reverse logistics capability now — whether they're councils redesigning waste services, government agencies developing policy, importers preparing for stewardship obligations, or waste operators investing in infrastructure — will be far better positioned than those that wait for the next waste facility fire or the next regulatory deadline to force their hand.

Because the products are already out there. Millions of them. And every single one of them is eventually coming back.

The question is whether there's a supply chain ready to receive them safely — or whether they end up on the kerb, in the bin, or in the back of a truck that catches fire on a Tuesday morning.

If your organisation is grappling with the reverse logistics challenge for electrified products, batteries or hazardous end-of-life materials, we'd welcome the conversation.

Sustainability

Transitioning Police Highway Patrols to Electric Fleets: A Supply Chain Challenge That Goes Far Beyond the Vehicle

Emma Woodberry
February 2026
Every state police force in Australia is now evaluating electric vehicles for highway patrol. But the real challenge isn't the car — it's the supply chain behind it. Charging infrastructure, depot redesign, parts management, workforce capability, and operational continuity all need to be rethought. Here's what's actually involved.

There's a moment in every fleet transition where the conversation shifts. It starts with the vehicle — what can we buy, what does it cost, does it pass the performance tests? And then, fairly quickly, the harder questions land. Where do we charge it? How do we keep it on the road? What happens to our depots? What about regional stations? Who maintains the thing? And what does this mean for how we roster, deploy and sustain an operational fleet that can't afford a single shift of downtime?

That's where Australian police forces are right now.

NSW Police has been running exhaustive trials at the Police Driver Training Centre in Goulburn, testing electric vehicles from multiple manufacturers through braking, cornering and pursuit-readiness assessments. Queensland Police took delivery of a Kia EV6 GT Line as their first fully electric highway patrol car. Victoria Police has tested Teslas. Western Australia recently rolled out Volkswagen Touareg R plug-in hybrids for highway patrol. And the BMW 530d — the workhorse of highway patrol in NSW and Victoria — is itself transitioning, with hybrid successors already entering fleets.

The direction of travel is clear: Australia's police highway patrol fleets will increasingly electrify over the coming decade. The operational performance of modern EVs — acceleration, handling, braking — is more than adequate for patrol work. In many cases it's superior. The economics of electric drivetrains, over a total cost of ownership lifecycle, are increasingly favourable. And the policy settings — federal and state emissions reduction targets, government fleet electrification mandates, sustainability reporting requirements — are creating momentum that isn't going to reverse.

But here's the thing that most of the public conversation misses: the vehicle is the easy part.

The genuinely complex challenge — the one that will determine whether this transition succeeds or stumbles — is the supply chain. Not in the narrow sense of "where do we buy the cars," but in the full operational sense: how do you redesign the infrastructure, logistics, maintenance, parts management, workforce capability and deployment models that sit behind a 24/7, mission-critical fleet?

That's a supply chain problem. And it deserves supply chain thinking.

Why this isn't just a procurement exercise

When police forces replaced Holden Commodores with BMWs, or transitioned from Ford Falcons to Kia Stingers, the underlying operating model didn't fundamentally change. You still refuelled at a petrol station. You still sent the car to a workshop with the same tools and the same technicians. The depot layout, the shift patterns, the parts supply chain — all broadly stayed the same.

Electric vehicles break that continuity.

The energy source changes entirely — from a liquid fuel that's dispensed in minutes at ubiquitous locations to an electrical charge that takes time, requires fixed infrastructure, draws significant power, and creates entirely new planning constraints. The maintenance profile changes — fewer moving parts, less frequent servicing, but different failure modes, different diagnostic tools, different technician skills, and a different parts supply chain. The depot and station infrastructure changes — electrical capacity, charging equipment, spatial layout, safety systems, and the relationship between the fleet and the built environment all need rethinking.

These aren't minor adjustments. They're structural changes to the operating model of a fleet that must be available around the clock, in all conditions, across vast geographies — with zero tolerance for capability gaps.

For government and defence agencies, where fleet availability is directly linked to public safety and operational readiness, the stakes are as high as they get.

The charging infrastructure challenge

Charging is where the supply chain complexity hits hardest. A highway patrol vehicle isn't a commuter car that drives 40 kilometres to an office and sits in a car park for eight hours. It's a high-utilisation asset that may cover hundreds of kilometres in a shift, across unpredictable routes, with no guaranteed return to base during operational hours.

That creates a set of infrastructure requirements that are fundamentally different from typical fleet electrification.

Depot charging is necessary but not sufficient. Every police station and highway patrol base will need charging infrastructure — that much is straightforward. But the electrical capacity of most existing stations was never designed for this. A single Level 2 (AC) charger draws around 7–22 kilowatts. A DC fast charger can draw 50–350 kilowatts. Multiply that across a fleet of patrol vehicles that all return to base within a similar window, and you quickly hit the limits of the site's electrical supply. Substation upgrades, switchboard replacements, cable runs, load management systems — these are capital works that require planning, approvals and lead times measured in months, not weeks.

Regional and remote stations face acute constraints. Highway patrol doesn't just operate out of metropolitan depots. It operates from stations in regional towns where grid capacity is limited, where the nearest fast charger might be an hour away, and where a vehicle being out of service for extended charging isn't operationally acceptable. Solving for regional deployment requires a different approach — potentially including on-site battery storage, solar generation, or strategic placement of dedicated police charging infrastructure along key patrol corridors.

En-route charging needs to be reliable and fast. Australia's public fast-charging network has grown significantly — reaching over 1,270 fast-charging sites by mid-2025 — but it wasn't designed for emergency services. Charger reliability, access priority, and coverage gaps on key highway corridors all create operational risk. Police forces will likely need either dedicated charging infrastructure or guaranteed-access arrangements at commercial sites to ensure vehicles can be recharged during shifts without compromising response capability.

Load management becomes an operational planning problem. When you have a depot with 15 patrol vehicles and 10 charging points, deciding which vehicles charge first, at what rate, and in what sequence is no longer a facilities management question — it's a fleet operations question. Smart charging systems that integrate with roster and dispatch data can optimise this, but they need to be designed, procured, installed and governed as part of the operating model, not bolted on afterwards.

This is where strategy and network design disciplines become critical. The charging network for a police fleet isn't just a facilities project — it's a supply chain network that needs to be designed around operational demand, geographic coverage, and reliability requirements.

Rethinking depot and station design

Charging infrastructure is one dimension. But the physical layout of police stations, depots and workshops also needs to change — and this is often underestimated.

Traditional police vehicle bays are designed for internal combustion engine (ICE) vehicles: fuel storage, exhaust extraction, oil and fluid management, and workshop layouts optimised for mechanical servicing. Electric vehicles introduce different spatial requirements. Charging bays need dedicated electrical infrastructure, cable management and potentially ventilation for thermal management. Workshop areas need to accommodate high-voltage battery safety protocols, including isolation procedures and specialist equipment. Storage for EV-specific parts and consumables — which differ materially from ICE parts — needs to be incorporated into existing stores.

For agencies managing hundreds of stations across a state, retrofitting these facilities is a substantial capital and logistics program. It requires site-by-site assessment, electrical engineering design, construction coordination, and a sequencing plan that doesn't compromise operational capability during the transition.

This connects directly to back-of-house logistics and warehousing and distribution thinking. How do you design physical infrastructure to support a changing fleet without disrupting current operations? How do you stage the rollout so that early sites become proof points, not bottlenecks? And how do you manage the parallel running of ICE and EV infrastructure during what will inevitably be a multi-year transition?

The maintenance and parts supply chain

The maintenance profile of electric vehicles is genuinely different from ICE vehicles — and for police highway patrol, those differences have operational consequences.

On the positive side, EVs have fewer moving parts, require less frequent routine servicing, and eliminate entire maintenance categories (oil changes, transmission servicing, exhaust system repairs). Over the vehicle lifecycle, this should reduce total maintenance cost and, critically, reduce unplanned downtime from mechanical failures.

On the other hand, the maintenance that EVs do require is different. Battery health management, high-voltage electrical systems, thermal management systems, regenerative braking calibration, and software updates all demand specialist tools, diagnostic equipment and technician training that most police fleet workshops don't currently possess.

The parts supply chain also shifts. The traditional police fleet MRO catalogue — filters, belts, brake components, fluids, mechanical wear parts — is largely replaced by a different set of items: battery modules, electric motor components, power electronics, charging connectors, and software-driven control units. Many of these are sourced through OEM-controlled supply chains with longer lead times and less aftermarket competition than traditional parts.

For agencies that manage fleet maintenance in-house, this means a significant capability uplift: training programs for existing technicians, recruitment of high-voltage specialists, investment in diagnostic and safety equipment, and a wholesale review of the parts catalogue, stocking policy and supplier base.

For agencies that outsource fleet maintenance, it means renegotiating service contracts, redefining performance requirements, and ensuring that service providers have the capability and capacity to support an electric fleet at the required service levels.

Either way, the procurement strategy for fleet maintenance and parts needs to be redesigned — not just tweaked. Category strategies, supplier agreements, inventory policies and performance frameworks all need to reflect the new operating reality.

Workforce capability and change management

Every transition of this nature has a people dimension, and this one is no exception.

Police fleet managers, workshop technicians, station managers, highway patrol officers, procurement teams and rostering staff are all affected. Each group needs to understand what's changing, why, and what it means for how they do their jobs.

Workshop technicians need structured training pathways to develop competence with high-voltage systems — including safety protocols that are materially different from ICE vehicle maintenance. Fleet managers need new planning tools and data to manage charging schedules, vehicle availability and lifecycle costs. Procurement teams need to understand new supplier markets, OEM relationships and total cost of ownership models. And frontline officers need confidence that the vehicles they're driving will perform when it matters and be available when they need them.

This isn't a memo and a half-day workshop. It's a sustained change management program that needs to be designed with the same rigour as the technical transition.

The agencies that handle this well will engage their workforce early, provide clear information about sequencing and support, build internal champions who can demonstrate the benefits from firsthand experience, and create feedback loops that allow the transition plan to adapt based on real operational experience.

Planning the transition: sequencing matters

One of the most consequential decisions in this transition is sequencing — the order in which vehicle types, station locations and capability investments are rolled out.

A common approach is to start with administrative and community engagement vehicles (lower risk, lower utilisation) before progressing to general duties and eventually highway patrol (higher risk, higher utilisation, higher performance requirements). This is the pattern most Australian police forces have followed so far, and it's sensible — it allows the organisation to build experience, test infrastructure, and develop maintenance capability before committing to the most demanding use cases.

Within the highway patrol transition specifically, sequencing decisions include which geographic areas to electrify first (metropolitan before regional, due to charging infrastructure density), which vehicle roles to prioritise (single-officer patrol versus pursuit-rated, for example), and how to manage the transition period where ICE and EV vehicles operate in parallel — requiring dual infrastructure, dual parts catalogues, and dual maintenance capabilities.

The transition period is often the most expensive and complex phase. Running two parallel fleet ecosystems simultaneously creates redundancy costs, training burdens and operational complexity that need to be carefully managed. A well-designed sequencing plan minimises the duration and cost of this parallel period while maintaining operational capability throughout.

This is fundamentally a planning and operations challenge. It requires demand modelling (how many vehicles, where, when), capacity planning (charging infrastructure, workshop capability, parts availability), scenario analysis (what if charging demand exceeds supply? what if a vehicle model is discontinued?), and a governance framework that allows the plan to adapt as real-world data becomes available.

Total cost of ownership: a different equation

Police fleet procurement has traditionally been driven by purchase price, performance specification and whole-of-life cost. Electric vehicles change the equation in several ways.

The upfront purchase price of EVs is currently higher than equivalent ICE vehicles for most segments relevant to highway patrol — though the gap is narrowing as battery costs decline and new models enter the market. However, the total cost of ownership (TCO) calculation is more favourable than the sticker price suggests when you factor in lower energy costs per kilometre (electricity versus fuel), reduced routine maintenance requirements, potential government incentives and fleet discounts, and residual value dynamics (which are still evolving for police-spec EVs).

On the other side of the ledger, the infrastructure investment required — charging equipment, electrical upgrades, depot modifications, workshop retooling — represents a significant capital commitment that doesn't apply to ICE fleet replacements. These costs need to be planned, funded and staged across the transition timeline.

The agencies that make the best decisions will be those that model TCO rigorously, across the full vehicle lifecycle, including infrastructure costs — and use that analysis to inform both procurement timing and budget allocation.

The supply chain resilience dimension

Police highway patrol fleets are, by definition, critical infrastructure. Any disruption to fleet availability has direct public safety consequences. That means the supply chain supporting these fleets needs to be designed with resilience as a primary consideration.

For electric fleets, resilience means ensuring that charging infrastructure has redundancy and backup power, that critical spare parts (particularly battery components and high-voltage systems) are held at appropriate service levels, that multiple supply pathways exist for key components to avoid single-source dependency, that maintenance capability is distributed geographically (not concentrated in a single workshop that becomes a single point of failure), and that the fleet management system can dynamically reallocate vehicles based on charge state, location and operational priority.

These are the same principles that apply to any mission-critical supply chain — but applied to a fleet context where the "customer" is public safety and the "service level" is uninterrupted operational capability.

Lessons from other fleet transitions

Australian police forces aren't the first organisations to navigate this transition. Commercial fleets, public transport operators, logistics companies and military organisations globally are all at various stages of electrification — and their experiences offer useful lessons.

Common themes include the importance of starting infrastructure planning well before vehicle procurement (charging infrastructure typically has longer lead times than vehicle delivery), investing in data and monitoring systems from day one rather than retrofitting them later (real-time charging data, vehicle health data and usage patterns are essential for optimising fleet operations), engaging with energy utilities early to understand grid capacity constraints and connection timelines, and not underestimating the change management requirement — the human dimension of the transition consistently takes longer than the technical one.

The organisations that succeed treat electrification not as a vehicle replacement program but as an operating model transformation — and plan accordingly.

How Trace Consultants can help

At Trace Consultants, we help government agencies and large fleet operators design and implement supply chain strategies for complex transitions — including fleet electrification.

We don't sell vehicles or charging equipment. We bring independent, supply-chain-focused thinking to the decisions that sit around the vehicle: the infrastructure, logistics, procurement, maintenance, workforce and operational planning that determine whether an electric fleet transition actually works in practice.

Fleet supply chain strategy and network design. We help agencies design the physical infrastructure network — depot charging, en-route charging, workshop locations, parts distribution — that supports an electric fleet across diverse geographies. This includes demand modelling, location analysis, capacity planning and sequencing strategy. Our strategy and network design approach ensures decisions are evidence-based and operationally grounded.

Procurement strategy for fleet transition. Electrification changes the procurement landscape — new vehicle categories, new OEM relationships, new maintenance service models, new parts supply chains. We help agencies develop procurement strategies that optimise total cost of ownership, build supply chain resilience and maintain competitive tension.

Maintenance and MRO supply chain design. We redesign the parts catalogue, stocking policies, supplier agreements and workshop capability models for an electric fleet — ensuring that maintenance supply chains support availability targets without over-investing in inventory. Our experience in MRO supply chains across government, defence and asset-intensive industries translates directly to fleet contexts.

Depot and station infrastructure planning. We support agencies with the back-of-house logistics and warehousing design work required to retrofit stations and depots for electric fleet operations — including spatial planning, electrical capacity assessment coordination, and construction sequencing that minimises operational disruption.

Workforce planning and change management. Transitioning to an electric fleet changes how people work — from technicians to fleet managers to frontline officers. Our workforce planning and change management teams help agencies design training programs, stakeholder engagement strategies and transition plans that bring people along rather than leaving them behind.

Resilience and risk management. For mission-critical fleets, we design supply chain resilience frameworks that identify vulnerabilities, build redundancy and ensure operational continuity through the transition and beyond.

Technology enablement. From fleet management systems to charging optimisation platforms to performance dashboards, we help agencies select and implement technology that supports evidence-based fleet management — without over-engineering or creating dependency on tools that don't integrate with existing systems.

The road ahead

The electrification of police highway patrol fleets in Australia is not a question of if, but when and how. The vehicle technology is ready. The policy direction is set. The economic case will strengthen as battery costs fall and charging infrastructure matures.

The agencies that get ahead of this transition — that start planning the supply chain, infrastructure and workforce dimensions now, rather than waiting until the first vehicles arrive — will find the process smoother, cheaper and less disruptive than those that treat it as a straightforward fleet replacement.

Because this isn't just about swapping what's under the bonnet. It's about redesigning the operating model that keeps patrol vehicles on the road, officers in the field, and communities safe. That's a supply chain challenge. And it deserves the same rigour, planning and investment that any critical infrastructure transition demands.

If your agency is planning or evaluating a fleet electrification pathway, we'd welcome the conversation.

Workforce Planning & Scheduling

Workforce Planning in Government Agencies: Why Productivity Gains Start With How You Plan, Roster and Deploy Your People

Emma Woodberry
February 2026
Government agencies across Australia are under pressure to deliver more, faster, with constrained budgets. The answer isn't more people — it's better workforce planning. Here's where the real productivity gains sit.

If you've worked inside or alongside an Australian government agency in the last five years, you'll have noticed a shift. The expectation to deliver more — more services, more responsiveness, more transparency — hasn't slowed down. But the resources available to deliver it haven't kept pace. Budgets are tight. Headcount caps are real. And the political and community appetite for "just hire more people" as a solution has, rightly, diminished.

So the question becomes: how do you get more from the people you already have?

Not by working them harder. That leads to burnout, turnover, and the kind of institutional knowledge loss that takes years to recover from. The answer is better workforce planning — a disciplined, evidence-based approach to making sure the right people, with the right skills, are in the right place, at the right time, doing the right work.

It sounds obvious. In practice, it's one of the most underdeveloped capabilities in the Australian public sector. And it's costing taxpayers far more than most agencies realise.

The productivity problem in plain terms

Australia's productivity story has been difficult reading for some time. The Productivity Commission's 2025 Annual Bulletin confirmed what many suspected: multifactor productivity growth has been near zero, and labour productivity has stalled. In the public sector specifically, the challenge is compounded by the nature of the work — services that are hard to automate, demand that's driven by population and policy rather than markets, and workforce structures that were often designed decades ago for a different operating environment.

The Australian Public Service alone employs around 150,000 people. State and territory agencies, local councils, statutory authorities and government-owned corporations add hundreds of thousands more. Labour is, by far, the largest operating cost in government — typically accounting for 60–80% of agency budgets. Even modest improvements in how that labour is planned, allocated and utilised can unlock significant productivity gains.

But the conversation in most agencies isn't framed that way. Workforce planning is often treated as an HR function — a compliance exercise focused on headcount reporting, vacancy management and organisational charts. It sits apart from operations, apart from service delivery, and apart from the financial planning processes that actually drive resource allocation.

That disconnect is where the productivity leaks start.

Where government agencies typically lose ground

Having worked with government organisations across Australia, the patterns are remarkably consistent regardless of whether you're looking at a federal department, a state health service, a water utility, a transport authority or a local council. The specifics differ, but the structural issues repeat.

Staffing levels are set by history, not demand. Most agencies inherit establishment structures that were set years — sometimes decades — ago. Roles are allocated by function, team or location based on what was needed at the time, and then carried forward with incremental adjustments. What's rarely done is a first-principles analysis of actual demand: how much work needs to be done, when, where, and with what skills? Without that foundation, you end up with some teams chronically under-resourced and others carrying capacity they don't consistently use.

Rostering and scheduling are manual and reactive. In operational areas — contact centres, field services, regulatory inspections, processing teams, custodial settings, hospital wards, aged care facilities — the quality of rostering directly drives service outcomes and cost. Yet many agencies still roster manually, using spreadsheets or basic tools that don't account for demand variation, skill requirements, fatigue management, or compliance constraints. The result is over-staffing at quiet times, under-staffing at peaks, excessive overtime, and heavy reliance on agency or casual labour to plug gaps.

The mix of employment types isn't optimised. Government workforces typically include a blend of permanent, fixed-term, casual, labour-hire and contractor staff. Each has different cost profiles, flexibility characteristics and capability implications. But the mix is often a product of accumulation rather than design. Agencies end up paying permanent premiums for variable work, or conversely, paying casual and agency premiums for work that's predictable enough to be filled by permanent or part-time staff. Neither extreme is efficient.

Capability gaps hide behind headcount. An agency might have the "right" number of people on paper, but if skills don't match the work — if investigators are spending time on administration, if specialists are doing generalist work, or if experienced staff are doing tasks that could be handled at a lower classification — productivity suffers. Capability planning, which looks at skills and competencies rather than just bodies, is still relatively immature in most government settings.

Data is fragmented and underused. Agencies typically have workforce data spread across HRIS systems, payroll, time and attendance, project management tools, and operational systems — none of which talk to each other particularly well. Getting a clear picture of who's doing what, when, and at what cost requires manual assembly that's done infrequently, if at all. Without that visibility, workforce decisions are made on instinct rather than evidence.

These aren't niche problems. They're systemic. And they represent an enormous opportunity — because fixing them doesn't require new legislation, massive technology investments, or wholesale restructuring. It requires better planning discipline and the analytical capability to support it.

What good workforce planning actually looks like in government

Effective workforce planning in government operates across three horizons, each with its own cadence and decision set.

Strategic workforce planning looks 1–5 years out. It answers questions about the size, shape and capability of the workforce needed to deliver on the agency's mandate in the medium term. This is where demographic trends, policy changes, technology impacts and labour market shifts get factored in. It informs recruitment pipelines, training investments, graduate programs and succession planning. Done well, it prevents agencies from being caught flat-footed by retirements, skill shortages or demand shifts they could have anticipated.

For government and defence agencies in particular, strategic workforce planning needs to account for long lead-time capabilities — security clearances, specialist technical skills, regional deployment requirements — that can't be sourced quickly from the market. This is where planning discipline really earns its keep.

Tactical workforce planning operates on a 2–12 week horizon. It translates strategic intent into operational reality: converting demand forecasts into rosters, managing leave, training days and secondments, setting overtime and agency-use guardrails, and ensuring compliance with enterprise agreements and award conditions. This is the engine room of workforce productivity, and it's where most agencies have the most to gain.

The key principle at this level is demand-driven planning. Rather than building rosters around available staff and hoping the work fits, you start with a clear picture of what needs to be done — volumes, service levels, complexity, location — and then design the workforce deployment to match. The gap between these two approaches is where most of the productivity leakage lives.

Operational scheduling is the day-to-day and hour-to-hour allocation of people to tasks. It's about managing disruptions — sick leave, demand spikes, urgent priorities — without defaulting to expensive fixes like overtime or agency staff. Strong scheduling depends on having clear escalation protocols, flexible deployment options, and real-time visibility of who's available and what they're capable of.

These three horizons are connected. Strategic planning sets the boundaries. Tactical planning fills in the detail. Operational scheduling manages the reality. When they're aligned, the workforce feels well-managed — people know what's expected, work is distributed fairly, and the agency consistently meets its service commitments. When they're disconnected — which is the norm — you get firefighting, frustration and waste.

The role of technology — and its limits

It's tempting to reach for technology as the solution. And there's no question that modern workforce management systems, AI-enabled demand forecasting, automated rostering engines and real-time dashboards can make a material difference to how agencies plan and deploy their people.

But technology is an accelerant, not a substitute. An automated rostering tool running on flawed demand assumptions will produce rosters that are efficiently wrong. A predictive analytics platform drawing on fragmented data will generate forecasts that nobody trusts. And a workforce dashboard that nobody looks at is just an expensive screensaver.

The agencies that get the most from technology investments are the ones that do the process and governance work first: define the demand model, clean the data, clarify roles and decision rights, standardise the planning cadence, and then use technology to execute faster, more accurately and at scale.

This is particularly important in government settings where enterprise agreements, award conditions, and workforce policies create a complex compliance environment. Automated tools can check compliance in seconds rather than hours — but only if the rules are correctly configured and maintained.

Trace Consultants' .Workforce solution is designed for exactly this context: helping organisations build insight-led workforce plans that balance operational demand, cost constraints and employee wellbeing — and providing the analytical foundation that makes technology investments worthwhile.

Demand forecasting: the foundation most agencies skip

If you ask a team leader in a government processing centre how many staff they need tomorrow, they'll probably give you a confident answer based on experience. If you ask them how they know, you'll often get something closer to "it's always been about this many" or "it depends."

Neither answer is wrong, exactly. But neither is precise enough to plan efficiently.

Proper demand forecasting for workforce planning uses historical data (workload volumes, processing times, service request patterns), known future events (policy changes, seasonal peaks, program deadlines, court schedules), and capacity models that translate workload into labour hours, accounting for productivity rates, skill requirements and non-productive time (training, meetings, breaks, administration).

The output is a demand profile — a picture of how much work needs to be done, by whom, and when. That profile becomes the blueprint for everything downstream: how many people to roster, what skills to schedule, where to deploy them, and how much it should cost.

Most government agencies don't have this. They have headcount budgets and establishment structures, which is a fundamentally different thing. The headcount tells you how many people you can afford. The demand model tells you how many people you need. The gap between the two is where productivity either hides or gets unlocked.

Getting demand forecasting right is closely linked to planning and operations capability more broadly. The same disciplines that drive effective supply chain planning — demand sensing, capacity modelling, scenario analysis — apply directly to workforce planning, just with different inputs.

The workforce mix question

One of the most consequential decisions in government workforce planning is the mix of employment types. Full-time permanent staff provide stability and institutional knowledge but carry higher fixed costs. Part-time and flexible arrangements can align capacity more closely with demand patterns. Casual and agency staff provide surge capacity but at a premium cost and often with lower productivity and engagement.

The right mix depends on the demand profile. Work that is stable, predictable and requires deep expertise warrants permanent resourcing. Work that fluctuates — seasonally, cyclically, or in response to policy events — warrants a more flexible component. And work that spikes sharply but infrequently is best covered by contingency arrangements rather than permanent headcount.

The mistake most agencies make is defaulting to one approach: either over-relying on permanent staff (and carrying idle capacity at times) or over-relying on contingent labour (and paying premium rates for work that could be planned). Neither extreme is efficient, and both carry hidden costs that don't show up in simple headcount reporting.

A structured workforce composition analysis — looking at demand variability by time of day, day of week, week of year and work type — almost always reveals opportunities to improve the mix. In many cases, agencies can reduce total labour cost while improving service coverage by shifting a proportion of permanent hours to part-time or flexible arrangements, reducing casual and agency usage for predictable work, redesigning shift patterns to better match demand profiles, and introducing tiered roles that match skill requirements to task complexity.

This kind of analysis requires data, modelling capability and a willingness to challenge inherited assumptions. It also requires effective change management, because changes to workforce structure directly affect people's jobs, routines and livelihoods. The agencies that handle this well communicate openly, involve staff in the design process, and demonstrate clearly that the changes are about fairness and effectiveness — not just cost-cutting.

Organisational design and workforce planning are inseparable

You can't plan the workforce effectively if the organisation's structure doesn't support it. And in government, organisational structures are often the product of machinery-of-government changes, political decisions, historical precedent and incremental growth — rather than deliberate design aligned to service delivery.

Common issues include spans of control that are either too narrow (lots of layers, slow decisions) or too wide (overloaded managers, poor oversight); functional silos that create duplication and poor coordination; classification structures that don't match the actual work being done; and unclear accountability for service outcomes, which makes it hard to know whether the workforce is performing well or not.

Workforce planning works best when it's connected to organisational design — ensuring that the structure, roles, reporting lines and governance frameworks are set up to enable efficient service delivery. Sometimes the biggest productivity gain isn't changing how many people you have, but changing how they're organised.

Measuring what matters

One of the frustrations in government workforce planning is the tendency to track inputs (headcount, FTE, vacancy rates) rather than outputs (work completed, service levels met, cost per outcome). Input metrics are easy to report but tell you very little about whether the workforce is productive.

Better metrics for government workforce productivity include labour cost per unit of service delivered, productive hours as a percentage of paid hours, roster accuracy (how closely the roster matched actual demand), overtime and agency spend as a percentage of total labour cost, schedule adherence, and time to fill critical capability gaps.

These aren't exotic measures. They're the kind of operational KPIs that service organisations in the private sector have tracked for decades. The difference is that government agencies haven't traditionally been set up — in terms of data, systems or culture — to measure at this level.

Building this measurement capability is part of building a genuine planning and operations discipline. It requires investment in data integration, reporting tools and, most importantly, a management culture that uses the data to drive decisions rather than just report compliance.

The APS reform context

The federal government's APS Reform agenda and the APS Workforce Strategy have placed workforce capability at the centre of the public service improvement conversation. The themes are clear: attract and retain the right skills, embrace technology and flexible workforce models, and strengthen leadership and integrity.

At the state level, similar reform agendas are playing out. Victoria's public sector workforce strategies, NSW's efficiency reviews, Queensland's service delivery modernisation — all share a common thread: the recognition that workforce productivity is a strategic issue, not just an operational one.

What's sometimes missing from these strategies is the operational execution layer. The strategies articulate the "what" — better skills, better technology, better flexibility. But the "how" — demand modelling, roster optimisation, workforce mix analysis, stores and deployment network design — is where the productivity gains actually materialise.

This is where practical, hands-on consulting capability makes a difference. Not strategy decks that sit on shelves, but working alongside agency teams to build the models, redesign the processes, implement the tools and embed the disciplines that turn workforce planning from a periodic exercise into a continuous operating capability.

How Trace Consultants can help

At Trace Consultants, we work with Australian government agencies — federal, state and local — to design and implement workforce planning, rostering and scheduling models that lift productivity, improve service delivery and reduce cost.

Our approach is grounded in data and operational reality. We don't treat workforce planning as an HR exercise or a technology project. We treat it as a supply chain problem: matching the supply of labour to the demand for services, efficiently, compliantly and sustainably.

Demand modelling and workforce sizing. We build bottom-up demand models that translate service requirements into labour hours — accounting for workload volumes, processing times, skill requirements, compliance obligations and non-productive time. The output is an evidence-based view of how many people are needed, where, when and with what capabilities. This replaces the "we've always had this many" approach with something defensible and optimisable.

Roster and schedule optimisation. We design roster patterns and scheduling frameworks that align staffing levels to demand profiles — reducing over-staffing in quiet periods, improving coverage during peaks, and minimising reliance on overtime and agency labour. For agencies with 24/7 or extended-hours operations — contact centres, hospitals, custodial settings, field services — this is where the biggest gains typically sit. Learn more about our approach to workforce planning and scheduling.

Workforce mix and composition analysis. We model the cost and service implications of different employment mixes — permanent, part-time, casual, agency, contractor — and help agencies design a workforce composition that matches demand variability. This often unlocks savings by reducing premium-cost labour for predictable work.

Organisational design alignment. When the challenge isn't just workforce numbers but organisational structure, we help agencies design operating models that clarify accountability, streamline decision-making and connect workforce planning to service delivery outcomes.

Technology enablement. We help agencies select and implement fit-for-purpose workforce management technology — from rostering platforms to demand forecasting tools to performance dashboards — ensuring that technology investments are grounded in clear processes and clean data. Our .Workforce solution provides a practical, modular toolkit for agencies building workforce planning capability.

Performance measurement and governance. We design KPI frameworks and governance cadences that give agency leaders ongoing visibility of workforce productivity — and the ability to act on what they see, not just report it.

Change management and capability building. Workforce planning improvements change how people work, how they're managed and how decisions are made. Our project and change management capability ensures that changes are adopted, understood and sustained — building internal capability rather than creating consulting dependency.

We bring cross-sector insight to government workforce challenges. The rostering disciplines used in healthcare and aged care, the demand planning rigour applied in retail and FMCG, the scheduling precision required in logistics and distribution — these same disciplines apply directly to government service delivery, and we bring that experience to every engagement.

The bottom line

Workforce productivity in government isn't about doing more with less in the way that phrase is usually used — as a euphemism for cutting. It's about doing more with what you have, by planning better, deploying smarter and measuring honestly.

The levers are there. Demand-driven workforce sizing. Optimised rosters. Smarter employment mixes. Clear organisational structures. Practical technology. Honest measurement. None of these are revolutionary. But they require a level of analytical discipline and operational focus that most agencies haven't historically brought to workforce management.

The agencies that get this right don't just reduce cost. They improve service. They reduce burnout. They make better use of the talented people they already have. And they build the kind of institutional capability that compounds over time — making each year's planning better than the last.

If your agency is grappling with workforce productivity, cost pressure, or the gap between strategic ambition and operational capacity, we'd welcome the conversation.

Asset Management and MRO

MRO Supply Chains in Australia: Why Getting the Fundamentals Right Has Never Mattered More

James Allt-Graham
February 2026
MRO supply chains are often the most neglected and most expensive part of an organisation's operations. Here's what Australian businesses need to fix — and where the real gains sit.

There's a pattern that plays out across almost every industry in Australia. An organisation invests heavily in its core supply chain — the products it sells, the services it delivers, the assets it builds. Procurement teams run competitive tenders. Warehouses are designed with precision. Inventory models are refined every quarter.

And then there's MRO.

Maintenance, repair and operations spend — the parts, consumables, tools, services and supplies that keep things running — is often treated as an afterthought. It sits in a grey zone between procurement, operations, maintenance, and facilities. Nobody truly owns it. Everyone touches it. And the costs, risks and inefficiencies quietly compound.

If you've spent any time in Australian mining, energy, utilities, transport, manufacturing, defence, healthcare or property, you'll recognise the symptoms: fragmented purchasing, bloated stores, emergency freight charges, duplicate parts under different names, contractors ordering whatever they want, and maintenance teams who have stopped trusting the system and started hoarding.

The scale of the problem is significant. Australia's MRO market was valued at approximately AUD 13.6 billion in 2025 and is projected to keep growing as infrastructure ages, asset fleets expand, and operating complexity increases. For many organisations, MRO accounts for 5–15% of total operating expenditure — yet it receives a fraction of the strategic attention given to direct materials or services.

This article is about changing that. Not with theory, but with practical levers that Australian organisations can pull to improve cost, availability and control across their MRO supply chains.

What actually makes MRO supply chains so difficult?

MRO isn't just "stuff you buy that isn't for resale." It behaves fundamentally differently from direct procurement and finished goods supply chains, and those differences are what make it hard to manage well.

Demand is unpredictable. Unlike production materials where demand is driven by a forecast or sales plan, MRO demand is driven by asset failure, condition, usage and maintenance schedules. Some demand is plannable (preventative maintenance creates known requirements), but a significant proportion is reactive. That makes traditional forecasting methods less effective and inventory planning more nuanced.

The SKU count is enormous. A single manufacturing site can carry thousands of MRO line items — from bearings, seals and filters through to electrical components, lubricants, safety gear, and specialist tools. Multiply that across a multi-site operation and the catalogue grows rapidly, often with poor standardisation and rampant duplication.

Criticality varies wildly. A $3 gasket can shut down a $20 million production line. A $50,000 spare motor might sit on a shelf for years and never be needed — but if it is needed and it isn't there, the cost of downtime dwarfs the holding cost. MRO inventory decisions are fundamentally about risk, not just dollars.

Procurement is fragmented. MRO purchases often happen through a mix of channels: blanket purchase orders, catalogue buys, spot purchases, maintenance credit cards, contractor-managed procurement, and the occasional "just grab it from the hardware store" approach. Spend visibility is poor, and leverage is wasted across dozens of suppliers who could be consolidated.

Data is messy. This might be the single biggest barrier. MRO catalogues are notorious for inconsistent naming, duplicate entries, missing specifications, and mismatched units of measure. When the data is unreliable, everything downstream — from inventory planning to procurement analytics — is compromised.

These challenges aren't unique to any one sector. They show up in mining and heavy industry, in healthcare and aged care, in property and hospitality, and across manufacturing and FMCG. The specifics change, but the structural problems are remarkably consistent.

The hidden cost of getting MRO wrong

Most organisations dramatically underestimate the total cost of a poorly managed MRO supply chain. The line items on the P&L only tell part of the story.

The obvious costs are purchase prices and freight. The less obvious — and often larger — costs include emergency and expedited shipping (which can run at three to five times normal freight rates), unplanned downtime caused by stockouts of critical spares, excess and obsolete inventory tying up working capital, duplicated purchases because nobody could find what was already in the store, maverick buying that bypasses negotiated agreements, contractor mark-ups on materials procured outside the organisation's supply chain, and write-offs on parts that were never needed or can no longer be identified.

One of the most telling indicators is the ratio of emergency purchases to planned purchases. In well-managed MRO environments, emergency buys might represent less than 5% of total MRO transactions. In organisations where the supply chain hasn't been deliberately designed, that number can easily sit above 20–30%. Each emergency buy isn't just expensive in freight terms — it's a signal of a planning, inventory or data failure upstream.

The other hidden cost is people's time. When maintenance planners spend hours searching for parts, chasing suppliers, or working around a system they don't trust, the real cost isn't just their salary — it's the maintenance work that doesn't get done, the scheduled jobs that slip, and the knock-on effect on asset reliability.

Five areas where Australian organisations should focus

There's no single fix for MRO supply chains. Improvement requires working across several interconnected areas simultaneously. But based on what we see working with Australian organisations across sectors, these are the five areas that consistently deliver the most value.

1. Get the catalogue and master data right

This is unglamorous work, and it's exactly why most organisations avoid it. But it's foundational. You cannot optimise what you cannot see clearly.

A fit-for-purpose MRO catalogue means every item has a unique, standardised description that allows maintenance teams to find what they need quickly. It means duplicates are identified and merged. It means units of measure are consistent. It means items are linked to the assets they serve through bills of materials. And it means there are clear rules for creating new items — so the catalogue doesn't immediately start degrading again.

The payoff is significant. Clean data unlocks better inventory planning, more effective procurement, faster issue resolution in stores, and more meaningful reporting. It also makes technology investments — like predictive analytics or automated replenishment — actually viable.

This isn't a one-off project. It's a governance discipline that needs to be embedded in operating rhythms. Trace works with organisations to build catalogue governance frameworks that are practical enough for frontline teams to follow — not just theoretically correct. You can read more about our approach to technology enablement and data-led supply chain improvement.

2. Segment inventory by criticality, not just cost

Most MRO inventory systems default to ABC classification — sorting items by annual spend. It's a useful starting point for counting priorities, but it's a terrible proxy for operational risk.

A $12 seal that protects a critical pump from failure is infinitely more important than a $5,000 fitting that sits on a redundant line. If you manage both with the same service-level target, you'll either over-invest in low-risk items or under-invest in high-risk ones.

A better approach is criticality-based segmentation. This considers safety impact, asset availability risk, lead time and supply vulnerability, substitution options, and the consequence of not having the part when it's needed. The output is a tiered model: critical spares get near-perfect availability targets and tight governance; routine consumables get simple min-max replenishment; and everything in between gets a policy proportionate to its risk profile.

This is where MRO inventory strategy intersects with maintenance strategy. Preventative maintenance creates plannable demand that can be kitted and staged in advance. Predictive maintenance gives early warning signals that allow procurement lead times to be built in. Corrective maintenance generates the most unpredictable demand — and that's exactly where critical spares buffers earn their keep.

The goal is availability without tying up unnecessary cash. Done well, organisations can often reduce total MRO inventory value while simultaneously improving stockout performance on the items that matter most.

3. Design the stores network deliberately

Even with the right inventory policies, availability suffers when parts are in the wrong place. And in Australia, geography makes this problem worse.

Many organisations inherit stores networks that grew organically — a cage here, a container there, a "temporary" store that became permanent, and satellite locations that exist because teams didn't trust transfers from the central store. The result is duplicated stock, poor utilisation of space, inconsistent management practices, and significant effort spent moving parts between locations.

A deliberate stores network design considers the maintenance footprint (where work happens), asset criticality by location, travel times and transport reliability, outage and shutdown schedules, after-hours access requirements, and the balance between centralised control and decentralised responsiveness.

Common models include a central store with regional satellite depots for fast-moving items, hub-and-spoke arrangements for distributed asset fleets, vendor-managed inventory for high-volume consumables, and dedicated staging areas for planned outages or shutdowns.

There's no universal answer — but there is a universal principle: inventory location is a design decision, not an accident. For organisations reviewing their physical network, Trace's warehousing and distribution and strategy and network design teams can model options and quantify the trade-offs.

4. Build a proper MRO procurement strategy

MRO procurement is frequently under-managed. Spend is spread across hundreds of suppliers and thousands of transactions, making it harder to aggregate, harder to negotiate, and harder to govern than direct categories.

The starting point is visibility. You need to know what you're spending, with whom, for what, and at what price — and then you need to be honest about how much of that spend is managed versus unmanaged.

From there, MRO procurement strategy typically involves category segmentation (grouping spend into manageable categories like bearings, electrical, lubrication, PPE, tools, services, and so on), supplier rationalisation (consolidating volume with fewer, better suppliers where it makes sense — while maintaining dual sourcing for critical categories), framework agreements (establishing pre-negotiated pricing, service levels and ordering pathways that make compliant purchasing faster than non-compliant purchasing), performance-based contracting for services like facilities maintenance, fleet, and inspection where outcomes matter more than hourly rates, and governance that tracks maverick buying, emergency purchasing and contract compliance as leading indicators.

The trap to avoid is optimising purely for unit price. In MRO, total cost of ownership includes freight, lead time risk, quality consistency, supplier responsiveness, returns handling, and the administrative cost of managing the transaction. A supplier who is 5% cheaper but unreliable on delivery can cost far more in downtime and expediting.

Trace's procurement team brings structured category management disciplines to MRO — including for adjacent categories like property and facilities management, fleet, and plant and equipment that often sit outside traditional procurement scope.

5. Fix the governance — but make it practical

The best MRO supply chains don't feel heavily governed from the frontline. Parts are available when needed. Ordering is quick. The catalogue is trusted. Stores are accurate. It just works.

That feeling of simplicity is actually the product of deliberate design: clear roles between maintenance planning, stores, procurement and engineering authority; standard processes for kitting, issuing, receiving and returns; a fast exception pathway for genuine urgencies; cycle counting that builds accuracy over time; and performance reporting that drives improvement, not blame.

The biggest governance risk in MRO is drift. A system that's well-designed at implementation gradually degrades as people leave, workarounds accumulate, and discipline relaxes. The antidote is embedding governance into regular operating rhythms — weekly stores reviews, monthly procurement performance reporting, quarterly inventory health checks — rather than relying on annual audits to catch problems after they've compounded.

This is where organisational design and project and change management become relevant. Getting the structure, accountability and change approach right from the outset dramatically improves the chances that improvements stick.

The technology question

It would be easy to suggest that technology is the answer to MRO supply chain challenges. And there's no doubt that better systems — inventory management platforms, e-procurement tools, IoT-enabled condition monitoring, and predictive analytics — can make a real difference.

But technology deployed on top of messy data, undefined processes, and unclear accountability tends to automate the chaos rather than fix it. The organisations that get the most from MRO technology investments are the ones that sort out the fundamentals first: clean the catalogue, define the inventory policies, clarify the procurement rules, and then use technology to enforce and enhance what's already working.

That said, there are areas where technology is genuinely transformative for MRO. Predictive maintenance tools that link asset condition data to parts demand are moving from pilot to practice across Australian mining, energy and manufacturing. E-procurement platforms that make compliant purchasing easier than non-compliant purchasing are reducing maverick spend. And visibility dashboards that surface stockouts, emergency buys and aged inventory in real time are giving supply chain and maintenance leaders the information they need to act, rather than react.

Trace's technology team works alongside our supply chain and procurement specialists to ensure technology decisions are grounded in operational reality — fit-for-purpose, not over-engineered.

Why this matters now

Several forces are converging to make MRO supply chain performance more important for Australian organisations than it has been in the past.

Infrastructure is ageing. Roads, bridges, rail, water treatment plants, power stations, hospitals, schools — much of Australia's public and private infrastructure was built decades ago and is now entering the part of its lifecycle where maintenance intensity increases. That means more MRO spend, more spares demand, and more risk if the supply chain isn't fit for purpose.

The energy transition is adding complexity. New asset types — solar farms, wind turbines, battery storage systems, grid-scale infrastructure — are expanding the range of parts and services that operators need to manage. At the same time, legacy assets must continue operating reliably during the transition. The organisations navigating this successfully are treating MRO as a strategic enabler, not a procurement afterthought.

Labour markets remain tight. Skilled maintenance technicians, planners and stores personnel are in short supply across Australia. When you can't simply throw more people at the problem, the supply chain around maintenance has to be more efficient — fewer wasted trips, fewer stockouts, less time searching for parts, more wrench time.

And supply chains globally remain less predictable than they were pre-pandemic. Lead times for specialist components have improved from their worst but haven't returned to historical norms. Geopolitical disruption, shipping volatility and concentrated manufacturing bases all add risk that needs to be actively managed through buffer strategies, supplier diversification and better demand planning.

For organisations looking to build supply chain resilience into their MRO operations, the time to act is before the next disruption — not during it.

How Trace Consultants can help

At Trace Consultants, we work with Australian organisations to design, optimise and implement MRO supply chain strategies that deliver measurable improvements in cost, availability and control.

Our approach is practical, data-led and grounded in operational reality. We don't deliver theoretical frameworks and walk away — we work alongside your teams to build solutions that stick.

MRO inventory strategy and optimisation. We help organisations segment inventory by criticality, set defensible service levels, design replenishment policies, and reduce excess and obsolete stock — delivering higher availability with lower working capital. Our approach connects inventory decisions directly to maintenance strategy and asset criticality.

Stores network and warehouse design. From central stores through to regional depots and field replenishment models, we design physical networks that put the right parts in the right place. This includes stores layout, workflow design, receiving and issuing processes, and accuracy improvement programs. Learn more about our warehousing and distribution capabilities.

MRO procurement and category strategy. We bring structured category management to MRO spend — including supplier rationalisation, framework agreement design, performance-based contracting, and governance that reduces maverick purchasing. Our procurement team also covers adjacent categories like facilities management, fleet, and plant and equipment.

Catalogue and master data governance. We help organisations clean, standardise and govern MRO catalogues — building the data foundation that everything else depends on. This includes duplicate identification, naming conventions, BOM linkage, and ongoing governance frameworks.

Technology enablement. We support organisations to select and implement fit-for-purpose technology for MRO inventory management, e-procurement and performance reporting — always grounded in clear processes and clean data.

Operating model and organisational design. When the challenge isn't just the supply chain but the structure around it, we help design operating models that clarify accountability, streamline decision-making and align maintenance, procurement and stores functions.

Program delivery and change management. MRO improvement programs touch multiple teams and change established ways of working. Our project and change management capability ensures that improvements are adopted, embedded and sustained.

We work across sectors — from government and defence through to FMCG and manufacturing, health and aged care, and property, hospitality and services — bringing cross-sector insight to challenges that are remarkably similar beneath the surface.

The bottom line

MRO supply chains rarely make the boardroom agenda. They don't have the visibility of a major capital project or a customer-facing logistics network. But for organisations that depend on asset reliability — and in Australia, that's most of them — MRO is one of the highest-leverage areas to improve.

The organisations that get this right don't just spend less on parts. They have fewer unplanned outages, faster maintenance turnaround, more productive technicians, better supplier relationships, and more confident capital planning. They've moved MRO from a cost centre that nobody wants to own into a capability that genuinely supports operational performance.

It doesn't require a massive technology transformation or a multi-year program. It starts with visibility, discipline and a deliberate strategy — applied consistently across the fundamentals of data, inventory, procurement, stores and governance.

If you'd like to pressure-test your current MRO supply chain or explore where the biggest opportunities sit, get in touch with our team. We'd welcome the conversation.

Strategy & Network Design

Practical Ways to Leverage AI in Supply Chains for Retailers

Shanaka Jayasinghe
February 2026
Australian retailers are spending more on AI every year. But most of the value isn't in flashy chatbots or personalisation engines — it's buried in the supply chain. Here's where to start and what actually works.

The Hype Is Loud. The Opportunity Is Real — but Specific.

Every retail conference in Australia right now features at least three panels on AI. The vendor pitches are relentless. The consulting decks are thick with promises about "transformative potential" and "AI-powered supply chains of the future." And somewhere in the middle of all that noise, a head of supply chain planning at an Australian retailer is trying to work out whether AI can actually help them solve the problems they're dealing with this quarter — not in 2030, but right now.

The honest answer is: yes, but not everywhere equally, and not without doing some unglamorous groundwork first.

AI's most immediate and measurable impact in Australian retail isn't happening in customer-facing applications like chatbots or visual search. It's happening in the supply chain — in demand planning, inventory positioning, warehouse operations, transport optimisation, and supplier management. These are the areas where even modest improvements in accuracy or efficiency translate directly into margin, working capital, and customer experience.

The challenge for most mid-market and growing retailers is knowing where to start, what's realistic given their data and systems maturity, and how to avoid spending six months on a proof of concept that never makes it to production. This article is our attempt to cut through the noise and lay out what's actually working in retail supply chains today, what's genuinely practical for Australian businesses, and where the effort is best spent.

1. Demand Forecasting: Where AI Earns Its Keep Fastest

If you're going to start anywhere, start here. Demand forecasting is the single area where AI delivers the most consistent, measurable value for retailers. The reason is straightforward: traditional forecasting methods — exponential smoothing, moving averages, even basic statistical models — rely heavily on historical sales patterns. They work reasonably well when demand is stable and predictable. They fall apart when it isn't.

Post-pandemic, Australian retail demand has been anything but predictable. Consumer behaviour shifted dramatically during COVID, and the patterns haven't fully reverted. Weather volatility, promotional cannibalisation, social media-driven spikes, cost-of-living pressures, and channel-shifting between online and in-store all make traditional forecasting less reliable than it used to be.

Machine learning models handle this complexity better because they can ingest a much wider range of demand signals — not just historical sales, but weather data, event calendars, promotional plans, social media trends, competitor activity, even local economic indicators — and identify non-linear relationships that statistical models miss. Coles, for instance, has been using AI models informed by over 100 variables to forecast fresh produce demand across its store network, helping reduce waste while keeping availability high. Woolworths has invested heavily in AI-driven demand planning, and publicly noted that their availability metrics are at their best levels since before COVID.

You don't need to be Coles or Woolworths to benefit. The practical starting point for most mid-market retailers is to layer a machine learning forecasting engine onto your existing demand planning process — not to replace your planners, but to augment them. Start with your highest-volume or most volatile categories, where forecast error has the biggest impact on either stockouts or markdowns. Measure the improvement in forecast accuracy (typically expressed as mean absolute percentage error, or MAPE) and the downstream impact on inventory and service levels.

The technology is more accessible than many retailers assume. Cloud-based forecasting tools from providers like o9 Solutions, Blue Yonder, and others now offer scalable AI forecasting that doesn't require a massive IT build. For organisations already invested in the Microsoft ecosystem, Power BI combined with Azure Machine Learning can deliver surprisingly capable forecasting at a fraction of the cost of a full APS deployment.

One word of caution: AI forecasting is only as good as the data it's trained on. If your sales data is riddled with stockout periods (where you can't distinguish between zero demand and zero availability), if your promotional history is incomplete, or if your product hierarchy is a mess, the model will underperform. Getting the data foundations right is the unsexy but essential first step. Our team regularly works with retailers on exactly this kind of planning and operations uplift — cleaning the data, structuring the inputs, and building the processes around the technology so that it actually delivers.

2. Inventory Optimisation: Putting Stock Where It Matters

Better demand forecasting is only half the equation. The other half is using those improved forecasts to position inventory more intelligently across the network.

Most Australian retailers with multi-tier distribution — central DC to state-based DCs to stores — still set safety stock parameters using rules of thumb that haven't been revisited in years. The result is often too much stock in the wrong places: slow movers clogging DC capacity while fast movers go out of stock at store level, or excess inventory built up as a buffer against forecast uncertainty that the AI forecasting engine has already reduced.

AI-powered inventory optimisation takes the improved demand signal and combines it with lead time variability, service level targets, storage constraints, and cost-to-serve data to recommend optimal stock levels at each node in the network. For retailers with complex, multi-echelon distribution networks, the shift from single-node to network-wide inventory optimisation can unlock working capital reductions of 15–25% while maintaining or improving product availability.

The practical application for most retailers starts with a segmentation exercise. Not every SKU warrants the same treatment. AI helps here too — clustering algorithms can segment your product range based on demand characteristics (volume, variability, trend, seasonality, lifecycle stage) and assign differentiated inventory policies to each segment. Your fast-moving, predictable staples get one treatment; your long-tail, intermittent-demand items get another; and your new product introductions get a third.

This kind of work doesn't necessarily require a massive technology investment upfront. We've helped retailers build pragmatic inventory optimisation models using a combination of analytics tools and structured planning processes, which can be scaled up as the organisation's data and technology maturity grows.

3. Warehouse Operations: AI on the Floor

Warehouse automation gets a lot of attention — Woolworths' $2 billion-plus investment in automated fulfilment centres at Auburn and Moorebank is a case in point. But you don't need to build a fully automated DC to get value from AI in warehouse operations.

The more immediate opportunities for most retailers sit in three areas.

Labour planning and task allocation. AI can forecast pick volumes by hour and day, allowing warehouse managers to roster the right number of staff for the actual workload rather than relying on averages. This is especially valuable in environments with variable demand — promotional peaks, seasonal surges, online order spikes — where getting labour allocation wrong means either paying for idle time or scrambling to cover shortfalls. Our workforce planning and scheduling team works with distribution operations to build exactly these kinds of demand-driven labour models.

Slotting optimisation. AI can analyse order patterns, pick frequencies, and product characteristics to recommend optimal product placement within a warehouse — positioning fast-moving items closer to pack stations, grouping frequently co-ordered items together, and dynamically re-slotting as demand patterns shift through the year. This reduces average pick path length, improves throughput, and reduces labour cost per unit — often by 10–20% in facilities that haven't been re-slotted in a while.

Quality and exception management. Machine learning models can identify patterns in receiving errors, mis-picks, short-ships, and returns, flagging root causes that manual review might miss. For retailers with high return rates (particularly in online fashion), AI-driven analysis of return reasons, product attributes, and supplier patterns can inform both warehouse process changes and upstream decisions about sizing, product descriptions, and supplier quality.

If you're operating or designing a warehousing and distribution network, AI should be a consideration at the strategy level — not just for automation decisions, but for the planning, labour, and process optimisation that surrounds the physical infrastructure.

4. Transport and Last-Mile Delivery: Smarter Routing, Lower Costs

Transport is one of the largest and most volatile cost lines in a retail supply chain. For Australian retailers, the challenge is amplified by geography — long distances between population centres, urban congestion in metro areas, and the growing customer expectation for fast (and increasingly free) home delivery.

AI-powered route optimisation is now well-established in the logistics market. Dynamic routing engines consider real-time traffic conditions, delivery windows, vehicle capacity, driver hours, and delivery density to generate optimised routes that reduce kilometres driven, fuel consumed, and delivery time. The technology is especially impactful for last-mile delivery, where the final leg to the customer's door can account for more than half of total shipping cost.

But the AI opportunity in transport goes beyond routing. Predictive models can forecast delivery volumes by region and day, allowing retailers to pre-position inventory and schedule transport capacity more efficiently. For retailers managing their own fleet or negotiating with carriers, AI-driven analysis of freight spend, carrier performance, and lane-level cost structures can identify savings that are invisible in aggregated reporting.

One area we're seeing growing interest in is the integration of transport planning with demand and inventory planning — what some call "end-to-end" or "connected" planning. When your demand forecast, inventory positioning, and transport scheduling are all informed by the same AI-driven demand signal, the entire chain becomes more responsive and less wasteful. This is the direction that leading supply chain strategy is heading, and it's where the compounding benefits of AI really start to stack up.

5. Supplier Performance and Procurement Intelligence

This one doesn't get as much airtime as demand forecasting or warehouse automation, but it's arguably where AI has the most untapped potential in Australian retail.

Most retailers track supplier performance — DIFOT (delivery in full, on time), quality defects, lead time reliability — but they do it reactively, looking at what happened last month. AI enables a shift from reactive reporting to predictive management. Machine learning models can identify early warning signals of deteriorating supplier performance — lead time drift, increasing defect rates, changes in order acknowledgement patterns — and flag risks before they hit the shelf.

For procurement teams, AI can also accelerate spend analysis, identifying savings opportunities across categories by analysing pricing trends, contract compliance, maverick spend, and market benchmarks. What used to take a procurement analyst weeks of spreadsheet work can now be surfaced in hours.

Trace has developed its own .DIFOT and .SIFOT solutions specifically to give organisations real-time visibility into supplier and service delivery performance — the kind of visibility that turns procurement from a transactional function into a strategic one.

6. Sustainability and Waste Reduction

AI is increasingly playing a role in helping retailers meet their sustainability commitments — and it's happening through supply chain operations, not just through marketing and reporting.

Demand forecasting improvements directly reduce food waste for grocery retailers. Smarter inventory positioning reduces the volume of markdowns and write-offs. Transport optimisation cuts fuel consumption and emissions. Even warehouse energy management can be improved through AI-driven monitoring and scheduling of heating, cooling, and lighting systems.

For retailers reporting under Australia's Climate-related Financial Disclosure regime or working towards ESG targets, the supply chain is where the largest and most measurable emissions reductions tend to sit. Scope 3 emissions — those embedded in your supply chain rather than your own operations — typically dwarf Scope 1 and 2 for retailers. AI-driven visibility into supplier emissions, transport carbon intensity, and packaging waste gives sustainability teams actionable data rather than estimates and averages.

Our supply chain sustainability capability helps retailers connect operational improvement with sustainability outcomes — because in practice, the two are often the same initiative viewed through different lenses. A project that reduces food waste through better demand forecasting is simultaneously a cost initiative and a sustainability initiative. The AI is the same; the reporting lens is different.

What Separates the Winners from the Pilot Graveyard

We've seen plenty of AI pilots in retail supply chains that delivered promising results in a controlled environment and then stalled. The pattern is depressingly consistent: a data science team builds a model, demonstrates it on a subset of data, presents impressive accuracy metrics to leadership — and then it sits on a shelf because nobody figured out how to integrate it into the planners' daily workflow, the IT team couldn't connect it to the ERP, or the business case wasn't compelling enough to justify the change management effort.

The retailers that extract real, sustained value from AI share a few common traits.

They start with a business problem, not a technology. The question isn't "how can we use AI?" It's "what's costing us money, losing us sales, or creating risk — and can AI help solve that specific problem?" The best AI initiatives in retail supply chains are narrow in scope, clear in expected outcomes, and tied to a specific financial or operational metric.

They invest in data foundations before models. Clean, consistent, well-governed data is not glamorous. It doesn't make the conference agenda. But it's the single biggest determinant of whether an AI initiative succeeds or fails. Master data quality, demand history integrity, lead time accuracy, and system integration are the prerequisites — not afterthoughts.

They embed AI into existing processes. AI doesn't work as a separate layer that planners occasionally consult. It works when it's woven into the daily planning rhythm — generating forecasts that flow into replenishment, optimising stock levels that drive purchase orders, flagging exceptions that planners review and action. The technology needs to fit the workflow, not the other way around.

They measure ruthlessly. Forecast accuracy, inventory turns, service levels, cost-to-serve, waste percentages — the organisations that succeed with AI track these metrics with discipline and hold teams accountable for improvement. AI is a tool, not magic. If you can't measure the impact, you can't justify the investment or the continued effort. And the measurement needs to be ongoing — not just a before-and-after comparison at the end of the pilot, but a continuous performance dashboard that your planning team reviews weekly.

How Trace Consultants Can Help

At Trace Consultants, we work with retailers across Australia and New Zealand to improve supply chain performance through better planning, smarter technology, and practical execution. AI is increasingly part of that picture — but always in service of a business outcome, never as an end in itself.

Here's where we add the most value for retail clients exploring or expanding their use of AI in the supply chain.

Diagnostic and opportunity assessment. We start by understanding where your supply chain is losing money or underperforming — whether that's forecast accuracy, inventory positioning, warehouse productivity, transport cost, or supplier performance. We quantify the opportunity, identify where AI can deliver the highest return, and build a prioritised roadmap that accounts for your data maturity, systems landscape, and organisational readiness. Our retail sector experience means we understand the specific pressures and trade-offs that retail supply chains face.

Demand planning and S&OP improvement. We help retailers redesign their planning and operations processes — from statistical forecasting through to integrated business planning — incorporating AI-powered tools where they add value and building the process discipline that ensures the technology gets used.

Inventory strategy and network optimisation. We model retail distribution networks, assess inventory policies across echelons, and design optimised inventory strategies that balance service, cost, and working capital. Our strategy and network design capability is grounded in practical implementation, not just modelling.

Technology enablement. We're technology-agnostic — we recommend and help implement the platforms that fit your needs, from enterprise APS solutions to pragmatic Power Platform tools (Power BI, Power Apps, Power Automate) that deliver quick wins without massive IT projects. Our .Solutions suite, including .DIFOT, .Planner, and .Workforce, provides practical, modular tools designed for supply chain teams.

Warehouse and distribution optimisation. From warehouse design and layout optimisation to WMS selection, labour planning, and process improvement, we help retailers build distribution operations that are efficient, scalable, and ready to take advantage of AI and automation as they mature.

Change management and capability building. We know that technology adoption fails without people. Every engagement includes a focus on building internal capability, training teams, and embedding new processes through structured change management — so that improvements last well beyond our engagement.

What makes Trace different is that we're practitioners, not theorists. Our consultants have sat in the planning room, built the dashboards, calibrated the models, and tracked the P&L impact. We're a boutique firm that puts senior people on every engagement — you get the team you were promised, not a junior analyst learning on the job.

The Practical Path Forward

AI in retail supply chains is not about a single transformative project. It's about a series of targeted, well-executed improvements that compound over time. Start with the problem that's costing you the most. Fix your data. Pick a technology approach that fits your scale and maturity. Measure everything. Build on what works.

The Australian retailers who are getting this right — from the big end of town through to mid-market operators — aren't the ones with the flashiest AI strategies. They're the ones who've done the disciplined work of connecting better data, better models, and better processes into a supply chain that's measurably more responsive, efficient, and resilient.

If you'd like to explore what AI could practically deliver for your retail supply chain, we'd welcome the conversation.

Trace Consultants is an Australian supply chain and procurement consultancy with offices in Melbourne, Sydney, Brisbane, and Canberra. We work with retailers, FMCG producers, Defence, government, and infrastructure clients to deliver measurable improvements in supply chain performance. Visit our insights page for more articles, or get in touch to speak with our team.

Planning, Forecasting, S&OP and IBP

Multi-Echelon Inventory Optimisation for Australian Supply Chains

Mathew Tolley
February 2026
Most Australian organisations still set safety stock location by location. Multi-echelon inventory optimisation offers a fundamentally better approach — but only if you get the implementation right.

The Inventory Problem Hiding in Plain Sight

Here's something we see regularly when working with supply chain teams across Australia and New Zealand: an organisation that's spent real money on demand planning tools, invested in warehouse automation, and negotiated hard with suppliers — yet still carries too much of the wrong stock in the wrong places.

The symptoms are familiar. A distribution centre in western Sydney is bursting at the seams with slow-moving SKUs while a regional depot in Queensland keeps running out of fast-movers. Safety stock buffers are set independently at each node, often by well-intentioned planners using rules of thumb that haven't been revisited in years. The finance team is asking pointed questions about working capital, and customer service metrics are stubbornly stuck below target despite all that inventory investment.

If this sounds familiar, you're not alone. And the root cause is almost always the same: the organisation is optimising inventory at each location independently, without considering how stock levels at one point in the network affect every other point.

This is the problem that multi-echelon inventory optimisation — commonly abbreviated as MEIO — is designed to solve. It's not new in theory, but it's increasingly practical for mid-market and enterprise organisations in Australia, thanks to advances in planning technology, better data availability, and a sharper focus on supply chain efficiency driven by years of disruption.

What Is Multi-Echelon Inventory Optimisation?

Let's cut through the jargon. A "single-echelon" approach to inventory management means each location in your supply chain — your central warehouse, your regional distribution centres, your retail stores or customer-facing depots — sets its own inventory targets in isolation. Each node determines its own safety stock, reorder points, and order quantities based on its own demand history, its own lead times, and its own service level objectives.

That approach works well enough for simple supply chains. If you've got one warehouse shipping directly to customers, single-echelon planning is probably sufficient.

But most Australian supply chains aren't that simple. You might have imported goods arriving at a port, moving to a central DC, then redistributing to state-based warehouses, and onwards to branch locations or stores. You might have manufacturing sites feeding multiple distribution tiers. Defence sustainment networks, in particular, can involve four or five echelons from original equipment manufacturer through to the point of use at a maintenance facility or deployed unit.

In these multi-tier networks, the inventory decisions at one level directly affect every other level. If your central DC carries more safety stock, your regional DCs can probably carry less — because replenishment from the centre is more reliable. Conversely, if your upstream supply is unreliable, every downstream node needs to buffer independently, driving up total inventory investment across the network.

Multi-echelon inventory optimisation treats the entire network as an interconnected system. Rather than optimising each node independently, it determines the optimal placement and quantity of inventory across all echelons simultaneously, balancing total cost against desired service levels across the whole chain. The result is typically lower total inventory with equal or better customer service — because stock is positioned where it creates the most value, not where it happens to have accumulated.

Why It Matters Now for Australian Organisations

There are several converging pressures making MEIO increasingly relevant for Australian supply chain leaders.

Working capital is under scrutiny. After years of supply chain disruption — pandemic-era shortages, container shipping volatility, geopolitical uncertainty — many organisations overcompensated by building inventory buffers at every point in the network. CFOs are now asking whether all that stock is actually needed, or whether capital could be redeployed more productively. MEIO provides the analytical framework to answer that question with data rather than opinion.

Service expectations keep rising. Whether you're serving retail customers expecting next-day delivery, government agencies with strict availability targets, or Defence platforms that can't afford parts shortages, the bar for service performance continues to lift. The paradox is that meeting higher service levels doesn't necessarily require more inventory — it requires smarter inventory placement. That's exactly what MEIO delivers.

Supply chains are getting more complex, not less. The push towards sovereign manufacturing capability in Defence, the growth of omnichannel retail, the regionalisation of supply networks in response to geopolitical risk — all of these trends are adding echelons and complexity to Australian supply chains. The more complex the network, the greater the gap between single-echelon and multi-echelon performance.

Technology has caught up with the theory. MEIO has been well-understood in academic literature for decades. What's changed is that the technology platforms to implement it — advanced planning systems (APS), digital twins, cloud-based optimisation engines — are now accessible to mid-market organisations, not just global multinationals with massive IT budgets. Platforms from vendors like o9 Solutions, Kinaxis, Blue Yonder, and others now offer MEIO capabilities that can be deployed on realistic timescales and budgets.

Single-Echelon vs. Multi-Echelon: What's Actually Different?

To make this concrete, consider a simplified Australian distribution network: an import warehouse in Melbourne receiving goods from overseas suppliers, two regional DCs (one in Sydney, one in Brisbane), and a network of 20 branch locations across the eastern seaboard.

Under a single-echelon approach, each of the 23 nodes calculates its own safety stock independently. The Melbourne warehouse looks at its lead time from overseas suppliers and the variability of demand from its two regional DCs. Each regional DC looks at its lead time from Melbourne and the variability of demand from its branch locations. Each branch looks at its lead time from the regional DC and the variability of customer demand.

The problem is that this cascading approach doesn't account for the interdependencies. If the Melbourne warehouse reliably holds sufficient stock, the regional DCs don't need as much buffer — but a single-echelon model doesn't capture that relationship. Every node buffers against its own worst-case scenario, and the total network inventory ends up significantly higher than it needs to be.

Under a multi-echelon approach, the optimisation model considers the entire network simultaneously. It might determine that shifting more safety stock upstream to the Melbourne warehouse — where a single unit of buffer protects both the Sydney and Brisbane DCs — reduces total network inventory while maintaining or improving end-customer service. Or it might identify that certain fast-moving, high-variability items should carry safety stock at the branch level (close to the customer) while slower, more predictable items should buffer centrally.

The results can be striking. Published research and vendor case studies consistently report inventory reductions of 15–30% with maintained or improved service levels when organisations move from single-echelon to multi-echelon approaches. The exact figure depends on the complexity of the network, the quality of the data, and how far the existing inventory policy is from optimal — but the direction is almost always the same: less total inventory, better-positioned stock, improved service.

The Practical Challenges of MEIO Implementation

If MEIO is so effective, why isn't everyone doing it? Because the theory is easier than the practice. Here are the real-world challenges we see when working with organisations across Australia.

Data Quality and Availability

MEIO models are only as good as the data that feeds them. You need accurate demand data at each echelon, reliable lead time information for each supply link, inventory holding costs by location, and clear definitions of service level targets by customer segment or product group. Many organisations discover, when they start an MEIO initiative, that their data is patchy, inconsistent, or locked in spreadsheets and legacy systems. The data cleansing and integration work often takes longer than the optimisation modelling itself.

Organisational Alignment

In many organisations, inventory decisions are decentralised. Each regional warehouse manager or branch operations lead sets their own stock levels, often based on local experience and relationships rather than network-wide optimisation. Moving to MEIO requires a cultural shift: accepting that the optimal inventory level at your location might go down because the network as a whole performs better with stock positioned elsewhere. That's a hard conversation, especially when individual performance metrics are tied to local service levels rather than network performance.

Technology Integration

MEIO doesn't operate in a vacuum. It needs to connect with your ERP system for transactional data, your warehouse management system for inventory positions, your demand planning tools for forecast inputs, and your procurement systems for lead time and supply reliability data. Getting these systems to talk to each other — reliably, in near-real-time — is a significant integration challenge, particularly for organisations running legacy ERP platforms or operating across multiple disconnected systems.

Change Management

Even with the right data and technology, MEIO only delivers results if planners actually use the optimised parameters. If your team doesn't trust the model outputs and continues to override recommended safety stock levels based on gut feel, you'll have invested in technology without capturing the value. Building planner confidence through pilot programmes, transparent model logic, and progressive rollout is essential.

Where MEIO Delivers the Most Value in Australia

Not every supply chain needs multi-echelon optimisation. For a simple, single-tier distribution model, the incremental benefit over good single-echelon planning may not justify the investment. But for several sectors that are prominent in the Australian market, MEIO can be transformative. The common thread across these sectors is complexity: multiple tiers of distribution, high stakes for stockouts, significant capital tied up in inventory, and networks that span a geography where freight costs and lead times really matter.

Defence and Aerospace Sustainment

Defence supply chains are textbook multi-echelon environments. Parts flow from OEMs and repair facilities through national supply depots, to base-level stores, to unit-level holdings, and ultimately to the maintenance point on a platform. The penalties for stockouts are severe — an unavailable part can ground an aircraft or delay a vessel's return to service. At the same time, Defence organisations hold billions of dollars in inventory, much of which is slow-moving or obsolete. MEIO offers a way to improve availability while reducing the total inventory investment — a priority that aligns with ongoing sustainment reform programmes and the focus on supply chain resilience under AUKUS. Trace has worked extensively with Defence clients on inventory management and supply chain strategy, and we consistently find that multi-echelon thinking unlocks significant value in these complex networks.

Retail and FMCG

Retailers and FMCG distributors with multi-tier distribution networks — central DC to state DC to store — are natural candidates for MEIO. The challenge of balancing product availability at shelf with working capital efficiency is precisely the trade-off MEIO is designed to manage. This is especially true for organisations with long product tails where a large number of slow-moving SKUs create disproportionate inventory holding costs. MEIO can help rationalise safety stock across the tail while protecting availability on the fast-movers that drive revenue. Our team regularly supports retail and FMCG supply chain planning across Australia and New Zealand.

Rail, Transport, and Infrastructure

Maintenance supply chains for rail operators, infrastructure managers, and asset-intensive utilities share many characteristics with Defence sustainment. Parts are distributed across depots and workshops, lead times for specialised components can be long, and the consequences of stockouts — delayed maintenance, asset downtime, service disruptions — are operationally and commercially significant. Inventory optimisation in transport and infrastructure is an area where we see growing demand for multi-echelon approaches.

Health and Aged Care

Healthcare supply chains — particularly those serving hospital networks, aged care providers, and pharmaceutical distributors — involve multiple distribution tiers with stringent service requirements. Product expiry management adds another layer of complexity. MEIO can help health and aged care organisations reduce waste from expired stock while maintaining the availability levels that patient care demands.

Getting Started: A Pragmatic Approach

For organisations considering MEIO, we'd recommend a phased approach rather than a big-bang implementation. The organisations that extract the most value from MEIO are those that treat it as a capability to build, not a project to deliver.

Start with a diagnostic. Before investing in technology, understand where the opportunity lies. Map your supply chain echelons, quantify your current inventory investment by tier and product group, and benchmark your actual service levels against targets. This diagnostic often reveals that a significant portion of your inventory is in the wrong place — and that's the foundation for building the business case for MEIO. In our experience, the diagnostic phase also surfaces data gaps and system limitations that need to be addressed before any optimisation model can be trusted.

Pilot on a bounded scope. Don't try to optimise the entire network in one go. Choose a product group or network segment that's representative but manageable — perhaps a single product category flowing through your full distribution network, or a single regional sub-network. Run the MEIO model, compare the results to current policy, and validate the recommendations with your planners. This builds confidence and identifies data or process issues before you scale. A well-run pilot typically takes eight to twelve weeks and provides enough evidence to secure investment for broader rollout.

Invest in the enablers. MEIO technology is only one piece of the puzzle. You'll also need clean master data, integrated systems, trained planners, and governance processes to maintain optimised parameters as conditions change. Don't underestimate the investment in these enablers — they're often what separates successful MEIO implementations from expensive shelf-ware. This is particularly important in the Australian context, where many organisations are still running legacy ERP systems or fragmented planning environments that weren't designed for cross-network optimisation.

Measure and iterate. Define clear KPIs — total inventory investment, inventory turns, service levels by echelon, working capital as a percentage of revenue — and track them rigorously through the pilot and rollout phases. Use the data to refine the model, adjust parameters, and demonstrate value to leadership and finance teams. The best MEIO implementations are living systems that are continuously updated as demand patterns shift, supply conditions change, and the network evolves.

How Trace Consultants Can Help

At Trace Consultants, we work with organisations across Defence, government, retail, FMCG, transport, and aged care to design and implement supply chain strategies that deliver measurable results. Multi-echelon inventory optimisation sits at the intersection of several of our core capabilities.

Inventory strategy and network design. We help clients understand their current inventory position, map their supply chain network, and identify where multi-echelon optimisation can deliver the greatest impact. Our strategy and network design capability combines analytical rigour with deep operational experience — we don't just model the optimal network, we help you build the business case and execute the transition.

Demand planning and S&OP. MEIO depends on quality demand signals. We help organisations improve their planning and operations processes, from demand forecasting and Sales & Operations Planning through to integrated business planning, ensuring that inventory optimisation decisions are grounded in realistic demand expectations.

Technology selection and implementation. We're technology-agnostic, which means we recommend the platforms that best fit your requirements and maturity level — whether that's an enterprise APS solution, a specialised MEIO tool, or a pragmatic approach built on Power Platform (Power BI dashboards, Power Apps workflows, and Power Automate integrations) to get you started without a massive technology investment. We don't just recommend technology — we build and implement it.

Data analytics and modelling. Our consultants are hands-on with data. We build the models, clean the data, run the scenarios, and translate the outputs into actionable recommendations. Whether it's a network optimisation model, a safety stock simulation, or a scenario analysis for a new distribution strategy, we bring the analytical depth combined with practical supply chain experience.

Change management and capability building. We know that the best model in the world is worthless if your planning team doesn't use it. That's why every engagement includes a focus on building internal capability, training planners, and embedding governance processes that sustain performance after we step back. Our project and change management approach ensures that improvements stick.

What sets us apart is that we don't stop at the strategy deck. Our consultants have done this work in practice — they've cleaned the data, built the models, sat with planners to calibrate the parameters, and tracked the results through to the balance sheet. We're a boutique firm that puts senior people on every engagement, with deep expertise in supply chain and procurement across both public and private sectors.

The Bottom Line

Multi-echelon inventory optimisation isn't a silver bullet, and it's not the right starting point for every organisation. If your demand data is unreliable, your master data is a mess, or your S&OP process doesn't exist, you've got foundational work to do first. But for organisations with multi-tier supply chains, reasonable data maturity, and a genuine appetite to improve working capital efficiency without sacrificing service, MEIO represents one of the highest-return investments in supply chain performance available today.

The Australian supply chain landscape — with its vast geography, import dependency, growing complexity in Defence and infrastructure, and increasingly demanding customers — makes MEIO especially relevant. The organisations that get this right will carry less inventory, deploy capital more effectively, and deliver better service than their competitors. Those that don't will keep wondering why their warehouses are full and their customers are still waiting.

If you're interested in exploring what multi-echelon inventory optimisation could deliver for your organisation, get in touch with our team. We'd welcome the conversation.

Trace Consultants is an Australian supply chain and procurement consultancy with offices in Melbourne, Sydney, Brisbane, and Canberra. We specialise in strategy, operations, and technology across Defence, government, retail, FMCG, health, and infrastructure sectors. Learn more at traceconsultants.com.au.

Warehousing & Distribution

Network Design and Warehouse Strategy: Getting the Foundations Right in a Shifting Australian Market

Shanaka Jayasinghe
February 2026
Australia's industrial property market is at an inflection point. Vacancy is expected to tighten, rents are climbing, and speculative supply is falling. For organisations making decisions about where to locate, how many facilities to operate, and what those facilities should look like inside, the window to get this right is narrowing. Here's what to consider.

There's a question that sits at the heart of every supply chain, and most organisations don't ask it often enough: is our distribution network actually set up to deliver what we're promising to customers, at a cost we can sustain?

It sounds simple. It's anything but. Network design and warehouse strategy are among the most consequential decisions a business makes — and they're also among the stickiest. Once you've signed a lease, built out a facility, and configured your operations around a particular footprint, you're locked in for years. Get it right and you create a platform for lower costs, faster service, and the flexibility to adapt as conditions change. Get it wrong and you bake inefficiency into the business in ways that are expensive and painful to undo.

In Australia right now, these decisions carry even more weight than usual. The industrial property market is shifting, customer expectations keep ratcheting up, construction costs remain elevated, and the economics of distribution are being reshaped by everything from e-commerce growth to interest rate movements. Organisations that approach network design and warehouse strategy with rigour — grounded in data, not gut feel — will outperform those that don't.

This article lays out how to think about these decisions in the current Australian context, where the common pitfalls are, and how to avoid them.

Why Network Design Matters More Than Most Executives Realise

When people hear "supply chain strategy," they tend to think about procurement contracts, transport rates, or warehouse management systems. Those things matter, but they're all downstream of a more fundamental question: what does the physical network look like?

Network design determines how many distribution centres, warehouses, cross-docks, or fulfilment centres you operate, where they're located, which customers and channels each facility serves, and how inventory is positioned across the network. It shapes your transport costs, your service levels, your working capital, and your ability to respond when things go wrong — whether that's a supplier delay, a demand spike, or a natural disaster.

In Australia and New Zealand, geography amplifies every network decision. The distances between population centres are vast. Freight costs are a significant share of total cost-to-serve. Labour markets vary enormously from one corridor to the next. And port dependencies — particularly for import-heavy businesses — add another layer of complexity that has to be factored into any serious network analysis.

A centralised network with one or two large distribution centres might offer economies of scale in warehousing but push transport costs higher and extend delivery times to regional areas. A decentralised network with multiple smaller facilities brings you closer to customers but increases inventory, complexity, and overhead. The right answer depends entirely on the specifics of your business — your product characteristics, order profiles, service commitments, and growth trajectory.

This is where strategy and network design capability becomes essential. It's not about having a strong opinion on centralised versus decentralised. It's about having the analytical tools and the industry experience to model both options (and everything in between), stress-test them against realistic demand scenarios, and quantify the trade-offs so leadership can make an informed decision.

The Current State of Australia's Industrial Market

Anyone making network decisions in 2026 needs to understand what's happening in the industrial property market, because the window of opportunity is shifting.

Through 2024 and into early 2025, Australia's industrial vacancy rates edged upward as a wave of new supply entered the market. National vacancy averaged around 2.8% through the first half of 2025 and rose to approximately 3.7% by the third quarter — still below the pre-2020 equilibrium of 5%, but enough to give tenants more negotiating leverage than they'd had in years.

That breathing room won't last. Speculative supply is forecast to fall by around 46% over the 2026–2027 period compared to the prior two years. Construction feasibility constraints — driven by elevated build costs and higher required economic rents — are causing developers to delay or shelve projects. At the same time, consumer spending is recovering, leasing activity is strengthening, and gross take-up is projected to reach 3.3 million square metres in 2026 and climb to 3.8 million in 2027.

The result is a market that's heading back towards tighter conditions. National vacancy is expected to peak at just under 4% by mid-2026 before declining to around 2.5% by late 2027. Prime net face rents are forecast to grow at roughly 3.9% per annum through 2026 and 2027, with effective rental growth accelerating as incentives pull back.

For occupiers, the implication is clear: the time to be making network decisions is now, while there's still some choice in the market. Waiting 18 months and hoping conditions will be the same is a gamble that the data doesn't support.

But — and this is the critical point — making a property decision is not the same as making a network decision. Signing a lease because a good deal is available in a particular submarket is not a strategy. It's a reaction. The property decision should follow the network decision, not the other way around. You need to know what network configuration best serves your business before you go looking for buildings.

Where Businesses Get Network Design Wrong

Having worked with organisations across retail, FMCG, government, manufacturing, and services, there are a handful of patterns that come up again and again when network design goes off the rails.

Starting with the building, not the question. This is the most common mistake. A lease comes up for renewal, a property agent presents an opportunity, or the board decides "we need a new DC." The response is to go find a building. But without first understanding the flows, the demand patterns, the cost-to-serve, and the service requirements, you end up with a facility decision disconnected from the network it sits in. The right sequence is always: strategy first, then network modelling, then facility requirements, then property search.

Ignoring the total cost picture. Transport and warehousing costs behave inversely — push one down and the other tends to go up. Organisations that optimise for warehouse cost alone (usually by consolidating into fewer, larger facilities) often find their freight bill blows out. The reverse is also true. A proper network model captures warehousing costs, inbound and outbound transport, inventory carrying costs, and fixed overheads to give a genuine total cost-to-serve view. Without this, you're optimising one line item at the expense of the whole.

Designing for today, not for where you're heading. Networks have long asset lives. Leases are typically 5–10 years, and the capital invested in fit-out, racking, and automation can take even longer to pay back. A network designed purely for today's volumes and channels may be undersized — or oversized — within a few years. Good network design incorporates demand scenarios that account for growth, channel shifts, seasonal variation, and potential disruption. This is where planning and operations capability intersects with network strategy.

Underestimating the labour dimension. Two sites that look identical on a spreadsheet can perform very differently depending on the local labour market. Wage rates, turnover, skills availability, commute patterns, and proximity to competing employers all affect your ability to staff a facility reliably. In Australia, where workforce planning and scheduling is already a challenge for most sectors, this should be a first-order consideration in any location analysis — not an afterthought.

Treating the warehouse as a black box. Network models tend to treat warehouses as cost-per-pallet or cost-per-order nodes. That's fine at a strategic level, but it's not enough when you're making real investment decisions. The internal design of the warehouse — layout, storage media, pick methodology, dock configuration, automation level — directly determines throughput capacity, cost, accuracy, and scalability. Network design and warehouse design need to be done in parallel, not sequentially, because they influence each other in ways that matter.

Warehouse Strategy: More Than Layout and Racking

A warehouse strategy defines what your facilities need to do, how they should be configured to do it, and what investments are required to achieve the desired performance. It sits between your network strategy (which tells you how many facilities, where, and what they serve) and your operational execution (which is the day-to-day running of the site).

For Australian businesses, getting warehouse strategy right is particularly important because of several converging pressures.

E-commerce continues to grow. Online fulfilment demands a fundamentally different warehouse operation than store replenishment. Picking individual items rather than full cases or pallets, managing returns at scale, and meeting next-day or same-day delivery windows all require specific design considerations — from pick-face layout to packing stations to outbound sortation. Many businesses that bolted on e-commerce capability during the pandemic are now discovering that their warehouse operations aren't scaled or designed for the volumes that online represents as a permanent channel. Designing warehouses that can serve both store and online channels efficiently is one of the core challenges for in-store and online retail supply chains.

Labour is scarce and expensive. Australia's warehouse workforce has been under pressure for several years, and the outlook doesn't suggest that's about to change. This makes the case for automation more compelling — but automation is a significant capital investment that needs to be justified against realistic volume projections and operational requirements. The question isn't whether to automate; it's what to automate, when, and to what extent. A phased approach — sometimes called "automation-when-ready" — designs the facility to accommodate future automation while being operationally effective with manual or semi-automated processes in the near term.

Sustainability expectations are rising. Warehouses consume significant energy, particularly when refrigeration is involved. The design of a facility — its orientation, insulation, lighting, HVAC, and solar potential — affects both operating costs and emissions. For organisations with supply chain sustainability commitments, warehouse strategy needs to integrate environmental performance from the outset, not treat it as a retrofit.

Construction costs remain elevated. Building a new warehouse, or fitting out an existing shell, is substantially more expensive than it was three years ago. Material costs, labour shortages in the construction industry, and longer approval timelines all contribute. This means every design decision carries more financial weight, and the cost of getting it wrong — overbuilding, underspecifying, or choosing the wrong location — is higher than it's been in a long time.

Connecting Network and Warehouse Decisions

One of the most common structural problems we see is a disconnect between network-level decisions and facility-level decisions. The network team models the optimal number and location of DCs. The property team goes and finds buildings. The operations team then has to make whatever they've been given actually work. Each group is doing sensible things in isolation, but the lack of integration means the result is suboptimal.

A centralised network with one or two large facilities might call for a high-throughput, automated warehouse with significant investment in materials handling equipment. A decentralised network with several regional facilities might favour smaller, more flexible operations with lower automation and greater reliance on labour. If the network configuration and the warehouse design aren't aligned, you end up with facilities that are either over-engineered for the flows they handle or under-equipped for the volumes they need to process.

At Trace Consultants, we deliberately run network and warehouse design modelling in parallel. The network model tells us the optimal flows, and the warehouse design work tells us what it takes to process those flows operationally. The two inform each other iteratively until we arrive at a solution that works on paper and will work in practice.

This integrated approach also extends to transport. Dock door numbers, yard layout, staging space, and dispatch scheduling all depend on the outbound transport plan — which in turn depends on the network configuration. Similarly, inbound logistics — container receival, cross-dock operations, putaway sequencing — need to be designed around the reality of how product arrives, not an idealised assumption.

The Role of Data — and Its Limits

Network design and warehouse strategy both depend heavily on data: demand data, order profile data, SKU data, transport data, cost data, and facility performance data. Good data enables good modelling. Poor data produces models that look precise but are actually misleading.

The first step in any network or warehouse project should be a thorough data review — cleaning, validating, and enriching the data to ensure it's fit for modelling. This includes understanding what the data does and doesn't capture. Most ERP and WMS systems will give you transactional data, but they rarely capture the nuances that affect warehouse design — things like handling complexity, product fragility, storage requirements, or the real-world constraints that operators deal with every day.

This is why the best network and warehouse design work combines quantitative modelling with qualitative insight. The model gives you the numbers, but experienced operators and site managers give you the context that turns a theoretical optimum into a practical solution. It's also why technology selection — from network modelling tools to WMS platforms — should be guided by what your organisation actually needs, not by what's newest or most feature-rich.

What About 3PL Versus In-House?

For many Australian businesses, the network design question is inseparable from the operating model question: should we run our own warehouses and transport, or outsource to a third-party logistics provider?

There's no universally right answer. In-house operations offer control, visibility, and the ability to invest in capability over time. 3PL arrangements offer flexibility, variable cost structures, and access to scale without capital commitment. Most organisations end up with some hybrid — perhaps owning their primary DC and using 3PLs for overflow, regional distribution, or specialised services.

What matters is that the operating model decision is made in the context of the network strategy, not independently of it. A poorly structured 3PL arrangement can erode the benefits of an optimised network, while a well-designed outsourcing model can extend the network's reach without proportional cost increases. Trace Consultants helps organisations evaluate insource versus outsource options as part of the broader network design process, ensuring the operating model supports the strategy rather than constraining it.

Resilience: The Dimension That's Easy to Forget

Most network design exercises focus on cost and service — and rightly so, since these are the primary performance dimensions. But the events of the past five years have taught us that resilience deserves equal attention.

A network that's optimised purely for efficiency can be dangerously fragile. A single DC serving the entire east coast is efficient right up until it's affected by flooding, fire, industrial action, or a pandemic-related shutdown. At that point, the cost of disruption far exceeds whatever savings the centralised model delivered.

Building resilience into network design means designing for redundancy where it matters — whether that's maintaining safety stock in a secondary location, designing facilities that can flex capacity, or structuring supplier and transport contracts to enable rapid redirection. Trace Consultants' resilience and risk management practice helps organisations stress-test their networks against realistic disruption scenarios and build contingency into their supply chain architecture.

How Trace Consultants Can Help

At Trace Consultants, we see network design and warehouse strategy as two halves of the same strategic coin. Our approach brings them together into an integrated process that delivers clear, quantified recommendations aligned with your commercial objectives.

Here's what that looks like in practice:

Rapid diagnostic and cost-to-serve analysis. We start with a sharp review of your demand flows, service promises, and current cost structure to identify the two or three levers that will have the greatest impact. This work gives leadership a clear picture of where the opportunities are before committing to a full design programme.

Scenario-based network modelling. We model multiple network configurations — varying facility count, locations, and inventory posture — and quantify the transport, warehousing, and inventory implications of each. We include emissions estimates and risk stress-tests so decisions are robust, not just efficient. Our strategy and network design team brings deep experience across retail, FMCG and manufacturing, government and defence, and health and aged care sectors.

Warehouse strategy and design. We develop layout options, storage media recommendations, process designs, and automation roadmaps tailored to your operational reality. Workforce requirements and safety are embedded from the start, not bolted on at the end. Our warehousing and distribution practice has helped organisations achieve significant reductions in warehouse operating costs and meaningful improvements in throughput and fulfilment accuracy.

Implementation support. Strategy without execution is just a slideshow. We stay involved through property fit-out, process change, systems implementation, and project and change management to ensure that what's designed on paper translates into real-world performance.

Independent, solution-agnostic advice. We don't sell property, equipment, or software. Our recommendations are shaped entirely by your operational requirements and strategic objectives. That independence is what allows us to give advice that genuinely serves our clients' interests.

The Stakes Are Higher Than They Look

Network design and warehouse strategy decisions lock in a significant portion of your cost base and service capability for years. In an environment where industrial property is tightening, construction costs remain high, customer expectations continue to rise, and the competitive landscape shifts fast, these decisions deserve more rigour than they typically receive.

The organisations that approach this work systematically — starting with strategy, grounding decisions in data, modelling scenarios, integrating network and facility design, and planning for resilience — will build supply chains that perform today and adapt tomorrow. Those that make property decisions opportunistically, design warehouses in isolation from the network, or skip the analytical work will find themselves locked into suboptimal configurations that are expensive to unwind.

If your network was designed for a different era — fewer channels, different volumes, a different cost environment — it's probably costing you more than you realise. And with the Australian industrial market heading into a tightening cycle, the window to act on better information is closing.

Trace Consultants can help you see clearly, decide confidently, and move quickly. We keep it practical, transparent, and focused on outcomes your board, your operations team, and your customers will recognise.

Trace Consultants is an Australian supply chain and procurement consultancy with offices in Sydney, Melbourne, Brisbane, and Canberra. To discuss your network design or warehouse strategy, speak to one of our team.

Resilience & Risk Management

Supply Chain Resilience in Energy and Renewables: What Australian Organisations Need to Get Right

Shanaka Jayasinghe
February 2026
As renewables surpass 50% of quarterly grid supply for the first time, Australia's energy transition is accelerating. But the supply chains underpinning it are under enormous strain. Here's what organisations need to address — and where to start.

Australia's energy transition depends on resilient supply chains.

Australia's energy landscape is shifting faster than most people expected. In the final quarter of 2025, renewables and storage supplied more than half of the National Electricity Market's energy needs for the first time ever. Wholesale electricity prices dropped significantly. Coal generation hit an all-time quarterly low. And battery storage discharge nearly tripled compared to the same period a year earlier.

On the surface, the numbers look encouraging. But behind every solar panel installed, every wind turbine erected, and every battery commissioned is a supply chain — and right now, those supply chains are under serious pressure.

If Australia is going to sustain this momentum and meet its legislated target of net-zero emissions by 2050, then the conversation needs to move beyond megawatts and policy targets. It needs to focus on procurement lead times, component availability, workforce capacity, logistics infrastructure, and the dozens of other practical realities that determine whether a project gets built on time, on budget, and to spec — or whether it stalls.

This article unpacks the supply chain challenges facing Australia's energy and renewables sector, and what organisations across the value chain can do to build genuine resilience.

The Scale of the Challenge

Australia is no stranger to large infrastructure rollouts, but the energy transition represents something fundamentally different in both pace and complexity. The country needs to simultaneously retire ageing coal-fired generation, build out gigawatts of new renewable capacity, deploy battery energy storage systems at scale, expand transmission networks across vast distances, and integrate distributed energy resources into an increasingly decentralised grid.

Each of these workstreams has its own supply chain, and each of those supply chains has its own set of dependencies, bottlenecks, and vulnerabilities.

Consider solar. The build-out phase is moderately to highly complex because Australia relies heavily on imported panels and components, predominantly from China. Wind is more complex still — turbines are larger, heavier, require specialised transport, and involve components sourced from multiple countries. Battery energy storage systems introduce yet another layer: lithium, cobalt, nickel, and rare earth elements all need to be sourced, processed, and manufactured into cells before they even reach Australian shores.

The Australian Energy Market Operator's Draft 2026 Integrated System Plan reinforces what the industry already knows — that renewable energy, backed by storage and connected through upgraded networks, remains the lowest-cost pathway to reliable electricity. But the plan also acknowledges that making this happen depends on resolving supply chain constraints that are already causing delays and cost overruns.

Where the Pressure Points Are

Critical Minerals and Component Sourcing

Australia is one of the world's largest producers of lithium, yet the vast majority of processing happens offshore — particularly in China. This creates a vulnerability that's become increasingly visible as trade tensions have escalated. The 10% tariff imposed on Australia by the United States in early 2025, while partially offset by the subsequent US-Australia Critical Minerals Production Tax Credit Agreement, highlighted just how quickly geopolitical shifts can ripple through supply chains.

For organisations involved in energy storage, solar, and grid infrastructure, the sourcing question is no longer just about price. It's about security of supply, lead time reliability, and the geopolitical risk embedded in concentrated supply chains. The growing push to diversify away from Chinese equipment and towards suppliers in South Korea, Europe, India, and Southeast Asia is sensible — but it takes time, relationship-building, and a procurement strategy that's fit for purpose.

Transmission and Grid Infrastructure

You can build all the solar farms and wind turbines you want, but if you can't connect them to the grid, they don't generate a single watt. Transmission has been one of the most stubborn bottlenecks in Australia's energy transition. AEMO's updated Transmission Cost database showed real cost increases of between 25% and 55% for overhead transmission line projects compared to earlier estimates. Planning approvals are slow, particularly in New South Wales, where the process can take four to seven years and cost developers millions just to apply.

The supply chains feeding transmission projects — steel, conductors, transformers, switchgear — are global in nature and subject to the same pressures affecting other heavy infrastructure. When every state government is simultaneously investing in transport, defence, health, and education infrastructure, the competition for materials and skilled labour becomes fierce. This is where strategy and network design becomes critical — understanding not just what needs to be built, but when, where, and in what sequence to avoid the worst of the supply chain congestion.

Workforce Shortages

The energy sector needs people, and it doesn't have enough of them. Estimates suggest the industry requires tens of thousands of additional skilled workers by the end of the decade — electrical engineers, wind turbine technicians, battery specialists, project managers, and a range of trade roles. The Clean Energy Council's launch of the "Clean energy, job ready" programme in mid-2025 was a positive step, but the gap between current capacity and what's needed remains significant.

Workforce challenges don't just affect construction timelines. They affect maintenance schedules, operational efficiency, and the ability to commission assets safely. For organisations managing energy assets or building out new capacity, workforce planning and scheduling needs to be treated as a strategic capability, not an afterthought. Getting the right people in the right place at the right time — and retaining them — is fundamental to keeping projects moving.

Logistics and Warehousing

The physical movement of energy infrastructure components across Australia presents its own set of challenges. Wind turbine blades can exceed 80 metres in length. Battery modules require temperature-controlled handling. Solar panel shipments arrive in enormous volumes that need staging, storage, and last-mile distribution to often remote project sites.

Australia's geography makes this particularly demanding. Long distances between ports and project sites, limited road and rail infrastructure in regional areas, and seasonal access constraints all add complexity. The design of warehousing and distribution networks for energy projects needs to account for these realities — factoring in laydown areas, staging logistics, just-in-time delivery constraints, and the capacity of regional freight corridors.

Lessons from Other Infrastructure Rollouts

Australia has been here before — maybe not at this exact scale, but the patterns are familiar. The National Broadband Network rollout is probably the most instructive parallel. That project suffered from global supply shortages of fibre-optic cables, network equipment, and skilled technicians — issues that weren't sufficiently anticipated during the planning phases.

The parallels with the energy transition are uncomfortably close. Heavy reliance on imported components, workforce bottlenecks, regulatory delays, and insufficient supply chain planning all contributed to cost blowouts and schedule slippage on the NBN. The lesson is clear: supply chain planning needs to be proactive, not reactive. Waiting until a project is underway to discover that a critical component has a 12-month lead time is not a supply chain strategy — it's a supply chain crisis.

The organisations that will navigate this transition most successfully are those that invest early in understanding their supply chain risks, mapping their dependencies, and building contingency into their plans. This is the domain of resilience and risk management — not as a compliance exercise, but as an operational discipline that protects project delivery and financial performance.

What Good Looks Like: Building Resilience into Energy Supply Chains

So what does a resilient energy supply chain actually look like in practice? It starts with visibility — knowing where your components come from, who your suppliers' suppliers are, and where concentration risks exist. From there, it's about making deliberate choices to mitigate the risks that matter most.

Supplier Diversification and Strategic Procurement

Relying on a single source for critical components is a well-understood risk, but many organisations in the energy sector still haven't addressed it adequately. Building a diversified supplier base takes effort — qualifying new suppliers, negotiating terms, managing quality across multiple sources — but the payoff in resilience is substantial.

Strategic procurement in the energy sector also means thinking differently about lead times. When global demand for batteries, inverters, and transformers is surging simultaneously, the organisations that have locked in supply agreements and built buffer stock strategies will be in a far stronger position than those operating on thin margins and short planning horizons. Trace Consultants' procurement practice works with organisations to develop sourcing strategies that balance cost, quality, and supply security across the full procurement lifecycle.

End-to-End Supply Chain Visibility

You can't manage what you can't see. For complex energy projects with multi-tier supply chains spanning multiple countries, real-time visibility into supplier performance, logistics status, and inventory levels is essential. This doesn't necessarily mean implementing the most expensive technology platform available — it means having the right data, the right processes, and the right governance to make informed decisions quickly.

Trace Consultants works with organisations to design and implement planning and operations frameworks that give leadership teams the information they need to anticipate problems rather than react to them. Whether it's tracking component deliveries across a multi-site build programme or managing MRO inventory for an operational fleet of wind turbines, the principle is the same: visibility drives better decisions.

Scenario Planning and Risk Modelling

Energy supply chains are exposed to a wide range of risks — geopolitical disruption, natural disasters, regulatory changes, currency fluctuations, and demand volatility, to name a few. Scenario planning allows organisations to stress-test their supply chains against plausible future states and develop contingency plans that can be activated quickly when conditions change.

This is particularly important in the current environment, where trade policy can shift rapidly and the competitive landscape for critical minerals is evolving. Organisations that have modelled multiple sourcing scenarios and understand the cost and timeline implications of each are better positioned to pivot when disruptions occur.

Network and Infrastructure Design

For energy companies, network utilities, and project developers, the physical configuration of supply chain infrastructure — laydown areas, warehouses, maintenance depots, and distribution routes — has a direct impact on project delivery and operational efficiency. Getting this right requires a combination of spatial analysis, demand modelling, and an understanding of local infrastructure constraints.

Trace Consultants' strategy and network design practice brings together these disciplines to help energy sector clients design supply chain networks that are efficient, scalable, and resilient. Whether it's determining the optimal location for a regional maintenance hub or designing the logistics footprint for a large-scale solar roll-out, the approach is grounded in data and shaped by operational reality.

Sustainability and Circular Economy Considerations

The energy transition is, at its core, a sustainability story — but the supply chains supporting it need to be sustainable too. The carbon footprint of manufacturing and transporting solar panels, wind turbines, and batteries is significant. End-of-life management for these assets is becoming an increasingly pressing issue as early-generation installations approach decommissioning.

Organisations that build circular economy principles into their supply chain design — including component recycling, refurbishment, and responsible disposal — will be better positioned both ethically and commercially. Trace Consultants' supply chain sustainability practice helps clients quantify the emissions embedded in their supply chains and identify practical steps to reduce them.

The Hydrogen Question — and What It Tells Us About Supply Chain Reality

It's worth briefly touching on hydrogen, because the story there is instructive. A few years ago, green hydrogen was being talked about as a pillar of Australia's energy future. But production costs of $5–6 per kilogram — well above the $2 target needed for commercial viability — combined with infrastructure gaps and uncertain demand have forced a reckoning. Several flagship hydrogen projects have been cancelled or scaled back, including multi-billion dollar developments in Queensland.

The supply chain lesson here isn't that hydrogen is dead — it's that supply chain fundamentals matter more than ambition. Without reliable access to affordable electrolysers, skilled installation crews, and functioning export infrastructure, even the most generously funded projects will struggle. The same principle applies across the energy sector: you need to build the supply chain before you build the asset, or at the very least, build them in parallel.

This is where organisational design plays an underappreciated role. Many energy companies have project teams that are brilliant at engineering and construction, but lack the internal supply chain capability to manage the procurement, logistics, and inventory challenges that large-scale projects demand. Getting the organisational structure right — with clear accountability for supply chain performance and the right expertise embedded in project teams — can be the difference between a project that delivers on schedule and one that spirals.

The Role of Government and Industry Collaboration

This isn't a challenge that any single organisation can solve alone. Building resilient energy supply chains requires collaboration between project developers, component manufacturers, logistics providers, government agencies, and training organisations.

Government has a critical role to play — in fast-tracking planning approvals, investing in transmission infrastructure, supporting domestic manufacturing capability, and funding workforce development programmes. The Albanese Government's approval of 123 renewable energy projects since 2022 and the successful Cheaper Home Batteries scheme — which saw 200,000 batteries installed in just six months — demonstrate what's possible when policy settings and market conditions align.

But industry needs to do its part too, by sharing demand forecasts, investing in supplier development, and committing to the long-term partnerships that give supply chain participants the confidence to invest in capacity. The energy sector can also learn from how other industries — particularly FMCG and manufacturing — have built mature, collaborative supplier ecosystems that balance efficiency with resilience.

For government agencies navigating energy policy and infrastructure delivery, Trace Consultants provides supply chain advisory services that bridge the gap between policy intent and operational execution. Our experience working with government and defence clients gives us a practical understanding of the procurement, logistics, and workforce challenges that public sector agencies face in delivering complex programmes.

How Trace Consultants Can Help

The energy and renewables sector is at an inflection point. The policy settings are broadly right, the economics of renewables are compelling, and public support for the transition is strong. But none of that matters if the supply chains underpinning the transition can't deliver.

At Trace Consultants, we work with energy companies, utilities, project developers, government agencies, and infrastructure investors to design, optimise, and de-risk their supply chains. Our team brings deep expertise across the disciplines that matter most in this sector:

Supply Chain Strategy and Network Design — We help clients model optimal supply chain configurations, taking into account demand forecasts, infrastructure constraints, cost-to-serve, and resilience requirements. Whether you're planning a new renewable energy roll-out or rethinking the logistics of an existing asset portfolio, we bring the analytical rigour and operational experience to get it right.

Procurement Excellence — From strategic sourcing of critical components to supplier qualification and contract management, our procurement team helps clients build supply bases that are competitive, reliable, and resilient. We understand the unique challenges of procuring for energy projects — long lead times, technical specifications, and the need to balance cost with supply security.

Resilience and Risk Management — We help organisations identify, quantify, and mitigate supply chain risks through structured risk assessment frameworks, scenario modelling, and contingency planning. In a sector where a single supplier failure can delay a project by months, this capability is not optional — it's essential.

Warehousing, Distribution and Logistics — Our warehousing and distribution team designs logistics networks that work in the real world — accounting for the oversized, heavy, and temperature-sensitive nature of energy components, the remoteness of many project sites, and the constraints of Australian transport infrastructure.

Workforce Planning — We help energy organisations build workforce plans that align with project timelines, operational requirements, and the realities of a tight labour market. This includes demand modelling, skills gap analysis, and the design of rostering and scheduling systems that maximise productivity.

Technology and Digital Enablement — We support clients in selecting, implementing, and optimising supply chain technology that delivers genuine visibility and control — from planning systems and inventory management to predictive analytics and digital twins.

Project and Change Management — Transforming a supply chain is a complex undertaking that requires disciplined project and change management. We embed ourselves in our clients' operations to ensure that strategies are implemented, benefits are realised, and change is sustained.

The Bottom Line

Australia's energy transition is one of the most significant infrastructure programmes the country has ever undertaken. It will reshape how electricity is generated, stored, distributed, and consumed for decades to come. But the pace and success of that transition will ultimately be determined not just by policy ambition or investment appetite — but by the strength and resilience of the supply chains that make it happen.

The good news is that the foundations are being laid. Renewables are now cost-competitive with fossil fuels. Battery storage costs have plummeted. Government policy is supportive. And the market is responding, with close to 7 GW of renewable capacity added to the grid in 2025 alone. But scaling from here to an 82% renewables target by 2030 — let alone net zero by 2050 — will require a step change in supply chain maturity across the sector.

The organisations that invest now in understanding their supply chain vulnerabilities, building strategic procurement capabilities, designing resilient logistics networks, and developing the workforce to deliver and maintain energy assets will be the ones that thrive in the transition ahead. Those that treat supply chain as a second-order concern will find themselves managing one crisis after another.

For those looking to take that step, Trace Consultants is ready to help. We don't just advise — we work alongside your team to deliver measurable outcomes. Because in a sector moving this fast, strategy without execution is just a slideshow.

Trace Consultants is an Australian supply chain and procurement consultancy with offices in Sydney, Melbourne, Brisbane, and Canberra. To discuss how we can support your energy or renewables supply chain, speak to one of our team.