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

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.

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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.

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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.

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