How Food, Grocery, and FMCG Organisations in Australia and New Zealand Can Benefit from Boutique Consultants with Specialist Experience and Knowledge

What Is Cost-to-Serve?

Cost-to-serve (CTS) is the total cost of getting a product from your distribution network into the hands of a specific customer, through a specific channel, at a specific service level.

It sounds straightforward. In practice, most Australian businesses have never calculated it — and the ones that have are often surprised by what they find.

The standard formula aggregates costs across the full fulfilment chain: inbound freight, warehousing (receiving, storage, pick-and-pack), outbound transport, customer service, returns handling, and any channel-specific compliance costs such as retailer DIFOT penalties or promotional execution. Divide that total by volume — per case, per pallet, per order — and you have a cost-to-serve figure you can compare across customers, channels, channels, SKUs, and regions.

The insight that makes CTS powerful is this: your gross margin on a product tells you very little about whether you're actually making money on it. A customer buying at full price but demanding daily small-order drops, bespoke labelling, extended payment terms, and high return volumes can easily cost more to serve than the margin they generate. A customer on a discount but ordering full pallets, paying on time, and never claiming can be one of your most profitable accounts.

Without CTS visibility, you're flying blind on profitability — and most pricing, ranging, and service-level decisions are effectively guesswork.

Why Cost-to-Serve Matters More Now

Margin pressure in Australian retail and distribution has intensified sharply over the past three years. Input cost inflation, labour cost increases, fuel surcharges, and the structural shift toward e-commerce have all pushed up the cost side of the equation. At the same time, major retail customers have continued to demand faster replenishment cycles, higher service levels, and more promotional and compliance activity from their suppliers.

The result is a hidden profitability problem. Businesses that were marginally profitable on their full customer and SKU mix in 2021 may be loss-making on significant portions of that mix today — without it showing up in their headline P&L.

Several specific dynamics are accelerating this in Australia:

Omnichannel fulfilment complexity. Retailers are increasingly requiring suppliers to support multiple fulfilment modes simultaneously — bulk DC delivery, direct-to-store, click-and-collect top-up, and e-commerce pick. Each mode has a different cost profile. Suppliers who haven't modelled the cost difference between a full pallet DC replenishment and a store-by-store eaches delivery are systematically mispricing their service commitments.

Geographic dispersion. Australia's population spread creates legitimate cost-to-serve variation that is not always captured in standard pricing structures. Serving a customer in Perth or Darwin from an east coast DC has a materially different transport cost profile to serving the same customer from Sydney to Melbourne. Regional and remote service commitments made without CTS modelling frequently turn out to be loss-making.

Retailer compliance regimes. Major grocery and hardware retailers have tightened DIFOT requirements and compliance penalty frameworks over the past several years. Suppliers who absorb these penalties without attributing them to specific customer CTS models are distorting their profitability picture.

SKU proliferation. The range expansion that occurred through COVID — safety stock build, innovation acceleration, channel-specific variants — has left many suppliers carrying a long tail of SKUs that are expensive to warehouse, slow-moving, and ordered in small quantities. The true cost of maintaining these SKUs in the range is rarely visible in standard reporting.

The Whale Curve: Why the Top Line Lies

One of the most useful outputs of a cost-to-serve analysis is the profitability waterfall — sometimes called the whale curve.

The whale curve plots customers (or SKUs, or channels) ranked from most to least profitable, showing cumulative profit contribution. In most businesses, the shape is consistent: the top 20–30% of customers generate 150–200% of total profit. The middle tier is roughly break-even. The bottom 20–30% destroys 50–100% of the profit generated at the top.

The headline business is profitable. The CTS breakdown shows that a significant portion of that profit is being destroyed by customers, channels, or SKUs that cost more to serve than they contribute.

This pattern repeats across industries. In Australian consumer goods distribution, it is common to find that 20–30% of products show margin erosion before they've even been delivered — driven by inbound costs, warehousing complexity, and order profile inefficiency. In retail distribution, small-format independent accounts, long-tail SKUs, and promotional compliance activities are the most common culprits.

The whale curve is not an argument for firing your bottom-tier customers. It is an argument for understanding what's driving their cost to serve — and making conscious, informed decisions about whether to fix it, reprice it, or exit.

What Drives High Cost-to-Serve

Before you can reduce CTS, you need to understand what drives it. The major cost levers in Australian retail and distribution are:

Order Pattern and Order Size

Small, frequent orders are the single biggest CTS driver in most distribution businesses. Every order triggers a fixed transaction cost — order processing, pick labour, despatch documentation, transport — regardless of the number of cases involved. A customer who orders twice weekly in small drops costs structurally more to serve than a customer who orders fortnightly in full pallet quantities.

The economics here are stark. In a typical pick-and-pack operation, the fixed cost per order often exceeds the variable cost per case for small orders. A three-case order can cost as much to process as a 30-case order in transaction terms.

Transport Mode and Delivery Distance

Outbound freight is typically the largest single component of CTS for high-frequency retail replenishment. The cost difference between a full truckload (FTL) delivery, a less-than-truckload (LTL) consolidation, and a courier parcel is not linear — it's exponential on a per-unit basis.

Australian geographic realities amplify this. Delivering to regional WA, rural NSW, or remote QLD from an east coast DC often makes accounts structurally uneconomical at current pricing — a problem that becomes visible only when transport costs are attributed to individual customers rather than pooled.

Warehousing Complexity

SKUs that are slow-moving, require special handling, have short shelf lives, or are stored in non-standard locations carry a higher warehousing CTS than fast-moving, shelf-stable lines in standard racking. If your CTS model pools warehousing costs across all SKUs equally, you're subsidising your complex lines with your simple ones.

Pick path complexity also matters. An order that requires picks across multiple zones, multiple temperature environments, or multiple storage systems (ambient, chilled, frozen, hazmat) costs more to fulfil than a single-zone order of equivalent case count.

Returns and Reverse Logistics

Returns are consistently undercosted in standard P&L reporting. The true cost of a return includes inbound freight, receival labour, quality assessment, repackaging, restocking or disposal — and in many cases, the customer service and credit administration overhead. For categories with high return rates (grocery promotions, seasonal apparel, consumer electronics), returns can add 3–8% to the effective CTS.

Compliance and Promotional Requirements

Retailer-specific labelling, ticketing, promotional setup, and compliance documentation add cost that standard margin reporting typically ignores. Bespoke EDI integration, retailer-specific carton labelling, and promotional execution requirements are real costs — but they're often absorbed in overheads rather than attributed to the customers that drive them.

Customer Service and Account Management Overhead

High-touch accounts that require frequent contact, complex queries, dispute resolution, or intensive account management carry a CTS that pure logistics models don't capture. In B2B distribution, the cost of servicing a high-maintenance $500K account can exceed the cost of managing a low-touch $2M account.

How to Build a Cost-to-Serve Model

CTS analysis doesn't require a six-figure software investment. Most businesses can build a working model using their existing ERP data, a structured activity-based costing approach, and a reasonably detailed understanding of their operational cost pools.

Step 1: Define the Unit of Analysis

Decide what you're measuring CTS at. Common choices are: per customer, per channel, per SKU, per order, or per delivery zone. Most useful CTS models are multi-dimensional — capable of slicing by customer AND channel AND SKU simultaneously.

Step 2: Map Your Cost Pools

Identify the major cost categories that sit between the factory gate (or supplier) and the customer. Typical cost pools for a retail distribution business include:

• Inbound freight and receival
• Warehousing (storage, pick-and-pack, despatch)
• Outbound freight (primary transport, last-mile)
• Returns and reverse logistics
• Order management and customer service
• Compliance and promotional execution
• Account management overhead

Step 3: Define Cost Drivers for Each Pool

For each cost pool, identify the activity driver — the operational event that causes costs to be incurred. Warehousing costs are driven by picks, pallets, and storage days. Outbound freight costs are driven by weight, cubic volume, zone, and frequency. Order management costs are driven by order count and line count.

This is the activity-based costing (ABC) step. It's the most analytically intensive part of the model — but it's also where the insight lives. Pooling costs without activity drivers produces averages that obscure the actual variation.

Step 4: Attribute Costs to Customers, Channels, and SKUs

Once you have cost pools and cost drivers, you can attribute costs to individual customers based on their actual consumption of each activity. A customer who placed 104 small orders last year at an average of 3 cases per order gets a very different CTS attribution than a customer who placed 12 orders at an average of 60 cases.

Step 5: Build the Waterfall and Whale Curve

Plot gross margin minus CTS for each customer (or SKU, or channel) to produce a net contribution figure. Sort and chart. The whale curve will show you the profitability distribution across your portfolio — and the outliers that warrant immediate attention.

Step 6: Validate and Sense-Check

CTS models are only as good as their inputs. Before acting on the output, validate against operational intuition. If the model says your largest customer is loss-making, check the assumptions. If it says your most demanding small customer is profitable, check whether all their compliance costs have been captured.

How to Reduce Cost-to-Serve: Seven Levers

Once you've built the model and identified where cost is being generated, there are seven primary levers for reduction.

1. Order Consolidation and Minimum Order Incentives

The fastest lever for most businesses is reducing order frequency and increasing order size. Minimum order quantities (MOQs), minimum order values (MOVs), or frequency-based pricing (lower per-case cost for less frequent, larger orders) all shift the order profile toward better CTS economics.

This doesn't require unilateral customer mandates. Many customers will voluntarily adjust their ordering patterns when the economic trade-off is made transparent — particularly if the cost saving can be shared in the form of a price incentive.

2. Route and Zone Optimisation

A systematic review of transport routing and zone structure can yield 10–20% freight cost reduction without service level compromise. This includes: zone skipping (bypassing intermediate DCs for high-volume lanes), milk run consolidation (combining multiple small-drop customers on a single run), and carrier mix optimisation (matching carrier selection to shipment profile rather than applying a single carrier blanket rate).

3. SKU Rationalisation

CTS analysis typically reveals a long tail of SKUs that are slow-moving, expensive to warehouse, ordered infrequently, and generate negative contribution after costs. A disciplined SKU rationalisation programme — informed by CTS data rather than just sales velocity — can reduce warehousing complexity, improve pick efficiency, and free up working capital.

The threshold for rationalisation should be explicit: SKUs below a CTS-adjusted contribution floor, with no strategic reason for retention (ranging requirements, range defence, anchor product), are candidates for deletion.

4. Channel-Specific Pricing and Service Tiers

If different channels (wholesale, direct-to-store, e-commerce, export) have materially different cost-to-serve profiles, that difference should be reflected in pricing or service-level structures. Charging the same price for a full DC pallet and a single-unit ecommerce pick is a structural mispricing problem.

Service tier design — defining explicit service levels (frequency, lead time, minimum order, compliance requirements) and pricing them accordingly — is one of the most effective structural tools for CTS management. Customers who want high-frequency, small-drop, bespoke service pay for it. Customers who consolidate and standardise benefit from a lower cost base.

5. DC Network Optimisation

For businesses with multiple distribution points, CTS analysis often reveals that the DC network is not optimally positioned relative to the customer base. A DC located to minimise inbound freight may be suboptimal for outbound last-mile costs. Adding a spoke location, repositioning inventory closer to high-density customer clusters, or shifting to a cross-dock model for certain customer segments can each reduce outbound CTS materially.

This is a longer-term lever — DC network changes involve lease commitments, capex, and operational transitions — but CTS modelling provides the business case rigour to make the decision with confidence.

6. Returns Rate Reduction

Reducing return rates directly reduces CTS. The primary levers are: improving order accuracy (the right product, right quantity, right condition), reducing promotional over-ordering (better promotional forecasting and agreement on sell-through responsibility), and improving packaging quality (reducing transit damage returns).

Where returns are unavoidable, streamlining the reverse logistics process — consolidated return pickups, automated credit processing, rapid quality assessment and restocking — reduces the per-unit cost of handling them.

7. Customer Collaboration

For strategically important customers with high CTS, a collaborative approach often yields better outcomes than unilateral repricing. Sharing CTS data with the customer — showing them what their ordering patterns, compliance requirements, and return rates are costing — enables joint problem-solving.

Many major retailers and FMCG customers have incentive to participate in CTS reduction programmes because the savings flow both ways. A supplier who reduces their CTS can pass some of the saving through in pricing; a retailer who adjusts their ordering behaviour reduces their own receiving and processing costs. The model that works is explicit: savings are identified jointly, and the split is agreed in advance.

Australian-Specific Considerations

Several features of the Australian market shape how CTS plays out in practice.

Retail concentration. Two grocery majors account for around 65% of Australian grocery sales. For FMCG suppliers, these accounts are non-negotiable to serve — but they are also the accounts most likely to have high compliance costs, tight DIFOT requirements, and intensive promotional activity. Managing CTS on these accounts is about optimisation rather than exit: how do you structure the relationship to minimise avoidable costs while maintaining the ranging and promotional position you need?

Independent and foodservice channels. The independent grocery, foodservice, and convenience channels in Australia involve high delivery frequency, geographic fragmentation, and significant account management overhead. CTS analysis on these channels frequently reveals that the segment as a whole is marginal — but within it, there are clusters of accounts with strong fundamentals and clusters with genuinely negative economics. The insight from CTS modelling allows selective focus rather than blanket withdrawal.

3PL relationships. Many Australian mid-market businesses operate through third-party logistics providers. CTS modelling in a 3PL environment requires integrating the 3PL's cost data — typically through open-book arrangements or detailed activity reporting — into the CTS model. Businesses that rely on 3PL summary invoices without activity-level detail are almost certainly missing significant CTS insight.

E-commerce growth. The continued growth of e-commerce in Australian retail has created CTS complexity that most traditional FMCG and retail distribution businesses are still working through. The per-unit cost of fulfilling an e-commerce order from a warehouse designed for pallet-level replenishment is typically 4–8x the per-unit cost of a DC-to-DC pallet move. Without explicit CTS modelling of the e-commerce channel, these costs are absorbed in the blended rate — and the e-commerce P&L is systematically overstated.

Common Mistakes in CTS Programmes

Building the model but not acting on it. CTS analysis is intellectually satisfying. It's also easy to use as a reason to keep analysing rather than making hard decisions. The businesses that extract value from CTS work are the ones that set a clear decision framework before they start — and commit to acting on the output.

Treating CTS as a one-time exercise. CTS changes as your business changes. Customer ordering patterns shift. Transport costs move. SKU mix evolves. A CTS model that was accurate two years ago may be materially wrong today. The businesses that get sustained value from CTS have embedded it as an ongoing operational metric — not a project.

Pooling costs too broadly. A CTS model that allocates warehousing costs equally across all SKUs, or splits transport costs evenly across all customers in a zone, will produce averages that obscure the variance. The whole point of CTS is to surface that variance. If your model produces a tight, normally distributed result across your customer base, the model probably isn't granular enough.

Failing to include all cost pools. The most commonly missed cost pools in Australian CTS models are: promotional execution labour, customer-specific compliance costs, EDI and systems integration costs, and customer service overhead. Omitting these systematically understates the CTS of your most demanding accounts.

Using CTS to justify price increases without a conversation. Informing a major customer that you're repricing because your CTS analysis shows they're unprofitable is a relationship risk. The more effective approach is to lead with the data, propose a joint problem-solving process, and frame repricing as a last resort rather than a first move.

How Trace Consultants Can Help

Cost-to-serve analysis is one of the highest-ROI supply chain engagements available to Australian retailers and distributors — but only if the model is built with sufficient rigour, validated against operational reality, and connected to a clear decision and implementation framework.

Trace Consultants works with retailers, FMCG businesses, and distributors to design and execute CTS programmes from the ground up. Our work typically spans three phases:

Diagnostic. We build the CTS model using your ERP, WMS, TMS, and financial data — structured to your specific cost pools and operational drivers. We produce the whale curve, identify the high-CTS segments, and quantify the opportunity.

Strategy. We work with your commercial and operations teams to design the response: which levers to pull, in what sequence, with what customer engagement approach. We model the financial impact of each lever under conservative, base, and upside assumptions.

Implementation. We support execution — whether that's carrier renegotiation, SKU rationalisation, service tier redesign, or DC network review — and establish the ongoing CTS reporting framework so the improvement is sustained.

Typical outcomes from Trace CTS engagements range from 10–25% reduction in cost-to-serve for target customer and SKU segments, with payback periods of six to eighteen months depending on the lever mix and business scale.

If your business is feeling margin pressure but can't identify where it's coming from — or if you suspect your pricing and service model hasn't kept pace with your cost structure — a CTS diagnostic is a logical starting point.

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Trace Consultants is an Australian supply chain and operations consultancy with offices in Sydney, Melbourne, Brisbane, and Canberra. We work with retailers, FMCG businesses, distributors, and government clients to improve supply chain performance, reduce cost, and build operational resilience.

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Supply Chain Network Design: When and Why to Redesign Your Network

Most Australian businesses are running supply chain networks that were designed for a version of the business that no longer exists. The distribution centre footprint was established when the customer base was different. The transport lanes were set up before e-commerce changed the order profile. The inventory positioning was designed when lead times were shorter and supply was more predictable. The outsourcing decisions were made when labour was cheaper and property was more affordable.

Networks don't fail suddenly. They drift — gradually and quietly — out of alignment with the business they are supposed to serve. Costs creep up. Service levels erode at the margins. The planning team invents workarounds that become permanent. Capacity constraints appear in some nodes while others are underutilised. And at some point, the cumulative effect of years of drift becomes visible in the P&L: logistics costs that are too high relative to revenue, service levels that are persistently below target, and a supply chain that is too slow, too expensive, and too fragile to support the business's growth ambitions.

Network design is the discipline of addressing that drift — of asking whether the current network configuration is still the right one, and if not, what it should look like and how to get there. This article explains what supply chain network design involves, what triggers a redesign, how the process works, and what Australian organisations can realistically expect to achieve.

What Supply Chain Network Design Actually Is

Supply chain network design is the process of determining the optimal structure of a supply chain — the number, location, size, and role of facilities, and the flow of goods between them. It addresses questions like:

Where should warehouses, distribution centres, and fulfilment nodes be located to optimise the cost of reaching customers at the required service levels? How many nodes does the network need — is the current footprint the right size, or is it too large (too many facilities, too much fixed cost) or too small (too much freight cost, too slow to serve regional markets)? What role should each facility play — national distribution centre, state hub, cross-dock, regional spoke, returns processing? How should inventory be positioned across the network to minimise total stockholding cost while maintaining availability? What is the right mix of owned, leased, and third-party logistics infrastructure? And how should the network be configured to balance cost, service, resilience, and sustainability — rather than optimising one dimension at the expense of the others?

Network design is distinct from day-to-day logistics management. It operates at a strategic level, informing decisions that take years to implement, cost significant capital to execute, and shape the supply chain's performance for five to ten years or more. Getting it right matters enormously. Getting it wrong — designing a network around assumptions that prove incorrect, or failing to adapt as conditions change — creates cost and service problems that are difficult and expensive to undo.

Why Networks Drift Out of Alignment

Understanding why supply chain networks become suboptimal over time is the starting point for knowing when and why to redesign.

Business growth and geographic expansion. A network designed to serve a business turning over $200M in revenue from three states will not necessarily perform well when the business reaches $500M and is serving all states and New Zealand. Volume growth strains capacity. Geographic expansion stretches transport lanes. Customer concentration shifts. The network that was right at one scale and geography is simply not the right network at a different scale and geography.

Channel mix change. E-commerce has fundamentally altered the order profile for most Australian retail and FMCG businesses — from full-pallet and full-carton orders shipped to store, to individual item picks shipped to residential addresses. The warehouse design, pick process, labour model, and carrier arrangements optimised for the former are typically wrong for the latter. Channel mix shifts create misalignment between the network as designed and the demand it is actually serving.

Mergers and acquisitions. Post-M&A supply chain integration is one of the most common triggers for network redesign. Combining two businesses typically produces a network with duplicate facilities, overlapping territories, incompatible systems, and a total logistics footprint that is larger than required. Capturing the synergies of a merger requires rationalising the combined network — which is a network design exercise.

Cost environment changes. Logistics costs in Australia have changed materially over the past five years — fuel costs, labour costs in warehousing and transport, industrial property rents in the major capital cities, and sea freight rates have all moved significantly. A network design optimised for the cost structure of 2019 may produce a different answer when reoptimised for 2025 costs. The relative economics of different configurations — more nodes closer to customers versus fewer, larger facilities further away — shift as the cost inputs change.

Lease expiry events. A warehouse lease expiry is one of the most reliable triggers for network review in Australia. It creates a natural decision point — renew, relocate, or redesign — and the cost of signing a new long-term lease on an existing facility that is in the wrong location, the wrong size, or the wrong configuration is very high. Lease events should always be preceded by a network review, not followed by one.

Supply chain disruption and resilience concerns. The events of 2020–2022 exposed the fragility of highly centralised, cost-optimised networks. Organisations that operated single distribution centres, single-source supply arrangements, or geographically concentrated production were disproportionately impacted by port congestion, supplier shutdowns, and freight capacity shortages. Many Australian businesses have since been reviewing their networks specifically to build in resilience — through network decentralisation, additional inventory nodes, or alternative transport routes.

Sustainability and emissions targets. Scope 3 emissions disclosure requirements — now mandatory for eligible Australian entities under the ASIC climate-related financial disclosure regime from January 2025 — are making the emissions profile of logistics networks a financial reporting issue, not just a sustainability aspiration. Freight movements are typically the largest component of a business's Scope 3 logistics emissions. Network designs that minimise freight kilometres, optimise modal mix, and reduce empty running are increasingly evaluated against both cost and emissions criteria simultaneously.

When Is the Right Time to Redesign?

The honest answer is that network design should be a continuous discipline — a periodic review of whether the current network configuration remains optimal, not a once-a-decade project triggered by crisis. In practice, most Australian organisations review their networks reactively rather than proactively, which means they tend to redesign in response to visible pain rather than in anticipation of it.

The clearest signals that a network review is overdue are:

Logistics costs are rising faster than revenue. If freight, warehousing, and handling costs are consuming a growing share of revenue — particularly if this is happening while volume is also growing — the network is likely absorbing inefficiency somewhere. This might be too many nodes creating cross-transfer cost, carrier routes that are inefficient relative to customer concentration, or inventory positions that require excessive replenishment movements.

Service levels are persistently below target. When the supply chain is working hard and DIFOT is still below where the business needs it to be, the problem is often structural — the network is not configured to reach customers within the required timeframes — rather than operational. Operational improvements (faster pick, better transport booking) can recover some performance, but they cannot compensate for a network that is fundamentally in the wrong place relative to demand.

Capacity is the binding constraint. When the answer to operational problems is consistently "we don't have enough space" or "we don't have enough throughput capacity," the conversation shifts from operational improvement to strategic investment. That investment decision — where to add capacity, in what form, at what scale — is a network design question.

A major business change is imminent. New customer contracts, geographic expansion, significant M&A activity, channel strategy changes, or major supplier shifts all change the demand or supply parameters the network needs to serve. The right time to review network design is before those changes take effect, not after the consequences are visible.

The network hasn't been reviewed in five or more years. Even in the absence of obvious pain signals, a network that has not been formally reviewed in five years has almost certainly drifted out of optimality. Business conditions, cost structures, customer expectations, and available logistics infrastructure all change over a five-year horizon. A periodic review — even one that concludes the current configuration is broadly correct — provides the evidence base for that conclusion and identifies incremental improvements that may have accumulated.

How a Network Design Process Works

A well-structured network design process follows a logical sequence from diagnostic through to implementation planning. The time and resource investment scales with the complexity of the network and the scope of the decisions being made, but the core structure is consistent.

Step 1: Define the design problem. Before building any models, establish what question the network design is actually trying to answer. Is this a facilities location exercise — where should nodes be? Is it a make vs. buy question — which activities should be insourced versus third-party? Is it a capacity investment decision — how much space, where, and when? The design problem definition also establishes the design constraints: the service level requirements the network must meet, the cost parameters it must operate within, and the capital available for investment.

Step 2: Gather and validate data. Network design modelling is only as good as the data underpinning it. The minimum data requirement is: customer locations and order profiles (volumes, frequencies, parcel sizes), current facility locations, costs and capacities, freight rates and transit times by lane, and inventory holding data by location and SKU category. In practice, assembling this data from multiple source systems — ERP, WMS, TMS, carrier invoices — is often the most time-consuming part of the process. Data quality issues should be surfaced and resolved before modelling begins, not discovered after scenarios have been built.

Step 3: Model the current state. Build a model that replicates the current network accurately enough to be a reliable baseline. Validate it against actual cost and service data. A model that cannot reproduce current-state costs within an acceptable tolerance cannot be trusted to accurately evaluate future-state scenarios.

Step 4: Develop and evaluate scenarios. The network design process is fundamentally a scenario evaluation exercise. Typically three to five design scenarios are developed — ranging from incremental adjustments to the current network to more radical reconfigurations — and each is evaluated against the design criteria: total cost (logistics, inventory, fixed facility costs), service level performance, capital requirement, implementation risk, and resilience. Scenarios should include sensitivity analysis — testing how the results change if key assumptions (demand growth rate, freight costs, property costs) prove incorrect.

Step 5: Develop the recommended design and implementation roadmap. The recommended design is not just the scenario with the lowest modelled cost — it is the scenario that best satisfies the full set of design criteria given the organisation's strategic priorities and risk appetite. The implementation roadmap translates the recommended design into a sequenced set of actions — facility decisions, lease transactions, systems changes, organisational adjustments — with timelines, capital requirements, and dependencies mapped out.

What Good Network Design Delivers

A well-executed network redesign typically delivers improvement across multiple dimensions simultaneously. Logistics cost reduction of 10–20% is achievable in networks that have not been reviewed recently and have accumulated significant structural inefficiency. Service level improvement — measured in DIFOT, lead time to customers, or reliability of delivery windows — is frequently an outcome alongside cost reduction, because a well-positioned network can reach more customers more reliably than one that has drifted out of alignment with the demand it serves.

Working capital reduction is a third benefit that is often underweighted in network design business cases. Repositioning inventory across the network — concentrating safety stock where it provides the most service benefit and eliminating buffer stock held redundantly across multiple nodes — releases working capital that partially or fully offsets the capital cost of network change.

The resilience benefit is harder to quantify but has become more commercially visible since the supply disruptions of recent years. Networks with greater geographic distribution, multiple transport modes, and strategic inventory positioning are less vulnerable to single-point failure than highly centralised networks optimised purely for cost.

The Australian Context: What Makes Network Design Different Here

Australia's geography creates specific network design challenges that don't apply in the same way in more compact markets.

Population concentration on the eastern seaboard — Sydney, Melbourne, Brisbane, and their surrounds account for the majority of retail consumption — means that for many businesses, the core network design question is how to serve the east coast efficiently while still reaching Perth, Adelaide, Darwin, and regional centres at acceptable cost and service levels.

The distances involved in Australian freight are significant by global standards. Sydney to Perth is over 4,000 kilometres by road. Melbourne to Brisbane is 1,750 kilometres. These distances make transport cost a major variable in network design and create genuine trade-offs between the cost of additional nodes (which reduce freight distance but add fixed facility cost) and the cost of long-haul freight from a centralised network.

Industrial property markets in Sydney, Melbourne, and Brisbane have tightened significantly over the past five years, with vacancy rates at historic lows and rents rising sharply in key logistics corridors. Network designs that were cost-optimal when framed in 2018 or 2019 property terms may produce different answers when current market rents are applied — and property market dynamics need to be explicitly modelled in any current network design exercise.

The Australian logistics infrastructure landscape also shapes network design options: the rail freight network offers cost and emissions advantages for high-volume, long-distance freight but with service and flexibility trade-offs that not all supply chains can absorb; the sea freight lanes between east and west coast ports are a viable cost-effective option for non-time-critical product; and last-mile delivery infrastructure in regional and remote Australia remains more constrained and expensive than in metro markets.

Common Network Design Mistakes

Several failure modes appear consistently in Australian network design projects.

Starting with a site, not a strategy. Organisations that begin a network review with "we need a new warehouse in Melbourne" rather than "what does our network need to look like to support our business strategy" tend to make property decisions that aren't properly anchored in logistics economics. Site selection should be the output of a network design process, not the premise of one.

Optimising for cost alone. Networks designed purely to minimise total logistics cost frequently underperform on service and resilience in ways that create costs elsewhere — lost sales, customer chargebacks, expedited freight. The design criteria need to reflect the full commercial picture.

Using last year's demand. Network designs based on current demand profiles rather than projected future demand build a network for the past, not the future. Design horizon should be at least five years, with sensitivity testing across demand growth scenarios.

Underestimating implementation complexity. A network redesign model that produces a compelling answer on paper can be undermined by implementation challenges: lease break costs, equipment relocation, staff impacts, systems cutover risk, and customer service disruption during transition. Implementation planning is part of the design process, not an afterthought.

Not reviewing as conditions change. A network design completed and implemented is not complete. The business continues to change, costs continue to move, and customer requirements continue to evolve. Building in a regular network review cadence — typically every three to five years for most Australian businesses — ensures the network doesn't drift back out of alignment before the next crisis triggers a reactive review.

How Trace Consultants Can Help

At Trace Consultants, network design is one of our core capabilities. We help Australian organisations answer the hard questions about whether their current network is still the right one — and if not, what it should look like and how to get there.

Strategy & Network Design. We lead end-to-end network design projects — from data gathering and current-state modelling through scenario development, evaluation, and implementation roadmap. We bring the modelling rigour and commercial judgement to translate a design that looks right on a map into one that works in practice.

Lease event and facility decision support. When a lease event, capacity constraint, or facility decision is imminent, we help organisations make it strategically rather than reactively — reviewing whether the current configuration remains appropriate before committing to new long-term property obligations.

Post-M&A network integration. We help organisations rationalise combined networks after acquisition — identifying consolidation opportunities, modelling the cost and service impact of different integration configurations, and planning the transition to avoid customer service disruption.

Resilience & Risk Management. For organisations reviewing their networks specifically through a resilience lens, we assess single-point-of-failure risk, model the cost of resilience investments (additional nodes, strategic inventory positions, alternative transport lanes), and develop network configurations that balance cost efficiency with robustness.

Warehousing & Distribution design. Network design determines where facilities should be; warehousing and distribution design determines what those facilities should look like inside — layout, process design, technology, and automation. We work across both levels.

We have worked across FMCG and manufacturing, retail, health and aged care, property and hospitality, and government and defence. The network design methodology is consistent; the sector context — what service levels are required, what cost constraints apply, what regulatory environment operates — shapes the answer.

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The Right Starting Point

If you suspect your network has drifted — if costs are high, service is inconsistent, and the supply chain feels more complicated than it should — the right starting point is a structured current-state assessment. A rapid diagnostic that maps the current network, benchmarks costs and service against industry peers, and identifies the primary misalignments between the network as designed and the business as it operates today.

That assessment typically takes four to six weeks. It produces a clear picture of whether a full network redesign is warranted, what the primary design questions are, and what the realistic improvement opportunity looks like. For organisations facing an imminent lease event or capacity decision, it provides the evidence base to make that decision strategically rather than by default.

Networks built for yesterday's business cost more and deliver less than they should. The question is simply how long to leave it before doing something about it.

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Make vs Buy: How Australian Businesses Should Decide What to Insource and What to Outsource

Most Australian businesses make insource versus outsource decisions reactively. A 3PL contract comes up for renewal and someone asks whether they should bring warehousing back in-house. A manufacturing cost blows out and leadership floats offshoring production. A service delivery problem surfaces and the instinct is to outsource it away. A new COO arrives with a view that the previous leadership outsourced too much — or not enough.

What's missing in almost every case is a structured framework for making the decision. Not a gut feel about control, not a cost comparison that only captures the visible numbers, and not a benchmarking exercise that tells you what other businesses do without telling you whether it's right for yours. A deliberate, repeatable analytical process that answers a clear question: for this specific activity, at this point in time, does make or buy produce the better outcome for our business?

This article sets out that framework. It applies to manufacturing and production decisions, to logistics and distribution, to procurement and service delivery, and to the workforce planning dimension that most make vs buy analyses get wrong. It is written for Australian operations and supply chain leaders who are making real decisions — not consulting to a theoretical case.

What Make vs Buy Actually Means

Make vs buy — also called insource vs outsource — is the decision about whether to perform an activity using your own people, assets, and processes, or to contract it to an external party.

The question is deceptively simple. The answer rarely is. Because the right answer depends on a combination of factors — cost, capability, strategic importance, risk, flexibility, and market maturity — that interact in ways that a simple cost comparison doesn't capture. And because the decision isn't made once: it needs to be revisited as your business changes, as the market for external providers matures or deteriorates, and as your strategic priorities shift.

In supply chain and operations, make vs buy decisions arise constantly. Should we operate our own fleet or use a carrier? Should we run our own warehouse or use a 3PL? Should we manufacture this component or buy it from a supplier? Should we manage our own procurement function or engage a managed service? Should we run our own maintenance team or outsource facilities management? Each of these is a make vs buy decision. Each deserves structured analysis rather than habit or instinct.

The Four Dimensions of the Decision

A rigorous make vs buy analysis evaluates four dimensions. Cost is the one everyone starts with. The other three are the ones that most often determine whether the decision is the right one.

1. Total Cost — Not Just the Visible Numbers

The most common error in make vs buy analysis is comparing the wrong costs. Businesses compare the external provider's quote against the internal cost of production — and the internal cost is almost always underestimated.

A genuine total cost comparison for the make option includes: direct labour (including on-costs, leave loading, superannuation), management overhead attributable to the activity, facilities cost (lease, utilities, maintenance), equipment and asset depreciation, technology and systems costs, quality and rework costs, and the opportunity cost of capital tied up in assets. Many organisations include the first two and miss the rest. The resulting comparison flatters the insource option.

The buy option has its own hidden costs: transition costs (one-time, but real), contract management overhead, the cost of service failures that the contract doesn't fully compensate, and the price escalation that typically occurs once you're locked into a relationship and switching is expensive. A provider who prices competitively to win the work may look quite different at renewal.

Total cost comparison also needs to account for volume and volatility. A fixed-cost insource model looks cheap at high volume and expensive at low volume. A variable-cost outsource model looks expensive at high volume and attractive when throughput drops. Australian businesses with significant demand seasonality — retail, hospitality, agricultural processing — need to model cost across their actual volume range, not just at average throughput.

2. Strategic Importance and Core Capability

The second dimension is whether the activity is core — whether it contributes to your competitive differentiation, embeds proprietary knowledge or intellectual property, or defines the experience your customers have with your brand.

Activities that are strategically core should generally be insourced. Not because outsourcing them is always more expensive, but because control over them is itself valuable. A food manufacturer whose product quality depends on a specific production process has a strategic reason to keep that process in-house, even if a contract manufacturer could produce the same output at lower unit cost. A retailer whose customer experience is defined by last-mile delivery has a strategic reason to think carefully before handing that entirely to a carrier whose brand accountability to your customers is limited.

Activities that are not strategically core — that are important operationally but don't differentiate you in the market — are candidates for outsourcing. Transactional procurement, standard warehousing for commodity products, routine fleet management, basic facilities maintenance: these are areas where a specialist provider will typically deliver better performance at lower cost than an in-house team for whom these activities are peripheral.

The test is not whether an activity is important. Almost everything is important. The test is whether doing it better than your competitors, or doing it differently, creates value that customers recognise and pay for.

3. Market Maturity — Is There a Provider Who Can Actually Do It?

The third dimension is often overlooked entirely: whether there is an external market that can credibly deliver the activity at the required standard.

In Australia, the market for external providers varies enormously by activity and geography. 3PL capability in Melbourne and Sydney is mature, competitive, and capable of servicing complex accounts. 3PL capability in Darwin or regional Western Australia is thinner, more expensive, and carries greater transition risk. Cold chain logistics is more constrained than ambient. Specialist manufacturing capability for certain product categories is limited domestically.

Before deciding to outsource, you need to know whether a credible market exists — and whether the providers in that market have the capability, capacity, and financial stability to be a reliable long-term partner. A make vs buy decision that concludes "buy" when the market can't actually deliver is not a useful outcome. It produces a poor outsourced arrangement that the organisation then spends years trying to exit.

Market maturity also affects the leverage you have. In a competitive market with multiple capable providers, you can drive hard commercial terms and hold providers accountable through genuine re-tendering. In a thin market with one or two credible options, your leverage is limited and your exposure to provider failure is higher.

4. Risk — What Happens When It Goes Wrong

The fourth dimension is risk: what is your exposure if the activity is performed badly, and how does that exposure differ between insource and outsource?

For activities where failure has severe consequences — production stoppages, customer delivery failures, regulatory non-compliance, safety incidents — the make vs buy decision needs to explicitly account for risk. Outsourcing transfers operational execution but it does not transfer accountability for outcomes. If a contract manufacturer produces defective product, the reputational and regulatory consequences land with you. If a 3PL mismanages your inventory, your customers experience the failure.

The risk question cuts both ways. For some activities, insourcing concentrates risk — a single internal team failure can cascade through the whole operation. For others, outsourcing to a single provider creates dangerous dependency — and the right answer is either to insource for control or to use multiple providers to maintain resilience.

Supply chain resilience analysis is increasingly integrated into make vs buy decisions for this reason. The question isn't just which option costs less under normal operating conditions. It's which option holds up when conditions aren't normal — and in the current Australian operating environment, that's not a hypothetical.

The Hybrid Model: Neither Fully In nor Fully Out

One of the most important developments in Australian operations practice over the last decade is the normalisation of hybrid models. The binary choice — fully insource or fully outsource — is increasingly a false one.

Hybrid models take many forms. A business might own and manage its distribution centre but use a 3PL workforce to run the operation — capturing the asset ownership and process control benefits of insourcing with the labour flexibility and specialist capability of outsourcing. A manufacturer might insource final assembly and quality control while outsourcing component production. A procurement function might manage strategic category strategy and supplier relationships in-house while outsourcing transactional purchasing to a managed service.

The logic of a hybrid model is that different elements of an activity have different strategic importance and risk profiles. Disaggregating the activity and applying the make vs buy framework to each element independently often produces a more nuanced and commercially optimal answer than applying it to the whole.

The challenge of hybrid models is governance and accountability. When responsibility is split between internal and external parties, the interfaces between them become critical. Clear contractual and operational boundaries, well-defined KPIs, and strong contract management capability on the internal side are prerequisites for hybrid models to work.

When to Revisit the Decision

Make vs buy is not a one-time decision. It needs to be revisited when circumstances change materially — and in Australian operations, circumstances change frequently.

Triggers for revisiting an existing insource or outsource arrangement include: significant volume growth or decline that changes the cost economics; a contract renewal that provides a natural market test; a deterioration in provider performance that raises the question of whether the capability exists in-house or elsewhere; a strategic shift that changes what is and isn't core to the business; or a market development — new providers entering, existing providers exiting — that changes the competitive landscape.

The mistake many Australian businesses make is treating the current arrangement as the default and requiring a high burden of proof to change it. In practice, both directions of change — insourcing an outsourced activity, or outsourcing an insourced one — are legitimate strategic moves that should be evaluated on their merits when circumstances warrant, not defended on the basis of inertia.

Common Mistakes in Australian Make vs Buy Decisions

A few patterns consistently produce poor outcomes.

Deciding on cost alone. Unit cost comparisons that ignore strategic importance, risk, and transition costs produce decisions that look right in the spreadsheet and wrong in practice. The businesses that have brought back in-house activities that were outsourced cheaply — only to find that the capability had atrophied and the market option was now the only viable one — understand this lesson the hard way.

Outsourcing a broken process. Outsourcing does not fix underlying process problems — it transfers them, usually at a premium. A warehousing operation with poor inventory accuracy will not improve simply because a 3PL takes it over. The process needs to be understood and stabilised before the outsource decision is made, otherwise the transition embeds the dysfunction into a contract that is expensive to exit.

Underestimating transition costs and risks. The one-time cost of transitioning an activity — either direction — is systematically underestimated in make vs buy analysis. Transition involves knowledge transfer, system integration, parallel running periods, staff redeployment or redundancy, and a period of elevated management overhead. These costs need to be included in the financial model, amortised over the planning horizon, to get an accurate picture of when the new arrangement becomes economic.

Ignoring the workforce dimension. Make vs buy decisions in operations almost always have a workforce planning dimension that gets handled separately — if at all. Insourcing requires hiring or redeploying people with the right skills. Outsourcing may require redundancy or redeployment of existing staff. The workforce implications affect both the cost and the feasibility of the decision, and they need to be integrated into the analysis rather than treated as an afterthought.

Not defining what success looks like before you decide. A make vs buy decision should specify, upfront, the performance outcomes the chosen model needs to deliver — service levels, cost targets, quality standards — and how those will be measured. Decisions made without this definition produce arrangements that drift, because there's no agreed baseline against which performance can be evaluated.

How Trace Consultants Can Help

At Trace Consultants, we help Australian businesses make rigorous insource versus outsource decisions — and then implement whichever path produces the better outcome.

Make vs buy analysis and decision support. We build the full analytical framework: total cost modelling across both options, strategic importance assessment, market scan for external capability, risk evaluation, and workforce implications. We produce a recommendation the CFO and COO can defend to the board — not a balanced presentation that leaves the decision unmade.

Procurement operating model design. For businesses evaluating their procurement function specifically — whether to build internal category management capability, use a managed service, or operate a hybrid — we design the operating model that fits the scale, complexity, and strategic priorities of the organisation.

Warehousing and distribution market assessment. For businesses evaluating whether to insource or outsource logistics, we provide an independent view of the external market — what providers can actually deliver, at what cost, and under what contractual terms — to ensure the buy option is being compared accurately.

Workforce planning integration. We integrate the workforce implications into the make vs buy analysis — modelling the headcount, capability, and cost implications of both options, and designing the transition approach for whichever direction is chosen.

Implementation and transition management. We manage the transition — in either direction — to ensure it is executed without service disruption and delivers the financial outcomes the decision was based on.

We work across retail and FMCG, manufacturing, health and aged care, government and defence, and hospitality. The framework is consistent. The right answer for each client is not.

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Getting Started: The Question Before the Question

Before running a make vs buy analysis, the most useful thing is to get clarity on what is actually driving the question. Is it a cost problem? A performance problem? A strategic realignment? A contract renewal that has forced the issue?

The answer shapes the analysis. A cost-driven review needs a rigorous total cost model. A performance-driven review needs to first determine whether the performance problem is inherent to the current model or fixable within it — because outsourcing a performance problem you don't fully understand is one of the most reliable ways to make it worse. A strategic realignment needs to start with a clear articulation of what is and isn't core before any financial modelling begins.

If you're facing an insource versus outsource decision — for logistics, manufacturing, procurement, or service delivery — and you want to make it well, that's the right starting point for a conversation.

The Bottom Line

Make vs buy is one of the most consequential decisions in operations. Done well, it produces arrangements that are cost-effective, resilient, and aligned with the strategic direction of the business. Done poorly — on the basis of cost alone, without proper transition planning, or without revisiting assumptions as circumstances change — it produces exactly the kind of lock-in and underperformance that keeps operations leaders awake at night.

The framework isn't complicated. The discipline to apply it rigorously, every time, is.

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