Supply Chain Business Case: 2026 Guide

Airport Back-of-House Logistics: A 2026 Guide for Australian Airport Operators and Tenants

The front-of-house experience at an Australian airport is the part the public sees: check-in, security, retail, food and beverage, lounges, boarding. The back-of-house is the part that makes the front of house possible. Catering trucks delivering meals into airside, waste containers moving in the opposite direction, retail and F&B replenishment to outlets across the terminal, cleaning consumables, linen for lounges and crew rest areas, ground handling supplies, fuel, maintenance parts, and the security screening that every airside-bound item must pass through. When the BOH operating model works, no passenger ever notices it. When it fails, the front of house fails with it.

Airport BOH is also one of the most under-discussed parts of the Australian aviation system. The capital expansion programme across the four ACCC-monitored airports (Sydney, Melbourne, Brisbane and Perth) is now reported by the ACCC at around $20 billion of proposed major infrastructure spend over the next decade. Western Sydney International opens in late 2026 with capacity to handle 10 million passengers initially and a long-term design horizon of 82 million by the 2060s. Melbourne Airport announced a $4.5 billion terminal expansion in March 2026. Brisbane is delivering its $5 billion Future BNE programme. Perth is progressing a new terminal. Almost every dollar of that capital programme has BOH operating model implications, and most of them are still being worked through.

This guide is the practitioner's framework for airport BOH logistics in Australia in 2026. It covers what airport BOH actually includes, why it is uniquely complex compared with other major-venue BOH operations, the strategic decisions facing operators and major tenants under the current capacity expansion programme, the demand modelling discipline that determines whether new terminals open with a viable operating model, the workforce and security architecture, and the common failure modes that the new generation of terminal projects can avoid.

What airport back-of-house actually covers

Airport BOH operations break into three connected zones, each with its own operating model, regulatory regime, and security framework.

Landside BOH. Everything that supports terminal operations outside the security cordon. Goods inwards docks, central stores, food and beverage consolidation, retail replenishment to landside outlets, waste consolidation, recycling and organics flows, cleaning consumables, linen, ground transport services, and the back-of-terminal logistics for landside concessions. Landside BOH has standard logistics characteristics but with the volume, peak shape, and 24/7 operating rhythm of an airport.

Airside BOH. Everything that crosses the security boundary into the secure zone. In-flight catering trucks, airside F&B outlet replenishment, airside retail (duty-free, news, convenience), lounge supplies, crew supplies, cleaning and consumables, waste being moved off airside, and the supply chains that support ground handling, refuelling, and aircraft turnaround. Every airside-bound item must be security-screened. Every airside operator must hold appropriate ASIC clearance. Every vehicle movement is governed by airside driving authorisations and route control.

Cross-cutting infrastructure. Loading docks (typically multiple, dedicated to landside and airside flows), screening facilities for goods, storage rooms, waste compactors, dock management systems, and the IT and operational systems that govern access, scheduling, and traceability across both zones.

The point of this segmentation is that airport BOH is not one operating model. It is two operating models with a security boundary between them, plus the infrastructure that connects them. Designing the two without recognising they are different operating problems is one of the most common BOH design failures we see across major-venue operations.

Why airport BOH is uniquely complex

Other major venues (stadiums, hospitals, integrated resorts, shopping precincts) all have demanding BOH operating environments. Trace's existing analysis on hospital and stadium BOH covers many of the recurring patterns. Airport BOH differs from all of them on five dimensions.

Security screening of every airside-bound item. Every pallet, every roll cage, every catering trolley crossing the security boundary must be screened. The screening regime has progressively moved from X-ray to 3D CT scanning at major airports for both passenger and goods security, with implications for throughput, layout, equipment, and the timing of deliveries. A screening process that adds even small amounts of cycle time per movement can cascade into significant peak-period congestion.

24/7 operations with concentrated demand peaks. Domestic and international flight schedules produce peaks that are predictable but acute. Catering uplift before international departures, retail replenishment for morning departures, F&B replenishment to support arrival hours, waste flows in the opposite direction. The operating model must absorb the peaks without over-resourcing the trough hours.

Multiple tenants and operators. A typical Australian capital city airport hosts multiple ground handlers, multiple in-flight caterers, multiple retail concessionaires, multiple F&B operators, multiple lounge operators, and multiple cleaning and waste contractors. The airport operator does not directly control most BOH movements. It governs them through tenant agreements, operating protocols, and shared infrastructure.

Airside permits and driving authorisations. Vehicles operating airside must hold appropriate authorisations, follow defined routes, and operate within structured movement protocols. Driver training, ASIC clearance, vehicle compliance, and route adherence all sit inside the BOH operating model.

Cold chain and food safety at scale. International catering uplift involves the largest single cold-chain flows on most airport sites. Cold-chain integrity, food safety compliance, and traceability are non-trivial operational requirements rather than peripheral concerns.

The combined effect is that airport BOH is one of the most demanding operating environments in Australian logistics, and the design discipline required to make it work is correspondingly higher.

The 2026 Australian context: a generational capacity expansion

The current Australian airport capacity expansion programme is the largest the country has seen in a generation. The implications for BOH operating models are material at every airport touched by the programme.

Western Sydney International opens in late 2026 with initial capacity of around 10 million passengers per year and a staged expansion path that ultimately accommodates up to 82 million passengers and four terminals on a 24/7 operating basis. WSI is the first major Australian airport to be designed from a clean sheet in over 50 years. The BOH design choices being embedded today shape the operating model for decades.

Melbourne Airport announced a $4.5 billion terminal expansion in March 2026, including five additional wide-body gates, expanded check-in, security and lounge areas, a new $500 million tote-based baggage handling system increasing capacity to over 4,000 bags per hour, and a planned third runway in 2031. Each expansion phase has corresponding BOH implications: new outlet locations, new airside catering volumes, new screening throughput requirements, new waste flows, and new vehicle movement patterns.

Brisbane Airport is progressing its $5 billion Future BNE transformation across international and domestic terminals, with a third terminal planned.

Perth Airport is delivering the new terminal and runway development reported by the ACCC as part of the $20 billion sector capital programme, with completion timelines extending into 2031.

Sydney Airport has proposed integration of T2 and T3 domestic terminals as part of its programme.

The ACCC has reported that the four largest airports invested $1.5 billion in aeronautical facilities in 2024-25, a 43.6 per cent increase on the prior year, with collective proposed major infrastructure spend approaching $20 billion over the next decade. Almost every project in that pipeline reshapes BOH operations directly or indirectly.

For airport operators, major tenants (airlines, ground handlers, caterers, concessionaires), and the design and construction partners delivering the expansion programme, the BOH operating model is not a peripheral consideration. It is one of the central design questions that determines whether the new infrastructure delivers its operational promise.

The strategic BOH decisions facing operators

Underneath the construction programme, every Australian airport operator now faces a set of strategic BOH decisions. These are not tactical fit-out decisions. They are operating model choices with twenty-year implications.

Centralised versus distributed stores. Concentrating receiving, screening and storage in a single landside facility versus distributing across multiple terminal-adjacent stores. Centralisation reduces footprint and improves screening throughput but increases internal vehicle movements and adds dependency on internal logistics flows. Distribution reduces internal movements but multiplies fixed infrastructure cost. The right answer varies by airport scale, terminal geometry, and tenant mix.

Operator-run versus tenant-managed BOH. How much of the BOH operation the airport operator runs directly versus how much sits with tenants. Catering, retail, F&B, cleaning, and waste are most commonly tenant-managed under operating protocols. The airport operator's role is governance, infrastructure, scheduling, and exception management. The boundary between operator and tenant responsibilities is the most consequential operating model decision in any new terminal project.

Screening capacity and configuration. Whether screening is centralised in a single facility, distributed across multiple dock locations, or implemented in a hybrid model. The cycle time, equipment type (X-ray, 3D CT, EDS for explosives), and operational protocol all interact with the broader BOH flow design. New 3D CT capability adds throughput and reduces secondary inspection rates but requires footprint and capital investment.

Demand modelling and capacity sizing. What level of BOH throughput the operating model is being designed for. This is the area where most BOH design exercises in our experience are weakest.

Technology and digital enablement. Dock booking systems, vehicle access control, security clearance management, waste tracking, F&B replenishment scheduling, and the data layer that allows the airport operator to manage the BOH operating model rather than just observe it.

Sustainability and waste. Waste segregation, organics processing, single-use plastic reduction, packaging reduction, and the broader sustainability agenda all sit inside the BOH operating model. Most Australian airports have material sustainability commitments that depend on BOH design and operating discipline to deliver.

Demand modelling: the most consistently under-done discipline

The most common pattern in major-venue BOH design, and airports in particular, is demand modelling that is too coarse to support sound design decisions. The most frequent failure mode is forecasting BOH demand from passenger volumes alone.

Passenger volume is the headline metric and is usually well-forecast, but it is not the right driver for most BOH demand. The right drivers vary by flow type.

Airside catering demand is driven by international departure schedules, aircraft size mix, sector lengths, and meal service patterns. Not by total passenger numbers.

Retail and F&B replenishment demand is driven by the number, type, size, and location of outlets across the terminal, their hours of operation, and the dwell time patterns of passengers in different terminal zones. Not by total passenger numbers.

Waste demand is driven by the same outlet count, plus front-of-house traffic patterns, plus cleaning shift patterns. Not by total passenger numbers.

Lounge supply demand is driven by lounge count, lounge size, lounge service model (food, drinks, amenities) and operating hours. Not by total passenger numbers.

In every case, forecasting from passenger volume alone produces design estimates that are systematically wrong. Outlet count, outlet mix, outlet operating hours, and the per-outlet demand profile are what drive most BOH flow demand. The dock capacity sized from a passenger-volume forecast often turns out to be undersized or oversized by significant margins.

The right demand modelling discipline starts with the outlet and service inventory, models the per-outlet flow demand, aggregates upward, and stress-tests against peak schedule patterns and growth scenarios. This is structurally different from passenger forecasting. Done properly, it produces design estimates that survive contact with operating reality.

Tenants, concessionaires, and the operating model question

Most airport BOH movements are not generated by the airport operator. They are generated by tenants: airlines, ground handlers, in-flight caterers, retail concessionaires, F&B operators, lounge operators, cleaning contractors, and waste contractors. The airport operator's role is to design the operating environment, govern the protocols, and manage exceptions.

The operating model question is where to set the line between airport operator and tenant responsibility. The choice has commercial, operational, and risk implications.

At one end of the spectrum, the airport operator owns and runs significant BOH infrastructure (loading docks, screening, central stores, internal logistics), with tenants paying for service. This concentrates control and standardises operations but expands the airport operator's operating footprint.

At the other end, tenants run their own BOH operations against operating protocols set by the airport. This minimises the airport operator's footprint but requires sophisticated governance and creates inconsistencies across operators.

Most Australian airports operate a hybrid model. The airport runs shared infrastructure (docks, screening, key internal flows) and governs tenant operations through agreements and protocols. The hybrid model is operationally viable but requires deliberate design.

The single most common BOH operating model failure we see in major-venue work is the absence of clear boundary definition. When the operating model is ambiguous, accountability for exceptions becomes contested, scheduling discipline erodes, and the front-of-house experience suffers.

Workforce, ASIC and the security architecture

Airport BOH workforces sit inside a more demanding security and compliance architecture than most logistics environments. Airside-bound workers, vehicle drivers, screening staff, and stores teams all hold ASIC (Aviation Security Identification Card) or equivalent clearances. Driving authorisations are airside-specific. Training requirements are extensive. Background-checking lead times can be material.

The workforce planning implications are significant.

Recruitment lead times include ASIC clearance, which can take meaningful time depending on the applicant's background. Workforce planning has to absorb the lead time, not be surprised by it.

Retention has commercial value in this environment. A cleared, trained airside worker has higher replacement cost than an equivalent worker in a non-airport setting. Retention discipline matters more.

Contractor management has to maintain visibility of which contractor workers hold which clearances, when those clearances expire, and what training is current. The data discipline is non-trivial.

Peak-period workforce supply is harder to flex than in standard logistics environments because of the clearance requirement. The operating model must build workforce capacity in advance rather than scale reactively.

For more on workforce planning in complex operating environments, our Workforce Planning and Scheduling practice covers the methodology that applies.

Where airport BOH operating models fail

In our experience working with infrastructure operators and major-venue clients across hospitality, healthcare, retail precincts and adjacent sectors, the recurring BOH failure patterns at airports are five.

Demand modelling from passenger volume alone. As covered above. The single most common upstream design error, and the one with the largest downstream cost. Outlet count, outlet mix, and per-outlet demand profile are what should drive most BOH flow estimates, not passenger forecasts.

Under-designed loading dock capacity. Loading docks are usually the binding constraint on landside BOH performance. Under-designed dock capacity shows up as queuing, vehicles waiting on public roads, missed delivery windows, and cascading peak-period failures. Dock capacity design is one of the highest-leverage BOH design decisions.

Ambiguous operator-tenant boundaries. The BOH operating model that does not clearly define operator versus tenant responsibility produces accountability gaps that surface as exceptions and disputes. Clear boundaries from day one are worth significant operating efficiency over the asset life.

Under-resourced screening capacity. Screening throughput is the binding constraint on airside-bound flows. Under-resourced screening cascades into late catering uplifts, missed retail replenishment windows, and operating disruption. The investment in screening capacity is rarely the part of a BOH budget that gets cut without consequence.

Treating BOH as the last design priority. Many major venue projects, airport projects included, treat BOH as the residual design exercise after the front-of-house and architectural decisions have been made. The result is BOH layouts that are technically functional but operationally awkward. Bringing BOH design into the master planning phase alongside front-of-house design is one of the highest-return changes a project can make.

The common thread is treating BOH as an afterthought rather than as a core element of the operating model. The new generation of Australian airport infrastructure projects has the opportunity to do this differently.

How Trace Consultants can help

Trace Consultants advises Australian infrastructure operators, major tenants, and the design and delivery partners working on Australia's major-venue and airport BOH operations. Our positioning is deliberate: senior-led, partner-anchored, vendor-agnostic, with practical operating experience across hospitality, healthcare, infrastructure, and retail BOH environments.

Master planning and BOH design. We work with master planners, architects, infrastructure operators, and project teams to embed BOH design discipline into the master planning phase rather than the fit-out phase. Our Back-of-House Logistics practice covers loading dock design, central stores, internal flows, screening configuration, and the integrated operating model.

Demand modelling for major venue capacity expansion. We build defensible demand models from outlet count, service mix, and per-outlet drivers, stress-tested against schedule patterns and growth scenarios. The deliverable is a working model the project team can use across design decisions, not a single point forecast.

Operating model design and tenant protocol architecture. Where the BOH operating model spans airport operator and tenant responsibilities, we design the operating boundaries, the governance architecture, and the protocols that make the hybrid model work.

Workforce planning and ASIC-cleared workforce design. Our Workforce Planning and Scheduling practice covers the workforce demand modelling, supply analysis, recruitment lead time management, and rostering discipline that the security-cleared workforce environment requires.

Technology and digital enablement. Dock booking systems, vehicle access control, security clearance management, waste tracking, and the broader BOH technology stack sit inside our Technology practice.

Commissioning and operational readiness. Where the BOH design transitions into operational reality, our Project and Change Management practice supports the commissioning, operational readiness, and post-go-live stabilisation work that determines whether the design intent is delivered in operation.

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Where to begin

If you are an airport operator, a major tenant, or a project team scoping BOH work, start with three questions. What does the demand profile actually look like at the outlet, flow type, and time-of-day level, not at the aggregated passenger volume level? Where are the binding capacity constraints in the BOH operating model today, and where will they be after planned expansion? Where do the operating boundaries sit between airport operator and tenant responsibility, and are they clear enough to govern exceptions?

If those three questions surface material gaps, the next step is a structured BOH operating model review.

Frequently asked questions

What is airport back-of-house logistics? The set of operations that supports passenger-facing airport functions: goods receiving, security screening of airside-bound items, central stores, internal logistics flows, food and beverage replenishment, retail replenishment, in-flight catering, waste flows, lounge supplies, ground handling support, and the operating model that governs all of them across landside and airside zones.

What is the difference between landside and airside BOH? Landside BOH operates outside the security cordon and supports terminal landside functions. Airside BOH crosses into the secure zone and must comply with airside operating protocols, ASIC clearance requirements, and security screening for all goods entering the zone. The two operate as related but distinct operating models.

Why is airport BOH more complex than other major-venue BOH? Five factors: security screening of every airside-bound item, 24/7 operations with concentrated demand peaks, multiple tenants and operators, ASIC clearance and airside driving authorisation requirements, and cold-chain and food safety requirements at significant scale.

What is ASIC clearance? Aviation Security Identification Card, the mandatory background-checked credential required for workers operating in airside zones at Australian airports. Lead times for clearance can be material and must be planned for in workforce supply.

Why should BOH be designed from outlet count rather than passenger volume? Most BOH flow demand is driven by outlet count, outlet mix, operating hours, and per-outlet demand patterns, not by total passenger numbers. Forecasting from passenger volume alone systematically produces design estimates that miss the actual drivers of demand. The right method starts with the outlet and service inventory and models per-outlet flow demand.

What is the typical scale of Australian airport capacity expansion in 2026? The ACCC has reported the four monitored airports (Sydney, Melbourne, Brisbane, Perth) invested $1.5 billion on aeronautical facilities in 2024-25, with collective proposed major infrastructure spend approaching $20 billion over the next decade. Western Sydney International opens in late 2026, Melbourne announced a $4.5 billion expansion in March 2026, and Brisbane is progressing its $5 billion Future BNE programme.

How does 3D CT screening change BOH operations? Three-dimensional computed tomography (3D CT) provides higher-resolution imaging of screened items, generally reducing secondary inspection rates and improving throughput compared with traditional X-ray. The implications for BOH operations include screening capacity sizing, layout, equipment investment, and timing of deliveries.

Who is responsible for BOH operations at an airport: the airport operator or the tenants? Most Australian airports operate a hybrid model. The airport operator runs shared infrastructure (loading docks, screening, key internal flows) and governs tenant operations through operating protocols. Tenants (airlines, caterers, retailers, F&B operators, ground handlers, cleaners, waste contractors) run their own BOH operations against the protocols. The boundary between operator and tenant responsibilities is one of the most consequential operating model decisions in any airport BOH design.

When should BOH design be considered in a major airport project? During master planning, alongside front-of-house design, not during fit-out. Treating BOH as a residual design exercise produces layouts that are technically functional but operationally awkward. The cost of doing BOH design early is small. The cost of doing it late is significant.

What are the most common BOH operating model failures at airports? Demand modelling from passenger volume alone, under-designed loading dock capacity, ambiguous operator-tenant boundaries, under-resourced screening capacity, and treating BOH as the last design priority.

Airport back-of-house operations are the half of the airport business the passenger never sees and the operator cannot afford to get wrong. The current Australian airport capacity expansion programme is the largest in a generation, and the BOH operating model implications of that programme are still being worked through across multiple projects.

If you are scoping a new terminal, expanding existing infrastructure, or running an existing airport BOH operation under capacity pressure, the work starts with the operating model.

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Related reading: Back-of-House Logistics · Project and Change Management · Workforce Planning and Scheduling · Technology · Strategy and Network Design · Insights

How to Build a Decision-Grade Supply Chain Baseline: A Framework for Australian Businesses

The most expensive mistakes in supply chain transformation are made before the transformation itself begins. They are made in the baseline. A weak current state model leads to a flawed business case, which leads to a misaligned future state, which leads to a programme that overruns, underdelivers, and damages the credibility of the leadership team that sponsored it. The technology is rarely the problem. The baseline is.

Yet in our experience advising Australian organisations across retail, FMCG, hospitality, health and aged care, government, and 3PL, the baseline is also the most consistently underweighted piece of any transformation. Programme teams spend months on the future state, the technology, the change plan, and the implementation roadmap. They spend weeks, sometimes days, on the current state. The asymmetry shows up later in every business case argument, every executive decision review, and every variance conversation post go-live.

This guide is the framework for building a decision-grade supply chain baseline: one that survives board scrutiny, supports scenario modelling, and underpins the downstream decisions on network, technology, operating model, and investment. It covers what a baseline actually is, what "decision-grade" means in practice, the components that have to be in the model, the methodology to build it, how to handle the data gaps that exist in every Australian business, and the common pitfalls that turn a baseline exercise into wasted effort.

What is a supply chain baseline?

A supply chain baseline is the structured, defensible, quantified representation of the supply chain as it operates today. It is the answer to the question "what does our supply chain actually cost, how does it actually perform, and what does it actually look like" before anyone starts proposing what it should become.

A baseline is not a slide pack. It is a model. A working representation of the network, costs, service levels, inventory positions, and capability that the business can interrogate, stress-test, and use as the starting point for every transformation decision that follows.

There is a useful distinction between three terms that are often used interchangeably and should not be. A current state assessment is the exercise of investigating and documenting how the supply chain operates today. A baseline is the quantified model that comes out of that assessment. A diagnostic is the analytical interpretation of the baseline against benchmarks, peers, or future requirements. The assessment is the activity. The baseline is the artefact. The diagnostic is the insight.

The baseline is what survives. Years later, when the transformation is complete and the next one is being scoped, the diagnostic insights have aged and the assessment work has been forgotten, but the baseline model (if it was built properly) is still being refreshed and used.

Why decision-grade matters

Most supply chain baselines are not decision-grade. They are good enough to populate a slide deck but not good enough to defend against a sceptical CFO, a challenging board, or a transformation steering committee asking pointed questions about variance.

A decision-grade baseline meets three tests.

The defensibility test. Can the model be defended line by line under scrutiny from a sceptical executive? Can every cost be traced to a source system? Can every allocation be explained by the cost driver behind it? Can every service level be reconciled to operational records? If the answers are not yes, the baseline will fail at the first hard meeting and the transformation will lose credibility before it has started.

The operability test. Can an operator look at the baseline and recognise the business they actually run? A baseline that the operations team rejects as not reflecting reality is dead on arrival, regardless of how analytically elegant the model is. The baseline must be built with operational input, validated with operational stakeholders, and accepted as a fair representation of how the business actually works.

The scenario test. Can the baseline support scenario modelling? Can it be re-run with different assumptions to test future state options? Can it produce comparable cost, service, and capability views under different network configurations, demand patterns, or operating models? A baseline that cannot move is a snapshot, not a tool.

A baseline that passes all three tests becomes a permanent commercial asset. A baseline that passes only one or two is consumable: useful for the immediate purpose, but not durable enough to support the decisions that follow.

The five components of a decision-grade baseline

A baseline that is going to support real transformation decisions has five components. Models that omit any of them tend to produce conclusions that do not survive contact with the board.

Component one: the network baseline. A complete description of the physical supply chain: facilities (DCs, warehouses, cross-docks, manufacturing sites, retail nodes), flows (inbound, inter-site, outbound, returns), volumes (units, lines, cases, pallets, cubic metres), and the geographic structure that connects them. The network baseline is what every future state network design alternative is compared against.

Component two: the cost baseline. The full cost-to-serve picture covering inbound freight, warehousing and handling, outbound transport, last mile, returns, customer service, technology, and the share of overhead that genuinely scales with serving activity. The cost baseline must reconcile to the financial P&L within an acceptable tolerance. A cost baseline that cannot reproduce reported supply chain cost is not a baseline.

Component three: the service baseline. The current service performance picture: DIFOT, order accuracy, dock-to-stock time, dispatch lead time, perfect order rate, returns rate, customer satisfaction, and the service differentiation patterns (or absence of them) across customers, channels, and segments. Service is the constraint that bounds every cost decision. A baseline that ignores it produces commercially indefensible recommendations.

Component four: the inventory baseline. The current inventory position by SKU, location, and stock class. Holding, ageing, obsolescence, slow-moving, dead, in-transit, safety stock, and cycle stock. Inventory is the working capital lever and the service lever simultaneously, and the inventory baseline is what every future state working capital case is built on.

Component five: the capability baseline. The honest assessment of the organisation's current process maturity, system landscape, data discipline, and team capability. Capability is often the missed component. A transformation that the current organisation cannot execute will fail regardless of how good the future state design is. The capability baseline anchors the change programme.

The five components form an interconnected system. Network, cost, service, and inventory views all need to reconcile against each other. The capability view sets the speed limit on what the transformation can achieve.

The methodology: seven steps to a decision-grade baseline

The end-to-end methodology for building a decision-grade baseline breaks into seven steps. Each has its own data, validation, and governance discipline.

Step one: scope the baseline. Define the boundaries of the baseline before any data is gathered. Geography, business units, channels, time period, level of granularity. A baseline scoped too tightly cannot answer the questions the business actually has. A baseline scoped too broadly produces a model too large to maintain and too aggregated to act on. Scope to the decisions the baseline will support, not to the data that happens to be available.

Step two: define the data model. Specify the structure of the baseline upfront: dimensions, hierarchies, time grain, cost categories, service measures, and the link tables that allow the views to combine. A baseline built without a data model on day one ends up with structural inconsistencies that surface late and cost time to fix. Spend the design effort early.

Step three: gather and validate the data. Pull data from the source systems (finance, ERP, WMS, TMS, planning systems, payroll, customer service) and validate every input against the source. Where data is incomplete, mark it. Where data is questionable, flag it. Where data is missing, document the gap and decide whether to estimate, exclude, or commission a focused collection effort. Data integrity at this step is what makes the baseline defensible later.

Step four: model the current state. Build the working baseline model that reproduces actual cost and service performance within an acceptable tolerance. A baseline that cannot reproduce current-state costs to within an acceptable variance cannot be trusted to evaluate future-state scenarios. The tolerance varies by use case, but typical practice is reconciliation within 5 per cent at total level and within 10 per cent at segment level. Where the model cannot reconcile, the data has a problem, the allocation logic has a problem, or the business has an unexplained variance that itself is a finding.

Step five: stress-test the baseline. Walk the baseline through operational stakeholders. Test it against known facts. Test the outliers. Test the segments where the output looks counter-intuitive. Either the model is wrong, the data is wrong, or the business has a real insight it did not previously have. All three are valuable outcomes.

Step six: document everything. A decision-grade baseline is documented to a standard that allows anyone to interrogate any number. Data sources, allocation logic, assumptions, exclusions, and known limitations are all captured. The documentation is what makes the baseline durable and what allows it to survive personnel changes.

Step seven: hand over and maintain. A baseline that is built and then abandoned is half a baseline. The handover plan covers how the model is owned, who maintains it, what the refresh cycle is, and how it integrates with the ongoing planning and reporting rhythm of the business. A working baseline that is refreshed quarterly is worth more than a perfect baseline that is built once and shelved.

The full methodology typically runs eight to fourteen weeks for a mid-market Australian business, depending on scope, data quality, and the level of granularity required. Enterprise-scale or multi-business-unit baselines can run longer. The single biggest variable is the state of the data when the work begins.

How to handle data gaps

Every Australian business has data gaps. The decision-grade approach is not to pretend they do not exist. It is to handle them transparently.

Categorise the gap. Is it missing, incomplete, low-quality, or inconsistent across sources? Each category has a different remediation approach. Missing data may need to be collected. Incomplete data may need to be sampled and extrapolated. Low-quality data may need to be cleaned. Inconsistent data may need to be reconciled at source.

Quantify the materiality. A data gap that affects 0.5 per cent of total cost can be handled with a documented estimate. A data gap that affects 30 per cent of total cost is a project in itself. Materiality determines the level of remediation effort.

Use defensible proxies. Where data is genuinely unavailable, use industry-standard or operationally-validated proxies. Cost per kilometre for line haul. Cost per pallet-day for storage. Cost per line picked for warehouse labour. Document the proxy and its source. A proxy is defensible. A guess is not.

Mark data quality in the model. Every input in the baseline should carry a data quality flag: actual, calculated, estimated, or proxy. The model output should aggregate the data quality view alongside the cost and service view, so users can see which conclusions are most and least defensible.

Resist the urge to build the perfect baseline. Perfect is the enemy of decision-grade. A baseline that is 90 per cent complete with known gaps documented is more useful than a baseline that is still being built six months later. Decision-grade does not mean perfect. It means defensible.

How a baseline gets used downstream

A decision-grade baseline is not the deliverable. It is the foundation for the decisions that follow. Six downstream uses recur across Australian businesses.

Network design. Every network design alternative is evaluated against the baseline. The baseline answers "what does the current network cost and how does it perform". The network design exercise answers "what alternative configurations would deliver better cost-service trade-offs". Without a credible baseline, network design conclusions are unfalsifiable. The baseline feeds directly into our Strategy and Network Design practice work.

Business case development. Every transformation business case quantifies the value at stake against the current state. The baseline is what defines that current state. A weak baseline produces a soft business case that does not survive CFO scrutiny.

Operating model redesign. Future-state operating model decisions are evaluated against current-state operating cost and capability. Without a capability baseline, the change effort required is consistently underestimated.

S&OP and planning transformation. Planning maturity and forecast accuracy improvements are measured against the baseline. The baseline anchors the value case and the performance management cadence.

Cost-to-serve and customer profitability. Cost-to-serve analysis is one of the most valuable applications of the baseline. The cost baseline component feeds directly into CTS modelling and customer or channel profitability decisions.

Automation and technology investment. Automation business cases are built on baseline labour, accuracy, and throughput data. The baseline is what makes the savings case defensible.

A baseline that is built once and then used across all six downstream applications repays its cost many times over. A baseline that is built for one purpose and then redone separately for each subsequent decision is expensive and inconsistent.

Where baselines go wrong

In our experience advising Australian operations and finance leaders, baseline exercises underdeliver for five recurring reasons. All of them are avoidable.

Treating the baseline as a slide pack rather than a model. A 60-page current state report with no underlying working model dies on the day the next executive question is asked. The baseline must be a model the business owns, not a deliverable the consultant takes away.

Skipping the data quality discipline. A baseline that quietly absorbs bad data without flagging it produces conclusions that look authoritative until they are challenged. The defensibility comes from the data discipline, not from the polish of the output.

Building the baseline without operational input. A baseline built entirely in the analytical team without ground-truth from the operators is rejected on arrival. The operators have to recognise their business in the model. If they do not, the baseline is unusable regardless of how methodologically sound it is.

Failing the reconciliation test. A cost baseline that cannot reconcile to the P&L is not a baseline, it is a hypothesis. Reconciliation to financials is mandatory, not optional.

Building it once and never refreshing it. Supply chains move. A baseline that is not refreshed within twelve months has aged past usefulness. The maintenance plan is part of the baseline, not an afterthought.

The common thread is treating the baseline as a project artefact rather than as a permanent commercial asset. The businesses that get the most from their baselines are the ones that build them once, properly, and then keep them alive.

The 2026 context

The Australian environment in 2026 has made baselines more commercially important than at almost any point in the last decade.

Sustained cost pressure across inbound freight, energy, last-mile distribution, and labour means that supply chain cost is a permanent board-level conversation. The Deloitte 2026 Global Retail Industry Outlook reports that 95 per cent of retail executives surveyed expect global trade policies to push costs higher in the year ahead. Tariff volatility, including the 10 per cent baseline US tariff and higher rates on specific categories, has added cost uncertainty across import-exposed Australian businesses.

Channel and operating model change continues. Omnichannel fulfilment, ecommerce growth, and reverse logistics have rewritten the cost structures retailers and FMCG businesses operate against. Health and aged care reform has rewritten the workforce and logistics cost picture for those sectors. Government procurement and program review continues to push for value-for-money transparency that depends on credible cost baselines.

Transformation programmes are being scoped and approved at pace under that pressure. The risk is that they are being scoped against baselines that are weeks-old slide packs rather than living models. The cost of that asymmetry will show up over the next 24 months in transformation overruns, business case re-baselines, and post-investment reviews that struggle to demonstrate the value originally promised.

The baseline is the cheapest insurance in the transformation portfolio.

How Trace Consultants can help

Trace Consultants advises Australian organisations on supply chain current state assessments, baseline modelling, and the downstream decisions baselines support. Our positioning is deliberate: we deliver decision-grade models the business owns, not slide packs the consultant retains. We are vendor-agnostic, partner-led, and senior on every engagement.

Current state assessment and baseline modelling. We scope, build, validate, and hand over decision-grade baseline models that survive executive scrutiny. The deliverable is a working model the business can interrogate, refresh, and use across the downstream decisions that follow.

Network design and strategy. Where the baseline informs network design, our Strategy and Network Design practice translates the baseline into network footprint, DC role, and infrastructure decisions.

Operating model and capability. Where the baseline exposes operating model or capability gaps, our Planning and Operations and Organisational Design practices support the future-state design work.

Transformation programme delivery. Where the baseline anchors a transformation business case and programme, our Project and Change Management practice supports the delivery.

Procurement and supplier baselines. Where the baseline informs procurement or supplier strategy, our Procurement practice translates the baseline into category and sourcing decisions.

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Where to begin

If you are early in this journey, start with three questions. What decisions is the baseline being built to support (network design, business case, operating model, S&OP transformation, cost-to-serve, automation investment)? What data is available in your source systems today and what would need to be collected or estimated? Who will own the baseline once it is built, and is there a maintenance cycle in place to keep it alive?

If those three questions point to a baseline exercise, scope it tightly to the decisions it will support, design the data model upfront, and treat the build as a programme rather than a project.

Frequently asked questions

What is a supply chain baseline? A supply chain baseline is the structured, defensible, quantified representation of the supply chain as it operates today. It is a working model of network, cost, service, inventory, and capability that the business can interrogate and use as the starting point for transformation decisions.

How is a baseline different from a current state assessment? A current state assessment is the activity of investigating and documenting how the supply chain operates today. A baseline is the quantified model that comes out of the assessment. The assessment is the exercise. The baseline is the artefact.

How long does it take to build a decision-grade baseline? A decision-grade baseline for a mid-market Australian business typically takes eight to fourteen weeks. The single biggest variable is the state of the data when the work begins. Enterprise-scale or multi-business-unit baselines can take longer.

What data do you need to build a baseline? At minimum: finance data covering supply chain cost categories, ERP transaction data covering volumes and flows, WMS and TMS operational data covering warehousing and freight, planning system data covering forecast and inventory positions, service performance data, and the organisational structure and headcount data covering capability. Data gaps are normal and managed transparently in the model.

What does decision-grade actually mean? A baseline is decision-grade when it is defensible to a board, executable by an operator, and useful for scenario modelling. Defensibility means every number can be traced to a source. Executability means the operators recognise the business in the model. Scenario usefulness means the model can be re-run with different assumptions to test future state options.

Does the baseline need to reconcile to the P&L? Yes. A cost baseline that cannot reproduce reported financial cost within an acceptable variance is not a baseline. Typical practice is reconciliation within around 5 per cent at total level and within around 10 per cent at segment level. Where reconciliation fails, either the data, the allocation logic, or the business has an unexplained variance, and that itself is a finding.

How is a baseline used after it is built? The most common downstream uses are network design, business case development, operating model redesign, S&OP and planning transformation, cost-to-serve and customer profitability analysis, and automation and technology investment cases. A single baseline can support all six.

How often should a baseline be refreshed? A working baseline used for ongoing commercial decision-making is typically refreshed quarterly or at minimum annually. Cost structures, channel mix, and operating model shift over time. A baseline that is not refreshed within twelve months has typically aged past usefulness.

Can a small or mid-market business build a decision-grade baseline? Yes. The methodology is scalable and the tooling required is well within mid-market reach. A small-to-mid-market baseline typically involves less data complexity than an enterprise-scale model and can be delivered in a tighter timeline.

Who should own the baseline once it is built? Ownership typically sits with the supply chain, operations, or transformation leadership team, with finance as a key partner for the cost reconciliation discipline. External advisors build the baseline. The business owns it.

A decision-grade supply chain baseline is one of the highest-leverage investments an Australian business can make before committing to a transformation programme. Built well, it survives years, supports every downstream decision, and pays back many times over. Built badly, it produces a slide pack that lasts a month and a business case that does not survive its first hard meeting.

If you are scoping a transformation, a network redesign, a business case, or an operating model change in 2026, the baseline is where the work begins.

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Related reading: Strategy and Network Design · Planning and Operations · Project and Change Management · Organisational Design · Procurement · Insights

Cost-to-Serve Analysis: A Framework for Australian Businesses

For most Australian businesses, the standard profit and loss statement hides as much as it reveals. The headline margin looks acceptable. The board accepts the numbers. The CFO signs them off. Underneath, a meaningful share of customers, channels, and SKUs is being sold at a loss. Another meaningful share is generating profit so far above the average that the business does not realise how dependent it has become on a small handful of relationships. The averages disguise the structure. The structure is where the commercial decisions live.

Cost-to-serve analysis is the methodology that exposes the structure. Done well, it produces a decision-grade view of which customers, channels, products, and orders are genuinely profitable, which are marginal, and which are quietly destroying value. Done badly, it produces a 200-page report that sits on a shelf because nobody can defend the numbers or act on them.

This guide is the practitioner's framework for building a cost-to-serve model that survives scrutiny and drives action. It covers what CTS is, why it matters now in the Australian market, the methodology end-to-end, the common traps that derail a CTS exercise, and how to turn the insight into commercial outcomes.

What is cost-to-serve analysis?

Cost-to-serve (CTS) is the total cost of getting a product or service from your business to a specific customer, through a specific channel, at a specific service level. It allocates supply chain, operational, and serving costs to the customers, channels, products, and orders that actually drive them, rather than spreading them as averages across the business.

A standard P&L tells you what the business as a whole earned and what it cost. A CTS analysis tells you which customers, channels, and SKUs actually generated that profit and which absorbed it. The same gross margin product can be wildly profitable through one channel and loss-making through another. The same channel can be highly profitable for one customer segment and value-destroying for another. CTS makes those differences visible.

The Australian Food and Grocery Council has described cost-to-serve as a methodology to determine the likely financial outcomes of supply chain investment and collaborative engagement. The more practical framing is simpler: CTS is the analytical tool that lets a business make defensible commercial decisions about pricing, channel mix, customer rationalisation, service differentiation, and operating model change.

Why CTS matters now in Australia

Three forces have made CTS more commercially relevant in 2026 than at almost any point in the last decade.

The first is sustained cost pressure. Inbound freight, energy, last-mile distribution, labour, and warehousing have all moved structurally higher since 2022. The Circana 2026 Australian FMCG outlook puts annual category spend at more than $175 billion against a backdrop of "uneven growth" and persistent cost and competition pressures. The Deloitte 2026 Global Retail Industry Outlook reports that 95 per cent of retail executives surveyed expect global trade policies to push costs higher in the year ahead. Margins that survived the inflationary years through pricing power are now exposed where pricing power has reached its limit.

The second is channel complexity. The shift to omnichannel fulfilment, ecommerce growth, marketplace expansion, and direct-to-consumer models has created cost structures that the standard P&L was never designed to handle. A product moving through five different channels has five different cost-to-serve profiles. Without analysis, businesses make pricing and investment decisions on a blended average that is no longer fit for purpose.

The third is the maturing data and analytics environment. The tools, data, and computing capacity required to run a credible CTS analysis are now within reach of mid-market businesses, not just enterprise. Five years ago, a CTS exercise was a multi-month consulting engagement. Today it can be a structured six-to-twelve-week diagnostic with a working model handed back to the business.

The convergence of these three forces means that the businesses doing CTS well are pulling further ahead of those that are not. The gap is widening.

The five components of a decision-grade CTS model

A CTS model is decision-grade when it is defensible to a board, executable by an operator, and useful for scenario modelling. Achieving all three requires five components.

Component one: a complete cost taxonomy. Every cost in the supply chain and serving operation must be classified, attributed, and allocated. The taxonomy typically covers inbound freight, warehousing (storage, handling, value-add), outbound transport (line haul, last mile, returns), pick and pack labour, customer service and account management, technology and systems overhead, working capital tied up in inventory, and a share of overhead that genuinely scales with serving activity. A CTS model that omits any major cost category produces unreliable conclusions.

Component two: a defensible allocation logic. Costs must be allocated to customers, channels, products, and orders using drivers that reflect actual cost causation. Warehousing cost allocated by units handled is different to warehousing cost allocated by cubic metres stored, by lines picked, or by orders processed. The right driver depends on the cost behaviour, not on what data is most easily available. The allocation logic must be transparent, documented, and defensible when challenged.

Component three: customer, channel, product, and order dimensions. A CTS model needs to slice the same cost view across at least four dimensions: customer (or customer segment), channel (in-store, ecommerce, wholesale, marketplace, direct, third-party), product (or product category), and order profile (size, frequency, mix, delivery type). The same SKU sold to the same customer through different channels generates different CTS. The four dimensions in combination are where the commercial insight lives.

Component four: a clear service-cost relationship. CTS is not just about cost. It is about the cost of delivering a specific service level to a specific customer. A model that ignores the service dimension misses the most important commercial lever: service differentiation. Pareto-style customers receiving Pareto-style service is the default in most businesses. The opportunity is usually to differentiate.

Component five: a working scenario engine. A static CTS report is a snapshot. A decision-grade CTS model is dynamic, allowing the business to test scenarios: what happens to channel profitability if last-mile freight rises by 15 per cent, if minimum order quantities change, if a customer is moved to a different fulfilment model, if a low-volume SKU is delisted, if delivery frequency to a customer segment is reduced. The scenario engine is what turns CTS from a diagnostic into a decision tool.

A model with all five components becomes a permanent commercial asset. A model missing any of them tends to be used once and shelved.

The methodology: six steps to a working CTS model

The end-to-end methodology for building a decision-grade CTS model breaks into six steps. Each step has its own data, governance, and quality requirements.

Step one: scope the model. Define the question the CTS analysis is answering. Channel profitability for an omnichannel retailer is a different model to customer profitability for a B2B distributor. SKU profitability for an FMCG manufacturer is different again. The scope drives the data, the granularity, and the timeline. Trying to answer every question in one model usually produces a model that answers none of them well.

Step two: build the cost taxonomy and gather the data. Define every relevant cost category and identify the source system for each. Finance systems provide the totals. ERP and WMS provide transaction volumes. TMS provides freight detail. Workforce systems provide labour data. Where data is missing or weak, decisions about proxies, estimates, and quality flags are made transparently. The data gathering step is typically the longest and the most underestimated.

Step three: define the allocation logic. For each cost category, choose the allocation driver based on cost causation. Document the choice and the rationale. Where multiple drivers could be defended, pick the one that best reflects operational reality and note the alternative. The discipline at this step is what makes the model defensible later.

Step four: build the model. Construct the working CTS model, typically in Excel for transparency or in a dedicated analytics platform for scalability. The model should allow each cost to be traced from its source to its allocation destination. Audit trails matter. Models that hide their logic in macros or undocumented formulas fail the defensibility test.

Step five: validate and stress-test. Reconcile model totals back to the P&L. Test the allocation logic with operational stakeholders. Identify customers, channels, or SKUs where the model output looks counter-intuitive and investigate. Either the data is wrong, the allocation logic is wrong, or the business has a real insight it did not previously have. All three outcomes are valuable.

Step six: interpret and act. The model output is not the deliverable. The deliverable is the decisions the model enables. Whale curves, customer-channel matrices, scenario outputs, and action lists are all built from the model. Without an activation plan, the analysis is unfinished.

In our experience, the businesses that get the most value from CTS treat it as a programme, not a project. The first model takes ten to fourteen weeks to build for a mid-market business. The ongoing operational use case extends for years.

The whale curve: what CTS typically reveals

The most-cited CTS output is the profitability waterfall, often 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 to 30 per cent of customers typically generate well in excess of total profit, often in the range of 150 to 200 per cent. The middle tier sits around break-even. The bottom 20 to 30 per cent typically destroys a meaningful share 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 absorbed by customers, channels, or SKUs that cost more to serve than they contribute.

This pattern repeats across sectors. In Australian retail and distribution, it shows up as ecommerce-fulfilment loss-making at certain order sizes. In FMCG, it shows up as long-tail SKUs absorbing distribution and slotting cost. In healthcare and aged care, it shows up as small remote sites driving disproportionate logistics cost. In hospitality back-of-house, it shows up as low-volume venues with the same fixed supply chain overhead as high-volume venues. In 3PL operations, it shows up as legacy clients on rates that no longer reflect cost-to-serve.

The whale curve is not the end of the analysis. It is the start of the commercial conversation.

Where CTS analyses go wrong

In our experience advising Australian operations and finance leaders, CTS exercises underdeliver for five recurring reasons. All of them are avoidable.

Scoping too broadly. Trying to build one CTS model that answers customer profitability, channel profitability, SKU profitability, and route-level profitability simultaneously usually produces a model too complex to maintain and too aggregated to act on. Scope tightly. A focused customer-and-channel CTS model is more valuable than a sprawling everything-at-once model.

Treating the model as the deliverable. A 200-page CTS report with no activation plan is a document, not an outcome. The model is the means. The deliverable is the commercial decision: customer rationalisation, channel re-pricing, service differentiation, network change, operating model redesign.

Allocating costs without defensibility. Allocations that cannot be defended to a sceptical commercial counterpart (sales director, channel head, key account manager) get rejected at the first scrutiny meeting. Spend the time on allocation logic upfront. The defensibility of the model is what makes it usable.

Ignoring service in the cost view. A CTS model that does not capture service differentiation by customer or channel misses the biggest commercial lever. The right answer to a loss-making customer is rarely to walk away. It is usually to move them to a different service profile. The model needs to enable that conversation.

Building a snapshot, not a tool. A static CTS analysis ages quickly. Cost structures shift, channel mixes evolve, customer behaviour changes. A working CTS model that the business can rerun quarterly or annually is worth ten static reports.

The common thread is treating CTS as an analytical exercise rather than a commercial discipline. The businesses that get the most from CTS embed it in their commercial decision-making, not just their finance team's quarterly reporting pack.

Activation: turning CTS insight into commercial outcomes

The point of CTS is action. The most valuable activations typically fall into one of six categories.

Customer rationalisation and re-pricing. The bottom of the whale curve usually contains customers being sold below cost. Some of these are strategic. Most are accidents of legacy pricing, account drift, or service creep. CTS makes the case for re-pricing, renegotiation, or in some cases exit.

Channel and fulfilment model redesign. Where a channel is structurally unprofitable, the answer is rarely to stop selling. It is usually to redesign the fulfilment model: minimum order quantities, delivery frequency, click-and-collect substitution, third-party fulfilment, or pricing changes that reflect the true cost of service.

SKU rationalisation. The long tail of SKUs typically absorbs disproportionate slotting, holding, replenishment, and complexity cost. CTS provides the defensible case for delisting decisions that the commercial team has often resisted on the basis of customer relationships.

Service differentiation. Differentiating service levels by customer value is one of the highest-return outcomes of a CTS exercise. Premium service to premium customers. Standard service to standard customers. Lower-touch service to low-value customers. The CTS model defines the segmentation.

Network and operating model change. CTS exposes the cost penalty of network configurations that no longer fit demand patterns. It feeds directly into network design, DC consolidation, and operating model decisions covered in our Strategy and Network Design practice.

Embedded commercial decision-making. The highest-value use of CTS is permanent: making the model available to commercial, account management, and pricing teams as a live decision tool. Every new customer, every pricing decision, every channel investment runs through the CTS model before approval. The discipline pays back continuously.

Sector applications

CTS is sector-universal in concept but sector-specific in application.

Retail and ecommerce. Channel CTS is the dominant question: in-store versus click-and-collect versus home delivery versus marketplace. Multi-channel CTS exposes the true unit economics of online fulfilment, which in many Australian retailers has been masked by blended margins. The Trace In-store and Online Retail sector page covers the broader retail context this work sits inside.

FMCG and manufacturing. Customer and SKU CTS is the dominant question: which trade customers, which categories, which SKUs are generating profit and which are absorbing it. The FMCG and Manufacturing sector page covers the broader context.

Hospitality and integrated resorts. Venue and outlet CTS is the dominant question: which venues, which F&B outlets, which back-of-house flows are profitable at their current cost-to-serve. Hospitality back-of-house is one of the most under-analysed cost pools in the Australian market.

Health and aged care. Site and service-line CTS is the dominant question: which sites, which clinical or care service lines, which patient or resident cohorts carry which cost-to-serve. The Trace Health and Aged Care sector page covers the broader healthcare supply chain context.

3PL and distribution. Client and rate-card CTS is the dominant question: which 3PL clients are on rates that no longer reflect cost-to-serve, and which value-add services are being delivered below cost. This typically informs rate-card resets at contract renewal.

Government and defence. Service-line and program CTS is the dominant question: which programs and service lines carry which true cost when fully allocated. This is increasingly relevant under the Commonwealth's renewed focus on procurement value-for-money and program-level cost transparency.

How Trace Consultants can help

Trace Consultants advises Australian organisations on cost-to-serve diagnostics, modelling, and activation. Our positioning is deliberate: we deliver decision-grade models, not 200-page reports. We sit with the business through activation, not just analysis. We are partner-led, senior on every engagement, and commercially anchored.

Cost-to-serve diagnostic and modelling. We scope, build, and validate CTS models tailored to the commercial question the business is trying to answer. The deliverable is a working model the business owns, not a static report.

Activation and commercial outcomes. Our Planning and Operations practice supports the activation of CTS insight into customer rationalisation, channel redesign, SKU rationalisation, and service differentiation programmes.

Network design and operating model change. Where CTS analysis exposes structural network or operating model issues, our Strategy and Network Design practice translates the insight into network footprint, DC consolidation, and operating model decisions.

Procurement leverage. Where CTS analysis identifies supplier or category cost issues, our Procurement practice supports sourcing and category strategy responses.

Sector depth. CTS in retail, FMCG, hospitality, health and aged care, 3PL, and government has its own data, allocation, and activation nuances. Trace's sector practices bring the operational depth required to make the model defensible and the activation credible.

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Where to begin

If you are early in this journey, start with three questions. What is the specific commercial question you are trying to answer (channel profitability, customer profitability, SKU rationalisation, service differentiation, network change)? What data do you have today, and what would you need to build a defensible model? Who owns activation once the model is built, and do they have the authority to act?

If those three questions point to a CTS exercise, scope it tightly. Run a focused ten-to-fourteen-week diagnostic on the most valuable commercial question, build the model, and use the first activation to fund the next. A working CTS programme typically expands organically once the first activation delivers a measurable outcome.

Frequently asked questions

What is cost-to-serve analysis? Cost-to-serve is the total cost of getting a product or service from your business to a specific customer, through a specific channel, at a specific service level. CTS analysis allocates supply chain and serving costs to the customers, channels, products, and orders that actually drive them, rather than averaging them across the business.

How is CTS different from activity-based costing? ABC and CTS share methodological DNA but differ in scope. ABC is a finance-led costing discipline applied across the whole business. CTS is typically a commercially-focused analysis applied to the supply chain and serving cost pools, oriented toward decisions about customers, channels, products, and service levels. In practice, the two methodologies often overlap.

How long does a CTS analysis take? A focused CTS diagnostic for a mid-market Australian business typically runs ten to fourteen weeks from kick-off to activation plan. Enterprise-scale or multi-business-unit CTS programmes can run longer. The longest step is almost always data gathering and validation.

What data do you need for a CTS analysis? At minimum: sales by customer, channel, and SKU; cost data from finance for the relevant cost categories; transaction volumes from ERP, WMS, and TMS; service level data; and operational drivers (cubic metres stored, lines picked, kilometres travelled, orders processed). Data gaps are normal and managed transparently in the model.

What is a whale curve? The whale curve, sometimes called the profitability waterfall, plots customers (or SKUs, or channels) ranked from most to least profitable, showing cumulative profit contribution. The typical shape across most businesses shows the top 20 to 30 per cent of customers generating well in excess of total profit, the middle tier near break-even, and the bottom 20 to 30 per cent destroying value.

Can a small or mid-market business run a CTS analysis? Yes. The methodology is scalable and the tools required are well within mid-market reach in 2026. A small-to-mid-market CTS analysis typically requires less data complexity than an enterprise-scale model and can be delivered in a tighter timeline.

Does CTS apply to services businesses, not just product businesses? Yes. CTS methodology applies to any business serving customers through different channels at different service levels, including health and aged care, professional services, government program delivery, and hospitality. The cost categories shift but the framework holds.

What is the most common mistake in CTS analysis? Treating the model as the deliverable rather than the activation. A defensible model with no activation plan does not change the business. The deliverable is the commercial decision the model enables.

How often should a CTS model be refreshed? A working CTS model used for commercial decision-making is typically refreshed quarterly or at minimum annually. Cost structures, channel mix, and customer behaviour shift, and a model that is not refreshed loses its decision-grade status quickly.

Can CTS analysis support a business case for transformation investment? Yes, and it often does. CTS quantifies the value at stake from network change, channel redesign, automation investment, and operating model redesign, providing the commercial anchor for the business case.

Cost-to-serve analysis is one of the highest-leverage commercial disciplines in supply chain and operations. Done well, it permanently changes how a business prices, segments, serves, and invests. Done badly, it generates a report nobody can defend. The difference is the methodology, the data discipline, and the commitment to activate rather than just analyse.

If you are facing margin pressure, channel complexity, or transformation investment decisions in 2026, a cost-to-serve analysis is one of the first places to look.

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Related reading: Planning and Operations · Strategy and Network Design · Procurement · Warehousing and Distribution · Insights