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

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

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

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

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

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

The technology illusion

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

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

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

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

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

The five things that actually determine whether a transformation succeeds

1. Process redesign before system design

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

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

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

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

2. Executive alignment and sustained sponsorship

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

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

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

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

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

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

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

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

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

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

4. Data foundations that actually support decision-making

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

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

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

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

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

5. Organisational design that reflects the new operating model

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

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

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

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

The failure patterns we see most often

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

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

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

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

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

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

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

What good looks like in practice

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

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

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

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

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

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

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

What this means for Australian supply chains

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

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

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

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

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

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

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

How Trace can help

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

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

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

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

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

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

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

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

And yet, the results are overwhelmingly disappointing.

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

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

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

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

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

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

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

Let's look at the specific patterns.

Pattern one: starting with the technology instead of the problem

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

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

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

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

Pattern two: the data foundation isn't there

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

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

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

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

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

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

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

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

Pattern three: bolting AI onto broken processes

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

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

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

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

Pattern four: the pilot trap

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

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

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

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

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

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

Pattern five: underinvesting in people and change

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

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

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

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

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

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

Pattern six: no clear success metrics

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

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

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

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

Pattern seven: trying to do too much at once

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

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

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

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

What the organisations that succeed are doing differently

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

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

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

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

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

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

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

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

What this means for Australian supply chain leaders

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

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

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

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

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

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

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

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

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

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

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

The role of advisory in AI adoption

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

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

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

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

The bottom line

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

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

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

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

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

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Choosing an Advanced Planning System is one of those decisions that looks straightforward on the surface and turns out to be fiendishly difficult in practice. The vendor landscape is broad. The functionality overlaps are significant. Every platform claims AI-powered demand forecasting, scenario modelling, inventory optimisation and seamless integration with your ERP. The demos are polished. The reference customers are carefully curated.

And yet, a significant proportion of APS implementations either fail outright, deliver far less value than the business case projected, or land in a state where the organisation is technically "live" but planners are still running their actual decisions through spreadsheets because the system doesn't work the way they need it to.

The root cause, in most cases, isn't bad technology. It's a selection process that failed to answer the right questions. The RFx was structured as a technology procurement exercise — feature lists, architecture requirements, integration specifications, pricing schedules — when it should have been structured as a planning capability assessment: does this system, configured for our data, our products, our network and our planning process, actually produce better plans than what we're doing today?

That distinction — between buying a technology product and selecting a planning capability — is what separates organisations that get genuine value from their APS investment from those that spend 18 months implementing a system nobody trusts.

Why APS selection is different from other technology procurement

Before diving into how to structure the RFx process, it's worth understanding why demand planning and APS selection doesn't follow the same playbook as procuring an ERP, a WMS, a TMS or most other enterprise technology.

The problem you're solving is harder to define. When you select a warehouse management system, the functional requirements are relatively concrete: receive stock, put it away, pick it, pack it, ship it, count it. The processes are physical, observable and well-understood. When you select a demand planning or supply chain planning system, the requirements are inherently more abstract. You're trying to improve the quality of decisions about uncertain futures — how much to make, where to hold stock, when to order, how to allocate constrained supply. The "right answer" changes daily. The value is in the system's ability to help planners navigate ambiguity, not execute a defined process.

Vendor capabilities genuinely vary in ways that are hard to assess. The Gartner Magic Quadrant for Supply Chain Planning Solutions now includes more than 20 vendors. The Leaders — Kinaxis, Blue Yonder, o9 Solutions, Logility and others — all claim comprehensive functionality across demand planning, supply planning, inventory optimisation, S&OP and production scheduling. But underneath those claims, the actual capabilities differ substantially. Some platforms are genuinely strong in statistical and machine-learning forecasting but weaker in constraint-based supply planning. Others excel at interactive scenario modelling but struggle with the algorithmic optimisation that drives inventory decisions. Some are purpose-built for specific industries — retail, FMCG, process manufacturing — while others are horizontal platforms that require significant configuration to work well in any specific context.

A standard RFx feature checklist — "does the system support promotional uplift modelling? Yes/No" — doesn't surface these differences. Every vendor will tick the box. The question isn't whether the feature exists but whether it works well enough, in your context, with your data, to make materially better decisions than your current approach.

The implementation matters as much as the software. APS aren't systems you install and switch on. They're systems you configure, calibrate and continuously tune — and the quality of that work determines whether the system produces plans worth following. The same platform can deliver transformative results in one organisation and be a $2 million shelf ornament in another, depending on how it was implemented, how the data was structured, how the planning processes were designed, and whether planners were genuinely brought along through the change.

This means the RFx process needs to evaluate not just the software but the implementation approach, the partner ecosystem, the support model and the vendor's track record in environments comparable to yours.

The six mistakes that derail most APS selection processes

Having supported organisations across FMCG and manufacturing, retail and consumer and other sectors through APS selection and implementation, we see the same patterns of failure repeatedly. Understanding them is the first step to designing a process that avoids them.

Mistake one: starting with the vendor shortlist instead of the planning problem

Too many APS selection processes begin with a list of vendors and a request for information rather than a clear articulation of what the organisation actually needs from its planning capability. What are the specific planning decisions that need to improve? Where is forecast accuracy weakest and why? Which inventory positions are structurally wrong — too much in some places, not enough in others? Where are supply and demand decisions disconnected? What does the S&OP process need that it doesn't currently have?

Without clear answers to these questions — grounded in data, not assumptions — the RFx process has no anchor. It becomes a general-purpose technology evaluation rather than a targeted assessment of which system best addresses the organisation's specific planning challenges.

This upfront work — what we'd describe as planning maturity assessment and requirements definition — is the most important phase of the entire selection process. It determines everything that follows: the evaluation criteria, the weighting, the scenarios for demonstration, and ultimately the commercial justification for the investment.

Mistake two: evaluating against feature checklists

The traditional approach to technology RFx involves compiling a requirements matrix — sometimes running to hundreds of rows — with each requirement scored on a scale (e.g. "fully met, partially met, not met, requires customisation"). Vendors complete the matrix, an evaluation panel scores them, and the highest-scoring platform is identified.

This approach works reasonably well for transactional systems with well-defined functional requirements. It works poorly for APS, for two reasons. First, the requirements that matter most in planning — forecast accuracy, scenario modelling quality, user experience for planners, speed of exception management — are qualitative and contextual, not binary. Second, vendors are skilled at completing these matrices in ways that maximise their score without necessarily reflecting how the system will perform in your environment. A planning system might technically "support" promotional uplift modelling, but if it requires three months of custom configuration and a data science team to maintain, that's a very different proposition from one where it's a native, well-tested capability.

Mistake three: accepting canned demos

Every APS vendor has a demonstration environment loaded with sample data and pre-configured scenarios designed to make the system look spectacular. These demos are useful for understanding the user interface and general approach, but they tell you almost nothing about how the system will perform with your data, your products, your demand patterns and your network complexity.

The organisations that make the best APS selection decisions insist on scripted demonstrations using representative samples of their own data. This doesn't mean a full proof-of-concept at the RFx stage — that comes later — but it does mean providing shortlisted vendors with a defined dataset and a set of planning scenarios that reflect your real-world challenges, then evaluating how each system handles them. The difference in insight between a canned demo and a data-driven demonstration is enormous.

Mistake four: underweighting implementation and change

Most RFx evaluation frameworks allocate the majority of their weighting to software functionality and price. Implementation approach, partner capability, change management methodology and ongoing support model might get 10-15% of the total score. This is backwards for APS, where the implementation and change journey typically determines 60-70% of the eventual outcome.

A system with excellent algorithmic capabilities but a weak implementation partner, a rigid configuration approach, or no credible change management methodology is a worse bet than a system with good-enough algorithms backed by a team that understands your industry, your data challenges, and how to get planners actually using the system to make decisions.

Mistake five: treating cost as licensing cost

APS pricing models vary enormously — from per-user licensing to volume-based pricing to flat annual subscriptions with modular add-ons. But the licensing cost is typically 30-40% of the total cost of ownership. Implementation services, data engineering, integration development, testing, training, change management, and ongoing support and optimisation make up the rest.

RFx processes that evaluate cost on licensing alone — or that allow vendors to quote implementation at unrealistically low levels to win the deal — set the organisation up for budget overruns and scope compromises during implementation. The RFx should require vendors and implementation partners to provide a realistic total cost of ownership estimate across a defined horizon (typically five years), including all the components that actually drive spend.

Mistake six: ignoring the planning process and people dimension

An APS is a tool. Its value depends entirely on the planning process it supports and the people who use it. Yet most RFx processes focus almost exclusively on the tool and pay limited attention to whether the organisation is ready to use it effectively.

Questions about planning and operations process design — how will the S&OP process change? What will the planner's daily workflow look like? How will exceptions be managed? What governance will ensure the system stays calibrated? — are rarely part of the vendor evaluation. Neither are questions about strategic workforce planning — do we have the right planning roles? Do our planners have the analytical skills to use an advanced system effectively? What training and development is needed?

These aren't implementation details to be figured out later. They're fundamental to whether the investment will deliver returns, and they should inform the selection decision.

How to structure an APS RFx process that works

With those pitfalls in mind, here's a practical framework for running an APS selection process that actually surfaces the information needed to make a good decision.

Phase one: planning capability assessment (4-6 weeks)

Before engaging the market, invest in understanding your own planning capability — where it's strong, where it's weak, and where the biggest opportunities for improvement sit.

This means conducting a structured assessment of current planning processes: demand planning, supply planning, inventory management, S&OP, production scheduling. It means analysing forecast accuracy by product segment, customer and time horizon. It means quantifying the inventory opportunity — how much working capital is tied up that shouldn't be, and where service levels are being missed despite high stock levels. It means mapping the data landscape: what's available, what's reliable, what's missing, and what integration is needed.

The output of this phase is a planning capability baseline and a set of prioritised requirements — not a generic feature list, but a specific articulation of the planning decisions that need to improve, the data and analytical capabilities required to improve them, and the process and organisational changes that will be needed alongside the technology.

This is strategy and network design work as much as it is technology work. It ensures the RFx is anchored in business value rather than vendor marketing.

Phase two: market scan and shortlisting (2-3 weeks)

With clear requirements in hand, conduct a structured market scan to identify the vendors and implementation partners most likely to be a good fit. This should consider the vendor's strength in your specific industry (FMCG, manufacturing, retail, resources), the maturity of their demand planning and forecasting capabilities relative to your needs, their track record in the ANZ market — including local implementation partners and support, their architecture and integration approach relative to your ERP and data landscape, and their pricing model and how it scales with your business.

The goal is to shortlist three to four vendors for detailed evaluation — enough for genuine competitive tension, not so many that the evaluation becomes unmanageable. Gartner's Magic Quadrant and Critical Capabilities reports are useful inputs here, but they should be one data point among several, not the sole basis for shortlisting. As independent analysis has noted, the Magic Quadrant format tends to reward vendors with broad market presence and comprehensive feature claims without necessarily distinguishing between deep capability and surface-level functionality.

Phase three: RFx design and issue (3-4 weeks)

The RFx document itself should be designed to generate the specific information needed for a high-quality evaluation — not to demonstrate procurement rigour through volume of documentation.

The most effective APS RFx documents we've seen include several key components. First, a business context section that gives vendors genuine insight into the organisation's planning challenges, data environment and improvement priorities — because better-informed vendors produce better responses. Second, a focused set of functional requirements organised around planning capability areas (demand planning, supply planning, inventory optimisation, S&OP, analytics) rather than exhaustive feature checklists. Third, a set of planning scenarios that vendors will be asked to demonstrate during evaluation — specific, data-driven scenarios that reflect the organisation's real challenges, such as "forecast a product with high promotional variability," "optimise safety stocks across a three-tier distribution network," or "model the impact of a supply constraint on customer service." Fourth, clear requirements for implementation approach, team composition, methodology, timeline and change management. Fifth, a total cost of ownership template that requires vendors to itemise licensing, implementation, integration, data engineering, training, change management and ongoing support costs over a defined horizon.

The scenarios are critical. They're what transform the evaluation from a paper exercise into a practical test of capability. Providing a representative dataset to shortlisted vendors — anonymised if necessary — and requiring them to demonstrate their system against defined scenarios reveals more in a two-hour session than a 200-page written response ever will.

Phase four: evaluation (4-6 weeks)

The evaluation phase should combine three streams of assessment.

Written response evaluation against the RFx criteria, focused on approach, methodology, team capability and total cost rather than feature compliance. This provides the baseline comparative view.

Scripted demonstrations where each shortlisted vendor works through the defined planning scenarios using the provided dataset. These sessions should be attended by planners, not just IT and procurement, because planners are the ones who will recognise whether the system handles their real-world challenges effectively. Evaluation should focus on the quality of outputs (does the forecast look sensible? Does the inventory recommendation align with service targets?), the usability of the planner interface (can a planner understand and act on what the system produces?), and the transparency of the system's logic (can planners see why the system is recommending what it's recommending, and override it when they have better information?).

Reference checks and site visits with comparable organisations that have implemented the same platform. These should go beyond the vendor's curated reference list — ask for references in your industry, your geography, your scale, and at a similar stage of planning maturity. The questions that matter most are: how long did implementation actually take versus plan? What was the real total cost versus budget? How quickly did planners adopt the system? What worked well and what would they do differently?

The evaluation criteria should weight practical demonstration performance, implementation credibility and total cost of ownership at least as heavily as written response quality. A common weighting structure might allocate 30% to demonstrated planning capability, 25% to implementation approach and team, 20% to total cost of ownership, 15% to architecture, integration and scalability, and 10% to vendor viability and support model.

Phase five: proof of concept (4-8 weeks, optional but recommended)

For high-value APS investments, a proof of concept with the preferred vendor — or in some cases, two finalists — provides a final layer of validation before committing. The PoC should use a defined subset of the organisation's actual data and test a specific set of planning capabilities in realistic conditions.

This isn't a full implementation. It's a structured test that answers the question: does this system, with our data, produce materially better planning outputs than our current approach? If the answer is yes — demonstrably, measurably — the business case for investment is validated. If the answer is equivocal, that's critical information to have before signing a multi-year contract.

Phase six: commercial negotiation and contracting (3-4 weeks)

With a preferred vendor identified and validated through demonstration and potentially PoC, the commercial negotiation phase should address the full scope of the relationship — not just licensing terms.

Key negotiation areas include licensing structure and scalability (what happens as user numbers or data volumes grow?), implementation pricing and risk sharing (fixed fee for defined scope versus time-and-materials), performance commitments (are there contractual commitments to system performance, forecast accuracy improvement, or implementation timeline?), data ownership and portability (if you leave, can you take your configuration, models and data?), support model and SLAs (response times, availability commitments, upgrade frequency), and contract term and exit provisions.

This is procurement work that benefits from experience with technology contracts specifically — understanding the commercial levers that matter in SaaS agreements, the risks that need to be allocated, and the governance mechanisms that protect the organisation's interests over a multi-year relationship.

The Australian and New Zealand context

For organisations operating in Australia and New Zealand, several characteristics of the local environment make APS selection particularly consequential.

Long inbound supply chains with variable lead times — particularly for imported products coming through congested port infrastructure — mean that demand planning accuracy and inventory optimisation have a disproportionate impact on both working capital and service levels. The buffer you need across a 12-week ocean lane from Asia is fundamentally different from what a European business needs across a 3-day road lane from a neighbouring country.

Seasonal and promotional demand patterns in Australian markets — particularly in FMCG, retail and agriculture — create concentrated demand peaks that expose the limitations of basic forecasting approaches. An APS that handles promotional uplift well can be worth millions in reduced waste and improved on-shelf availability.

The relatively small scale of the ANZ market means that some global APS vendors have limited local presence — fewer implementation consultants, smaller support teams, longer response times. The RFx process should explicitly evaluate local capability, not just global credentials. An outstanding platform with no credible ANZ implementation partner is a risk, not an asset.

Working capital pressure in the current interest rate environment makes inventory optimisation — one of the core APS capabilities — directly material to the balance sheet. Every dollar of unnecessary inventory carries a real financing cost that didn't matter as much when rates were near zero.

These factors reinforce why the RFx process needs to be tailored to ANZ operating conditions rather than run as a generic global technology evaluation. The scenarios used in evaluation should reflect Australian supply chain realities: long lanes, seasonal peaks, promotional volatility, concentrated retail customers and distributed geographic networks.

For more on how APS capabilities map to ANZ supply chain challenges, Trace has published a detailed guide on Advanced Planning Systems and how they transform supply chain planning in Australia and New Zealand.

Process readiness matters as much as system selection

One of the most important — and most frequently overlooked — aspects of APS selection is the question of process readiness. The best demand planning system in the world won't deliver value if the organisation doesn't have a functioning S&OP process, if planners don't have time to work with the system because they're buried in manual data manipulation, or if there's no governance rhythm for reviewing and acting on system outputs.

The RFx process should include an honest assessment of organisational readiness: do we have the planning processes, the roles, the data discipline and the executive sponsorship to implement and sustain an APS effectively? If the answer is "not yet," that doesn't mean abandoning the investment — but it does mean sequencing process improvement and capability building alongside (or ahead of) the technology implementation, and reflecting that reality in the implementation plan and timeline.

The organisations that get the most from their APS investment typically spend as much effort on project and change management — redesigning planning processes, upskilling planners, building governance mechanisms, securing stakeholder buy-in — as they do on configuring and deploying the technology. The RFx process should evaluate vendors and implementation partners on their ability to support this change dimension, not just their technical capability.

How Trace Consultants can help

At Trace Consultants, we help Australian organisations navigate APS selection and implementation from a position of genuine planning expertise — not just technology procurement capability.

We work across the full selection lifecycle:

Planning capability assessment. We assess your current demand planning, supply planning, inventory management and S&OP processes against best practice, quantify the improvement opportunity, and define the specific requirements that should drive your APS selection. This is planning and operations consulting grounded in practical supply chain experience.

Market scan and shortlisting. We help you navigate the vendor landscape with an informed, independent view — identifying which platforms are genuinely strong for your industry, your scale and your planning maturity, and which are better suited to different contexts.

RFx design and management. We design and manage the RFx process end to end — from document development through evaluation, demonstration management, reference checking and recommendation. Our approach emphasises scenario-based evaluation over feature checklists, ensuring the selection is based on demonstrated capability rather than written claims.

Commercial negotiation. We support procurement negotiations with APS vendors and implementation partners, bringing benchmarks and experience with technology contract structures to ensure the commercial terms protect your interests and reflect realistic expectations.

Implementation readiness. We help you prepare for implementation — designing the target planning processes, defining the organisational structure and roles, building the data readiness plan, and establishing the change management approach that will determine whether planners adopt the system or revert to spreadsheets.

Ongoing optimisation. Post-implementation, we support organisations in tuning and optimising their APS — recalibrating forecasting models, refining inventory parameters, improving S&OP process integration, and building internal capability to sustain and extend the system's value over time.

Our independence from any APS vendor means our recommendation is based entirely on which platform best fits your requirements, your data, your organisation and your budget — not on partnership arrangements or reseller margins. We've worked with organisations implementing platforms across the spectrum, from the major Leaders through to focused niche players, and our advice reflects that breadth of experience.

Getting this decision right

An APS investment is typically a multi-year, multi-million dollar commitment. The system you select will shape how your organisation plans demand, manages inventory, allocates supply and makes trade-off decisions for years to come. The planners who use it daily will either be empowered to make better decisions faster — or frustrated by a tool that doesn't fit their reality.

The RFx process is where you create the conditions for one of those outcomes or the other. A process that's rigorous about the right things — planning capability, practical demonstration, implementation credibility, total cost, change readiness — will consistently lead to better decisions than one that's rigorous about the wrong things: feature checklists, compliance documentation and licensing price.

If your organisation is considering an APS investment and you want to make sure the selection process is designed to deliver a system your planners will actually use, we'd welcome the conversation.

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