In Conversation at Trace: Nai Yan Yeap on what value really means, building client relationships, and the resilience gap in supply chains

Rhys joined Trace as a Manager in 2025, bringing nearly two decades of operational experience across retail and hospitality. Most recently at The Star Entertainment Group, he led strategy, process improvement, and stakeholder engagement across one of Australia's most complex hospitality environments—and as a founding member of The Star's Modern Slavery Working Group, championed dignity and transparency across their supply chains.

We sat down with Rhys to talk about what nearly two decades in operations taught him about consulting, why credibility matters more than methodology, and wher e he sees the biggest opportunities for organisations to improve experience and performance at the same time.

You spent almost 20 years inside businesses before moving into consulting. What does that background change about the way you work with clients?

RE: It makes me instinctively sceptical of solutions that look good on paper but haven’t been stress-tested against operational reality. I’ve worked across import, procurement, supply chain, and hospitality transformation — environments where the gap between a well-designed recommendation and what’s implementable can be significant. That sits with me when I’m building work for a client.

It also shapes how I enter a problem. I’m not leading with methodology, but with curiosity about what’s actually happening: where the friction is, what the team is working around, what the data is telling us. The guiding principle across my career has been understanding a system from the inside before trying to change it. In consulting, that instinct turns out to be a genuine asset.

The more industry-specific crafts of structuring problems, communicating recommendations, and managing engagements with rigour, is something I’ve continued to focus on over the last 18 months. But the underlying orientation was already there, and I think that combination is what led to my role here at Trace.

Your career has sat the intersection of customer experience and operational efficiency, which are often talked about as competing priorities. Do you see them that way?

RE: The tension is usually more apparent than real. When you trace most customer experience failures back to their root cause, you find an operational problem—a process gap, an inventory issue, a workflow creating inconsistency at the point of service. Improving the operation tends to improve the experience.

What makes it feel like a trade-off is usually a time horizon issue. The customer impact of an operational gap compounds until it shows up as a complaint, a lost regular, or a revenue trend. My approach has always been to make that linkage explicit early. Once it’s visible, the “balance” tends to resolve itself. 

Hospitality is one of the most operationally pressured environments there is. How do you get people to engage with improvement work when they're already flat out?

RE: You must start with the problems people are already feeling. Walking into a busy hospitality operation with a pre-determined transformation roadmap is a fast way to lose credibility. What works is listening first—understanding the workarounds, the friction points, the things that make people's days harder—and building improvement work around those, rather than arriving with a pre-formed agenda. In my experience, the most effective work comes from building strong relationships across all levels, from frontline teams through to the C-suite. In hospitality operations, you earn credibility and buy-in by proving your solutions hold up on the floor under pressure, not just in a workshop..

As a founding member of The Star's Modern Slavery Working Group, you focused on dignity and respect across supply chains. What drove you to get involved in that work, and what did you learn about the gap between policy and practice?

RE: Having spent almost a decade in both local and international procurement and supply chain, I had a clear view of how far removed conditions at the end of a supply chain can be from the values in a head office policy document. That visibility made it hard to stay on the sidelines.

What I learned is that the gap between policy and practice is rarely about bad intent, it’s structural. The commercial pressures flowing down through a supply chain don’t always carry the same values that sit in a sustainability framework. Closing that gap requires real visibility before it requires better policy; and that’s something I carry into supply chain advisory work now.

Treating people in supply chains with dignity creates better business outcomes, not just better ethics, that's something you've advocated for. Can you give an example of where you've seen that play out in practice?

RE: One place it starts is procurement decisions. Chasing the lowest price sounds rational, but it often just transfers cost pressure onto the supplier, and I’ve seen firsthand how that pressure creates the conditions for underpayment and poor working conditions further down the chain. The cheapest option frequently isn’t the best one when you account for what it costs elsewhere.

More broadly, supplier relationships built on transparency rather than pressure mean problems such as capacity constraints or quality risks surface earlier and get resolved before they become expensive. The alternative produces failures that are costly when they eventually land: disruptions, reputational damage, compliance issues.

It also shows up internally. The way people within an operation are treated shapes the culture guests encounter. Teams that feel respected bring a different quality of attention to the work, and customers notice, even when they can’t articulate why. Dignity isn’t separate from performance; it’s a driver of it. 

Looking ahead, where do you see the biggest opportunities for organisations to improve customer experience and operational performance at the same time?

RE: Visibility is key. Most organisations are generating operational data they’re not utilising effectively: demand patterns, process flow times, where and when things fail. When that data is properly structured and accessible to decision-makers, improvements follow naturally because you’re working on evidence rather than assumption.

Closely related is the smart use of AI to reduce administrative burden on employees, such as reporting, scheduling, and coordination tasks that consume time without directly serving a customer. Deploying that time back toward the guest experience is a meaningful gain; it’s about using existing resources better, not reducing headcount.

The third is supply chain resilience. The last few years exposed how brittle lean, cost-optimised supply chains can be. The redesign work happening now, in diversifying sources, building in redundancy, and improving responsiveness, has a direct customer benefit when done well. Fewer stockouts, fewer service failures, fewer moments where the business can’t deliver on what it’s promised.

Rebuilding Australia's Grid for the AI Era: The Supply Chain and Workforce Challenge

Australia is in the middle of the largest rebuild of its electricity grid in living memory. Coal is leaving the system, renewables are arriving at pace, and thousands of kilometres of new high voltage transmission are being planned and built to connect the two. That task was already stretching the country's energy transmission supply chain and its skilled workforce to the limit. Then artificial intelligence arrived, and with it a wave of data centre demand that lands directly on the same wires, the same equipment order books, and the same pool of electricians and engineers.

The popular framing is that Australia is "rebuilding the grid for AI." That is not quite right, and the distinction matters. The grid rebuild is driven first by the energy transition: the retirement of ageing coal generators and the commitment to 82 per cent renewable electricity by 2030. AI and the data centres that power it are not the cause of the rebuild. They are a powerful new load arriving on top of it, compressing timelines that were already tight and intensifying constraints that were already binding.

For Australian leaders in energy, infrastructure, government, and the businesses that supply them, the practical message is blunt. The constraint is no longer ambition, policy, or even capital. The constraint is whether the physical equipment and the skilled people can be secured fast enough. This article looks at the problem through a supply chain lens: what is being built, how AI changes the demand picture, why decades of underinvestment have left Australia on the back foot, and what commercial and government organisations should actually do about it.

What is actually being rebuilt

The blueprint is the Australian Energy Market Operator's 2024 Integrated System Plan, the official roadmap for the National Electricity Market. Its central conclusion is stark: to replace retiring coal with firmed renewables, Australia needs around 10,000 kilometres of new transmission lines, with roughly 4,581 kilometres of that required just to hit the 2030 targets, according to the Department of Climate Change, Energy, the Environment and Water. AEMO costs the transmission component at about $16 billion and expects it to deliver net market benefits of around $22 billion. The urgency comes from coal: AEMO projects that 90 per cent of today's coal capacity will close by 2035, and all of it before 2040.

Funding the network side is the Commonwealth's Rewiring the Nation programme, a $20 billion pool of concessional finance and equity administered through the Clean Energy Finance Corporation. The money is now flowing into the projects that form the new backbone of the east coast grid: HumeLink and VNI West connecting New South Wales and Victoria, Project EnergyConnect linking New South Wales and South Australia, Marinus Link under Bass Strait to Tasmania, Sydney Ring South, QNI Connect into Queensland, and renewable energy zone infrastructure such as the Central-West Orana REZ.

The important context is this. Australia committed to the rebuild, set it a hard deadline, and underwrote it with public money, all before AI demand became a serious factor. The plan was always going to test the country's ability to source equipment and skilled labour. AI did not create that test. It raised the difficulty.

How AI changes the demand maths

Data centres are still a modest share of Australian electricity demand, but the trajectory is steep. In 2024-25, data centres consumed around 4 terawatt hours, about 2 per cent of the NEM, equivalent to the electricity used by more than 700,000 homes, according to analysis for AEMO by Oxford Economics Australia. The same work projects that demand triples to nearly 12 terawatt hours by 2030, lifting data centres to about 6 per cent of NEM electricity, and reaches roughly 34 terawatt hours by 2049-50, around 12 per cent of the grid.

The pipeline behind committed projects is far larger. In New South Wales alone there were 44 data centres in the development pipeline as at 31 March 2026, totalling 11.4 gigawatts, which is roughly the output of four Eraring power stations, Australia's largest coal generator. A CEFC and Baringa report released in December 2025 forecasts that data centres could account for up to 11 per cent of national electricity consumption by 2035, up from about 1 per cent today, with the sector attracting between $85 billion and $135 billion in investment and growing capacity fourfold within a decade. Around half of all planned capacity is clustered in Sydney, with Melbourne hosting roughly a quarter.

Two features make this demand hard to plan for. The first is geographic concentration. AI data centres cluster around existing fibre, land, and power, so the load piles up in a handful of corridors, principally Western Sydney and parts of Melbourne, putting acute, localised pressure on the transformers and substations that serve those pockets. The second is uncertainty. Networks are receiving connection applications well beyond what will ever be built, a phenomenon the industry calls "phantom demand," where developers lodge speculative requests to hold a place in the queue. This makes forecasting genuinely difficult and creates a real risk of over-building for loads that never materialise, or under-building for the ones that do. For anyone planning supply, the AI boom is not a single clean signal. It is a noisy, concentrated, fast-moving demand layer on top of an already demanding transition.

How Australia ended up on the back foot

Australia is not rebuilding this grid from a position of strength. It is rebuilding after decades in which it barely needed to build at all, and the supply chain muscle for large-scale grid delivery has wasted. This is the part of the story that gets least attention and matters most.

The original grid was largely built out in the post-war decades. From the 1990s, with the network broadly in place and the sector privatised and restructured, investment shifted toward maintenance and incremental upgrades rather than nation-shaping transmission. For roughly a generation, Australia simply did not do large grid construction at scale. Commentators have long observed that the country's energy infrastructure suffered from a lack of long-term investment and planning, and even the recent past tells the story: the electricity transmission sector contracted slightly through the early 2020s before the current build began to turn it around.

Two things happened to the supply chain as a result. First, the domestic manufacturing base narrowed. As recently as 2000, government analysis found that 75 to 85 per cent of Australia's transformer capacity was made domestically. A capable local base still survives, led by long-standing names such as Wilson Transformer Company, but the largest and highest-voltage units, along with many specialist high-voltage components such as HVDC cable and cable accessories, are now imported. Industry analysis has explicitly flagged that this reliance on imported high-voltage specialist items has exposed supply chain vulnerabilities. Second, the skills and project-delivery base thinned. The workforce that built the original grid aged out, apprenticeship pipelines shrank against flat demand, and the engineering and construction ecosystem that knew how to deliver large transmission contracted. The Australia Institute has documented that real productivity in the electricity sector fell by around a third between 2007 and the early 2020s, with hands-on field roles giving way to administrative ones.

This is supply chain atrophy, and it behaves the same way in every industry. When a capability is not needed for twenty or thirty years, suppliers consolidate or exit, skilled people retire without being replaced, and the institutional knowledge of how to build at scale fades. None of it vanishes overnight, but rebuilding it takes years. Australia is trying to rebuild it at exactly the moment global demand for the same equipment and people is at a record high. That is what being on the back foot means in practice, and it is why the recovery is not simply about spending money. It is about re-standing-up manufacturing, trades, and delivery capability that were allowed to wither.

The equipment squeeze: availability is now the gating factor

The grid does not run on plans and finance. It runs on transformers, high voltage cables, switchgear, circuit breakers, conductors, and steel. Every one of those items now sits in a global queue, and a country that imports the largest units is near the back of it.

The International Energy Agency's 2025 report Building the Future Transmission Grid quantified the problem. Across the global market, it now takes two to three years to procure high voltage cables and up to four years to secure large power transformers, with lead times having almost doubled since 2021. Direct current cables, preferred for the long-distance interconnectors central to Australia's plan, can take more than five years. Wood Mackenzie's market surveys through 2025 told the same story, with standard power transformers averaging around 128 weeks for delivery and the largest units stretching to four years. Prices have moved with the lead times: the IEA found power transformer prices have risen roughly 75 per cent since 2019 and cable prices have nearly doubled, driven by raw materials such as grain-oriented electrical steel, which roughly doubled in price between 2021 and 2023.

The reason is that demand is not one wave but three, all peaking at once and drawing from the same supplier base: the global energy transition, the electrification of industry and transport, and the AI data centre build-out. Global power transformer trade was worth about USD 13.5 billion in 2023, and just four countries, China, Korea, Türkiye, and Italy, supply about half of it. A buyer in Sydney or Melbourne is bidding against utilities and hyperscalers worldwide for slots in that small set of factories.

The strategic consequence is simple to state and easy to underestimate: equipment availability has replaced capital and permitting as the primary constraint on infrastructure projects. A facility that breaks ground today cannot energise on a conventional timeline, because the transformer it needs was effectively ordered years before the business case was signed. Procurement of long-lead equipment must now happen before final investment approval, not after it. The order book, not the project schedule, sets the real delivery date.

The workforce squeeze: a harder constraint than steel

If equipment is the visible constraint, the workforce is the deeper one, because a skilled tradesperson cannot be imported overnight and an apprentice takes four years to train.

Jobs and Skills Australia's report The Clean Energy Generation estimates that Australia needs around 32,000 additional electricians by 2030 to deliver the renewable target, and roughly 85,000 more by 2050, well beyond projected supply. It notes that more than half of Australia's electrical engineers were born overseas, leaving the pipeline heavily reliant on migration. Modelling of the 2024 ISP by the RACE for 2030 research centre found electricity sector employment is likely to double by 2029, an increase of about 33,000 workers in five years. The supply side is not keeping up: the Powering Skills Organisation projects an energy sector shortfall of more than 14,000 electricians by 2030, and estimates Australia needs around 20,500 apprentice electricians to commence each year through to 2030, about 40 per cent above the recent average. Close to half of electrotechnology apprentices drop out before finishing, and roughly 2.4 energy workers are approaching retirement for every new entrant under 25. This makes the shortage structural, not cyclical.

Three dynamics turn these national figures into project-level risk. The work is front-loaded into a construction peak in the late 2020s before shifting to operations and maintenance, so everyone needs the same trades in the same few years. The bulk of the work is in regional Australia, competing for the same people needed by capital-city infrastructure and, increasingly, by data centres concentrated in a few corridors. And the competition is global: the United States needs around a million additional electricians, and the IEA estimates the worldwide net zero effort requires 30 million new clean energy workers by 2030. Australia is recruiting from the same pool as everyone else, and it is no longer the automatic destination of choice. It is no surprise that an alliance of industry groups, unions, community organisations, and environmental bodies has proposed that data centres setting up in Australia be required to contribute to local energy supply and skills, rather than simply drawing on capacity others have built.

The network design challenge, and how modelling solves it

The temptation is to manage equipment and workforce one project at a time. That badly underestimates the danger, because the constraints are correlated. The same narrow window sees transmission, renewables, electrification, and AI data centres all pulling on the same transformers, cables, and crews. When a scarce resource is drawn by an entire economy at once, the risk is not just higher cost. It is that a project cannot buy the equipment or hire the people at any price within its schedule. You cannot manage correlated, systemic risk with project-by-project workarounds.

This is why the problem is, at its heart, a supply chain network design problem before it is an engineering one. The relevant network is not only the wires. It is the end-to-end equipment supply network that feeds the build: a thin set of global suppliers, oversized heavy-lift logistics through constrained ports and road corridors, staging and laydown, strategic spares, and a workforce that must be deployed across dispersed regional sites in the right sequence. Designing that network well, under genuine uncertainty, is hard for three reasons that compound each other: demand is uncertain and inflated by phantom requests, supply is long-lead and globally contested, and the work is geographically concentrated and time-compressed. A single point forecast will be wrong, and any plan built on one will fail at the first delayed transformer.

This is where scenario modelling earns its place. Rather than betting on one demand future, you model several: high and low data centre uptake, phantom versus real connections, faster or slower coal exit. You test sourcing strategies against realistic distributions of lead times rather than optimistic averages. You quantify the cost and risk of holding strategic spares against the cost of sourcing reactively when a unit fails in service. And you identify the decisions that hold up across all those scenarios, robust choices rather than ones that are optimal only for a forecast that will not eventuate.

Network optimisation then turns that insight into decisions. It tells you where to source to reduce single-point dependence, where to position critical inventory including shared transformer spares pools, how to sequence and stage builds to smooth the equipment and labour peak rather than amplify it, and how to design the inbound logistics network for equipment that needs heavy-lift handling and special road routes. Done well, this is the difference between a pipeline that overwhelms the supply chain and one the supply chain can actually serve. This is core supply chain network design, and it is exactly the kind of problem that rigorous demand modelling and supply chain analytics are built to solve, so that ordering and staging decisions are made early enough to matter rather than reconstructed after a delay has already happened.

What commercial organisations should do

For network operators, renewable developers, engineering and construction firms, equipment suppliers, and the data centre operators building their own grid connections, the practical priorities are clear. Start with an honest diagnostic of where you are actually exposed on the critical path, then act on it.

Decouple procurement from financial close. Order long-lead items, transformers and HVDC cable in particular, ahead of final investment decision, and secure factory slots and supplier relationships early. Treating procurement as a task to be sorted once funding lands is the most common way projects slip by years.

Build visibility below the first tier. The binding constraint is often a sub-component, a bushing, a tap changer, grain-oriented steel, or a cable accessory, not the headline unit. Map that exposure so a single shortage cannot quietly hold up everything else.

Treat strategic inventory as a resilience asset, not a cost. Hold or pool critical spares, especially large transformers, rather than sourcing reactively. The carrying cost is small against the cost of a stranded, near-complete project.

Plan against scenarios, not a single forecast. Use scenario modelling and network optimisation to choose sourcing, inventory, and logistics decisions that perform across a range of demand and lead-time futures, rather than optimising for one number that is almost certain to be wrong.

Build the workforce as a multi-year capability. Map the trades and engineering skills each phase needs, smooth your pipeline to avoid the worst of the boom-bust peak, plan the transition from construction crews to long-term operations and maintenance teams, and invest in apprenticeships and retention rather than assuming you can simply hire at the peak.

Diversify and qualify supply. Reduce single-source dependence, qualify alternative suppliers before you need them, and consider domestic assembly, refurbishment, and local content where it improves resilience and speed.

What government should do

Government is both the system steward and, collectively, the largest client. Its choices shape whether the supply chain can cope. The most useful interventions are supply chain decisions, not new regulation.

Coordinate and smooth the national pipeline. Sequence the build so the country does not place its entire demand for transformers, cables, and crews into the same two or three years. Smoothing the pipeline is a supply chain decision with national consequences, and it is the single highest-leverage move available.

Use collective buying power. Aggregate and coordinate procurement across projects and jurisdictions to place early, large, bundled orders that secure scarce global manufacturing capacity, through framework and advance-purchase agreements rather than each project competing alone and late.

Invest deliberately in sovereign capability. Support domestic manufacturing, assembly, refurbishment, and a national strategic-spares pool where the resilience value exceeds the lowest-first-cost case. The value of supply chain security does not show up in a standard return calculation, and judging these investments on that basis alone guarantees they never get built.

Fund the workforce pipeline at scale. Treat training capacity, apprenticeships, migration pathways, and retention as infrastructure with a four-year lead time. The crews needed at the late-decade peak have to start training now.

Improve the quality of the demand signal. Reduce phantom connection requests so planners and suppliers can invest against real demand rather than speculative queues. Better signal quality lowers the risk of both over-building and under-building.

De-risk inbound logistics. Prioritise the ports, heavy-lift corridors, and oversized-load routes that imported equipment depends on, so the last mile does not become the bottleneck after a transformer has crossed an ocean.

How Trace Consultants can help

Trace works at the intersection of supply chain, procurement, and workforce, which is precisely where the grid rebuild is constrained. We help asset owners, infrastructure developers, government bodies, and the firms that supply them turn an ambitious build programme into a deliverable one.

Supply chain strategy and network design. We model the end-to-end equipment supply network, run the demand and lead-time scenarios, and optimise sourcing, strategic inventory, and inbound logistics so the pipeline is one the supply chain can actually serve. Explore our strategy and network design capability.

Procurement and category strategy. We help organisations bring forward and de-risk the procurement of transformers, cables, switchgear, and conductors, building the supplier relationships and contracting approaches that secure capacity early rather than competing for it late. See our procurement services.

Resilience and risk management. We build the supply chain risk frameworks that make correlated equipment and labour risk visible at board level, so it can be managed deliberately across a portfolio. Learn more about resilience and risk management.

Strategic workforce planning. We map the trades and engineering skills each phase requires, model supply against a tight and ageing labour market, and design the transition from construction to long-term operations and maintenance. Explore our workforce planning capability.

Planning technology. Where the challenge calls for it, we implement advanced planning and analytics tools that bring rigour to demand scenarios, long-lead procurement, and strategic inventory, turning uncertain forecasts into early, defensible decisions. See our technology services.

For government clients and defence-adjacent infrastructure, where security of supply and sovereign capability carry additional weight, we bring sector experience through our government and defence practice, and we support major build programmes with project and change management.

Conclusion

Australia is rebuilding its electricity grid for the energy transition, and AI data centre demand has arrived on top of that rebuild at the worst possible moment for a supply chain that spent a generation winding down. The capital is largely committed and the plan is clear. What is not guaranteed is that the transformers, cables, and skilled people can be secured fast enough to deliver it, especially after decades in which the country let that capability atrophy. This is a supply chain and workforce challenge before it is anything else, and it rewards organisations that plan early and punishes those that wait.

If you are delivering grid, generation, or data centre infrastructure in Australia, now is the time to pressure-test your supply chain and workforce plans against the reality of global lead times, a thin domestic base, and a tight labour market.

Explore our supply chain strategy and network design capability →

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Related reading: Resilience and risk management · Strategic workforce planning · Trace Insights

ERP vs APS: Why Your ERP Was Never Built to Plan

Walk into the planning team of almost any mid-sized or large Australian business and you will find the same thing. There is a multi-million-dollar ERP running the company, whether it is SAP, Oracle, or Microsoft Dynamics, and the people responsible for planning are quietly doing the actual work in Excel. The forecast lives in a spreadsheet. The safety stock calculation lives in another. The replenishment plan gets exported, massaged, and re-keyed back in. The ERP, for all its cost and reach, has become the place decisions get entered rather than the place they get made. That gap is the whole substance of the ERP vs APS question, and it is the reason advanced planning systems exist.

The question matters because the answer is rarely obvious from the inside. An ERP is a serious, expensive, business-critical system, and it is natural to assume that something so central must be capable of planning the supply chain it runs. It usually is not, at least not well, and the confusion between what an ERP does and what an advanced planning system, or APS, does costs Australian businesses a great deal in working capital, service, and wasted planner time. This article sets out the real difference between the two, which ERP planning modules businesses most commonly outgrow, what an APS genuinely adds, and, just as importantly, when your ERP is actually enough and you should not spend a dollar on anything more.

Why this question is landing on more desks right now

There is a practical reason the ERP vs APS conversation has become more common over the past two years, and it is worth naming because it changes the stakes. A large wave of ERP modernisation is underway, driven hardest by SAP. SAP has announced the end of mainstream support for ECC by December 31, 2027, after which no more security patches, compliance updates, or fixes will be delivered. Extended maintenance is available until 2030, at a significant cost premium. Yet the migration is running slowly. A Gartner report showed that in seven years licences for SAP S/4HANA have only been purchased by a third of SAP ECC customers, and 18 to 36 months is typical for a migration, longer in complex environments. Kellton + 3

This matters for planning because an ERP migration is the moment the planning gap becomes impossible to ignore. When a business re-platforms its core system, someone finally asks what the new ERP will do for demand forecasting, inventory optimisation, and replenishment, and discovers that the answer is "not much that it did not do before." The migration surfaces a decision that was always there but easy to defer: whether to keep planning in spreadsheets bolted onto a transactional system, or to put a purpose-built planning layer in place. Getting that decision right at the point of an ERP refresh is far cheaper than retrofitting it later, which is why so many Australian businesses are confronting ERP vs APS now rather than in a few years' time.

The cost of getting it wrong is not abstract. For most businesses, carrying costs typically range between 20% and 30% of total average inventory value per year, according to the Institute for Supply Management. Planning quality is the single biggest lever on how much of the wrong stock you hold, and a transactional system that cannot optimise leaves most of that lever untouched. Fishbowl Inventory

The core distinction: a system of record versus a system of decision

The cleanest way to understand ERP vs APS is to recognise that they are built to do fundamentally different jobs, and being excellent at one tells you almost nothing about being good at the other.

An ERP, or enterprise resource planning system, is a system of record. Its job is to transact and to hold the single, authoritative version of what is true: the orders, the inventory positions, the financials, the bills of material, the supplier records. It executes processes reliably at scale, it keeps the data consistent, and it is the backbone every other system relies on. SAP, Oracle, Microsoft Dynamics, and the rest are extraordinarily good at this. When you place a purchase order, receive stock, or close the month, the ERP is doing exactly what it was designed to do.

An advanced planning system is a system of decision. Its job is not to record what happened but to work out what should happen next: how much to forecast, how much buffer to hold and where, when and how much to reorder, how to sequence production, and where to position inventory across the network. It does this through optimisation, heuristics, and increasingly machine learning, generating plans, simulating trade-offs, and recommending the decision that best balances cost and service against the constraints of the business. Where the ERP answers "what is true," the APS answers "what is the best thing to do about it."

That distinction is the whole game, and it explains the most common misunderstanding in the market. The ERP planning modules disappoint not because they are badly built, but because they were designed to execute a plan, not to work out what the plan should be. Asking your ERP to optimise your supply chain is like asking your general ledger to set your pricing strategy. It holds the numbers faithfully. It was never meant to decide them.

Why the ERP planning module disappoints

Most ERPs ship with planning functionality, usually centred on material requirements planning, and businesses understandably assume this covers them. In practice the limitations show up quickly, and they are structural rather than something a better configuration will fix.

The foundation of ERP planning is MRP, which works backwards from a production or demand plan to determine what materials are needed and when. MRP is genuinely useful and it has been around for decades, but on its own it assumes infinite capacity. It will happily generate a plan that requires more output than your factory or your suppliers can actually deliver, because checking feasibility against real constraints is not what it does. The result is a schedule that looks tidy on screen and falls apart in the real world, which is why planners learn not to trust it and revert to manual workarounds.

The deeper issue is that ERP planning is rules-based, not optimisation-based. It applies the parameters you give it: a fixed lead time in a field, a reorder point someone set two years ago, a flat number of weeks of cover across the catalogue. It does not search across thousands of possible plans to find the one that minimises total cost at your target service level, because it has no optimisation engine to do so. This is the difference between a calculator and a strategist. The ERP will faithfully compute the consequences of the policy you entered. It will not tell you that the policy is wrong, or find you a better one.

Three specific gaps follow from this, and they are exactly where the money sits. ERP planning treats lead time as a single fixed number rather than the variable, shifting distribution it actually is, which forces businesses to either guess high and carry excess or guess low and run short. It sets safety stock one location at a time, with no ability to optimise inventory across a multi-tier network, so every warehouse buffers against the same uncertainty and the business pays for the same protection several times over. And it produces a single forecast number rather than a probabilistic range, which is far less honest about uncertainty and far less useful for setting buffers intelligently. None of these are configuration problems. They are the boundaries of what a transactional system was built to do.

This is the same reason spreadsheets eventually fail, and it is no coincidence that businesses outgrowing their ERP planning are usually drowning in spreadsheets at the same time. The ERP cannot do the sophisticated planning maths, so planners export the data and attempt it manually, which does not scale, breaks when the person who built the model leaves, and produces the familiar situation where two reports disagree and nobody can explain why. The spreadsheet is not the disease. It is the symptom of an ERP being asked to do a job it was never designed for.

Which ERP planning systems businesses outgrow

The ERP vs APS question looks slightly different depending on which platform you run, but the pattern is the same across all of them: the native planning is MRP and reorder-point logic, supplemented heavily by spreadsheets, and it is that combination businesses outgrow. These are the systems we most often see prospects planning in before they move to an APS.

SAP (ECC and S/4HANA). The core planning is MRP and production planning. Larger SAP sites often added SAP APO, the Advanced Planner and Optimizer, which was SAP's APS application, the first version of which launched in 1998. APO is now being retired, and how SAP has handled that retirement actually proves the point of this article. APO's Demand Planning and Supply Network Planning are now covered by SAP IBP, while Production Planning and Detailed Scheduling and Global Available-to-Promise live on as embedded functionalities in SAP S/4HANA. SAP mainstream maintenance for APO ends in 2027. The successor, SAP IBP, introduced in 2015, is a cloud planning suite and is itself an APS. In other words, SAP's own roadmap keeps the planning layer separate from the ERP core, which is exactly the distinction we are describing. SAP shops weighing this are really choosing between adopting IBP and going best-of-breed with a platform such as Kinaxis, o9, or Blue Yonder. Implement + 3

Oracle (E-Business Suite, JD Edwards, and Fusion Cloud SCM). Oracle's ERPs carry native planning that is commonly outgrown once multi-echelon optimisation, demand sensing, or genuine scenario modelling are needed. Oracle offers its own supply chain planning cloud, and many Oracle customers also evaluate independent APS platforms alongside it.

Microsoft Dynamics 365 (Finance and Supply Chain Management, formerly AX). Dynamics uses MRP-based master planning, which is adequate for simpler operations and very commonly outgrown as range, network complexity, and demand volatility grow. It is one of the platforms we most often see prospects supplementing heavily with spreadsheets.

NetSuite. Oracle's cloud ERP is ubiquitous among growing Australian businesses, and its demand planning is deliberately light. It is usually the first system a scaling business outgrows on the planning side, well before it outgrows the ERP itself.

Pronto Xi. A long-standing Australian ERP common in distribution, manufacturing, and retail, with reorder-point planning that is typically run alongside extensive Excel models.

TechnologyOne. The major Australian ERP across government, local councils, universities, and health, where its supply chain and planning capability is thin and planning generally lives outside the system entirely. For public-sector and asset-intensive organisations this is a frequent starting point.

Infor, Epicor, Sage, QAD, IFS, and MYOB Advance. The mid-market field, used widely across Australian manufacturing and distribution. Each carries some planning functionality, and all of it is commonly outgrown for the same structural reasons set out above.

What businesses move to splits into two groups. The first is best-of-breed advanced planning platforms such as Kinaxis, o9, Blue Yonder, RELEX, ToolsGroup, OMP, Logility, and GAINS, which handle optimisation, multi-echelon inventory, and demand sensing at scale. The second, at the smaller and more inventory-focused end, is lighter tools such as Netstock and Slim4. The right destination depends entirely on the size and complexity of the supply chain and the maturity of the planning process behind it.

What an APS actually does that your ERP cannot

An advanced planning system sits above the ERP, drawing on its data as the single source of truth and feeding decisions back into it for execution. It is not a replacement for the ERP and it does not compete with it. It does the planning the ERP was never built to do, and the modern generation of these platforms has moved well beyond the rules-based calculation that ERP planning offers.

The defining capability is optimisation. An APS can search across an enormous number of possible plans and find the one that best balances service and cost against the real constraints of capacity, minimum order quantities, shelf life, and supplier limits. This is the step-change, because it shifts planning from "compute the result of my assumptions" to "find me the best decision." On top of optimisation, the leading platforms layer heuristics and machine learning to handle problems that are too large or too uncertain for classical methods, and to incorporate the variables, such as promotions, pricing, and weather, that statistical forecasting alone struggles with.

Several specific capabilities follow, and they map directly onto the ERP's gaps. An APS performs multi-echelon inventory optimisation, looking at the whole network at once and deciding where it is cheapest and most effective to hold buffer, which typically delivers the same or better service from materially less stock. It supports demand sensing, reading short-term signals to adjust the near-term forecast so the plan reflects what is happening this week rather than what a model assumed last quarter. The more capable platforms can predict lead times rather than assuming them, turning the same machine learning techniques used in demand forecasting onto the inbound side. And critically, an APS enables scenario modelling and exception-based planning, so the business can test "what if" before committing, and planners can spend their time on the genuine exceptions rather than re-keying thousands of routine orders that a system should generate automatically.

The combined effect is significant and measurable. On Trace's own planning and operations work, we typically see forecast accuracy improvements in the range of 20 to 40 per cent where businesses move from spreadsheet-driven planning to a structured process supported by an advanced planning system, and inventory carrying cost reductions of up to 30 per cent off the back of better demand and inventory planning. Those two outcomes are linked, because the inventory reduction is largely a consequence of the accuracy and optimisation gain. We explore the underlying mechanics of this in our piece on how advanced planning systems transform supply chain planning and on demand, inventory, and replenishment as a source of competitive advantage.

The honest part: when your ERP is actually enough

Not every business needs an APS, and one of the more useful things a genuinely independent adviser can tell you is when the answer is no. The ERP vs APS decision is not a question of sophistication for its own sake, and buying planning software you do not need is its own kind of waste.

For a smaller business with a simple supply chain, a limited range, stable demand, and short, reliable lead times, the planning functionality in a modern ERP such as Dynamics 365 or NetSuite, supplemented by some well-built spreadsheets, can be perfectly adequate. The maths that defeats an ERP, multi-echelon optimisation across thousands of items, probabilistic forecasting, network design, only becomes necessary when the scale and complexity of the supply chain make manual approaches genuinely unworkable. If your business is not at that point, an APS is an expensive answer to a question you do not have.

There is a more important caution underneath this. An APS is not a fix for a broken planning operating model, and the businesses that get the least from these platforms are usually the ones that bought the technology hoping it would supply the discipline their organisation lacked. A planning system amplifies the quality of the process it sits on top of. Implement it on clean data, a sound segmentation, a working sales and operations process, and clear accountability, and it is transformative. Implement it on the same broken process that produced the spreadsheet chaos, and all you get is faster, more expensive chaos. If your planning problems are really process and ownership problems, no amount of ERP-versus-APS deliberation will solve them, and the right first move is to fix the operating model, a theme we return to constantly, including in our look at why S&OP so often fails in Australia.

The same logic applies to the current excitement about autonomous, agentic planning. Layering intelligent automation onto an immature data and process base produces confident, fast, wrong decisions, which is why we take a deliberately pragmatic view in our article on agentic AI in the supply chain. The sequence that works, every time, is people, then process, then technology.

The symptoms that you have outgrown ERP planning

If you are weighing ERP vs APS, the most reliable signal is not the size of your business but the behaviour of your planning function. A few patterns reliably indicate that your supply chain has outgrown what SAP, Oracle, Dynamics, or any other ERP can do for planning:

• Your planners spend most of their time maintaining spreadsheets rather than planning, exporting data out of the ERP, working it manually, and keying decisions back in.
• You set safety stock and reorder points by rule of thumb, typically a flat number of weeks of cover, because the ERP cannot calculate buffers properly against demand and lead time variability.
• You cannot model a change before you make it, so questions like "what happens to service if we consolidate two DCs" get answered by instinct rather than analysis.
• Two reports disagree and nobody can fully explain why, because several people maintain their own versions of the plan and there is no single optimised source.
• Your forecast is a single number that is regularly wrong, with no sense of the range of likely outcomes and no systematic way to improve it. If forecasting is your weak point specifically, our guide to improving demand forecasting accuracy is the place to start.
• You are carrying too much of the wrong stock and too little of the right stock at the same time, expediting freight in one category while writing down ageing inventory in another.

One or two of these can often be addressed with better process. When most of them are true at once, you have reached the limit of ERP planning, and an APS is worth serious evaluation, provided the process and data foundations are sound enough to build on.

Getting the sequence right, especially during an ERP migration

For the many Australian businesses currently migrating their ERP, an SAP move to S/4HANA in particular, there is a real opportunity and a real trap. The opportunity is that a re-platform is the natural moment to design the planning layer deliberately rather than inheriting whatever the new ERP happens to offer. The trap is assuming the new ERP will close the planning gap on its own. It will be a better, more modern system of record. It will still not be a system of decision, because that is not what an ERP is for, regardless of vintage. As SAP's own split of APO into IBP and embedded S/4HANA functionality shows, even the vendor treats planning as a separate layer.

The sensible approach is to treat the ERP and the planning capability as two related but distinct decisions, and to get the foundations right first. Establish clean master data, the item attributes, lead times, costs, and supplier information that any planning system runs on, because poor data produces wrong plans no matter how good the optimisation engine is, and data readiness is a workstream in its own right rather than a box to tick. Get the demand forecast trusted and separated from the sales target. Set inventory policy deliberately against a service target. Stand up a planning and sales-and-operations process that actually makes decisions. Do that, and you will capture much of the available value before you have spent anything on an APS, and you will be in a far stronger position to choose and implement one if you decide you need it. Our broader perspective on this sits across our planning and operations and technology capabilities, and the full picture is set out in our guide to supply chain planning for Australia.

How Trace Consultants can help

At Trace Consultants, we help Australian and New Zealand businesses navigate exactly this decision, and we do it without a platform to sell, which means our advice is about what your business actually needs rather than what we are trying to move. Our practitioners have built and run planning processes inside businesses and have selected and implemented planning systems across retail, FMCG, and manufacturing, so we know that the technology is the easy part and the operating model is where the value is won or lost.

We tell you honestly whether you need an APS at all. We assess where your planning capability genuinely sits today, across forecasting, inventory, lead time, and S&OP, and give you a clear-eyed view of whether your ERP and a tighter process would serve you, or whether the scale and complexity of your supply chain genuinely warrant an advanced planning system. No vendor incentive, just your numbers.

We fix the process and data before the technology. Through our planning and operations work, we get the demand forecast trusted, the inventory policy set deliberately, the master data clean enough to plan from, and the S&OP process making real decisions, so that whatever technology follows amplifies a process that already works rather than accelerating one that does not.

We select and implement the right platform, properly. When an APS is justified, we help you choose the platform that fits your situation, whether that is SAP IBP alongside an S/4HANA programme or an independent platform, and implement it on a sound foundation. We bring genuine implementation experience, including with platforms such as GAINS, and a structured methodology that treats the rollout as a capability change rather than a software install. More on our approach sits on our technology page.

We connect planning to the structural decisions. Where you hold inventory is both a planning decision and a network decision, so our strategy and network design work makes sure the two are designed together rather than in separate rooms, and our sector depth across FMCG and manufacturing and retail means the approach fits how your business actually operates.

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Where to begin

If you are weighing ERP versus APS, resist the urge to start with a vendor demonstration. Start by being honest about where your planning capability actually is and what is really causing the pain. A short diagnostic across forecasting, inventory, lead time, and your S&OP process will usually reveal quickly whether your problem is a technology gap that an APS would close or a process and data gap that no software will fix. For most businesses the value is not evenly spread, and two or three disciplines are quietly costing the majority of the lost value.

From there, the sequence is the same whether or not you end up buying a platform. Fix the data discipline and the process first. Get the forecast trusted and separated from the target. Set inventory policy against a service level rather than habit. Stand up a planning process that makes decisions. If, having done that, the scale and complexity of your supply chain still defeat what your ERP can do, then evaluate an APS from a position of strength, with a clean foundation for it to optimise. That is the difference between a planning system that transforms the business and one that becomes an expensive disappointment.

Your ERP is the system that runs your business, and a good one is worth every dollar. It was simply never built to plan your supply chain. Knowing the difference, and acting on it in the right order, is what separates the businesses that take cost out and free up capital from the ones that keep firefighting the symptoms with the most expensive tools money can buy.

Frequently asked questions about ERP vs APS

What is the difference between an ERP and an APS?

An ERP, or enterprise resource planning system, is a system of record. It transacts and holds the authoritative version of your orders, inventory, financials, and master data, and it executes processes reliably at scale. An advanced planning system, or APS, is a system of decision. It works out what should happen next, the forecast, the inventory buffers, the replenishment, the production sequence, using optimisation, heuristics, and machine learning. The ERP answers what is true. The APS answers what is the best thing to do about it.

Can SAP, Oracle, or Microsoft Dynamics handle supply chain planning?

They can all do basic planning, usually through MRP, but they were designed to execute a plan rather than to work out what the plan should be. ERP planning is rules-based rather than optimisation-based, assumes infinite capacity, treats lead time as a fixed number, sets safety stock one location at a time, and produces a single forecast rather than a probabilistic range. For a small, simple supply chain that can be enough. For a complex one it leaves most of the value on the table, which is why businesses on SAP, Oracle, and Dynamics so often end up doing the real planning in spreadsheets.

What happens to SAP APO, and do I need SAP IBP or a third-party APS?

SAP APO is being retired, with mainstream maintenance ending in 2027. Its demand and supply network planning move to SAP IBP, and its detailed scheduling embeds into S/4HANA. SAP IBP is a cloud APS in its own right, so businesses face a genuine choice between adopting IBP and selecting an independent platform such as Kinaxis, o9, or Blue Yonder. The right answer depends on your wider SAP roadmap, your planning complexity, and the maturity of your planning process, and it should follow a clear-eyed assessment rather than a default to the incumbent.

Do I need an advanced planning system?

Not necessarily. If your supply chain is small and simple, with stable demand and reliable lead times, your ERP plus good spreadsheets may be adequate. You should consider an APS when the scale and complexity of your supply chain make manual planning genuinely unworkable, when multi-echelon optimisation and probabilistic forecasting would deliver real value, and, crucially, only once your planning process and data are sound enough to build on. An APS amplifies a good operating model and accelerates a bad one.

Does an APS replace my ERP?

No. An APS sits above the ERP, draws on its data as the single source of truth, and feeds planning decisions back into it for execution. The two systems do different jobs and work together. You keep the ERP as your system of record and add the APS as your system of decision.

Should I sort out planning during an ERP migration?

Yes, an ERP migration such as a move to S/4HANA is the natural moment to design your planning layer deliberately, but do not assume the new ERP will close the planning gap on its own. A newer ERP is still a system of record, not a system of decision. Treat the ERP and the planning capability as related but distinct decisions, fix the data and process foundations first, and then decide whether an APS is justified.

Related reading: Supply Chain Planning: A Guide for Australia · How Advanced Planning Systems Transform Supply Chain Planning · How to Improve Demand Forecasting Accuracy · S&OP That Actually Works in Australia