The Hype Is Loud. The Opportunity Is Real — but Specific.
Every retail conference in Australia right now features at least three panels on AI. The vendor pitches are relentless. The consulting decks are thick with promises about "transformative potential" and "AI-powered supply chains of the future." And somewhere in the middle of all that noise, a head of supply chain planning at an Australian retailer is trying to work out whether AI can actually help them solve the problems they're dealing with this quarter — not in 2030, but right now.
The honest answer is: yes, but not everywhere equally, and not without doing some unglamorous groundwork first.
AI's most immediate and measurable impact in Australian retail isn't happening in customer-facing applications like chatbots or visual search. It's happening in the supply chain — in demand planning, inventory positioning, warehouse operations, transport optimisation, and supplier management. These are the areas where even modest improvements in accuracy or efficiency translate directly into margin, working capital, and customer experience.
The challenge for most mid-market and growing retailers is knowing where to start, what's realistic given their data and systems maturity, and how to avoid spending six months on a proof of concept that never makes it to production. This article is our attempt to cut through the noise and lay out what's actually working in retail supply chains today, what's genuinely practical for Australian businesses, and where the effort is best spent.
1. Demand Forecasting: Where AI Earns Its Keep Fastest
If you're going to start anywhere, start here. Demand forecasting is the single area where AI delivers the most consistent, measurable value for retailers. The reason is straightforward: traditional forecasting methods — exponential smoothing, moving averages, even basic statistical models — rely heavily on historical sales patterns. They work reasonably well when demand is stable and predictable. They fall apart when it isn't.
Post-pandemic, Australian retail demand has been anything but predictable. Consumer behaviour shifted dramatically during COVID, and the patterns haven't fully reverted. Weather volatility, promotional cannibalisation, social media-driven spikes, cost-of-living pressures, and channel-shifting between online and in-store all make traditional forecasting less reliable than it used to be.
Machine learning models handle this complexity better because they can ingest a much wider range of demand signals — not just historical sales, but weather data, event calendars, promotional plans, social media trends, competitor activity, even local economic indicators — and identify non-linear relationships that statistical models miss. Coles, for instance, has been using AI models informed by over 100 variables to forecast fresh produce demand across its store network, helping reduce waste while keeping availability high. Woolworths has invested heavily in AI-driven demand planning, and publicly noted that their availability metrics are at their best levels since before COVID.
You don't need to be Coles or Woolworths to benefit. The practical starting point for most mid-market retailers is to layer a machine learning forecasting engine onto your existing demand planning process — not to replace your planners, but to augment them. Start with your highest-volume or most volatile categories, where forecast error has the biggest impact on either stockouts or markdowns. Measure the improvement in forecast accuracy (typically expressed as mean absolute percentage error, or MAPE) and the downstream impact on inventory and service levels.
The technology is more accessible than many retailers assume. Cloud-based forecasting tools from providers like o9 Solutions, Blue Yonder, and others now offer scalable AI forecasting that doesn't require a massive IT build. For organisations already invested in the Microsoft ecosystem, Power BI combined with Azure Machine Learning can deliver surprisingly capable forecasting at a fraction of the cost of a full APS deployment.
One word of caution: AI forecasting is only as good as the data it's trained on. If your sales data is riddled with stockout periods (where you can't distinguish between zero demand and zero availability), if your promotional history is incomplete, or if your product hierarchy is a mess, the model will underperform. Getting the data foundations right is the unsexy but essential first step. Our team regularly works with retailers on exactly this kind of planning and operations uplift — cleaning the data, structuring the inputs, and building the processes around the technology so that it actually delivers.
2. Inventory Optimisation: Putting Stock Where It Matters
Better demand forecasting is only half the equation. The other half is using those improved forecasts to position inventory more intelligently across the network.
Most Australian retailers with multi-tier distribution — central DC to state-based DCs to stores — still set safety stock parameters using rules of thumb that haven't been revisited in years. The result is often too much stock in the wrong places: slow movers clogging DC capacity while fast movers go out of stock at store level, or excess inventory built up as a buffer against forecast uncertainty that the AI forecasting engine has already reduced.
AI-powered inventory optimisation takes the improved demand signal and combines it with lead time variability, service level targets, storage constraints, and cost-to-serve data to recommend optimal stock levels at each node in the network. For retailers with complex, multi-echelon distribution networks, the shift from single-node to network-wide inventory optimisation can unlock working capital reductions of 15–25% while maintaining or improving product availability.
The practical application for most retailers starts with a segmentation exercise. Not every SKU warrants the same treatment. AI helps here too — clustering algorithms can segment your product range based on demand characteristics (volume, variability, trend, seasonality, lifecycle stage) and assign differentiated inventory policies to each segment. Your fast-moving, predictable staples get one treatment; your long-tail, intermittent-demand items get another; and your new product introductions get a third.
This kind of work doesn't necessarily require a massive technology investment upfront. We've helped retailers build pragmatic inventory optimisation models using a combination of analytics tools and structured planning processes, which can be scaled up as the organisation's data and technology maturity grows.
3. Warehouse Operations: AI on the Floor
Warehouse automation gets a lot of attention — Woolworths' $2 billion-plus investment in automated fulfilment centres at Auburn and Moorebank is a case in point. But you don't need to build a fully automated DC to get value from AI in warehouse operations.
The more immediate opportunities for most retailers sit in three areas.
Labour planning and task allocation. AI can forecast pick volumes by hour and day, allowing warehouse managers to roster the right number of staff for the actual workload rather than relying on averages. This is especially valuable in environments with variable demand — promotional peaks, seasonal surges, online order spikes — where getting labour allocation wrong means either paying for idle time or scrambling to cover shortfalls. Our workforce planning and scheduling team works with distribution operations to build exactly these kinds of demand-driven labour models.
Slotting optimisation. AI can analyse order patterns, pick frequencies, and product characteristics to recommend optimal product placement within a warehouse — positioning fast-moving items closer to pack stations, grouping frequently co-ordered items together, and dynamically re-slotting as demand patterns shift through the year. This reduces average pick path length, improves throughput, and reduces labour cost per unit — often by 10–20% in facilities that haven't been re-slotted in a while.
Quality and exception management. Machine learning models can identify patterns in receiving errors, mis-picks, short-ships, and returns, flagging root causes that manual review might miss. For retailers with high return rates (particularly in online fashion), AI-driven analysis of return reasons, product attributes, and supplier patterns can inform both warehouse process changes and upstream decisions about sizing, product descriptions, and supplier quality.
If you're operating or designing a warehousing and distribution network, AI should be a consideration at the strategy level — not just for automation decisions, but for the planning, labour, and process optimisation that surrounds the physical infrastructure.
4. Transport and Last-Mile Delivery: Smarter Routing, Lower Costs
Transport is one of the largest and most volatile cost lines in a retail supply chain. For Australian retailers, the challenge is amplified by geography — long distances between population centres, urban congestion in metro areas, and the growing customer expectation for fast (and increasingly free) home delivery.
AI-powered route optimisation is now well-established in the logistics market. Dynamic routing engines consider real-time traffic conditions, delivery windows, vehicle capacity, driver hours, and delivery density to generate optimised routes that reduce kilometres driven, fuel consumed, and delivery time. The technology is especially impactful for last-mile delivery, where the final leg to the customer's door can account for more than half of total shipping cost.
But the AI opportunity in transport goes beyond routing. Predictive models can forecast delivery volumes by region and day, allowing retailers to pre-position inventory and schedule transport capacity more efficiently. For retailers managing their own fleet or negotiating with carriers, AI-driven analysis of freight spend, carrier performance, and lane-level cost structures can identify savings that are invisible in aggregated reporting.
One area we're seeing growing interest in is the integration of transport planning with demand and inventory planning — what some call "end-to-end" or "connected" planning. When your demand forecast, inventory positioning, and transport scheduling are all informed by the same AI-driven demand signal, the entire chain becomes more responsive and less wasteful. This is the direction that leading supply chain strategy is heading, and it's where the compounding benefits of AI really start to stack up.
5. Supplier Performance and Procurement Intelligence
This one doesn't get as much airtime as demand forecasting or warehouse automation, but it's arguably where AI has the most untapped potential in Australian retail.
Most retailers track supplier performance — DIFOT (delivery in full, on time), quality defects, lead time reliability — but they do it reactively, looking at what happened last month. AI enables a shift from reactive reporting to predictive management. Machine learning models can identify early warning signals of deteriorating supplier performance — lead time drift, increasing defect rates, changes in order acknowledgement patterns — and flag risks before they hit the shelf.
For procurement teams, AI can also accelerate spend analysis, identifying savings opportunities across categories by analysing pricing trends, contract compliance, maverick spend, and market benchmarks. What used to take a procurement analyst weeks of spreadsheet work can now be surfaced in hours.
Trace has developed its own .DIFOT and .SIFOT solutions specifically to give organisations real-time visibility into supplier and service delivery performance — the kind of visibility that turns procurement from a transactional function into a strategic one.
6. Sustainability and Waste Reduction
AI is increasingly playing a role in helping retailers meet their sustainability commitments — and it's happening through supply chain operations, not just through marketing and reporting.
Demand forecasting improvements directly reduce food waste for grocery retailers. Smarter inventory positioning reduces the volume of markdowns and write-offs. Transport optimisation cuts fuel consumption and emissions. Even warehouse energy management can be improved through AI-driven monitoring and scheduling of heating, cooling, and lighting systems.
For retailers reporting under Australia's Climate-related Financial Disclosure regime or working towards ESG targets, the supply chain is where the largest and most measurable emissions reductions tend to sit. Scope 3 emissions — those embedded in your supply chain rather than your own operations — typically dwarf Scope 1 and 2 for retailers. AI-driven visibility into supplier emissions, transport carbon intensity, and packaging waste gives sustainability teams actionable data rather than estimates and averages.
Our supply chain sustainability capability helps retailers connect operational improvement with sustainability outcomes — because in practice, the two are often the same initiative viewed through different lenses. A project that reduces food waste through better demand forecasting is simultaneously a cost initiative and a sustainability initiative. The AI is the same; the reporting lens is different.
What Separates the Winners from the Pilot Graveyard
We've seen plenty of AI pilots in retail supply chains that delivered promising results in a controlled environment and then stalled. The pattern is depressingly consistent: a data science team builds a model, demonstrates it on a subset of data, presents impressive accuracy metrics to leadership — and then it sits on a shelf because nobody figured out how to integrate it into the planners' daily workflow, the IT team couldn't connect it to the ERP, or the business case wasn't compelling enough to justify the change management effort.
The retailers that extract real, sustained value from AI share a few common traits.
They start with a business problem, not a technology. The question isn't "how can we use AI?" It's "what's costing us money, losing us sales, or creating risk — and can AI help solve that specific problem?" The best AI initiatives in retail supply chains are narrow in scope, clear in expected outcomes, and tied to a specific financial or operational metric.
They invest in data foundations before models. Clean, consistent, well-governed data is not glamorous. It doesn't make the conference agenda. But it's the single biggest determinant of whether an AI initiative succeeds or fails. Master data quality, demand history integrity, lead time accuracy, and system integration are the prerequisites — not afterthoughts.
They embed AI into existing processes. AI doesn't work as a separate layer that planners occasionally consult. It works when it's woven into the daily planning rhythm — generating forecasts that flow into replenishment, optimising stock levels that drive purchase orders, flagging exceptions that planners review and action. The technology needs to fit the workflow, not the other way around.
They measure ruthlessly. Forecast accuracy, inventory turns, service levels, cost-to-serve, waste percentages — the organisations that succeed with AI track these metrics with discipline and hold teams accountable for improvement. AI is a tool, not magic. If you can't measure the impact, you can't justify the investment or the continued effort. And the measurement needs to be ongoing — not just a before-and-after comparison at the end of the pilot, but a continuous performance dashboard that your planning team reviews weekly.
How Trace Consultants Can Help
At Trace Consultants, we work with retailers across Australia and New Zealand to improve supply chain performance through better planning, smarter technology, and practical execution. AI is increasingly part of that picture — but always in service of a business outcome, never as an end in itself.
Here's where we add the most value for retail clients exploring or expanding their use of AI in the supply chain.
Diagnostic and opportunity assessment. We start by understanding where your supply chain is losing money or underperforming — whether that's forecast accuracy, inventory positioning, warehouse productivity, transport cost, or supplier performance. We quantify the opportunity, identify where AI can deliver the highest return, and build a prioritised roadmap that accounts for your data maturity, systems landscape, and organisational readiness. Our retail sector experience means we understand the specific pressures and trade-offs that retail supply chains face.
Demand planning and S&OP improvement. We help retailers redesign their planning and operations processes — from statistical forecasting through to integrated business planning — incorporating AI-powered tools where they add value and building the process discipline that ensures the technology gets used.
Inventory strategy and network optimisation. We model retail distribution networks, assess inventory policies across echelons, and design optimised inventory strategies that balance service, cost, and working capital. Our strategy and network design capability is grounded in practical implementation, not just modelling.
Technology enablement. We're technology-agnostic — we recommend and help implement the platforms that fit your needs, from enterprise APS solutions to pragmatic Power Platform tools (Power BI, Power Apps, Power Automate) that deliver quick wins without massive IT projects. Our .Solutions suite, including .DIFOT, .Planner, and .Workforce, provides practical, modular tools designed for supply chain teams.
Warehouse and distribution optimisation. From warehouse design and layout optimisation to WMS selection, labour planning, and process improvement, we help retailers build distribution operations that are efficient, scalable, and ready to take advantage of AI and automation as they mature.
Change management and capability building. We know that technology adoption fails without people. Every engagement includes a focus on building internal capability, training teams, and embedding new processes through structured change management — so that improvements last well beyond our engagement.
What makes Trace different is that we're practitioners, not theorists. Our consultants have sat in the planning room, built the dashboards, calibrated the models, and tracked the P&L impact. We're a boutique firm that puts senior people on every engagement — you get the team you were promised, not a junior analyst learning on the job.
The Practical Path Forward
AI in retail supply chains is not about a single transformative project. It's about a series of targeted, well-executed improvements that compound over time. Start with the problem that's costing you the most. Fix your data. Pick a technology approach that fits your scale and maturity. Measure everything. Build on what works.
The Australian retailers who are getting this right — from the big end of town through to mid-market operators — aren't the ones with the flashiest AI strategies. They're the ones who've done the disciplined work of connecting better data, better models, and better processes into a supply chain that's measurably more responsive, efficient, and resilient.
If you'd like to explore what AI could practically deliver for your retail supply chain, we'd welcome the conversation.
Trace Consultants is an Australian supply chain and procurement consultancy with offices in Melbourne, Sydney, Brisbane, and Canberra. We work with retailers, FMCG producers, Defence, government, and infrastructure clients to deliver measurable improvements in supply chain performance. Visit our insights page for more articles, or get in touch to speak with our team.
