In Conversation at Trace: Tim Harris on systems, decision-making, and designing for resilience

There's a powerful narrative running through Australian boardrooms right now, and it goes something like this: global supply chains are broken, China is too risky, tariffs are making imports expensive, the government is offering incentives, and we should be making more things here — or at least closer to here. It's a narrative with genuine merit. But it's also a narrative that glosses over the economics, underestimates the complexity, and risks leading organisations into decisions they'll regret.

The reshoring conversation in Australia has intensified significantly since 2020. The pandemic exposed just how dependent the country had become on extended global supply chains — for PPE, for pharmaceuticals, for basic manufactured goods. Then came the geopolitical tensions with China, the Red Sea shipping disruptions, and the tariff shocks of 2025. Each new disruption reinforced the same message: distance is risk, and dependence on a single source is dangerous.

The Australian government has responded with policy settings that explicitly encourage domestic manufacturing. The Future Made in Australia Act, introduced in the 2024 federal budget, allocates approximately $22.7 billion over ten years to support clean energy and advanced manufacturing — including production tax credits for critical minerals processing and renewable hydrogen. The National Reconstruction Fund provides $15 billion in financing for projects that diversify and transform Australian industry, though it has faced criticism for slow deployment. A $5 billion Net Zero Fund, announced in September 2025 and delivered through the NRF, targets industrial decarbonisation.

Meanwhile, the Ai Group's 2025 Trade and Supply Chain Survey shows that 44% of Australian manufacturers intend to increase supply chain investment in 2026, with digital technologies and AI a focus. HKTDC's sourcing analysis confirms that many Australian businesses are adopting a China-plus-one strategy, diversifying their supplier base to reduce single-source reliance, with some considering reshoring operations and investing in local suppliers.

All of this creates momentum. And momentum, without rigorous analysis, is where expensive mistakes get made.

The unit cost trap

The most common mistake in reshoring analysis is comparing the wrong numbers. Specifically, comparing the unit manufacturing cost of a product made in Australia (or Vietnam, or Indonesia) against the unit cost from China — and concluding that the gap is too large to justify a move.

This comparison is misleading in both directions.

It understates the true cost of offshore sourcing by ignoring the hidden costs that accumulate across an extended supply chain: international freight and insurance, customs duties and tariff exposure, inventory carrying costs driven by long lead times, quality inspection and rework costs, travel and communication costs for supplier management, intellectual property risk, compliance management across jurisdictions, and the opportunity cost of slow responsiveness to market changes.

The Reshoring Initiative in the United States has documented this consistently. Their research shows that making sourcing decisions based solely on unit price typically results in a 20-30% understatement of true offshoring costs. When organisations conduct a proper total cost of ownership analysis — accounting for freight, duty, inventory, quality, risk and overhead — roughly 25% of what is currently sourced offshore would be more profitably reshored, even without tariff protection.

But the unit cost comparison can also overstate the case for reshoring by ignoring the genuine cost disadvantages of domestic production. Australian manufacturing labour costs are among the highest in the world. Energy costs have been rising. The domestic supplier base for many components and materials is thin or non-existent. Regulatory compliance costs are significant. Scale economies that exist in Chinese or Southeast Asian manufacturing clusters are difficult to replicate in a market of 26 million people. The Productivity Commission has cautioned that supporting industries without long-term competitive advantage can lead to ongoing costs — a warning that deserves attention in the current policy environment.

The honest answer, for most Australian businesses, sits somewhere in the uncomfortable middle: reshoring makes sense for some products, in some categories, under some conditions — and not for others. Getting that assessment right requires analytical rigour, not enthusiasm.

What total cost of ownership actually means in the Australian context

A proper TCO analysis for an Australian business considering reshoring or nearshoring should account for at least the following cost categories, each of which plays out differently depending on the product, the source market, and the supply chain configuration.

Manufacturing cost differential. The starting point, but only the starting point. Australian manufacturing wages are significantly higher than alternatives in Southeast Asia. But productivity differences, automation potential, and quality costs can narrow the gap substantially. For highly automated production where labour content is low, the wage differential matters less. For labour-intensive production, it matters enormously.

Logistics and freight. Australia is an island continent. Everything that arrives from offshore comes by sea or air, through a relatively small number of ports and airports. Freight costs from China to Australia have been volatile — spiking during the pandemic, normalising, then spiking again during Red Sea disruptions. But the baseline cost of shipping from Southeast Asia is actually lower than many assume for standard containerised goods. The logistics cost advantage of domestic production is real but often overstated for shelf-stable, non-perishable products. It's most significant for bulky, low-value items where freight is a large percentage of landed cost, and for products requiring temperature control or other special handling.

Inventory carrying costs. This is where the economics get interesting, and where many organisations undercount the cost of offshore sourcing. When your supply chain involves 4-6 month lead times from Asia (common for many Australian importers), you need to carry substantially more inventory than if you were sourcing domestically or from a nearby region. At current capital costs — the Reserve Bank cash rate has been elevated, and weighted average cost of capital for most Australian businesses sits between 8-12% — carrying excess inventory is expensive. A business with $50 million in inventory is spending $4-6 million per year just on the cost of capital tied up in stock. If domestic or nearshore sourcing could reduce that inventory by 30% through shorter lead times and more frequent ordering, that's $1.5-1.8 million in annual savings to offset against any unit cost premium.

Tariff and trade policy exposure. The tariff landscape has been volatile. Australian exporters paid approximately A$1.4 billion in additional duties following US tariff impositions, and the full effects of trade policy changes are still flowing through. The February 2026 US Supreme Court ruling that the use of IEEPA for tariffs was unlawful, and the subsequent replacement with 15% Section 122 tariffs, illustrates the unpredictability. For Australian importers, tariff exposure works differently — but the principle is the same: trade policy can change the economics of your supply chain overnight, and concentrated sourcing from a single geography amplifies that risk.

Quality and compliance costs. Managing quality across distance is expensive. Inspection regimes, factory audits, sample testing, rework and returns all cost more when your supplier is thousands of kilometres away. For products subject to TGA regulation, food safety requirements, or other Australian compliance standards, the cost of ensuring offshore suppliers meet requirements — and demonstrating that compliance to regulators — adds meaningfully to the total cost. Modern slavery reporting requirements add another layer of due diligence cost for extended supply chains.

Responsiveness and speed to market. This is harder to quantify but often strategically decisive. When your supply chain has 4-6 month lead times, you're making production decisions based on forecasts that are inherently uncertain. When demand shifts — a product takes off unexpectedly, a competitor launches, a promotion overperforms — you can't respond quickly. You either miss the opportunity or you've overcommitted to stock you can't sell. Domestic or nearshore sourcing with shorter lead times allows smaller, more frequent orders, better matching of supply to actual demand, and faster response to market changes. For businesses in categories with short product lifecycles, seasonal patterns, or promotional intensity, this responsiveness has real financial value.

Risk and resilience. The cost of supply chain disruption is notoriously difficult to quantify in advance but very real when it happens. The Ai Group found that 81% of Australian businesses that experienced supply chain disruptions reported increased costs, and 44% said disruptions constrained their growth. A diversified supply base — which might include some domestic or nearshore sourcing — reduces concentration risk. The question is whether the premium for that diversification is worth paying as a form of insurance, and the answer depends on how much disruption would actually cost your business.

The geography that matters: why "reshoring" for Australia is really "nearshoring to Southeast Asia"

When American companies talk about reshoring, they often mean bringing production back to the United States — a market of 330 million people with deep manufacturing capability, an extensive domestic supplier base, and massive government incentives. When Australian companies talk about reshoring, the reality is more nuanced.

Australia's manufacturing sector accounts for roughly 5.7% of GDP. The domestic supplier base for many manufactured components is thin. ABS data shows the sector employed approximately 902,000 people at June 2024, and while Industry Value Added grew modestly to $134.8 billion, EBITDA actually declined by $3.6 billion — highlighting the profitability pressures that make cost competitiveness a genuine challenge.

For most Australian businesses, the practical supply chain reconfiguration opportunity is not "bring everything back to Australia" — it's a combination of strategic decisions about what to make domestically, what to source from nearby alternative markets in the Asia-Pacific region, and what to continue sourcing from established offshore suppliers with better risk management.

The alternative markets have different profiles, and understanding those differences matters.

Vietnam has become the most common first alternative to China for Australian importers. It offers competitive labour costs, a growing industrial base, government support for export manufacturing, and improving infrastructure. It's also a member of both CPTPP and RCEP, providing preferential trade access. But capacity constraints are real — particularly for complex products — and quality consistency can be variable. Shipping times to Australia are somewhat shorter than from China, but not dramatically so for most trade lanes.

Indonesia is closer to Australia than any other major manufacturing economy, which creates genuine freight cost and lead time advantages. It has a large domestic market, competitive labour costs, and government interest in developing export manufacturing capability. But the manufacturing base is concentrated in certain categories, the regulatory environment requires careful navigation, and infrastructure outside Java can be challenging. The proximity advantage is most significant for bulky, low-value goods where freight cost is decisive.

India offers enormous scale and a strong engineering base, particularly for pharmaceuticals, chemicals, and IT-enabled services. But inland logistics infrastructure remains challenging, bureaucratic complexity is real, and quality consistency across suppliers varies widely. It's typically a stronger option for businesses with the resources to invest in supplier development and ongoing relationship management.

Domestic Australian manufacturing makes the strongest case in specific circumstances: where quality control and IP protection are paramount (defence, medical devices, specialised industrial products); where speed to market is a competitive differentiator; where the product is bulky and low-value relative to freight cost; where government procurement requirements mandate or incentivise local content; or where automation can offset labour cost disadvantage. The National Reconstruction Fund and Future Made in Australia incentives can shift the economics for certain product categories — particularly in clean energy, critical minerals processing, battery manufacturing, and medical technology.

The right answer for most Australian businesses is a deliberate portfolio approach: different products and components sourced from different locations based on a rigorous assessment of total cost, risk, responsiveness, and strategic importance. Not a wholesale shift in any one direction, but a conscious diversification of the supply base.

The five mistakes organisations make

Having set out the analytical framework, it's worth naming the specific mistakes we see most often when Australian organisations approach reshoring or supply chain reconfiguration.

Mistake one: comparing unit costs instead of total cost of ownership. We've covered this, but it bears repeating because it's so common. The unit price from the factory gate is the starting point of the analysis, not the answer. Organisations that make sourcing decisions on unit cost alone consistently underestimate the true cost of offshore supply by 20-30% — and overestimate the premium for domestic or nearshore alternatives by a corresponding margin.

Mistake two: underestimating the transition cost and timeline. Qualifying a new supplier — whether domestic, nearshore, or in an alternative offshore market — takes time. For simple products, six months might be sufficient. For complex manufactured goods, medical devices, food products, or anything with regulatory requirements, twelve to eighteen months is more realistic. And that's just qualification — building genuine dual-source capability, where the alternative supplier can reliably deliver at volume and quality, takes longer still.

During the transition, you're typically carrying cost in both the old and new supply chains: dual tooling, parallel testing, additional inventory buffers, project management overhead, travel costs. These transition costs are routinely underestimated and can be significant — enough to eliminate the projected savings from the switch for the first two to three years. Organisations that don't budget for the transition realistically either abandon the project partway through or deliver it over budget and behind schedule.

Mistake three: treating reshoring as an all-or-nothing decision. Some products should be reshored or nearshored. Others shouldn't. Treating the entire product portfolio the same way — either moving everything or moving nothing — misses the opportunity for a smarter, more nuanced approach. The most effective supply chain strategies segment the portfolio: critical components, high-risk categories, and products where responsiveness matters most get prioritised for diversification; commodity inputs where cost is the primary driver and supply risk is manageable stay with established low-cost sources.

Mistake four: ignoring the procurement capability required. Managing a diversified supply base across multiple countries is harder than managing concentrated sourcing from a single market. It requires more sophisticated procurement capability — in supplier identification and qualification, contract management, quality assurance, logistics coordination, trade compliance, and supplier relationship management. Many Australian organisations have procurement teams that are already stretched managing their existing supplier base. Adding new suppliers in new markets, with different languages, different regulatory frameworks, different quality systems and different commercial practices, requires capability investment that needs to happen concurrently with the sourcing transition.

Mistake five: making the decision reactively rather than strategically. The worst time to diversify your supply base is during a crisis — when you're scrambling for alternatives, your negotiating position is weak, and you don't have time for proper due diligence. The best time is before you need to, when you can assess alternatives methodically, qualify suppliers properly, negotiate from a position of strength, and build optionality into your supply chain before it's urgently needed.

The organisations that are best positioned right now began their diversification programs in 2022 or 2023, giving alternative suppliers time to qualify, build capacity, and prove their reliability before disruption hit. Organisations starting now are already behind — but starting late is better than not starting at all, because the forces driving supply chain reconfiguration (geopolitical tension, tariff volatility, Scope 3 reporting requirements, regulatory complexity) are structural, not cyclical. They're not going away.

What the policy environment means for Australian supply chain decisions

The Australian government's industrial policy settings are the most interventionist in a generation, and they create real incentives that can shift the economics for specific categories.

The Future Made in Australia Act commits approximately $22.7 billion over ten years. This includes production tax credits for critical minerals processing and renewable hydrogen, substantial funding through ARENA for clean energy technology manufacturing (batteries, electrolysers, solar components), and a $5 billion Net Zero Fund targeting industrial decarbonisation. The NRF's recent investments — including $75 million in Alpha HPA's alumina facility in Gladstone and $20 million in Diraq's quantum technology — signal the types of projects being supported.

For businesses operating in the targeted sectors — critical minerals, clean energy, battery manufacturing, medical technology, defence — these incentives can genuinely change the reshoring calculus. A production tax credit that covers a material portion of the cost disadvantage versus offshore production can make domestic manufacturing viable where it otherwise wouldn't be.

But there are important caveats. The incentives are concentrated in specific sectors. If your business makes consumer electronics, textiles, general industrial components, or other products outside the priority areas, the government support is limited. The NRF has faced criticism for slow deployment — stakeholders have noted that much of the funding remains unutilised. And the Productivity Commission has warned about the risks of supporting industries that don't have long-term competitive advantage, cautioning that the era of getting the most competitively priced goods on the global market is coming to a close, with consumers and businesses paying more as supply chains duplicate and fragment.

For supply chain leaders, the policy environment is a factor in the analysis, not the answer. Government incentives can shift the economics at the margin, and they should be factored into any TCO assessment. But they don't eliminate the underlying cost disadvantage of Australian manufacturing for most product categories, and they introduce their own risks — policy settings change with governments, sunset clauses expire, and eligibility criteria evolve. A reshoring decision that only works with a government subsidy is a decision that carries political risk alongside the commercial risk.

The Scope 3 connection

One dimension that's increasingly relevant but rarely integrated into reshoring analysis is the interaction with Scope 3 emissions reporting. Under AASB S2, large Australian organisations will be required to report Scope 3 emissions from their second reporting year — which for the first tranche (revenue exceeding $500 million) means mid-2026.

This changes the reshoring conversation in a subtle but important way. Transport emissions are a significant component of Scope 3 for most importers. Sourcing from Southeast Asian alternatives — particularly Indonesia, which is closer to Australia than China — reduces transport emissions compared to longer trade lanes. Domestic sourcing eliminates international transport emissions almost entirely.

At the same time, the emissions profile of manufacturing itself varies enormously by country. Manufacturing in a country with a coal-heavy energy grid may generate higher production emissions than manufacturing in Australia (which has a growing renewable energy share) or in a country with a cleaner energy mix. A proper Scope 3 analysis needs to consider both transport and production emissions — not just proximity.

The practical implication is that Scope 3 reporting requirements are adding another variable to the total cost of ownership calculation. Organisations subject to mandatory reporting will need visibility into the emissions profile of their supply chain, and sourcing decisions will increasingly need to account for emissions alongside cost, quality, risk and responsiveness. This doesn't automatically favour reshoring — but it does favour supply chain transparency, supplier engagement, and the kind of rigorous analysis that most organisations haven't yet invested in.

What a good reshoring assessment looks like

If your organisation is considering supply chain reconfiguration — whether that's reshoring to Australia, nearshoring to Southeast Asia, or diversifying away from concentrated offshore sourcing — the assessment should follow a structured approach.

Start with supply chain mapping. Before you can assess alternatives, you need to understand your current supply chain in detail. Where are your tier-one suppliers located? What about tier-two and tier-three? Where are the concentration risks — multiple products or components sourced from a single supplier, a single region, or a single trade lane? What percentage of your spend goes through your top ten suppliers? How many critical items are single-sourced? Many organisations can't answer these questions without significant data gathering across fragmented procurement systems and business units.

Segment your portfolio. Not every product or component needs the same supply chain strategy. Segment based on a combination of supply risk (concentration, geopolitical exposure, lead time vulnerability, supplier financial health), strategic importance (revenue impact, customer impact, substitutability), and value characteristics (unit cost, freight sensitivity, shelf life, quality criticality). High-risk, high-importance categories are the priority for diversification. Low-risk commodity inputs with well-functioning competitive supply markets are not.

Conduct total cost of ownership analysis for priority categories. For the categories you've identified as priorities, model the true total cost across alternative sourcing scenarios — current state, nearshore alternative, domestic alternative, diversified portfolio. Include all the cost categories discussed earlier: manufacturing cost, logistics, inventory carrying costs, tariff exposure, quality and compliance, responsiveness value, and risk premium. Be honest about the transition costs and timeline.

Assess alternative markets and suppliers. For each priority category, identify potential alternative suppliers in target markets. Assess them against capability, capacity, quality, financial stability, ESG compliance, and willingness to invest in the relationship. This is where deep procurement expertise matters — identifying and qualifying suppliers in unfamiliar markets requires knowledge of those markets, established networks, and structured evaluation frameworks.

Develop a phased transition plan. Don't try to move everything at once. Prioritise based on the combination of risk urgency, financial impact, and readiness (both your readiness and the alternative supplier's readiness). Plan for dual sourcing periods where you're running both old and new supply chains in parallel. Budget for the transition costs realistically. Set clear milestones and decision gates.

Integrate with broader supply chain strategy. Reshoring and nearshoring decisions don't sit in isolation. They connect to your network design (where are your distribution centres relative to where you're now sourcing?), your planning processes (shorter lead times from nearshore sourcing change inventory parameters and planning horizons), your warehousing and distribution configuration, your Scope 3 reporting obligations, and your resilience strategy. The best outcomes come from treating supply chain reconfiguration as an integrated strategic exercise, not a standalone procurement project.

The honest conclusion

Reshoring and nearshoring are real, strategically important options for Australian businesses. The forces driving supply chain reconfiguration — geopolitical volatility, tariff uncertainty, regulatory complexity, Scope 3 requirements, and the painful lessons of recent disruptions — are structural and enduring.

But the decision to reconfigure your supply chain should be based on rigorous analysis, not narrative momentum. The economics are more complex than most commentary suggests. The transition is harder, slower, and more expensive than most business cases assume. The right answer is almost always nuanced — a portfolio approach that puts different products and components in different places based on a clear-eyed assessment of total cost, risk, responsiveness, and strategic importance.

The organisations getting this right are treating it as a multi-year strategic program, not a reactive response to the latest disruption. They're investing in the analytical capability to make good decisions, the procurement capability to execute them, and the supply chain design thinking to integrate sourcing decisions with network, planning, and distribution strategy.

How Trace can help

At Trace, we work with Australian organisations across FMCG and manufacturing, retail and consumer, resources and energy, health and human services, and government and defence on exactly these decisions. Our work spans supply chain risk assessment and exposure mapping, total cost of ownership analysis across alternative sourcing scenarios, market analysis and alternative supplier identification, procurement strategy that integrates diversification with cost and service objectives, supplier qualification and evaluation frameworks, transition planning and project management, and network design that reflects the new sourcing configuration.

We're independent — we don't have commercial relationships with suppliers, logistics providers, or technology vendors that would influence our recommendations. Our advice is based entirely on what's right for your organisation.

We've written previously about how tariff disruption is affecting Australian businesses and about the broader challenges shaping Australian supply chains in 2026. If you're considering supply chain reconfiguration and want a rigorous, independent assessment of what makes sense for your business, get in touch.

Trace Consultants is an Australian supply chain and procurement consulting firm. We help organisations make better supply chain decisions — grounded in data, informed by deep operational experience, and independent of any vendor or supplier interest. Visit our insights page for more on the challenges shaping Australian supply chains.

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Every supply chain and operations leader in Australia has been told, repeatedly, that artificial intelligence will transform their function. The conference presentations, the vendor pitches, the board-level questions about "what we're doing with AI" — the pressure to adopt is relentless. And it's not wrong. AI is genuinely changing how demand is forecast, how inventory is positioned, how logistics networks are optimised and how operational decisions are made.

But there's an enormous gap between recognising that AI matters and actually deploying it in ways that deliver measurable operational improvement. The Australian Government's AI Adoption Tracker for Q1 2025 found that while awareness and interest in AI continues to grow, challenges like rapid technological change, skills gaps and funding constraints remain significant barriers. Only 14% of surveyed businesses said AI had definitely helped with supply chain and supplier management — though another 46% said it possibly had, suggesting many organisations are still in early stages where benefits are emerging but not yet clear.

The pattern we see across our client base tells a similar story. Most organisations aren't struggling with whether to adopt AI. They're struggling with where to start, how to prioritise, what's genuinely ready for production versus what's still experimental, and how to build the foundations — data, process, people — that determine whether AI tools actually work in their operational context.

This article is a practical guide to navigating that challenge. Not a survey of what's theoretically possible, but a framework for how Australian supply chain and operations teams can adopt AI in ways that generate real, sustainable value.

Start with the problem, not the technology

The single most common mistake in AI adoption — across every sector and every function — is starting with the technology. "We should use machine learning for demand forecasting." "We need a generative AI copilot for our planning team." "Let's build a predictive model for supplier risk."

These aren't bad ideas. But they're solutions looking for problems. The organisations that extract genuine value from AI start from the other direction: they identify the operational decisions that matter most and are currently made poorly, then ask whether AI can improve them.

In supply chain and operations, the decisions that typically offer the highest leverage for AI-assisted improvement fall into several categories.

Demand forecasting and planning. Most Australian organisations still forecast demand using a combination of statistical models (often basic ones embedded in their ERP), manual adjustments based on sales team input, and spreadsheet-based reconciliation. The result is forecast accuracy that hovers around 50-70% at SKU level for many businesses — adequate for rough planning but costly in terms of excess inventory, emergency orders and missed service targets. Machine learning can genuinely improve forecast accuracy, particularly for products with complex demand patterns driven by promotions, seasonality, weather, competitive activity or other external signals. But the improvement is contingent on having clean, granular historical data and a planning process that actually uses the improved forecast.

Inventory optimisation. Setting safety stock levels and reorder parameters is one of the most consequential decisions in supply chain management — and one of the most commonly done by formula or rule of thumb. AI-driven inventory optimisation can improve service levels while reducing working capital by dynamically adjusting parameters based on demand variability, lead time variability, supplier reliability and service level targets. In the current interest rate environment, the working capital benefit alone often justifies the investment. But it requires integration with your ERP, clean master data, and a governance process for managing exceptions.

Logistics and route optimisation. AI-powered routing and scheduling can reduce transport costs by 5-15% by optimising vehicle allocation, delivery sequences and consolidation opportunities — particularly for organisations with complex, multi-drop delivery networks. Australian geography makes this particularly valuable: the distances involved mean that even modest efficiency improvements translate to material cost savings.

Supply risk and disruption management. AI can monitor external data sources — news feeds, shipping data, weather forecasts, financial indicators — to provide early warning of supply chain disruptions. For organisations managing complex, global supply chains with long lead times, this capability shifts the response model from reactive to proactive. But the value depends on having clear escalation processes and decision frameworks that translate alerts into action.

Warehouse operations. From predictive slotting and pick path optimisation through to demand-driven labour scheduling, AI can improve warehouse throughput and reduce cost per unit handled. For organisations managing large warehousing and distribution operations, the compounding effect of small efficiency improvements across millions of transactions is significant.

Quality and maintenance prediction. In manufacturing and processing environments, AI-powered predictive maintenance can reduce unplanned downtime by identifying equipment failure patterns before they cause outages. Similarly, AI-driven quality prediction can reduce waste by identifying process drift earlier in the production cycle.

The point isn't that every organisation should pursue all of these simultaneously. It's that the starting point should be a clear-eyed assessment of which operational decisions are currently weakest, which have the largest financial impact, and which have the data and process foundations to support AI-assisted improvement.

The three foundations that determine whether AI works

Every failed AI initiative we've seen shares a common root cause: the technology was deployed on top of inadequate foundations. Specifically, one or more of three prerequisites was missing.

Foundation one: data readiness

AI is only as good as the data it learns from and operates on. This isn't a platitude — it's a practical reality that determines whether a machine learning model produces useful outputs or garbage.

For demand forecasting, you need clean, granular historical transaction data at the right level of detail (SKU-location-week, typically), with promotional activity, pricing changes and other demand-shaping events accurately captured. For inventory optimisation, you need reliable lead time data, supplier performance history and demand variability metrics. For logistics optimisation, you need accurate delivery windows, vehicle constraints and cost structures.

Most Australian organisations have this data somewhere in their systems — but not necessarily in a form that's accessible, consistent or trustworthy. Data quality issues, system fragmentation, inconsistent master data and manual workarounds are the norm, not the exception.

Investing in data readiness — cleaning historical data, establishing data governance, building integration pipelines, fixing master data — isn't exciting. It doesn't feature in conference keynotes. But it's the work that determines whether your AI investment delivers 15% forecast accuracy improvement or becomes an expensive experiment that nobody trusts.

Foundation two: process maturity

AI tools produce recommendations, forecasts, optimisation outputs and alerts. Those outputs only create value if there's a business process that consumes them — a planning cycle that reviews the forecast, a replenishment process that acts on inventory recommendations, a logistics process that implements optimised routes, a management rhythm that responds to risk alerts.

If your S&OP process is dysfunctional, a better demand forecast won't fix it — the improved forecast will just be ignored or overridden the same way the current one is. If your warehouse has no standard operating procedures for pick path management, an AI-optimised slotting recommendation won't translate to throughput improvement.

This is why process design and AI adoption need to be pursued together, not sequentially. The organisations that get the most from AI are the ones that redesign their planning and operations processes to take advantage of what AI makes possible — faster exception identification, better scenario modelling, more granular segmentation — rather than trying to bolt AI onto processes designed for a pre-AI world.

Foundation three: people and capability

AI doesn't replace planners, analysts or operations managers. It changes what they do. Instead of spending 80% of their time gathering, cleaning and reconciling data and 20% making decisions, AI should flip that ratio — handling the routine analytical work so that people can focus on the exceptions, the judgement calls and the cross-functional coordination that create value.

But this shift requires new capabilities. Planners need to understand what the AI model is doing well enough to know when to trust it and when to override it. Analysts need enough data literacy to configure, monitor and troubleshoot AI tools. Managers need the confidence to change established ways of working based on AI-generated insights.

Strategic workforce planning for AI adoption isn't about hiring data scientists (though some organisations will need them). It's about upskilling existing operational teams to work effectively with AI tools — understanding the outputs, managing the exceptions, and knowing when human judgement should prevail.

A practical adoption framework

With those foundations in mind, here's a framework for sequencing AI adoption in supply chain and operations that we've seen work consistently in Australian organisations.

Stage one: assess and prioritise (4-8 weeks)

Map the operational decisions where AI could add value against two axes: business impact (cost, service, working capital, risk) and readiness (data availability, process maturity, organisational appetite). The intersection of high impact and high readiness is where you start.

This assessment should be grounded in data — actual forecast accuracy metrics, inventory performance, logistics costs, service level achievement — not assumptions or vendor claims. It should also include an honest evaluation of data readiness: is the data that an AI model would need actually available, accurate and accessible?

The output is a prioritised roadmap with clear sequencing: what to tackle first, what requires foundation work before AI can be effective, and what to defer until earlier initiatives have proven the model.

This is strategy and network design work applied to the AI adoption question — ensuring investments are directed where they'll deliver the most value rather than where the technology is most exciting.

Stage two: build foundations (8-16 weeks, concurrent with stage three)

For the prioritised use cases, address the data, process and capability gaps identified in stage one. This might include data cleansing and enrichment for historical demand data, establishing master data governance for products, locations and suppliers, building data integration pipelines between source systems and AI tools, redesigning planning or operational processes to incorporate AI outputs, and developing training programs for the teams who will use AI tools.

This work isn't glamorous, but it's where the ROI is protected. Skipping it — or rushing it — is the most reliable way to ensure your AI initiative disappoints.

Stage three: prove value with targeted pilots (8-12 weeks)

Start with one or two well-defined use cases in controlled environments. A demand forecasting pilot for a specific product category in a specific market. An inventory optimisation trial for a subset of SKUs in a defined part of the network. A route optimisation test for a specific distribution operation.

The pilot should have clear success metrics defined upfront: forecast accuracy improvement measured against the current baseline, inventory reduction without service degradation, cost per delivery reduction, or whatever metric is relevant to the use case. It should run long enough to demonstrate performance across different demand conditions — not just the easy weeks.

Critically, the pilot should test the full operating model, not just the technology. Are planners using the AI outputs? Are the process changes working? Are exceptions being managed appropriately? Are the results genuinely better than the current approach, measured rigorously?

Stage four: scale what works (ongoing)

When a pilot demonstrates clear value, scale it — but deliberately. Scaling means extending the AI capability across more products, more locations, more operations, while simultaneously extending the process changes and capability development that make it work.

Scaling also means establishing the governance and operating rhythm for ongoing management: who monitors model performance? Who recalibrates when accuracy degrades? Who decides when the model should be overridden? Who owns the data quality that feeds it? These aren't implementation details — they're the mechanisms that determine whether AI delivers value in year one only or sustains it over time.

For organisations with complex, multi-site operations — whether in FMCG and manufacturing, retail and consumer, resources and energy, or government and defence — the scaling phase is where project and change management capability becomes critical. Rolling out new tools and processes across multiple sites, teams and geographies requires structured change management, not just technical deployment.

Navigating the vendor landscape

One of the most confusing aspects of AI adoption for supply chain leaders is the vendor landscape. It includes embedded AI capabilities within existing platforms (your ERP vendor's demand planning module, your WMS vendor's slotting optimisation), specialised best-of-breed AI tools for specific use cases (standalone demand sensing, logistics optimisation, or predictive maintenance platforms), Advanced Planning Systems with native AI and ML capabilities (the dedicated supply chain planning platforms covered in Trace's APS guide), and general-purpose AI and analytics platforms that can be configured for supply chain use cases (cloud ML platforms, business intelligence tools with predictive capabilities).

Each approach has trade-offs. Embedded capabilities are easier to deploy but may be less sophisticated. Best-of-breed tools may deliver superior results for specific use cases but create integration complexity. APS platforms provide comprehensive planning capability but require significant implementation investment. General-purpose platforms offer flexibility but require more internal technical capability to deploy.

The right answer depends on your specific use cases, your existing technology landscape, your internal capability, and your appetite for integration complexity. A structured evaluation — similar to the procurement RFx approach we recommend for APS selection — ensures you're comparing options on the dimensions that actually matter rather than being swayed by demos and marketing.

The governance dimension

As AI becomes more embedded in operational decisions, governance becomes a practical necessity, not just a compliance consideration. The Australian Government's 2025 Guidance for AI Adoption sets out six essential practices for responsible AI governance, reflecting a maturing regulatory environment.

For supply chain and operations specifically, the governance questions that matter most are practical ones. How do you ensure AI-driven decisions are explainable — that a planner can understand why the system is recommending a particular forecast or inventory level? How do you manage bias — ensuring that AI models don't systematically over-serve some customers or under-invest in some regions? How do you handle data privacy — particularly when AI tools process customer demand data, supplier information or employee productivity metrics? How do you maintain accountability — ensuring that humans remain responsible for decisions, even when those decisions are informed by AI?

These aren't theoretical concerns. They're design choices that need to be made during implementation and embedded in the operating model. Organisational design for AI-enabled operations includes defining who owns model performance, who governs data quality, who authorises changes to AI parameters, and who escalates when AI recommendations don't align with business judgement.

What to avoid

A few patterns reliably lead to disappointing outcomes in supply chain AI adoption:

The moonshot. Starting with an ambitious, multi-year AI transformation program that tries to reimagine the entire supply chain. These programs generate impressive slide decks and burn significant budget before delivering any operational value. Start small, prove value, scale what works.

The technology-first approach. Selecting an AI platform before clearly defining the operational problems it needs to solve. This leads to solutions searching for problems and features that don't map to your actual decision-making needs.

The data will sort itself out. Assuming that data quality issues will be resolved as part of implementation rather than addressing them upfront. They won't. And the AI model trained on poor data will produce poor recommendations that planners quickly learn to ignore.

The standalone pilot. Running an AI pilot in isolation from the business processes and people who need to use it. A model that sits on a data scientist's laptop producing impressive accuracy metrics but never integrating into the planning cycle is a science project, not an operational capability.

Ignoring change management. Deploying AI tools without investing in the training, process redesign and stakeholder engagement needed for adoption. If planners don't trust the tool, they'll route around it — and your investment will deliver a fraction of its potential value.

How Trace Consultants can help

At Trace Consultants, we help Australian organisations adopt AI in supply chain and operations from a position of deep operational expertise. We're not an AI vendor or a technology implementation firm. We're supply chain and operations consultants who understand where AI creates genuine value — and where it doesn't.

AI readiness assessment and roadmap. We assess your current operations, data landscape, process maturity and organisational capability to identify where AI will deliver the highest return — and what foundation work is needed to support it. This is strategy work grounded in operational reality.

Use case definition and prioritisation. We help you cut through the noise — identifying the specific operational decisions that AI can improve in your context, quantifying the potential value, and sequencing adoption based on impact and readiness.

Data and process readiness. We support the foundation work that determines whether AI delivers: data quality improvement, process redesign, master data governance, and integration planning. Our planning and operations expertise ensures that AI tools are embedded in processes that actually work.

Technology evaluation and selection. We help you navigate the vendor landscape with an independent, informed perspective — evaluating embedded capabilities, best-of-breed tools and APS platforms against your specific requirements. Our procurement and technology expertise ensures you select the right tool, not just the most impressive demo.

Pilot design and execution. We design and support AI pilots that test the full operating model — technology, process and people — with clear success metrics and a realistic path to scale.

Scaling and change management. We help organisations scale proven AI capabilities across sites, teams and geographies — managing the project and change dimensions that determine whether AI becomes embedded in how the organisation operates or remains a one-off experiment.

Organisational design and capability building. We help design the organisational structures, roles and governance mechanisms that sustain AI-enabled operations — and build the internal capability to manage, tune and extend AI tools over time through workforce planning and development.

The practical path forward

AI in supply chain and operations isn't a future state. It's happening now, in Australian organisations across every sector, in applications ranging from demand forecasting through to warehouse optimisation and logistics routing. The organisations capturing value aren't the ones with the biggest AI budgets or the most ambitious transformation programs. They're the ones that started with clear operational problems, built the foundations to support AI, proved value in controlled environments, and scaled deliberately.

If your organisation is ready to move beyond the question of whether to adopt AI and start answering the questions of where, how and in what sequence, we'd welcome the conversation.

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How has studying math prepared me for consulting?

I remember telling my sister last year I was going to try my hand in consulting.

“But how are you going to help businesses?” she replied incredulously, “Don’t you need like… business knowledge?”.  

Coming into consulting, without a business or economics background, I too was worried I would be at a disadvantage. I had just spent three years studying mathematical definitions and theorems, working with ‘imaginary numbers’ and building differential models to investigate biological issues, such as the spread of COVID-19. To me, this was a whole different world to that of excel sheets, operating models and client presentations. However, the past few months in my first role in the consulting world has made me realise how my degree has prepared me for consulting in so many unexpected ways; ways that studying technical business knowledge could have never. And so I’ve come to realise, actually, among the swarm of applicants with commerce backgrounds that typically reach for consulting roles, that perhaps my different background has given me an edge over the field, instead.

Maths shaped the way I think. It taught me how to confidently bring order to complexity: to take problems that initially feel overwhelming and break them down into structured, manageable components. Many problems had no obvious starting point and allowed for multiple pathways to a solution, each requiring fast judgement on which tools or approaches were most relevant. Rather than relying on precedent, I learned to reason from first principles. Through tackling lengthy, abstract problems, I was trained to work methodically, even without a clear end goal or complete information. I learnt to rely on a clear structure and disciplined logical thinking to recognise patterns and progress step by step towards a solution. That process ingrained disciplined problem solving and abstract thinking skills; the core to what consulting is.  

Maths taught me resilience and independence in the face of ambiguity and limited resources. As my classes grew more complex, practice problems became scarce as many concepts were too difficult to replicate, and a single question could require over five pages of working to solve fully. Hence, to consolidate my understanding, I often sifted through old forums and lengthy online workbooks, to be able to digest complex abstract theory. When exams arrived, we were sometimes given only a single practice paper, and often without answers, and so I found myself frequently reaching out to professors, tutors, and classmates to compare thinking processes and to clarify concepts. As a result, this process built initiative and independence, shaping the way I approach and complete work. It prepared me well for consulting, where projects often come with limited direction or guidance and involve navigating ambiguity, identifying what information is necessary and relying on your own research and judgment to progress effectively.

Maths made me comfortable with rapid learning. Specifically as I specialised in applied mathematics, I was consistently exposed and expected to understand the application of new programming languages and software tools towards solving math problems. This meant many long nights spent in front of YouTube, trying to grasp their usages. Thus, the need for rapid self-learning for new skillsets and complex software set me up well for the nature of consulting. Here, likewise, I am constantly thrown into new projects, new clients, new tools, new objectives: all while making sense of brand-new industries. The learning curve has been steep and relentless. However, my degree normalised such pace and gave me the persistence and resilience to get through it, making my first few months as a consultant a rather thrilling and stimulating journey of continuous learning.

So, while on the surface, abstract math theory may seem removed from the world of consulting, but importantly, it has shaped the way I think and approach unfamiliar problems. It has trained me to work rigorously, learn quickly, manage ambiguity and apply discipline to complex systems. It has instilled fundamental pattern recognition and logical thinking processes that no technical business knowledge could.

Now, I can safely answer my sister’s question.  

Yes, business knowledge is pivotal. But consulting, by its nature, is built on how one approaches and solves problems: and that is where studying maths made all the difference.