How to Improve Reverse Logistics in Online Retail

Online retail has transformed customer expectations across Australia and New Zealand. Fast delivery, flexible fulfilment options, and frictionless returns are no longer differentiators — they are baseline expectations.

While most retailers have invested heavily in forward logistics capabilities such as fulfilment centres, last-mile delivery, and order management systems, reverse logistics has often lagged behind. Returns are frequently treated as an unavoidable cost of doing business rather than a supply chain capability that can be actively designed, optimised, and improved.

As return volumes grow, particularly in categories such as apparel, consumer electronics, homewares, and health products, reverse logistics has become one of the largest hidden cost drivers in online retail. It impacts warehousing efficiency, inventory accuracy, transport spend, labour productivity, sustainability outcomes, and — critically — customer experience.

This article explores how online retailers can improve reverse logistics performance, reduce cost-to-serve, and design returns processes that support both commercial outcomes and customer loyalty.

Why reverse logistics matters more than ever in online retail

Return rates in online retail are materially higher than in physical stores. Customers buy multiple sizes, colours, or variants with the intention of returning items that do not meet expectations. Generous returns policies are often necessary to compete, but they come at a cost.

Poorly designed reverse logistics leads to:

• High manual handling and rework
• Congestion in warehouses not designed for returns
• Inventory sitting in limbo and unavailable for sale
• Increased transport and handling costs
• Write-offs due to slow processing or damage
• Poor visibility of return reasons and root causes

At the same time, returns play a major role in shaping customer perception. A slow, confusing, or inflexible returns experience can undermine an otherwise strong brand and fulfilment proposition.

Improving reverse logistics is therefore not just a supply chain issue — it is a commercial, operational, and customer experience priority.

Understanding reverse logistics beyond “returns”

Reverse logistics is often used interchangeably with “returns,” but in practice it is broader and more complex.

Reverse logistics can include:

• Customer returns
• Exchanges
• Repairs and refurbishments
• Warranty claims
• Recalls
• End-of-life or recycling flows
• Unsold inventory moving back through the network

Each of these flows has different handling requirements, cost profiles, and decision points. Treating all returned products the same way is one of the most common and costly mistakes retailers make.

The true cost of poor reverse logistics

Many retailers underestimate the total cost of reverse logistics because it is spread across multiple functions.

Costs typically sit in:

• Transport (returns freight, consolidation, cross-dock movements)
• Warehousing (handling, storage, congestion, space utilisation)
• Labour (manual inspection, sorting, repacking)
• Inventory (markdowns, write-offs, obsolescence)
• Customer service (queries, disputes, refunds)
• Technology (workarounds, manual processing)

When these costs are not visible end-to-end, decision-making becomes reactive and fragmented.

Retailers that take a structured approach to reverse logistics often find that returns erode margin far more than initially assumed, particularly when return volumes spike during peak trading periods.

Common challenges in online retail reverse logistics

Across Australia and New Zealand, several challenges appear consistently in online retail environments.

Returns designed around policy, not operations

Returns policies are often created by commercial or marketing teams without fully considering operational implications. This can result in:

• Unpredictable return volumes
• Inconsistent handling rules
• Manual decision-making on the warehouse floor

Warehouses not designed for returns

Many fulfilment centres are optimised for outbound flow, not inbound inspection and sorting. This leads to:

• Congestion at receiving docks
• Inefficient layouts
• Disruption to forward picking operations

Poor inventory visibility

Returned stock often sits in quarantine or limbo locations with limited system visibility, delaying resale and increasing markdown risk.

One-size-fits-all handling

High-value, low-value, resellable, damaged, and unsellable items are frequently processed through the same workflow, driving unnecessary cost.

Limited insight into return drivers

Without structured data on why items are returned, retailers struggle to address root causes such as product quality issues, inaccurate descriptions, or sizing problems.

Designing a better reverse logistics strategy

Improving reverse logistics starts with treating it as a designed capability, not an afterthought.

Segment reverse flows by value and outcome

Not all returns deserve the same treatment. Retailers should segment returns based on factors such as:

• Product value
• Condition on return
• Speed-to-resale requirement
• Likelihood of refurbishment or repair
• Sustainability considerations

This enables differentiated handling paths rather than a single, inefficient process.

Decide where returns should flow

Returns do not always need to return to the original fulfilment centre.

Options may include:

• Dedicated returns processing facilities
• Store-based consolidation
• Third-party returns hubs
• Direct-to-supplier or refurbisher flows

Network design plays a major role in reverse logistics cost and responsiveness.

Warehouse and fulfilment considerations for returns

Reverse logistics places very different demands on warehouse operations compared to outbound fulfilment.

Key considerations include:

• Dedicated space for returns receiving and inspection
• Clear segregation between sellable and non-sellable stock
• Fast-track paths for items suitable for immediate resale
• Ergonomic workstations for inspection and repacking
• Labour models that can flex with return volume variability

Retailers that explicitly design returns into warehouse layouts typically achieve faster cycle times and lower handling costs.

Inventory management and speed to resale

One of the biggest opportunities in reverse logistics is reducing the time between return and resale.

Delays lead to:

• Missed sales opportunities
• Increased markdowns
• Higher obsolescence risk

Improvement levers include:

• Clear decision rules for resale vs write-off
• Immediate system updates when condition is confirmed
• Priority processing for fast-moving SKUs
• Alignment between returns teams and inventory planners

When returns are processed quickly and accurately, they become a source of recovered value rather than margin erosion.

Using data to reduce returns at the source

Improving reverse logistics is not only about processing returns better — it is also about preventing avoidable returns.

Data from returns can be used to:

• Identify product quality issues
• Improve product descriptions and imagery
• Address sizing or specification inaccuracies
• Highlight packaging weaknesses
• Inform supplier performance discussions

Retailers that actively analyse return reasons often see improvements not just in logistics costs, but in customer satisfaction and conversion rates.

Sustainability and reverse logistics

Returns have a material environmental impact due to additional transport, packaging, and waste.

Improving reverse logistics can support sustainability objectives by:

• Reducing unnecessary transport movements
• Increasing resale and refurbishment rates
• Improving recycling and end-of-life pathways
• Minimising landfill through better sorting and recovery

For many organisations, reverse logistics is an increasingly important part of their broader sustainability narrative.

Technology and systems enablement

Technology plays a critical role in enabling efficient reverse logistics, but only when aligned to process and operating model design.

Key system considerations include:

• Clear visibility of returned inventory status
• Integration between customer service, warehouse, and finance systems
• Simple workflows for inspection and disposition decisions
• Data capture on return reasons and condition
• Reporting that supports continuous improvement

Technology should support faster decisions and clearer accountability, not add complexity.

Governance and ownership

One of the reasons reverse logistics underperforms is unclear ownership. Responsibilities are often split across:

• Ecommerce
• Customer service
• Warehousing
• Transport
• Finance

Improvement requires:

• Clear accountability for reverse logistics performance
• Defined KPIs covering cost, speed, recovery, and customer experience
• Regular review of return drivers and trends

Without governance, reverse logistics remains reactive and fragmented.

How Trace Consultants can help

Trace Consultants works with online retailers to design and improve reverse logistics capabilities that balance customer experience, cost-to-serve, and operational efficiency.

Our support commonly includes:

• Reviewing current reverse logistics processes and cost drivers
• Mapping end-to-end returns flows across transport, warehousing, and systems
• Designing segmented reverse logistics strategies
• Supporting warehouse layout and operating model changes
• Improving inventory visibility and speed to resale
• Developing performance metrics and governance frameworks
• Supporting technology and system alignment

Our approach is practical and grounded in how supply chains actually operate, helping retailers move beyond reactive returns handling to deliberate, data-driven reverse logistics design.

Turning reverse logistics into a competitive advantage

As online retail continues to grow across Australia and New Zealand, reverse logistics will only increase in scale and importance.

Retailers that treat returns as a necessary evil will continue to see margin leakage, operational disruption, and frustrated customers. Those that invest in structured reverse logistics design will be better positioned to:

• Control cost-to-serve
• Improve inventory recovery
• Support sustainability objectives
• Enhance customer trust and loyalty

Reverse logistics is no longer just about managing returns — it is about unlocking value from what comes back.

The question is not whether reverse logistics deserves attention, but whether it is being designed with the same rigour as the forward supply chain.

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Reducing COGS % and Improving Margin Through Range & Inventory Optimisation

Waste and SLOBs Reduction

Cost of Goods Sold (COGS) is often treated as something largely fixed—set by suppliers, market conditions and scale. Yet for many organisations across Australia and New Zealand, COGS % creeps up over time not because unit costs increase dramatically, but because inefficiencies accumulate quietly across the range and inventory portfolio.

New products are added. Old ones linger. Forecasts become less accurate. Inventory ages. Waste increases. Slow-moving and obsolete stock (SLOBs) grows. Markdowns become routine. Expediting costs rise. Eventually, margin erosion is accepted as inevitable.

In reality, a significant proportion of COGS pressure is self-inflicted.

Organisations that systematically optimise their product range and inventory policies can materially reduce COGS %, improve gross margin and release working capital—often without renegotiating a single supplier contract.

This article explores how range and inventory optimisation directly impacts COGS, why waste and SLOBs are persistent challenges, and how organisations can tackle them in a practical, sustainable way. It also outlines how Trace Consultants helps organisations drive measurable margin improvement through structured, data-led approaches.

Why COGS % Creeps Up Over Time

COGS rarely deteriorates overnight. It erodes gradually through a combination of small decisions that, individually, seem reasonable.

Range Expansion Without Discipline

New products are often introduced to:

• Respond to customer requests
• Match competitors
• Support promotions or growth initiatives

However, older products are rarely removed with the same rigour. Over time, ranges expand well beyond what demand can support efficiently.

The result:

• Lower volume per SKU
• Reduced forecast accuracy
• Smaller production or purchase runs
• Higher unit costs and logistics inefficiencies

Inventory Policies That Don’t Reflect Reality

Inventory parameters are often set once and rarely revisited. Safety stock, minimum order quantities and reorder points may no longer reflect:

• Actual demand variability
• True lead times
• Service priorities

This misalignment drives both excess stock and stock-outs—each of which increases COGS in different ways.

Poor Visibility of Waste and SLOBs

Waste and SLOBs are frequently treated as:

• “One-off write-offs”
• “The cost of doing business”

Without clear ownership and ongoing monitoring, these losses repeat year after year.

Forecast Bias and Optimism

Forecasts often reflect what the business hopes to sell, rather than what demand signals support. This optimism flows directly into excess inventory, markdowns and obsolescence.

How Range Complexity Drives COGS Up

Range complexity is one of the most powerful—and underestimated—drivers of COGS %.

The Hidden Cost of Too Much Choice

Each additional SKU introduces:

• Forecasting effort
• Inventory holding cost
• Planning variability
• Procurement and logistics overhead
• Risk of obsolescence

Even if a SKU is profitable on paper, it may still dilute overall margin by consuming capacity and attention.

Long-Tail SKUs and Margin Dilution

In many organisations, a small proportion of SKUs drive the majority of volume and margin. The long tail often:

• Sells infrequently
• Requires higher safety stock relative to demand
• Contributes disproportionately to waste and SLOBs

Without active range management, these SKUs quietly erode performance.

Range Decisions Are Often Decoupled from Supply Chain Reality

Product decisions are sometimes made without full consideration of:

• Minimum order quantities
• Shelf life or expiry risk
• Storage and handling complexity
• Transport and packaging inefficiencies

When these costs are not visible at decision time, they show up later in COGS.

Inventory Optimisation as a Margin Lever

Inventory is not just a balance sheet item. It is a direct driver of COGS through waste, write-downs, handling cost and inefficiency.

Excess Inventory Increases More Than Holding Cost

Holding cost is only part of the story. Excess inventory also increases:

• Damage risk
• Expiry and obsolescence
• Picking inefficiency
• Space constraints and overflow handling
• Discounting and markdown activity

All of these inflate COGS or reduce realised margin.

Stock-Outs Also Increase COGS

While counterintuitive, stock-outs can increase COGS by:

• Driving expediting and premium freight
• Forcing suboptimal substitution
• Increasing small-batch purchases
• Reducing production efficiency

Optimisation is about balance, not just reduction.

Waste: The Most Visible Symptom of Poor Alignment

Waste is one of the clearest indicators that demand, supply and inventory decisions are misaligned.

Common Drivers of Waste

Across industries, waste often stems from:

• Over-forecasting
• Poor promotion planning
• Inflexible minimum order quantities
• Long lead times combined with volatile demand
• Range changes not reflected in inventory run-down plans

Waste is rarely caused by one bad decision. It is the cumulative effect of many small misalignments.

Why Waste Is So Hard to Eliminate

Waste persists because:

• Accountability is diffuse
• It is often discovered too late
• Root causes are not systematically addressed

Without structural changes, waste reductions achieved one year often reappear the next.

SLOBs: The Silent Margin Killer

Slow-moving and obsolete stock is one of the most stubborn problems in supply chains.

What Creates SLOBs

SLOBs typically arise from:

• Range rationalisation delays
• Product lifecycle mismanagement
• Forecast bias
• Supplier constraints
• Lack of clear exit strategies for underperforming items

Once inventory becomes slow-moving, options narrow quickly.

The True Cost of SLOBs

Beyond write-offs, SLOBs:

• Occupy valuable warehouse space
• Complicate picking and replenishment
• Mask true demand signals
• Consume management attention

Left unchecked, they distort planning decisions and perpetuate poor outcomes.

Connecting Range, Inventory and COGS %

COGS improvement requires seeing the system as a whole.

Range decisions affect:

• Demand predictability
• Purchase economics
• Inventory requirements

Inventory policies affect:

• Waste and obsolescence
• Service levels
• Expediting and handling cost

Together, they determine:

• Gross margin
• Cash tied up in stock
• Operational efficiency

Treating these levers separately limits results. The biggest gains come from addressing them together.

Practical Approaches to Range Optimisation

Effective range optimisation is not about cutting aggressively. It is about making deliberate, evidence-based choices.

Segmenting the Range

Segmenting SKUs by:

• Volume contribution
• Margin contribution
• Demand variability
• Strategic importance

allows organisations to apply differentiated strategies rather than blanket rules.

Identifying Candidates for Rationalisation

Common indicators include:

• Persistent low volume
• High forecast error
• Disproportionate waste or markdowns
• Duplication or close substitutes

Importantly, decisions should consider total cost-to-serve, not just sales.

Managing Range Change Properly

Rationalisation without inventory exit planning simply creates SLOBs faster.

Effective programs include:

• Clear sell-down strategies
• Adjusted replenishment rules
• Supplier engagement
• Defined end-of-life processes

Inventory Optimisation Levers That Reduce COGS

Recalibrating Safety Stock

Safety stock should reflect:

• Actual demand variability
• True lead time performance
• Service priorities by SKU or segment

Many organisations carry excess safety stock because parameters were never revisited.

Improving Forecast Quality Where It Matters

Not all SKUs require the same forecast effort.

Focusing improvement on:

• High-value items
• High-risk items
• Items with high waste exposure

delivers far greater returns than chasing marginal gains everywhere.

Aligning Inventory Targets to Service Strategy

Different products justify different service levels.

Explicitly linking service targets to inventory investment:

• Improves margin transparency
• Prevents over-investment in low-impact areas

Governance: Where Most Initiatives Succeed or Fail

Many range and inventory initiatives start strongly and then fade.

Clear Ownership Is Critical

Successful programs clearly define:

• Who owns range decisions
• Who owns inventory targets
• Who is accountable for waste and SLOBs

Shared ownership often results in no ownership.

Embedding Ongoing Review Cadence

Range and inventory optimisation is not a one-off project.

Effective organisations embed:

• Regular range reviews
• Ongoing SLOB monitoring
• Periodic inventory parameter recalibration

This prevents issues from re-accumulating.

The Role of Data and Technology (Without Over-Engineering)

Advanced tools can help, but they are not a prerequisite for success.

What Matters Most

The biggest gains usually come from:

• Better use of existing data
• Clear metrics and definitions
• Consistent decision-making

Technology should support decisions, not complicate them.

Avoiding the “Tool First” Trap

Investing in sophisticated optimisation software without fixing:

• Data quality
• Governance
• Decision rights

often leads to underwhelming results.

How Trace Consultants Can Help

Trace Consultants works with organisations across Australia and New Zealand to reduce COGS %, improve margin and release cash through practical range and inventory optimisation.

Our approach focuses on sustainable improvement, not short-term fixes.

Diagnosing the True Drivers of Margin Leakage

We help organisations understand:

• Where COGS is really being lost
• How range complexity, waste and SLOBs interact
• Which levers will deliver the greatest impact

This creates a clear, prioritised improvement roadmap.

Range Optimisation and Rationalisation

Trace Consultants supports:

• SKU segmentation and performance analysis
• Identification of rationalisation opportunities
• Development of structured exit and transition plans
• Governance frameworks to prevent re-expansion

All decisions are grounded in data and operational reality.

Inventory Policy and Parameter Optimisation

We help recalibrate:

• Safety stock and reorder policies
• Service level targets by segment
• Inventory deployment across networks

The focus is on reducing excess without increasing risk.

Waste and SLOB Reduction Programs

Trace Consultants helps design and implement:

• Waste tracking and root cause analysis
• SLOB identification and disposition strategies
• Preventative controls to stop recurrence

This turns waste reduction into an ongoing discipline.

Embedding Capability and Governance

Sustainable results require capability uplift. We support:

• Role clarity and decision rights
• KPI design and performance management
• Review cadences and operating rhythms

This ensures improvements hold beyond the initial program.

Final Thoughts

Reducing COGS % and improving margin is rarely about one big initiative. It is about addressing the cumulative impact of range complexity, excess inventory, waste and SLOBs.

For organisations across Australia and New Zealand, the opportunity is significant:

• Margin improvement without supplier renegotiation
• Cash release without service degradation
• Simpler, more resilient operations

The key is treating range and inventory optimisation as core commercial disciplines—not just supply chain housekeeping.

When organisations align product decisions, inventory policies and governance around a shared understanding of value and risk, COGS % comes down, margin improves and performance becomes far more predictable.

If COGS pressure feels persistent despite strong sales or procurement efforts, the answer may lie not in buying cheaper—but in carrying, managing and ranging smarter.

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Demand Planning and Inventory Optimisation Using AI

Demand planning and inventory optimisation have always sat at the heart of supply chain performance. Get them right, and organisations unlock lower working capital, higher service levels and calmer operations. Get them wrong, and the consequences show up everywhere: stock-outs, excess inventory, expediting costs, frustrated customers and stressed teams.

Across Australia and New Zealand, many organisations feel like demand planning has become harder, not easier. Forecasts are less stable. Product ranges are broader. Promotions are more frequent. Supply is less predictable. And decision-makers are expected to respond faster, with fewer buffers and less tolerance for error.

This is where interest in artificial intelligence (AI) has surged.

AI is often positioned as a silver bullet for forecasting and inventory management. In reality, its value is far more nuanced—and far more powerful—when applied thoughtfully. AI does not replace demand planners or inventory managers. Instead, it augments their judgement, improves signal detection, and enables faster, more consistent decision-making at scale.

This article explores how AI is being used in demand planning and inventory optimisation, where it genuinely adds value, where organisations need to be careful, and how Trace Consultants helps organisations apply AI in practical, outcome-focused ways.

Why Demand Planning and Inventory Optimisation Are Under Pressure

Before looking at AI, it’s important to understand why traditional approaches are struggling.

Demand Has Become Less Predictable

Many planning processes were built around relatively stable demand patterns. Today, demand is influenced by:

• Rapid product innovation and SKU proliferation
• Promotional intensity and price volatility
• Channel shifts (B2B, B2C, online, omnichannel)
• Customer-specific ordering behaviour
• External disruptions and uncertainty

Simple time-series forecasting methods struggle to keep up with this level of complexity.

Inventory Buffers Are No Longer Acceptable

Holding excess inventory used to be the default response to uncertainty. Today, rising capital costs, storage constraints and executive focus on cash have reduced tolerance for “just in case” stock.

Organisations are expected to:

• Reduce inventory
• Improve availability
• Maintain resilience

Doing all three at once requires better planning tools and smarter trade-offs.

Manual Planning Doesn’t Scale

Spreadsheets, judgement-based overrides and manual exception handling can work in small, stable environments. They do not scale well when:

• SKU counts grow
• Lead times vary
• Supply constraints change frequently

Planners end up spending more time firefighting than improving outcomes.

What AI Really Means in Demand Planning and Inventory Optimisation

AI is a broad term that means different things to different people. In demand planning and inventory optimisation, it typically refers to the use of advanced algorithms to identify patterns, relationships and signals in large, complex datasets.

This often includes:

• Machine learning models that adapt as new data becomes available
• Pattern recognition across multiple demand drivers
• Probabilistic forecasting rather than single-point forecasts
• Scenario analysis and simulation
• Automated segmentation and parameter tuning

AI does not “guess the future.” It improves the quality of insights used to make decisions about the future.

How AI Improves Demand Planning

Moving Beyond Single-Number Forecasts

Traditional forecasting often produces a single number for each item and period. AI-based approaches are more likely to produce a range of outcomes, reflecting uncertainty.

This allows planners to:

• Understand forecast confidence
• Identify high-risk items
• Make differentiated decisions

For example, two items with the same average forecast may require very different inventory strategies if one has high volatility and the other is stable.

Better Use of Multiple Demand Signals

AI models can incorporate far more inputs than traditional methods, such as:

• Historical sales patterns
• Promotions and pricing changes
• Seasonality at different levels
• Customer behaviour
• External drivers (where relevant and available)

This helps surface patterns that are difficult to identify manually.

Improved Forecast Accuracy for Long-Tail Items

Low-volume or intermittent demand items are notoriously difficult to forecast. AI-based approaches are often better at identifying underlying patterns and reducing noise for these items, improving planning confidence.

Faster Adaptation to Change

Machine learning models can recalibrate more frequently as new data becomes available. This helps planners respond faster when demand patterns shift, rather than waiting for periodic forecast reviews.

How AI Improves Inventory Optimisation

Demand planning is only half the story. Inventory optimisation is where AI often delivers the most tangible financial impact.

Smarter Safety Stock Setting

Traditional safety stock formulas rely on simplified assumptions about demand variability and lead time. AI enables more nuanced approaches that:

• Reflect actual demand distributions
• Account for variable lead times
• Adjust dynamically as conditions change

This often leads to:

• Lower overall inventory
• Better service at critical items
• Reduced overstock in low-value areas

Differentiated Inventory Strategies

AI enables automated segmentation of inventory based on:

• Demand patterns
• Value
• Criticality
• Service requirements

This allows organisations to apply different inventory policies where they matter most, rather than treating all items the same.

Scenario Modelling and Trade-Off Analysis

AI-powered tools can simulate scenarios such as:

• Changes in service targets
• Supplier lead time variability
• Volume growth or decline
• Network changes

This helps decision-makers understand trade-offs before committing to changes.

Exception-Based Management

Rather than planners reviewing thousands of SKUs manually, AI helps identify:

• Items at risk of stock-out
• Items at risk of excess
• Items behaving abnormally

This shifts planning from reactive to proactive.

What AI Does Not Do Well (and Why That Matters)

AI is powerful, but it is not magic. Understanding its limitations is critical.

AI Does Not Replace Business Context

Algorithms do not understand:

• Strategic priorities
• Customer relationships
• Regulatory constraints
• Commercial commitments

Human judgement remains essential, particularly for exceptions and strategic decisions.

AI Is Only as Good as the Data

Poor data quality leads to poor outputs. Common challenges include:

• Inconsistent item master data
• Poor promotion tracking
• Incomplete lead time data
• Misaligned hierarchies

AI amplifies both good and bad data.

AI Cannot Fix Broken Processes

If planning processes are unclear, roles are poorly defined, or governance is weak, AI will not solve these problems. In fact, it may make them more visible.

Common Pitfalls in AI-Enabled Planning Initiatives

Many AI initiatives underperform because of predictable mistakes.

Starting with Technology Instead of the Problem

Organisations sometimes invest in AI tools without being clear on:

• What decisions need to improve
• Which outcomes matter most
• How success will be measured

This leads to impressive dashboards with limited real-world impact.

Over-Automation Without Trust

Planners need to trust AI outputs. If models are opaque or poorly explained, users override them or ignore them altogether.

Transparency and explainability matter.

Ignoring Change Management

AI changes how people work. Without structured change management, organisations see:

• Resistance
• Shadow spreadsheets
• Reversion to old habits

Successful programs invest as much in people as in algorithms.

Where AI Delivers the Most Value in Practice

Across industries in Australia and New Zealand, AI tends to deliver the most value when applied to:

• High SKU-count environments
• Networks with variable lead times
• Organisations under pressure to reduce working capital
• Businesses with complex promotional or seasonal demand
• Operations where planners are overloaded with manual tasks

The biggest gains often come not from perfect forecasts, but from better inventory decisions made faster and more consistently.

Integrating AI into Sales & Operations Planning (S&OP)

AI-enabled demand planning and inventory optimisation are most powerful when integrated into S&OP or Integrated Business Planning (IBP) processes.

This allows organisations to:

• Align demand and supply assumptions
• Quantify trade-offs between service, cost and cash
• Run scenarios collaboratively
• Make decisions with a shared fact base

AI strengthens S&OP by improving the quality and timeliness of inputs, not by replacing the process.

Technology Choices: One Size Does Not Fit All

There is no single “best” AI solution. Options range from:

• Advanced features within existing planning platforms
• Specialist inventory optimisation tools
• Low-code or spreadsheet-based solutions enhanced with AI
• Custom-built models for specific use cases

The right choice depends on:

• Business complexity
• Data maturity
• Internal capability
• Speed-to-value requirements

Overly complex solutions can delay benefits and increase dependency. Simpler, well-targeted tools often deliver faster results.

The Role of Low-Code and Practical AI

Not all AI needs to sit in large enterprise platforms. Increasingly, organisations are using:

• Low-code tools
• Smart spreadsheets
• Embedded algorithms

to automate specific planning decisions and workflows.

These approaches can:

• Deliver rapid benefits
• Improve planner productivity
• Complement core planning systems

They are particularly useful where flexibility and speed matter.

How Trace Consultants Can Help

Trace Consultants helps organisations across Australia and New Zealand improve demand planning and inventory optimisation using AI in a practical, outcome-focused way.

We do not start with tools. We start with decisions.

Clarifying the Planning Problem

We work with clients to define:

• Which planning decisions matter most
• Where value is being lost today
• What “good” looks like in terms of service, cost and cash

This ensures AI is applied where it makes a real difference.

Designing Fit-for-Purpose Planning Models

Trace Consultants helps design planning approaches that:

• Match business complexity
• Balance automation and human judgement
• Integrate with existing processes and systems

This includes demand forecasting, safety stock design and replenishment logic.

Supporting Technology Selection and Deployment

We support clients to:

• Assess AI-enabled planning tools objectively
• Avoid over-engineering
• Implement solutions that deliver value quickly

Our approach is vendor-agnostic and grounded in practical experience.

Embedding Change and Capability

We help organisations:

• Redesign planning roles and responsibilities
• Build trust in AI outputs
• Establish governance and review cadences
• Train teams to use insights effectively

This ensures benefits are sustained, not short-lived.

Linking Planning to Financial Outcomes

Critically, we help connect demand and inventory decisions to:

• Working capital
• Service performance
• Cost-to-serve

This allows executives to see the impact of planning decisions clearly.

Final Thoughts

AI is changing demand planning and inventory optimisation—but not in the way headlines often suggest.

The real value of AI lies in:

• Better visibility of uncertainty
• Smarter inventory trade-offs
• Faster, more consistent decisions
• Reduced manual effort

For organisations across Australia and New Zealand, the opportunity is significant. But success depends less on algorithms and more on how AI is integrated into everyday decision-making.

When applied thoughtfully, AI does not replace planners. It gives them better tools, clearer insights and more time to focus on what matters.

If your organisation is struggling with volatile demand, rising inventory pressure or planning processes that no longer scale, the question is not whether to explore AI—but how to do so in a way that genuinely improves outcomes.

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