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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Advanced Planning Systems

It was the week before a major seasonal peak and the planning team was sweating. Historical spreadsheets said one thing, the promotional plan suggested another, suppliers were reporting longer lead times, and the distribution centre was already running short on several high-turn SKUs. The operations team hinted at overtime and temporary hires; the finance director warned about working capital. The supply chain felt like an orchestra without a conductor — lots of good instruments, no single score.

Advanced Planning Systems (APS) exist to be that conductor. They bring together probabilistic forecasting, inventory optimisation, constraint-based supply planning and scenario modelling so organisations can make trade-offs deliberately rather than reactively. For businesses in Australia and New Zealand — where long inbound lanes, seasonal demand and labour constraints are common — APS offer a way to lift service, reduce inventory and improve decision speed.

This article is a practical, non-technical guide to Advanced Planning Systems targeted at supply chain leaders, planning managers and executives in ANZ. We’ll cover what APS are, the capabilities that matter, how to evaluate and implement them, data and organisational readiness, governance and change management, common pitfalls, and a pragmatic roadmap for success. We’ll also explain how Trace Consultants can help at every stage.

What is an Advanced Planning System (APS)?

An APS is software that supports medium- to long-range planning activities across forecasting, inventory, supply and resource planning. Unlike traditional ERP or spreadsheet-based planning, APS combine:

• Statistical and machine-learning forecasting to produce probabilistic demand views;
• Inventory optimisation that sets safety stocks and strategic buffers using volatility and service targets;
• Constraint-based supply planning that schedules production, distribution and procurement while respecting real-world limits (capacity, materials, labour);
• Scenario modelling and simulation to assess trade-offs across cost, service and risk; and
• Integration with S&OP/IBP to provide a single source of truth for strategic decisions.

APS are used to coordinate decisions across procurement, manufacturing, warehousing and distribution. They provide the ‘what-if’ capability managers need to choose the best course when demand, supply or capacity changes.

Why ANZ organisations should consider APS now

Several local market factors make APS particularly relevant for Australian and New Zealand organisations:

• Long inbound lanes and variable lead times. Imports, transhipment and port congestion create lead-time variability that APS model more effectively than fixed-lead routines.
• Seasonal and promotional peaks. Events, weather and tourism cycles create concentrated demand windows that benefit from scenario planning.
• Tight labour markets and automation uptake. Predictable plans reduce reliance on ad-hoc labour and support investment in automation and scheduling.
• Complex omnichannel requirements. APS helps balance store replenishment, online fulfilment and distribution-centre priorities with a single plan.
• Working capital pressure. Inventory optimisation reduces stock while maintaining service — a critical lever in a capital-constrained environment.

APS answer questions that matter in ANZ: how much buffer do we need across long lanes? How should we allocate scarce transport capacity during a peak? What is the true cost of achieving a small uplift in service? APS allow those questions to be modelled, measured and governed.

Core APS capabilities and why they matter

Not all APS are created equal. Here are the core capabilities and the business problems they solve.

1. Probabilistic forecasting

Traditional point forecasts (a single number) assume certainty. Probabilistic forecasting provides a range and confidence intervals, enabling planners to see risk and plan cover by service-level targets.

Why it matters: Better visibility of demand uncertainty drives smarter safety stock and promotion planning.

2. Multi-echelon inventory optimisation (MEIO)

MEIO models inventory across the entire network — from central DCs to regional depots and stores — and optimises holdings for service at each echelon.

Why it matters: Avoids local overstocking and suboptimal safety stock duplication, reducing total network inventory.

3. Constraint-based supply planning

APS can schedule production and distribution while respecting equipment, labour and material constraints, rather than assuming infinite capacity.

Why it matters: Produces feasible plans and highlights bottlenecks early, enabling investment or workarounds.

4. Distribution requirements planning and transportation optimisation

APS can incorporate transport slots, carrier capacity and yard constraints, coordinating deliveries with dock availability and minimising detention or overtime.

Why it matters: Reduces freight cost, truck dwell time and the risk of stockouts at stores.

5. Scenario analysis and optimisation

Managers can test scenarios (e.g. supplier outage, port delay, or promotion) and understand cost/service trade-offs, including probabilistic outcomes.

Why it matters: Supports robust decision-making under uncertainty and demonstrates business cases for resilience investments.

6. Integrated S&OP/IBP support

APS feeds consolidated plans into Executive S&OP or IBP processes, providing a quantitative backbone for strategic decisions.

Why it matters: Aligns finance, sales and operations with a single plan and transparent assumptions.

7. Demand sensing and short-term replenishment

Using high-frequency signals such as POS and web traffic, APS can sense demand and adjust near-term plans dynamically.

Why it matters: Improves responsiveness in volatile periods and reduces short-term stockouts.

8. Master data and product hierarchies

Robust product and supplier master data management enables consistent planning across systems.

Why it matters: Poor master data degrades model performance and undermines trust in outputs.

How to evaluate APS vendors

Evaluating APS vendors is less about feature checklists and more about fit to your operating model and delivery capability. Consider these evaluation dimensions:

1. Fit to operating model

Does the system support your mix of activities — manufacturing versus pure distribution, omni-channel fulfilment, and multi-echelon networks?

2. Modelling approach

Does the vendor offer probabilistic forecasting, multi-echelon optimisation and constraint-based planning? How transparent and explainable are the models?

3. Data and integration

Can the APS integrate with your ERP, WMS, TMS and demand sources? Are connectors available and is the system API-friendly?

4. Usability and decision support

Are planners able to run scenarios, override plans with business rules, and visualise trade-offs without heavy IT support?

5. Scalability and performance

For network-scale optimisation and many SKUs, the system must solve complex models quickly — especially if used in routine planning cycles.

6. Deployment model

SaaS vs on-premise considerations, data residency, SLAs and availability.

7. Implementation methodology and local support

Does the vendor or partner have experience implementing in ANZ realities — long lanes, seasonal patterns and the local regulatory environment?

8. Total cost of ownership

Consider licensing, implementation, data engineering, change management and ongoing support.

9. Roadmap and openness

Is the vendor investing in AI, probabilistic capabilities and scenario simulation? Will they maintain openness for custom integrations?

A structured RFP with scripted scenario demonstrations and a proof-of-concept on a representative data set reveals much more than vendor marketing.

Data readiness: the unsung prerequisite

APS delivers only as well as the data that feeds them. Data readiness is a practical, staged task.

Master data

Clean, consistent SKU definitions, BOMs, pack dimensions, supplier lead times and regions. Resolve duplicates and ensure consistent units of measure.

Transactional history

Sufficient, clean historical demand and shipment records. Flag anomalies and promotions; annotate events (extreme weather, sales campaigns) so models can account for them.

Supply-side data

Supplier reliability, production batch times, transit times by lane, port performance and carrier variability. Capture variance as well as averages.

Inventory data

Accurate location-level stock records, cycle counts and reconciliation processes.

External signals

Calendar data, public holidays, weather patterns and promotions at competitors if available. These enrich demand models.

Data pipelines and MVDP

Build a Minimal Viable Data Product — a lean, versioned dataset that supports early pilots. Implement robust transformation pipelines and provenance so models are reproducible.

Without data discipline, APS are at best brittle and at worst misleading. Spend time here first.

Implementation: a pragmatic roadmap

A common path to APS implementation looks like this:

1. Strategy & use-case prioritisation

Identify the highest-value use cases: probabilistic forecasts for promotional SKUs, MEIO for inventory reduction, or constraint-based planning where capacity is tight. Prioritise quick wins.

2. Proof of concept / pilot

Run a time-boxed pilot on a defined category and network slice. Deliver measurable KPIs — forecast error reduction, inventory covered or days-of-cover savings.

3. Scale and industrialise

Operationalise data pipelines, automate routine runs, and embed outputs into planning cycles. Add scenario libraries and governance.

4. Integrate with S&OP/IBP

Move APS outputs into monthly S&OP cycles, with executive dashboards and financial translation.

5. Continuous improvement

Monitor model performance, retrain, refine master data and expand models to more categories and regions.

A pilot-first approach reduces large upfront cost and validates assumptions under real operations.

Organisational change: people, processes and governance

APS is more about people and decisions than about software. Successful APS programmes invest in change.

The planner’s role changes

Planners move from data wranglers to decision-makers. Training is required in scenario interpretation, model limitations and exception handling.

S&OP integration

Set clear interfaces: who approves trade-offs, how financials are translated, and how risks are escalated. APS should support not replace human judgement.

Governance and ownership

Define owners for models, data, and outcomes. Establish model governance, drift detection and a retraining cadence. Maintain a model registry with version history and testing artefacts.

KPIs and incentives

Align KPIs to desired outcomes: service, inventory turns, working capital and customer experience. Avoid perverse incentives that game the system.

Change management

Run workshops, simulations and rehearsal exercises. Early wins demonstrate value and build advocacy.

Organisations that treat APS as a capability shift — not just a systems upgrade — capture the real value.

Common pitfalls and how to avoid them

APS projects fail for some recurring reasons. Here’s how to avoid them.

1. Over-ambitious scope

Start small. Pick focused pilots and extend by pattern. Large, multi-functional first-wave projects take longer and increase risk.

2. Neglecting data

Allocate time and budget for master data and transactional cleansing. Poor data leads to poor decisions.

3. Treating APS as an ERP replacement

APS complements ERP; it is not a transactional system. Keep responsibilities clear: ERP for transactions and finance, APS for optimisation and planning.

4. Poor integration planning

Plan middleware, APIs and data refresh cadence early. Real-time or near-real-time feeds are often necessary for lead-time variability.

5. Ignoring model governance

Assign ownership and monitoring. Models must be versioned and audited for reliability and bias.

6. Underinvesting in training

Invest in planners and S&OP owners so they can interpret results and override responsibly.

7. Expecting miracles from technology

APS improves decision-making quality and speed, but it requires organisational discipline to act on outputs.

Measuring value and ROI

APS delivers value across several metrics. A comprehensive business case should quantify:

• Forecast accuracy improvements (e.g. reduction in MAPE) and resulting stockout reduction.
• Inventory reduction through MEIO and dynamic safety stock (days-of-cover improvements and working capital release).
• Service level improvements and their revenue protection value.
• Reduced expedite and freight savings by better supply planning and early warning.
• Capacity utilisation improvements and avoided capital spend through better scheduling.
• Operational efficiency (planner hours saved, fewer manual reconciliations).

Aim to demonstrate payback through a combination of inventory, freight and labour metrics, with transparency on assumptions for demand variability and supplier reliability.

Advanced topics: machine learning, hybrid models and digital twins

Once the fundamentals are in place, organisations can explore advanced capabilities:

Hybrid models

Combine statistical forecasts with causal models and machine learning that ingest external signals for promotions, weather or marketing activity.

Reinforcement learning

Experimental approaches can tune replenishment policies in simulated environments, but these remain high-complexity and need rigorous validation.

Digital twins and simulation

Digital twins of supply chains and discrete-event simulation let planners stress-test scenarios — port delays, supplier outages and capacity changes — and quantify resilience trade-offs.

Prescriptive analytics

Beyond predicting, APS can prescribe actions — priority allocations, production sequencing or supplier nominations — that are optimisation-driven and explainable.

Advanced techniques should be phased and always validated against business logic and safety constraints.

Cloud versus on-premise: practical considerations

Cloud deployment is increasingly common for APS due to scalability and faster provisioning. Consider:

• Data residency and compliance. Ensure hosting complies with local and sectoral requirements.
• Performance and latency. APS optimisation can be compute-intensive; cloud scaling reduces solve-time and supports interactive scenario runs.
• Integration patterns. Cloud APIs facilitate integration, but plan secure connectivity to on-premise ERP systems.
• TCO. Factor cloud costs for heavy compute, not just software licences.

For many ANZ organisations, cloud-first APS coupled with hybrid integration strikes a pragmatic balance.

How Trace Consultants can help

Trace Consultants supports Australian and New Zealand organisations at every stage of the APS journey — from strategy to sustained value. Our approach focuses on pragmatic results and operational realism.

We can help with:

• Strategy & use-case prioritisation: Identify the high-value APS use cases for your business using a rapid diagnostic that balances commercial benefit and data readiness.
• MVDP & pilot design: Build a Minimal Viable Data Product and run pilots that prove value fast, focusing on categories or lanes that unlock the most benefit.
• Vendor selection & procurement support: Define the evaluation criteria, run scripted demos and manage commercial negotiation to secure fit-for-purpose solutions.
• Data engineering & master data: Deliver master data cleansing, lineage and transformation pipelines so models produce reliable outputs.
• Model governance & operationalisation: Set up model registries, performance monitoring, retraining cadences and role-based ownership.
• Integration architecture: Design API-first, event-driven integrations between APS, ERP, WMS and TMS to ensure reliable, auditable feeds.
• Change management & capability uplift: Transform planner roles through training, S&OP integration and scenario rehearsal workshops.
• Scenario modelling & digital twins: Build simulation environments to test resilience and business cases for inventory, capacity and supplier diversification.
• Post-rollout optimisation: Run slotting, replenishment and network tuning to capture further value after go-live.

Our delivery emphasises short pilots, measurable KPIs and a repeatable pattern to scale. We focus on embedding APS into everyday decision-making so the technology becomes part of how your organisation runs, not just a tool you use occasionally.

Practical 12-step roadmap to get started in the next 12 months

1. Run a rapid APS diagnostic: identify top 2–3 value use cases.
2. Form a cross-functional team with planning, procurement, operations, IT and finance.
3. Build a Minimal Viable Data Product for the pilot scope.
4. Select a short, high-impact pilot (e.g. probabilistic forecasting for promotional SKUs).
5. Design the pilot KPIs and baseline current performance.
6. Run the pilot in 8–12 weeks and measure outcomes.
7. Validate process changes and training needs for planners who will use APS outputs.
8. Scale to multi-echelon inventory optimisation once forecasting and data pipelines are stable.
9. Introduce constraint-based supply planning and scenario libraries.
10. Integrate APS outputs into S&OP/IBP with clear financial translation.
11. Establish model governance and automated monitoring.
12. Commit to continuous improvement with quarterly reviews and roadmap updates.

This roadmap focuses on rapid validation, human-centred change and progressive scale.

Final thoughts

Advanced Planning Systems are not magic, but they are powerful. When organisations treat APS as a capability — combining clean data, clear ownership, scenario-driven decision-making and disciplined governance — they unlock better forecasting, more efficient inventory, and supply plans that reflect the real world. For Australian and New Zealand businesses facing long lanes, seasonal peaks and constrained labour markets, APS is a lever to make operations more predictable, resilient and cost-effective.

Trace Consultants helps translate APS from concept to continuous benefit: rapid pilots, pragmatic engineering, governance and the change work that makes new planning practices stick. If you’d like a short diagnostic to identify the right APS pilots for your business and a practical roadmap to get started this quarter, Trace Consultants can prepare a tailored scope and deliverables pack.

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