When to Upgrade or Migrate to a New APS (Advanced Planning System)

There’s a moment most supply chain leaders recognise.

It’s late in the month. Forecast sign-off is due. Someone’s “final” demand file has three versions in circulation, and the only person who understands why the system is throwing exceptions is on leave. The replenishment plan looks wrong, but you can’t prove it quickly. Sales is frustrated because the forecast “doesn’t reflect reality”. Operations is frustrated because the plan “isn’t executable”. Finance is frustrated because nobody can explain the gap between what was planned and what was shipped.

And your planning team—smart, hardworking people—are stuck doing spreadsheet gymnastics to keep the wheels turning.

That’s usually when the APS question lands on the table:

Do we upgrade what we’ve got, or do we migrate to something new?

This article is a practical guide for Australian supply chain, operations, and finance leaders navigating that decision. We’ll cover:

• The clearest signs your APS is no longer fit-for-purpose
• Upgrade vs migration: how to choose the right path
• What “good” looks like in a modern planning stack
• Common traps (and how to avoid them)
• How Trace Consultants can help you get to value faster—without vendor bias

We’ll also reference common platforms used across ANZ—GAINS, RELEX, Anaplan, Logility, Kinaxis, Slimstock, Coupa, Blue Yonder, and o9—but the goal here isn’t to crown a winner. It’s to help you make a decision that fits your business, your constraints, and your maturity.

First: what an APS should be doing (and why it matters)

An Advanced Planning System (APS) is meant to help your organisation make better decisions across demand, inventory, supply, and execution—faster, with less manual effort, and with clearer trade-offs.

In practice, most APS programs sit across a few core capabilities:

• Demand planning and forecasting (baseline, promo, new products, lifecycle)
• Inventory optimisation (service levels, safety stock, policy, multi-echelon options)
• Replenishment planning (store/DC ordering, constraints, pack rounding, MOQ/MPQ)
• Supply planning (capacity, lead times, constraints, allocation, scenario planning)
• S&OP / IBP enablement (one set of numbers, trade-offs, governance, workflow)
• Exception management (alerts, prioritisation, resolution workflow)
• Scenario modelling (what-if analysis, stress testing, decisions with context)

When your APS is working well, you see it in outcomes:

• Planners spend more time managing exceptions, not massaging data
• Service improves (or holds) while inventory and waste reduce
• Decisions are faster, and the “why” is transparent
• S&OP becomes a decision forum, not a reporting meeting
• The organisation can scale complexity—range growth, new channels, new DCs—without adding headcount linearly

When your APS isn’t working, you also see it—just not in one neat dashboard.

The real reason APS decisions are hard

APS choices are rarely just software decisions. They’re operating model decisions that touch:

• How your organisation plans (cadence, roles, decision rights)
• How your data is governed (master data, hierarchies, ownership)
• How trade-offs are made (service vs working capital vs cost)
• How execution teams work with plans (DC constraints, supplier constraints, store realities)

That’s why APS upgrades and migrations can either unlock step-change performance—or become expensive, exhausting programs that deliver a nicer interface on top of the same problems.

Before you decide upgrade or migrate, get clear on why you’re doing it.

The clearest signs it’s time to upgrade or migrate

You don’t need all of these to justify a change. But if you recognise several, it’s time to take the APS question seriously.

1) Your APS is technically supported, but operationally abandoned

Maybe the vendor still supports it. Maybe IT can keep it running. But the business has quietly stopped trusting it:

• Key planners don’t use it day-to-day
• Teams export data “to do it properly in Excel”
• Exception messages are ignored because they’re too noisy or irrelevant
• The “official” plan isn’t the plan being executed

That’s not a planning maturity issue. That’s a system-and-process fit issue.

2) You’re spending more effort feeding the system than using it

If your planning calendar is dominated by data cleansing, manual overrides, rebuilding hierarchies, re-keying promotions, fixing integration errors, and reconciling versions, your APS is acting like a transactional burden—not a decision engine.

3) Your business has changed, but your planning design hasn’t

Common triggers in Australia include:

• Major range expansion (especially in retail and spare parts)
• New channels (e-commerce, marketplace, direct-to-consumer)
• New fulfilment models (dark stores, micro-fulfilment, ship-from-store)
• Increased import exposure and volatile lead times
• Supplier consolidation or new strategic suppliers
• M&A activity (multiple ERPs, multiple planning methods, misaligned policies)

If the operating context shifts, planning needs to shift too.

4) Service targets are rising, but you can’t hold inventory flat

This is one of the most common hidden APS problems: the system can generate a plan, but it can’t clearly explain trade-offs (or optimise the policy) in a way the business trusts.

When that happens, organisations often default to “just hold more stock”—and working capital balloons.

5) You can’t model constraints properly

A plan that ignores constraints is just a wish list.

If your APS can’t properly account for DC capacity (labour, dock doors, cut-offs), supplier constraints (MOQ, capacity, allocation), transport constraints, store constraints (shelf capacity, backroom limits), or manufacturing constraints (changeovers, finite capacity), it will keep producing plans that look right but fail in execution.

6) Upgrades have become risky and expensive

If every version upgrade feels like open-heart surgery—and the business dreads change windows—you may be approaching the point where incremental upgrades don’t make sense.

7) You can’t meet governance expectations (auditability, workflow, controls)

In many organisations, the APS becomes part of a broader governance system: forecast sign-off and accountability, assumption tracking, scenario approvals, change control, and data lineage.

If your APS can’t support that, S&OP becomes political instead of factual.

8) Your architecture is now a patchwork

A common pattern: APS plus spreadsheets plus custom scripts plus shadow databases plus reporting “fixes”.

You end up with a fragile ecosystem where nobody is sure what’s true, and every change breaks something downstream.

9) You need decision speed, and you can’t get it

When volatility hits (weather events, supplier disruptions, promo spikes), you need to sense and respond quickly.

If you can’t produce a credible re-plan in hours (or a day), you’re operating with lag.

10) Your planners are burning out (and you’re losing talent)

Good planners don’t want to spend their careers reconciling files. When the APS becomes a grind, attrition follows—and capability walks out the door.

Upgrade vs migrate: how to choose

A practical way to think about it:

Choose an upgrade when:

• The core engine is sound, and gaps are mostly configuration, data, or process
• The platform roadmap still aligns with your needs
• You can achieve improvements via modules, enablement, or targeted redesign
• Your biggest pain is adoption, workflow, or master data—not fundamental capability
• You need value fast and want to minimise disruption

Upgrades work best when the business is prepared to fix the real issues: planning design, data ownership, exception logic, and ways of working.

Choose a migration when:

• The platform can’t support critical requirements (constraints, scenarios, scale, channels)
• The product is end-of-life, or you’re stuck on a legacy version you can’t safely modernise
• Integration is brittle and costly, and modern integration patterns would materially reduce risk
• You have a step-change in business complexity (new network, new channel, new model)
• You need to standardise planning across merged entities or multiple ERPs
• The total cost of keeping the old platform alive exceeds the value it delivers

Migrations are harder—but sometimes they’re the only sensible way to reset the foundation.

A quick “APS decision test” you can run internally

Ask three groups the same question:

What decisions should our APS help us make weekly—and what decisions should it make automatically?

• If the answers are wildly different, you have a planning operating model gap.
• If the answers are aligned but the system can’t support them, you have a capability gap.
• If the system could support them but nobody uses it that way, you have an adoption/design gap.

That distinction is what separates smart upgrades from rushed re-platforming.

What to look for in modern APS platforms (without getting vendor-blinded)

The platforms you’ll see in the ANZ market each tend to have different strengths depending on industry, scale, and planning philosophy.

Rather than listing features, focus on fit across the areas below.

1) Planning philosophy and workflow

• Does it support your cadence (weekly, daily, event-based)?
• Can you embed sign-offs, controls, and governance?
• Can it help teams collaborate across sales, finance, operations?

This is where connected planning approaches (often associated with platforms like Anaplan) can suit certain operating models—particularly where cross-functional alignment is the core problem to solve.

2) Forecasting and demand signal handling

• Can it handle promotions, events, and causal factors?
• Can you incorporate external signals where appropriate?
• Can you manage lifecycle properly (new, seasonal, end-of-life)?

Retail-focused planning solutions (often considered in the RELEX conversation) can be relevant when range dynamics and store-level planning are central.

3) Inventory optimisation and policy

• Can you set policies by segment and service tier?
• Can you model lead time variability properly?
• Can you run multi-echelon logic where it matters?
• Can you explain the “why” behind recommended stock?

Inventory-optimisation specialists such as GAINS and Slimstock often come up when the goal is to lift service and reduce working capital through better policy—not just better forecasting.

4) Supply planning and concurrency

• Can it model constraints in a way your operations team trusts?
• Can it re-plan quickly when conditions change?
• Can you evaluate trade-offs across demand and supply in one place?

This is a common reason organisations explore platforms like Kinaxis—particularly for complex supply environments that need speed and scenario depth.

5) End-to-end integration (planning plus execution)

If your organisation is trying to connect planning decisions to execution reality (warehouse constraints, transport constraints, order management), broader suites like Blue Yonder may enter the discussion depending on your architecture direction.

6) Scenario modelling that leaders will actually use

A scenario isn’t useful if it takes a week to build, or if nobody trusts the assumptions.

Look for fast scenario creation, transparent assumptions, and outputs that support decisions—not just charts.

7) Data model and extensibility

Modern APS programs succeed or fail on data:

• Master data ownership
• Product and location hierarchies
• Lead time logic
• Pack rounding and ordering rules
• Service policies and segmentation

If the system requires perfect data to function, be honest: are you ready for that?

8) Integration and architecture fit

Most APS pain isn’t from the planning engine. It’s from the plumbing: ERP integration, POS and sales feeds, supplier feeds, warehouse and transport feeds, and master data synchronisation.

Your integration approach should reduce fragility, not increase it.

9) Total cost of ownership, not just licence cost

Don’t stop at subscription fees. Include implementation and change effort, ongoing admin and platform support, integration maintenance, data governance workload, and the cost of planner time wasted on manual work.

The migration traps that cost the most (and how to avoid them)

Trap 1: Treating APS as an IT project

APS is a business capability program. If the business doesn’t own the outcomes, the system will become shelfware.

Avoid it by setting clear decision outcomes, planning KPIs, and business ownership from day one.

Trap 2: Replicating broken processes in a new tool

If your current planning process is messy, migrating it “as-is” just makes the mess faster.

Avoid it by redesigning the planning operating model before (or alongside) system design.

Trap 3: Underestimating master data and hierarchy work

Planning hierarchies are not “just data”. They are the structure of how your organisation thinks.

Avoid it by allocating real ownership, real time, and clear governance to master data.

Trap 4: Over-customising early

Customisation feels like progress. It’s often future technical debt.

Avoid it by adopting standard patterns where possible, then iterating once value is stable.

Trap 5: Measuring success as go-live

Go-live is a milestone. Value is a sustained outcome.

Avoid it by planning for hypercare, adoption metrics, and continuous improvement.

A pragmatic APS upgrade or migration roadmap

Every organisation’s pathway is different, but the strongest programs tend to follow a similar sequence.

Step 1: Define the decisions you need to make (and the decisions you want automated)

Be specific. “Better forecasting” is not a decision.

Examples of decision statements:

• We will set service tiers by segment and enforce them through inventory policy.
• We will decide promo volume and supply feasibility in one forum.
• We will re-allocate constrained supply within 24 hours of disruption.

Step 2: Map current-state planning end-to-end (and be honest about workarounds)

Capture cadence and handoffs, where Excel is doing the real work, where assumptions are made (and who owns them), and where planners override the system and why.

Step 3: Identify quick wins before you buy anything new

Sometimes the best first move is stabilisation: fixing data feeds, tuning exception logic, and tightening governance. This can also tell you whether an upgrade path is viable.

Step 4: Clarify requirements that matter (not the “nice-to-have” list)

Strong requirements usually fall into:

• Critical capabilities (must have)
• Constraints and execution realism
• User workflow and governance
• Integration and data rules
• Performance and scalability expectations

Step 5: Choose upgrade vs migrate, then validate with a proof of value

A proof of value should test the hardest parts:

• Your ugliest SKU segments
• Your most constrained DC
• Your most volatile category
• Your most painful supplier constraints
• Your most politically sensitive planning decisions

Step 6: Build a business case that finance will back

A good APS business case includes inventory impact by segment, service impact and customer outcome, waste and obsolescence reduction, planner productivity and scalability, working capital and cash flow impacts, implementation and change costs, plus risk and resilience benefits.

Step 7: Implement in waves (and protect the business during transition)

Wave approaches reduce risk and build momentum:

• Start with a category, region, or DC scope that matters but is manageable
• Stabilise, then expand
• Use hypercare to cement new ways of working

How Trace Consultants can help

APS upgrades and migrations sit at the intersection of strategy, planning design, data, technology, and execution—and most organisations don’t have all those capabilities available at once.

Trace Consultants supports clients across the full journey: APS health checks, operating model and process design, requirements definition, vendor shortlisting and selection support, business case development, implementation governance, and change management to ensure adoption sticks.

Our approach is deliberately solution-agnostic. We help you clarify what you need, pressure-test options (including platforms such as GAINS, RELEX, Anaplan, Logility, Kinaxis, Slimstock, Coupa, Blue Yonder and o9), and then implement in a way that delivers measurable outcomes—not just a successful go-live.

A real example (anonymised)

In a value retail environment, an advanced planning and inventory optimisation implementation delivered an initial inventory reduction of around 10% while maintaining store service levels in the high 90s. The point isn’t the exact number—it’s that well-designed APS programs can create measurable outcomes when the planning design, data, and adoption are treated as first-class workstreams.

A simple checklist: are you ready to make the move?

If you can answer yes to most of these, you’re in a strong position:

• We can clearly explain what decisions the APS must improve
• We have executive sponsorship across supply chain, sales, and finance
• We know where the current APS is failing (capability vs adoption vs data)
• We have a realistic view of data quality and ownership gaps
• We’re willing to redesign ways of working—not just install a tool
• We’re prepared to implement in waves and invest in hypercare
• We have defined success metrics (service, inventory, planning accuracy, productivity)

If you answered no to several, that’s not failure—it’s a signal. The best next step may be a diagnostic, not a platform decision.

Closing thought

APS decisions aren’t about chasing the newest platform. They’re about building a planning capability that can keep up with the pace and complexity of modern supply chains in Australia—without relying on heroics, spreadsheets, and institutional memory.

If your APS is holding back service, cash, growth, or your team’s capacity, it’s worth asking the question now—while you can still choose the timing, the scope, and the pathway on your terms.

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Planning, forecasting and inventory optimisation: how to reset S&OP (and finally trust the numbers)

Most organisations don’t wake up one day and decide to “transform S&OP”.

They get there the hard way.

It starts with a few late deliveries. A couple of stockouts. Then a run of expedited freight that was meant to be a one-off, but somehow becomes standard operating procedure. Warehouse space fills up with the wrong things, while sales teams can’t get the products they actually need. Finance asks why working capital is climbing. Operations asks why the plan changes every week. And everyone quietly stops believing the forecast.

That’s the moment leaders start searching for answers:

• How do we improve forecast accuracy?
• How do we reduce inventory without blowing up service levels?
• How do we run an S&OP process that drives decisions, not just meetings?
• Do we need Integrated Business Planning (IBP), or do we just need to get the basics right?

For Australian and New Zealand organisations, these problems are amplified by geography, lead times, supplier variability, and the reality that “one network” often spans multiple states, islands, and customer expectations that keep rising.

This article is a practical playbook for resetting demand planning and forecasting, building an inventory optimisation approach that sticks, and implementing an S&OP / IBP cadence that improves service, cost, and working capital—without creating a bureaucracy.

Why planning is the highest-leverage problem you can fix

There’s a reason planners are often the most exhausted people in the business.

They sit at the intersection of sales, operations, supply, finance, and customer expectations. When planning is weak, everyone feels it:

• Customer service is stuck explaining late orders
• Warehouses get slammed with peaks they can’t resource
• Production runs the wrong sequence
• Procurement places last-minute orders at the worst possible prices
• Finance carries more inventory than it wants, and still can’t rely on availability

When planning is strong, the opposite happens:

• Inventory reduces and availability improves (because the right stock is in the right place)
• Expediting drops
• Service levels stabilise
• Decisions get faster
• People stop “working around the system”

That’s why S&OP (and its more mature cousin, IBP) consistently ranks as one of the most effective cross-functional management processes when it’s done properly.

The symptoms that tell you it’s time for a reset

If you’re seeing any of these, you’re not alone—and you’re probably overdue for a planning reset.

1) The forecast exists, but no one trusts it

• forecast accuracy is poor, or doesn’t improve over time
• planning teams spend more time explaining errors than improving the process
• the business runs on “the spreadsheet” or “the sales manager’s number” instead

2) Inventory is high, but availability is still patchy

The classic pain: “We have too much stock… just not the stock we need.”

3) Expediting is normalised

Premium freight, last-minute supplier orders, urgent production changes—it all becomes the hidden tax of poor planning.

4) You’re constantly reacting to promotions and events

Promotions, weather, channel shifts, competitor actions—if these are handled ad hoc, the plan whipsaws.

5) There’s no single view of demand

Different teams use different numbers:

• Sales has a view
• Finance has a view
• Operations has a view
• E-commerce has a view
  …and reconciliation becomes the work.

6) Meetings happen, but decisions don’t

Plenty of calendar time, limited outcomes. S&OP becomes a reporting ritual rather than a decision process.

The planning myth that causes most damage: “If we buy an APS tool, it’ll fix it”

Technology can help—sometimes dramatically.

But forecasting tools, APS platforms, and planning modules don’t fix:

• unclear decision rights
• inconsistent data
• lack of accountability
• demand signals that never make it into the process
• poor inventory policy discipline
• cross-functional misalignment

The best results come when organisations treat planning as a system:

• process + people + data + governance + technology

Get the system right, and the tooling becomes an accelerator—not a crutch.

Demand planning and forecasting: what actually improves accuracy

Forecast accuracy isn’t about picking the perfect algorithm. It’s about building an environment where the forecast can improve over time.

Start with the basics: define what “accuracy” means

Many organisations measure accuracy in ways that don’t help decision-making. A practical approach includes:

• Bias (are we consistently over or under forecasting?)
• Error (how far off are we, on average?)
• Stability (how much does the forecast change each cycle?)
• Service impact (what does the error cost us in stockouts or excess?)

A small improvement in bias can have a bigger commercial impact than a flashy improvement in a single accuracy metric.

Fix the demand signals before arguing about models

In many ANZ organisations, planning is undermined by:

• promotions not shared early enough
• pricing changes not reflected in the plan
• new product introductions without realistic ramp assumptions
• channel shifts (store to online, wholesale to DTC) not separated cleanly
• one-off customer orders treated as “base demand”

A good demand planning process separates:

• baseline demand (what happens without intervention)
• uplift events (promotions, campaigns, tenders, seasonality spikes)
• one-offs (large deals, project orders, exceptional events)

Build a “forecast that’s usable”, not “forecast that’s perfect”

Operational planning needs a forecast that is:

• timely
• stable enough to plan labour and production
• granular enough to position inventory
• explainable (so stakeholders can improve it)

In practice, it’s better to have a forecast that’s 80% accurate, consistent, and acted on, than a forecast that is theoretically brilliant but ignored.

Inventory optimisation: the goal isn’t “less stock”—it’s “less waste”

Inventory optimisation isn’t a single calculation. It’s a set of policies and behaviours that balance three competing forces:

1. service level expectations
2. supply variability and lead times
3. working capital constraints

Why “inventory down” targets backfire

If inventory reduction becomes a blunt KPI, teams respond predictably:

• they cut orders and hope
• service drops
• expedites rise
• customer dissatisfaction grows
• and inventory creeps back anyway

A more durable approach is to optimise the right stock:

• correct safety stock settings
• clear replenishment rules
• segmentation of SKUs by demand profile and criticality
• realistic lead times and variability assumptions

The inventory policies that matter most

For most organisations, the biggest gains come from tightening:

• Service level policy (what service levels do we target by product/customer segment?)
• Safety stock logic (based on variability, lead times, and service targets—not gut feel)
• Reorder points / reorder cycles (aligned to supply cadence and demand volatility)
• Min/max and order multiples (supplier constraints, pallets, MOQs)
• Lead time governance (actual vs assumed, and how often it’s updated)
• Obsolescence discipline (slow movers, end-of-life stock, returns and damaged goods)

SKU segmentation: the simplest tool that changes everything

Most businesses treat all SKUs the same, which is how you end up with:

• too much attention on low-value items
• not enough attention on high-risk availability items

Segmentation (ABC/XYZ, criticality, intermittency) helps you decide:

• which SKUs get tight service targets
• which SKUs can tolerate longer replenishment cycles
• which SKUs should be made-to-order or stocked differently
• where you need dual sourcing or risk buffers

S&OP vs IBP: what’s the difference, really?

S&OP (Sales & Operations Planning)

At its core, S&OP is a monthly (or 4-week) cadence that aligns:

• demand plan
• supply plan
• inventory plan
• capacity and constraints
• a set of decisions the business commits to

IBP (Integrated Business Planning)

IBP expands S&OP by formally integrating:

• financial planning (margin, revenue, cost, working capital)
• scenario planning and strategic trade-offs
• a stronger governance model, often with clearer executive ownership

Here’s the key point: many organisations don’t need “IBP branding” to get IBP outcomes.
They need a disciplined S&OP foundation that:

• produces one set of numbers
• makes decisions
• drives accountability

If your current process can’t consistently deliver those basics, start there.

What a “fit-for-purpose” S&OP cadence looks like

A good S&OP cadence is not about more meetings. It’s about the right meetings with the right inputs and clear decision rights.

A pragmatic structure looks like:

1) Demand Review

• baseline + uplift events
• key changes since last cycle
• risks and opportunities
• assumptions clearly documented

2) Supply Review

• capacity constraints
• supplier issues and lead time risks
• inventory outlook and exceptions
• feasible supply plan alignment to demand

3) Pre-S&OP (alignment)

• resolve cross-functional gaps
• confirm scenario options
• identify decisions required at executive level

4) Executive S&OP (decision meeting)

• agree the plan
• approve trade-offs (service vs cost vs cash)
• lock priorities and escalation paths
• confirm KPIs and accountability

The difference between a weak and strong process is simple:

• weak S&OP reports information
• strong S&OP commits to decisions

The metrics that drive better planning behaviour

Be careful: what you measure is what you get.

A balanced S&OP dashboard typically includes:

Demand

• forecast bias and error (by family/channel)
• forecast stability (change over change)
• promotion forecast accuracy (separate to baseline)

Supply

• schedule adherence (if manufacturing)
• supplier OTIF / lead time variability
• capacity utilisation and constraint weeks

Inventory and service

• fill rate / DIFOT / OTIF
• backorders and aged backorders
• days of cover / turns (by segment)
• obsolescence and slow movers

Financial

• working capital impact
• expediting cost
• gross margin impact of stockouts and substitutions

The best dashboards are exception-based. They highlight what needs a decision, not everything that happened.

A practical 8–12 week reset program that works in the real world

If you want a clear, time-boxed approach, this is a common structure.

Phase 1: Diagnose the current state (2–3 weeks)

• map planning processes and handoffs
• assess data quality and master data governance
• quantify value-at-stake: service loss, excess inventory, expediting
• identify the biggest drivers of variability (demand, supply, lead times)

Output: clear problem statements, a baseline, and priorities.

Phase 2: Redesign the planning framework (3–4 weeks)

• define demand planning method (baseline + event management)
• create inventory policy framework and segmentation approach
• design S&OP cadence, agenda, decision rights, and templates
• define core KPIs and reporting

Output: a fit-for-purpose operating rhythm, not an abstract model.

Phase 3: Pilot and embed (3–5 weeks)

• test the process on a pilot business unit/category
• refine templates and data feeds
• train teams and align stakeholders
• implement governance and continuous improvement rhythm

Output: a working process that the business adopts—not a document.

Quick wins you can deliver in 30 days (before a full reset)

If you need immediate traction, these moves are often safe and high impact:

• Align on one demand number for the next cycle (stop parallel forecasts)
• Separate baseline demand from promotions/events and document assumptions
• Create a top 20 exception list: SKUs driving most stockouts or excess
• Refresh lead time assumptions using actuals (even a simple override helps)
• Establish a weekly constraint call for the next 8 weeks (short-term execution alignment)
• Implement basic SKU segmentation to prioritise effort and service targets
• Review safety stock settings for the most critical and most variable SKUs
• Stop uncontrolled expediting by creating an approval gate and root-cause tracking

Quick wins don’t replace the reset, but they reduce leakage and rebuild confidence.

Planning in Australia & New Zealand: the local realities to design for

Planning frameworks need to reflect geography and operating conditions.

Common ANZ factors include:

• long domestic transport distances and variable regional access
• inter-island movements in NZ and weather-related variability
• port congestion or shipping schedule volatility for imported goods
• labour constraints affecting warehouse and production capacity
• high customer expectations on delivery windows (especially e-commerce)
• promotion-driven demand spikes in retail and FMCG
• regional and remote service commitments in government and essential services

A “global template” S&OP process often fails because it ignores these constraints. A fit-for-purpose approach builds them in from day one.

How Trace Consultants can help: forecasting, inventory optimisation and S&OP/IBP reset

Planning improvements only stick when they’re practical, adopted by the business, and supported by a governance rhythm.

Trace Consultants helps Australian and New Zealand organisations reset planning capability across demand planning and forecasting, inventory optimisation, and S&OP/IBP.

Support typically includes:

1) Planning diagnostic and value-at-stake assessment

• baseline performance across forecast, service, inventory and expediting
• root-cause diagnosis (data, process, operating model, governance)
• prioritised roadmap with quick wins and longer-term improvements

2) Demand planning and forecasting uplift

• baseline vs uplift event framework
• forecasting governance and accuracy/bias improvement
• demand signal integration (promotions, pricing, channel shifts)
• practical templates and routines for planners and stakeholders

3) Inventory optimisation and policy reset

• SKU segmentation and service level policy design
• safety stock and replenishment rule calibration
• lead time governance and variability handling
• slow mover and obsolescence management discipline

4) S&OP / IBP cadence design and facilitation

• fit-for-purpose meeting cadence and decision rights
• templates, dashboards, and exception reporting
• executive-level facilitation to drive decisions and alignment
• embedding accountability and continuous improvement

5) Technology and data enablement (where appropriate)

• planning data model and master data governance
• requirements for forecasting / APS tools (tool-agnostic)
• pragmatic automation opportunities that reduce manual effort

Most importantly, Trace’s approach focuses on adoption. The aim is not to produce a “perfect” model. It’s to build a planning system the business actually uses—and trusts.

Frequently asked questions

How long does it take to see results?

You can often see early improvements within one or two cycles if:

• you align to one demand number
• you reduce forecast bias
• you improve event planning discipline
• you reset key inventory policies on critical SKUs

More structural improvements (operating model, tech enablement) typically take longer, but they compound over time.

Do we need IBP?

If your organisation needs tighter linkage between financial outcomes and operational plans, IBP can be valuable. But many organisations get most of the benefit by getting S&OP fundamentals right first.

Can we reduce inventory without hurting service?

Yes—when you target the right inventory, not just “less inventory”. This usually requires:

• clearer service policies
• calibrated safety stock and lead time assumptions
• better segmentation
• improved demand signal capture

What’s the biggest cause of forecast inaccuracy?

It’s rarely the algorithm. It’s usually:

• missing or late demand signals
• unstructured event uplift management
• inconsistent master data
• lack of accountability for assumptions
• planning overridden without learning loops

The bottom line: better planning is a commercial advantage

When planning is weak, every part of the organisation pays a tax—expediting, inefficiency, customer dissatisfaction, and bloated working capital.

When planning is strong, the business becomes calmer:

• fewer surprises
• fewer firefights
• better service
• smarter inventory
• faster decisions

If your teams are spending more time reacting than planning, or your S&OP meetings feel like reporting rather than decision-making, it’s a strong signal that a reset will pay back quickly.

If you want to explore what a practical S&OP and inventory optimisation reset could look like for your organisation, Trace Consultants can help—from diagnostics and quick wins through to embedding a cadence that the business adopts.

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Retail Supply Chains: Demand and Replenishment Planning System Selection

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It’s 7:45am on a Monday. The trading team is already in “hotlist mode”: a seasonal line has sold through faster than expected, a promoted item has landed late, and the DC is sitting on slow-moving stock nobody wants. Store teams are asking why “the system” ordered too much of one product and not enough of another. Meanwhile, Finance is watching inventory creep up and asking the familiar question: How can we hold less stock without making service worse?

For many Australian retailers, this tension has become the new normal. Demand is more volatile, promotions are more complex, lead times are less forgiving, and customers expect high availability across store and online. In that environment, demand and replenishment planning is no longer a back-office process — it’s a competitive capability.

And that’s exactly why “system selection” matters. The right planning platform can help you:

• sense demand shifts earlier and respond faster
• order the right stock, in the right quantities, to the right locations
• reduce working capital tied up in inventory
• protect availability during promotions and peaks
• improve planner productivity through exception-based workflows

The wrong platform (or a platform chosen for the wrong reasons) can lock you into years of workarounds: spreadsheet shadow systems, manual overrides, mistrust in forecasts, and benefits that never quite land.

This article is a practical guide for Australian retailers on selecting demand and replenishment planning systems — including how to think about vendors such as RELEX, GAINS, o9, SAP, Kinaxis, and others shown in the analyst landscape you’ve probably seen floating around the industry.

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What “demand and replenishment planning” really covers

Let’s clear up a common confusion: retailers often use “forecasting”, “planning”, and “replenishment” interchangeably — but the system capabilities you need depend on which decisions you’re trying to improve.

At a minimum, demand and replenishment planning typically includes:

Demand planning (forecasting)

• baseline forecasts by item/location/channel
• seasonality, trend, events, and calendar effects
• promotions and price impacts
• new item introduction and lifecycle modelling
• intermittent/erratic demand handling (common in long-tail ranges)

Inventory and replenishment planning

• store and DC ordering policies (min/max, order cycles, safety stock)
• lead time and supplier constraints (MOQs, pack sizes, delivery days)
• multi-echelon logic (DC + store, sometimes supplier + DC + store)
• allocation and rebalancing (especially around promotions)
• shelf-life / freshness constraints (critical in grocery and QSR-adjacent retail)

Execution workflows

• exception management (what needs attention today)
• collaboration across merchandising, supply chain, and suppliers
• performance reporting and KPI tracking (availability, inventory, waste, forecast bias)

Some platforms focus heavily on retail replenishment execution (auto-ordering, store/DC workflows). Others major on enterprise planning and scenario orchestration (IBP-style planning, concurrent scenario modelling). Many promise to do everything — but in practice, strengths differ.

The trick is to be clear about the problems you’re solving first, then select the system that’s genuinely fit-for-purpose.

Why Australian retailers have some unique planning headaches

If you’ve worked across Australian retail (grocery, specialty, apparel, hardware, pharmacy, convenience, or department), you’ll recognise a few local realities that shape system requirements:

• Long supply lines and import exposure: Higher reliance on overseas manufacturing and shipping means longer, more variable lead times.
• A stretched geography: Serving metro and regional locations creates different replenishment rhythms, transport constraints, and store capacity realities.
• Weather and event sensitivity: Demand can swing sharply with heatwaves, storms, public holidays, sporting events, and localised disruptions.
• Omni-channel complexity: Click & collect, ship-from-store, endless aisle, marketplace ranges — all add noise to “true” demand signals.
• High SKU counts and range churn: Especially in specialty and apparel, where newness and lifecycle planning are central.
• Promotional intensity: Promotions can distort history, and poor promo modelling is one of the fastest ways to lose trust in a planning system.

A selection process that ignores these realities often ends with a platform that looks great in a global demo — but struggles in the day-to-day of Australian trading.

When it’s time to move beyond ERP and spreadsheets

Plenty of retailers run “good enough” planning with ERP replenishment parameters and a lot of human effort. But there are common signs you’ve outgrown it:

• Planners are spending most of their time fighting exceptions, not improving outcomes
• Forecasts are heavily overridden because the team doesn’t trust them
• Promotions create chaos: stock-outs in some locations, excess in others
• Store ordering is inconsistent, and “hero operators” are propping up the system
• Inventory is rising, but availability still isn’t where you want it
• You can’t easily model trade-offs (availability vs inventory vs waste vs capacity)
• New item demand is guesswork and repeated across teams in different spreadsheets

If that sounds familiar, you’re in the zone where a dedicated demand and replenishment platform can create value — but only if you select and implement it properly.

The vendor landscape: what the “Magic Quadrant” view does (and doesn’t) tell you

Industry analysts often group supply chain planning vendors into categories like leaders, challengers, visionaries, and niche players. In a recent snapshot (as at March 2025), vendors such as OMP, Kinaxis, and RELEX were positioned strongly, with players like o9 and Blue Yonder also prominent, and other well-known vendors (including SAP, Oracle, Logility, ToolsGroup, Manhattan Associates, and others) appearing across the broader landscape.

That kind of view is useful — as a starting point. It can help you build a longlist and sanity-check market relevance.

But here’s the catch: a quadrant doesn’t know your supply chain.

It doesn’t know:

• whether your core issue is promo forecasting or store ordering discipline
• whether your range is stable or churn-heavy
• whether you need true multi-echelon optimisation or simpler policies
• whether your master data is strong enough to support automation
• whether you need rapid deployment or can tolerate a longer program

So treat analyst positioning as a map, not a decision.

Your selection should be anchored in use cases, data reality, and operating model fit.

A practical system selection approach that actually works

Below is a selection approach we see succeed repeatedly — because it forces clarity early, tests vendors with your reality (not theirs), and avoids “feature theatre”.

1) Start with outcomes, not modules

Write down, in plain language, what “better” looks like. Examples:

• Improve on-shelf availability in priority categories
• Reduce DC overstocks and aged inventory
• Stabilise promotional execution and reduce post-promo residuals
• Increase planner coverage (more SKUs per planner with the same team)
• Reduce manual store ordering variability

Then translate those into measurable KPIs (availability, inventory turns, waste, forecast accuracy/bias, service level, order stability). You don’t need perfect baseline maths on day one — but you do need alignment on direction.

2) Get specific about planning decisions

A demand and replenishment platform supports decisions like:

• How much should we buy? (buy quantities, order cycles, supplier constraints)
• Where should we hold it? (store vs DC, allocation logic)
• When should we move it? (lead times, transport cadence, capacity windows)
• What should we do when reality changes? (exceptions, alerts, collaboration)

Document the key decisions, who owns them, and what data is used today. This becomes your requirements backbone.

3) Be honest about data readiness (and fix it early)

System selection often fails because the glossy demo hides a hard truth: planning tools are only as good as the data feeding them.

In retail planning, the usual pain points are:

• item/location history gaps or inconsistent hierarchies
• promotion flags that don’t match what happened in stores
• lead times stored as “best guess” rather than reality
• pack sizes, MOQs, and supplier calendars not maintained
• substitution effects not captured (especially in grocery/pharmacy)
• store capacity / shelf constraints not represented

A smart selection process includes a data diagnostic early — not as a separate “later” project. If you don’t know the state of your data, you’ll misjudge both vendor fit and implementation effort.

4) Define “must-have” capabilities by use case

Avoid 400-line requirements spreadsheets that nobody reads. Instead, define a set of use-case test scripts.

For example:

• A promoted line with cannibalisation and multiple price points
• A new item launch replacing an older item
• Seasonal demand with short selling windows
• A supplier disruption requiring reallocation and revised ordering
• A long-tail SKU with intermittent demand (stop/start pattern)

Then ask vendors to walk through these scenarios using realistic assumptions — ideally with a small proof-of-concept using your own data.

This is where differences between platforms become obvious.

5) Assess fit across five dimensions (not just “features”)

When comparing platforms such as RELEX, GAINS, o9, SAP, Kinaxis (and others), we recommend scoring across five practical dimensions:

Capability fit
Can it do what you need for demand forecasting, promo modelling, multi-echelon replenishment, and execution workflows?

Retail operating model fit
Does it support how your teams work — category, store ops, supply chain, suppliers — with the right exception and collaboration tools?

Integration and architecture fit
Will it integrate cleanly with ERP, POS, WMS, OMS, supplier systems, data platforms? Does it support APIs, event-based signals, or only batch?

Usability and adoption fit
Can planners and trading teams use it without a PhD? Are workflows intuitive? Is it configurable without endless customisation?

Commercial and delivery fit
Does the vendor have a delivery model that matches your timeline and internal capacity? Is the total cost of ownership realistic?

6) Don’t skip the “people and process” question

A planning platform is not a magic wand. If your replenishment process is unclear, store ordering discipline is inconsistent, or exception governance doesn’t exist, the system will amplify noise — not fix it.

A robust selection includes:

• role clarity (who overrides what, when)
• exception thresholds and decision rights
• how merchandising and supply chain collaborate
• how suppliers are engaged (and what data is shared)

This is also where benefits are won or lost.

Vendor considerations (a simple overview)

Every retailer asks: “So which system is best?” The more useful question is: “Which system is best for our context, constraints, and ambition?”

Here are some grounded, non-hype ways to think about a few common platforms:

RELEX

Often shortlisted by retailers looking for strong retail-specific forecasting and replenishment, particularly where store and DC execution and exception workflows matter. It’s commonly considered when the goal is to lift availability while improving inventory efficiency with practical retail logic (including promotion and assortment complexity).

GAINS

Frequently associated with replenishment optimisation and inventory planning, particularly in environments that want to get more disciplined about ordering policies, service levels, and multi-echelon behaviour. It can be a strong option when the key pain is replenishment outcomes and inventory settings rather than broader enterprise planning.

o9 Solutions

Often considered for organisations wanting an integrated planning environment with strong scenario and cross-functional planning capability. If you’re aiming to connect demand planning to broader commercial and supply decisions, it can be relevant — particularly where the ambition is bigger than “just replenishment”.

SAP (e.g., integrated planning options)

Common in retailers already running SAP as their backbone and looking for tighter integration. The trade-off is often between integration simplicity and best-of-breed retail planning depth — which is why it’s important to test SAP’s planning capabilities against your specific retail scenarios, not generic manufacturing-style planning demonstrations.

Kinaxis

Known for concurrent planning and scenario responsiveness in complex environments. It can make sense where retailers want stronger end-to-end planning orchestration across supply constraints, responsiveness, and scenario trade-offs — particularly when speed of replan matters.

Others you may see in the landscape

Depending on your needs, you may also assess platforms such as Blue Yonder, OMP, Oracle, Logility, ToolsGroup, Manhattan Associates, and specialist players. Some are stronger in broader supply chain suites, some in retail allocation or execution, and some in specific planning niches.

The selection takeaway: don’t choose a system because it’s famous — choose it because it wins your use-case tests with your data and your workflows.

The shortlist mistake almost everyone makes

Most shortlists are built on reputation, analyst positioning, and what peers are doing. That’s understandable — but it’s risky.

A better way to shortlist is to segment vendors by what you actually need most:

• If the priority is retail replenishment execution and store/DC workflows, weight vendors with deep retail replenishment DNA.
• If the priority is enterprise scenario planning and cross-functional orchestration, weight vendors known for planning concurrency and scenario modelling.
• If the priority is integration with an existing ERP stack, test “native” options — but still validate capability depth and usability.

This is also where you decide whether you’re looking for:

• a best-of-breed planning tool connected into your ecosystem, or
• a broader planning suite that attempts to cover multiple planning horizons and functions.

There isn’t a universal right answer — but there is a right answer for your operating model.

Proof-of-concept: where good selections are won

If you do one thing differently in your next system selection, do this: run a proof-of-concept that reflects real retail complexity.

A practical PoC doesn’t have to be months long. But it should include:

• a handful of categories with different demand patterns
• a mix of metro and regional stores
• promotion history and price changes
• DC-to-store lead times and supplier constraints
• clear evaluation criteria agreed upfront

Then assess:

• forecast performance (including bias, not just accuracy)
• replenishment recommendations and stability
• ability to handle constraints (MOQs, pack sizes, delivery days)
• usability: how quickly planners can act on exceptions
• integration approach: what’s required to industrialise it

This avoids the classic trap: buying a platform based on “demo confidence” and discovering reality only after contracts are signed.

Implementation realities: don’t underestimate the last mile

Even the best system selection can fall over reminding stores to:

• receive stock correctly
• keep inventory records accurate
• follow replenishment discipline
• trust automated recommendations

The best implementations treat demand and replenishment as a capability rollout, not just a technology project.

What tends to work well:

• start with a controlled scope (a few categories/regions)
• stabilise master data and supply parameters early
• build planner confidence through transparent logic and sensible overrides
• define governance: when to override, and how to learn from overrides
• invest in training that’s role-based and practical, not generic

What tends to go wrong:

• trying to “big bang” every category and store at once
• customising the platform heavily to replicate old habits
• relying on one or two power users to keep it running
• under-resourcing change management and store engagement

How Trace Consultants can help (without vendor bias)

System selection is one of those rare moments where you can set your retail supply chain up for years — or lock in complexity for years. It’s also an area where independent support pays for itself because the costs of a wrong decision are so high (time, distraction, implementation spend, and opportunity cost).

Trace Consultants supports Australian retailers through the full demand and replenishment planning journey, including:

1) Strategy and operating model clarity

• define the target planning outcomes and KPIs
• map planning processes and decision rights
• design exception workflows that match how retail teams operate

2) Requirements and use-case design (fit-for-purpose, not bloated)

• convert business pain points into testable system requirements
• build realistic retail scenarios for vendor demonstrations and PoCs
• establish scorecards that balance capability, usability, and integration

3) Data readiness and integration planning

• assess forecast/replenishment data quality and gaps
• define integration architecture with ERP, POS, WMS, OMS, data platforms
• prioritise the “data fixes” that actually move the needle

4) Vendor evaluation and selection support

• longlist/shortlist guidance based on your needs (not ours, not theirs)
• structured RFP management and commercial evaluation
• support contract negotiation with a focus on delivery outcomes

5) Implementation governance and benefits realisation

• program governance, risk management, and rollout planning
• change management support to drive adoption (especially with stores)
• KPI tracking to make sure the system delivers the promised outcomes

Importantly, we can do this vendor-agnostically — helping you choose the platform that best fits your retail context, then supporting delivery in a way that sticks.

A simple way to start (even if you’re not ready for an RFP)

If you’re early in the journey and not ready to talk to vendors yet, start with three practical steps:

1. List your top 10 planning “failure modes” (stock-outs, excess, promo misses, slow response, etc.)
2. Pick 3–5 categories that represent different demand patterns and agree your success metrics
3. Run a data diagnostic on those categories: demand history quality, promo flags, lead times, pack sizes, hierarchy consistency

That alone will sharpen your system requirements dramatically — and it will prevent you from buying a tool that your data and operating model can’t support.

Closing thought: choose the platform your teams will trust

Retail planning systems don’t fail because the maths is wrong. They fail because the business doesn’t trust them, or because the workflows don’t match how people actually work under pressure.

The best demand and replenishment planning platform is the one that:

• makes sensible recommendations in your real-world scenarios
• integrates cleanly into your ecosystem
• supports exception-based retail workflows
• is transparent enough to earn trust
• can be implemented without turning into a multi-year distraction

If you’d like a second set of eyes on your requirements, shortlist, or proof-of-concept approach — Trace Consultants can help you move faster, reduce risk, and land benefits you can actually see in store and in the DC.

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