Supply Chain Planning and Replenishment as a Service: The Managed Model That Makes Planning Stick

There’s a moment most supply chain leaders recognise.

It’s the end of the week. The plan has changed three times. Someone has “fixed” the forecast in a spreadsheet that only two people understand. A supplier is late again, so replenishment parameters get overridden. The DC is flooded with the wrong stock while stores (or sites, or wards) are missing the items that matter. Service is wobbling, working capital is climbing, and everyone is busy.

Planning isn’t failing because people don’t care. Planning fails because the system of planning isn’t set up to cope with reality.

That’s where Supply Chain Planning and Replenishment as a Service comes in.

This is not outsourcing your supply chain. It’s not “set-and-forget” automation. It’s a managed operating model that combines people, process, governance, data and technology to run planning and replenishment with consistency—so the organisation isn’t relying on heroic individuals to keep the wheels turning.

In this article, we’ll unpack:

• what Planning and Replenishment as a Service actually is
• when it makes sense for Australian organisations
• what the service covers (and what it shouldn’t)
• how to govern it so accountability stays with the business
• the KPIs that prove it’s working
• the common traps that derail it
• and how Trace Consultants can help you design, transition, and run a managed planning model that delivers measurable outcomes

If you want to explore Trace’s broader capabilities across planning, operating models, technology enablement and supply chain transformation, start here: Services.

What is Supply Chain Planning and Replenishment as a Service?

Supply Chain Planning and Replenishment as a Service is a managed service model where a specialist partner runs (or co-runs) core planning activities to an agreed cadence and standard, using clear governance and performance measures.

It typically includes some combination of:

• Demand planning (forecasting, demand sensing inputs, promo/event planning support)
• Supply planning (constrained supply plans, supplier collaboration, capacity alignment)
• Replenishment execution (ordering, exception management, parameter tuning)
• Inventory policy management (service levels, safety stock logic, segmentation)
• S&OP / IBP support (pre-S&OP packs, scenario modelling, decision logs)
• Master data and planning data quality (lead times, MOQs, order calendars, UOMs)
• Performance reporting (service, inventory, forecast accuracy, stability)
• Continuous improvement (root-cause analysis, rule improvements, automation)

The keyword is managed. This isn’t a body-shop model where you hire an extra planner and hope it helps. It’s a repeatable operating rhythm with defined roles, escalation pathways, and clear performance targets.

A good service model does two things at once:

1. stabilises day-to-day planning so service holds and noise reduces
2. lifts capability so the business becomes less dependent over time (even if the service continues)

Why “as a Service” is showing up in planning now

Australian supply chains have some unique pressure points:

• Long lead times and geographic distance magnify small planning errors
• Supplier variability can swing quickly, and recovery takes time
• Labour constraints make warehousing and transport capacity less elastic
• Multi-channel demand (store, online, wholesale, project) increases complexity
• High service expectations are colliding with cost reduction mandates
• Data fragmentation persists, even after ERP upgrades
• Key-person risk is real: one or two planners carry the institutional knowledge

Many organisations respond by trying to “fix planning” with a system project alone. But technology doesn’t solve planning by itself. Planning improves when the operating model improves—cadence, governance, data discipline, segmentation, and exception management.

A managed service model is attractive because it can deliver structure quickly without requiring the organisation to build a large, specialised planning function overnight.

Planning and Replenishment as a Service vs outsourcing: the difference that matters

Let’s clear up a common misconception.

Outsourcing often means shifting work away and hoping it comes back better. It can create distance from the business, slow decision-making, and weaken ownership.

Planning and Replenishment as a Service, done properly, is different:

• You keep decision rights. The business still owns service targets, inventory policy, and customer commitments.
• The cadence is transparent. Clear weekly and monthly rhythms, with measurable outputs.
• Exceptions are visible. You don’t lose control; you gain clarity and discipline.
• The partner is accountable to outcomes. Not just activity.
• Capability is lifted. Through standard work, playbooks, and coaching.

This model is often implemented as co-managed planning: some activities remain internal (e.g., commercial inputs, key account priorities), while the managed service runs the planning engine and performance discipline.

When does Planning and Replenishment as a Service make sense?

This model isn’t for everyone. But it’s a strong fit when one or more of these conditions exist.

1) Planning is dependent on a few individuals

• Key planners are overloaded
• Knowledge sits in people’s heads or spreadsheets
• Holidays or resignations create immediate risk

2) Forecasts exist, but aren’t trusted

• Bias is consistent (always too high, or always too low)
• Overrides are common, but learning doesn’t occur
• Promotions and events aren’t integrated properly
• Commercial teams don’t see planning as credible

3) Replenishment is noisy and reactive

• Order parameters are constantly overridden
• Supplier constraints are handled late
• Expedites are normalised
• The organisation is “chasing” service every week

4) Inventory is high, but availability still hurts

• Excess and obsolete is rising
• Range complexity has grown
• Service targets aren’t differentiated
• Safety stock settings don’t reflect reality

5) You’re implementing or stabilising technology

• New ERP / WMS / planning tools have gone live, but adoption is uneven
• Master data quality is limiting benefits
• Reporting isn’t aligned and definitions vary
• Teams are working around the system

6) You need speed without permanent overhead

• The organisation can’t hire quickly enough (or at all)
• Planning workload comes in waves
• A transformation program needs a stable BAU backbone

What the service typically includes: a practical service catalogue

A managed planning model works best when the service is defined in “plain English” terms: what gets done, when, by whom, and what output is produced.

Below is a practical view of the service catalogue many organisations adopt.

Demand planning (weekly and monthly)

• Baseline forecast creation and refresh
• Promo/event uplift integration (where applicable)
• Forecast accuracy and bias tracking
• Demand segmentation (stable vs volatile, lifecycle stage)
• Demand review facilitation packs (one version of the truth)

Supply planning (weekly)

• Supplier constraint alignment (capacity, allocations, lead time changes)
• Constrained supply plan creation
• Scenario assessment (what happens if supply slips, demand spikes)
• Forward cover reporting for critical ranges
• Coordination with logistics and warehouse capacity where relevant

Replenishment execution (daily / weekly)

• Order generation and review
• Exception-based replenishment management (not line-by-line firefighting)
• Parameter tuning process (lead times, order cycles, MOQs, pack sizes)
• Supplier order calendar management and compliance tracking
• Alignment with inbound scheduling where possible

Inventory policy and optimisation (monthly / quarterly)

• Service level policy design by segment
• Safety stock logic review and variability coverage
• Obsolescence prevention routines (lifecycle, slow-movers)
• Multi-echelon placement logic (where relevant)
• Working capital governance and targets

S&OP / IBP support (monthly)

• Pre-S&OP pack creation and performance narrative
• Scenario modelling and options framing
• Decision log maintenance (what was decided and why)
• Post-cycle action tracking and benefits follow-through

Data and reporting discipline (ongoing)

• Master data quality checks (lead time, UOM, MOQ, supplier calendars)
• Planning data pipelines and refresh routines
• KPI definitions and performance packs
• Exception taxonomy (so the same issue isn’t labelled five ways)

The operating rhythm: what “good cadence” looks like

Planning becomes reliable when it’s run like an operating system, not a series of emergencies.

A practical cadence often looks like this:

Daily (light touch)

• Exceptions triage (what changed, what needs action today)
• Supply disruptions flagged and escalated early
• Critical stock-out risks highlighted (not every low-stock line)

Weekly

• Forecast refresh and exception review
• Supplier alignment and inbound risk review
• Replenishment run and parameter exception review
• DC capacity constraints surfaced (if applicable)
• Short-cycle performance review: service misses, root causes, immediate actions

Monthly

• S&OP / IBP cycle support
• Inventory health review (excess, slow, obsolete risks)
• Policy adherence and override analysis
• Continuous improvement backlog prioritisation

Quarterly

• Segmentation refresh (range, lifecycle, demand shape)
• Safety stock and service policy recalibration
• Supplier performance review for planning-critical vendors
• Planning capability uplift plan (tools, process, training)

The governance model: keeping ownership where it belongs

A managed service succeeds or fails based on governance.

Done well, governance ensures:

• decision rights remain with the business
• escalations are timely and consistent
• performance is visible and measurable
• the service improves over time (not just runs)

A practical governance structure typically includes:

1) Operational planning huddle (weekly)

Attendees: planning leads, replenishment, procurement/supplier contacts, DC/operations rep
Focus: exceptions, supplier risks, near-term stability actions
Output: short action list with owners and due dates

2) Performance and policy review (monthly)

Attendees: supply chain leadership, finance partner, category/operational stakeholders
Focus: service vs inventory trade-offs, policy alignment, key drivers, decisions required
Output: decisions logged; policy changes approved; improvement backlog prioritised

3) Steering group (quarterly)

Attendees: senior leadership
Focus: strategic capability, technology roadmap, operating model refinement
Output: investment decisions, target-setting, risk posture alignment

Trace frequently supports governance design as part of broader operating model uplift and transformation work—see Project & Change Management and Technology.

KPIs that prove the service is working

A managed model needs more than activity measures. It needs performance proof.

Here are the KPI families that matter most.

Service outcomes

• OTIF / DIFOT (defined consistently)
• Fill rate by segment and channel
• Stock-out rate for critical items
• Backorder and cancellation trends
• Lead time adherence (customer-facing and internal)

Inventory and working capital outcomes

• Days of cover by segment (with context)
• Excess / slow-moving / obsolete trends
• Inventory turns (interpreted by segment, not averaged blindly)
• Stock balance stability (less “churn” and panic rebalancing)

Forecast and planning quality

• Forecast accuracy by segment and horizon
• Forecast bias (directional error)
• Plan stability (how often the plan changes)
• Override rate (and whether overrides improve outcomes)

Execution quality

• Order compliance to supplier calendars
• Expedite frequency and root cause distribution
• Supplier conformance signals that affect planning
• Exception closure rates (are issues resolved, or recycled?)

Service delivery quality (the managed model itself)

• Cycle-time reliability (are weekly and monthly outputs delivered on time?)
• Stakeholder satisfaction (simple pulse checks)
• Continuous improvement throughput (how many systemic fixes land per quarter?)

Technology: what you need (and what you don’t)

You don’t need a perfect tech stack to start. But you do need reliable data flows, clear definitions, and the ability to run an exception-led process.

Planning and replenishment as a service can run across different technology realities:

• ERP-driven replenishment with improved parameters and discipline
• Advanced planning systems (APS) where adoption needs stabilisation
• WMS/TMS integration improvements for better execution signals
• Lightweight analytics and workflow automation where it reduces noise

In many environments, quick wins come from:

• improving master data integrity
• reducing manual workarounds with small automation
• building clear exception views and decision packs
• establishing one “source of truth” performance pack

Trace often supports these enablers through solution-agnostic advisory plus practical implementation—see Solutions and Technology.

The biggest risks and traps (and how to avoid them)

Trap 1: Treating the service as a “black box”

If the business can’t see how decisions are made, trust erodes quickly.

Fix: make cadence, logic, and decision outputs transparent. Use decision logs. Keep governance disciplined.

Trap 2: Measuring success by “busyness”

Planning teams can be flat out and still not improve outcomes.

Fix: lock KPIs to service, inventory health, plan stability, and exception closure.

Trap 3: Running replenishment line-by-line

If the process becomes a manual review of every item, it collapses under scale.

Fix: run exception-led replenishment with segmentation and thresholds.

Trap 4: Not fixing master data

Bad lead times, MOQs, calendars, and UOM errors are silent killers.

Fix: embed master data routines and ownership in the service model. Treat data quality as BAU, not a one-off cleanup.

Trap 5: Lack of segmentation

If every SKU is treated the same, you get the worst of both worlds: high inventory and poor availability.

Fix: segment by demand shape, criticality, lifecycle, supplier variability, and service promise.

Trap 6: No change management

A managed model introduces structure. Without stakeholder alignment, people revert to old behaviours.

Fix: treat the transition as a change program—clear roles, training, stakeholder comms, and early wins.

A practical 90-day path to stand it up

If you’re considering Planning and Replenishment as a Service, here’s a pragmatic way to phase it.

Days 1–30: Diagnose and stabilise

• Confirm the problem statement and success measures
• Establish baseline reporting and KPI definitions
• Map current planning workflows and exception points
• Identify master data gaps that cause the most noise
• Implement a weekly cadence and visible action log

Days 31–60: Segment and standardise

• Build segmentation (items, suppliers, channels)
• Define replenishment exception rules and thresholds
• Standardise weekly/monthly packs and templates
• Set governance forums and escalation pathways
• Reduce overrides by improving parameters and confidence

Days 61–90: Embed and improve

• Shift from firefighting to exception-led routines
• Add scenario modelling to support decisions
• Formalise inventory policy governance
• Stand up continuous improvement backlog with owners
• Confirm benefits tracking approach with Finance

The goal by day 90 isn’t perfection. It’s a stable planning engine with transparency and a pathway to continuous improvement.

FAQs: Supply Chain Planning and Replenishment as a Service

Is this model only for retailers?

No. It’s common in retail, but it’s equally relevant in FMCG, manufacturing, healthcare supply chains, mining support logistics, and any environment where demand variability and supply uncertainty require disciplined planning.

Will this reduce headcount internally?

Sometimes it reduces the need for incremental headcount. More often, it protects the organisation from key-person risk, improves throughput, and frees internal capability to focus on strategic work rather than firefighting.

Can it work with our current systems?

Yes—if the service is designed to fit your data reality and process maturity. Many improvements come from better cadence, segmentation, and exception management, not from a new system on day one.

How do we make sure we don’t lose control?

You keep decision rights and governance. A good model makes planning more transparent, not less. Decisions are logged, exceptions are surfaced, and performance is measurable.

What’s the difference between this and “managed inventory” by suppliers?

Supplier-managed inventory is one mechanism in certain categories. Planning and replenishment as a service is a broader operating model that covers demand, supply, inventory policy, and governance across the network.

How Trace Consultants can help

Trace Consultants helps Australian organisations improve planning performance by designing solutions that work in real operations—where data is imperfect, stakeholders are busy, and the plan needs to be executable.

You can explore Trace’s broader service offering here: Services.

When it comes to Supply Chain Planning and Replenishment as a Service, Trace typically supports clients across five areas:

1) Planning diagnostics and stabilisation

We help you establish a clear baseline, identify the real drivers of service and inventory pain, and stabilise the planning cadence quickly—so the business stops living week-to-week.

Related reading and capability: Insights

2) Operating model design for planning and replenishment

We design practical role clarity, decision rights, escalation pathways, and governance rhythms that match your organisation’s reality—whether centralised, decentralised, or hybrid.

Supporting capability: Project & Change Management

3) Inventory policy, segmentation, and replenishment rule design

We help build segmentation that makes planning easier (not more complicated), improve safety stock logic, reduce noise, and align service targets to commercial intent.

Supporting capability: Strategy & Network Design

4) Technology enablement and practical tooling

Trace is solution-agnostic. We support planning technology stabilisation, reporting improvements, workflow automation, and data discipline—so planners spend time on decisions, not data wrangling.

Explore: Technology and Solutions

5) Transition into a managed planning service that’s measurable

We help you stand up the managed service model with clear service definitions, KPIs, governance and benefits tracking—so you can scale planning capability without building a huge internal machine.

If you’re looking to discuss what this could look like for your organisation—whether as a short stabilisation sprint or a longer-term co-managed model—start here: Contact

Closing thought: planning should be a capability, not a coping mechanism

If your supply chain planning depends on heroic effort, it’s only a matter of time before the cracks widen—service misses, inventory blowouts, expedite costs, frustrated stakeholders, and burnt-out teams.

Planning and Replenishment as a Service is one of the most practical ways to make planning consistent, transparent, and resilient—especially in the Australian context where distance and variability magnify every planning decision.

If you want to turn planning from a weekly scramble into a disciplined operating rhythm, Trace Consultants can help you design and deliver a managed model that works—on the ground, in the numbers, and in the boardroom.

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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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