Retail Supply Chains: Demand and Replenishment Planning System Selection
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.
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:
- List your top 10 planning “failure modes” (stock-outs, excess, promo misses, slow response, etc.)
- Pick 3–5 categories that represent different demand patterns and agree your success metrics
- 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.
