beyond data

    Demand & Inventory Forecasting

    10–20%

    reduction in working capital tied to inventory

    Forecast ranges, model drivers and human overrides. Plan inventory with uncertainty in view.

    Demand & Inventory Forecasting combines statistical and machine-learning forecasts at SKU and location level with confidence bands and visible model drivers. Planners compare service levels, stockout risk and working capital before approving replenishment. Human overrides carry a rationale and audit trail, so operational judgement and quantitative evidence can be evaluated together.

    The Problem

    What usually breaks

    Most growing Australian wholesalers, retailers, and food manufacturers still plan inventory in Excel. The demand planner exports sales history out of NetSuite, Pronto, or SAP Business One on a Monday morning, applies a year-on-year growth factor, overlays a gut feel about the upcoming promotion, and emails the file to purchasing. That is the forecast. It is also the reason working capital is typically 10 to 20% higher than it needs to be.

    The fix is not more dashboards. The fix is a forecast produced at SKU and location granularity, refreshed weekly, and fed directly into the replenishment logic the ERP already runs. The planner keeps the veto. The spreadsheet goes away. Stock-outs on A-lines drop, overstock on C-lines drops faster, and the cash that was sitting on the warehouse floor goes back to the business.

    In Your Business

    How this lands inside your operation

    Picture the Monday planning cycle. Your demand planner opens the forecast in the ERP, not in Excel. Every SKU at every DC has a number next to it, a confidence band, and the top three drivers the model used to get there. The planner reviews exceptions, the 50 or so lines where the model disagrees meaningfully with last year, approves or overrides each one, and the replenishment run goes out by 10am.

    Purchasing sees the same numbers. Finance sees the same numbers in the cash forecast. When a promotion lands, the planner flags it once and the model absorbs the lift. When a supplier lead-time slips, the safety-stock logic reacts the same day. The spreadsheet that used to run the business is gone, and nobody misses it.

    This is for you if…

    • Growing wholesalers, retailers, and FMCG manufacturers with lean planning teams across multiple SKUs, locations, suppliers, and spreadsheets.
    • Businesses running NetSuite, Pronto, MYOB Advanced, SAP Business One, or similar, with replenishment logic living partly in the ERP and partly in Excel.
    • Operations leaders who have a demand planner but no data science team, and know the planner is spending 60% of their week on spreadsheet maintenance.
    • COOs who have been quoted seven figures by a tier-one forecasting vendor and want a version sized for their business.

    This probably isn't the right fit if…

    • Single-site operators with a small SKU range where a cleaner spreadsheet will do more for less.
    • Organisations whose sales history is unreliable or less than 18 months deep. Fix the data first.
    How It Works

    The engagement, step by step

    1. 1

      Pull transactional sales history, promotion flags, and stock-on-hand from the ERP into the lakehouse, cleaned and reconciled.

    2. 2

      Classify every SKU by ABC value and XYZ volatility so the model fits each segment with an appropriate method.

    3. 3

      Train a hierarchical forecasting model that reconciles national, DC, and store-level demand without double-counting.

    4. 4

      Layer in external signals that actually earn their keep: public holiday calendars, BOM weather, promotion uplift curves.

    5. 5

      Publish the weekly forecast into the ERP replenishment screen with a confidence band and override field.

    6. 6

      Route exceptions to the demand planner as a ranked queue, highest cash impact first.

    7. 7

      Feed actual sales back into the model each week so forecast accuracy compounds, not decays.

    What we use, what we don't

    Forecast signals we use and don't use

    Most forecasting vendors sell a list of exotic inputs. For teams still reconciling forecasts in spreadsheets, half of them cost more than they earn. Here is the honest split.

    Signals we useSignals we explicitly don't use for lean planning teams
    Historical sales by SKU and location, 2–5 years where availableReal-time POS streaming. Weekly batch is enough unless per-minute stock decisions are already operational.
    Promotion flags and discount depth from the ERP or trade-promotion systemThird-party weather AI vendors. The free BOM data is good enough for 95% of FMCG use cases.
    Seasonality and public holiday calendars (including state-specific ones)Social-media sentiment feeds. Too noisy, too expensive, and rarely predictive for replenishment decisions.
    BOM weather data for temperature-sensitive categoriesSatellite imagery of competitor car parks. Yes, vendors sell this. No, you don't need it.
    ABC / XYZ classification to fit method to SKU behaviourReal-time competitor price scraping. Useful for pricing; noise inside a demand forecast.
    Supplier lead-time variance and inbound shipment statusGeneric "AI demand-sensing" black boxes. If we can't explain the drivers, the planner won't trust it.
    Stock-on-hand and open purchase orders across DCs and storesBespoke deep-learning models trained from scratch. Your data volume doesn't support them.
    Outcomes

    What changes once this is in

    Working capital released

    10–20% reduction in inventory value carried, driven mostly by smaller safety-stock buffers on stable lines and fewer overstocked C-class SKUs sitting for quarters.

    Stock-outs down on the lines that matter

    Service level on A and B SKUs lifts as the forecast sees promotion lift, seasonality, and lead-time variance earlier than a spreadsheet ever could.

    Planner time redirected

    The demand planner stops rebuilding the forecast from scratch each week. They spend that time on the 50 exceptions that actually move money, not the 5,000 that don't.

    Cashflow forecast gets honest

    Finance finally has an inventory line in the cash forecast that reflects reality. The CFO stops being surprised in month three of each quarter.

    Capabilities

    The capabilities behind this

    Advanced Analytics & ML

    Forecasting, simulation and optimisation for consequential decisions, with visible uncertainty and quantified trade-offs.

    Learn more

    AgileData Platform

    One governed data platform across your ERP, CRM, finance, and operational systems. We build it, we run it, and it is live in two weeks.

    Learn more

    Insight-to-Action Systems

    Connect evidence, scenarios and quantified trade-offs to human approval, approved action and outcome learning.

    Learn more
    Common Questions

    Frequently asked

    Let's talk about Demand & Inventory Forecasting.

    30-minute call, no slides, no obligation. We'll tell you plainly whether this is the right fit for what you're trying to do.

    See how Advanced Analytics & ML delivers it

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