reduction in working capital tied to inventory
SKU-level demand forecasts that feed replenishment directly, so planners manage exceptions instead of rebuilding spreadsheets every Monday.
Demand & Inventory Forecasting is a statistical and machine-learning forecasting layer that predicts weekly demand at SKU and location granularity, then pushes those numbers straight into the replenishment and production plans inside your ERP. It replaces the Excel-plus-gut-feel cycle with a forecast the demand planner can trust, override, and audit.
Most mid-market 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.
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.
Pull transactional sales history, promotion flags, and stock-on-hand from the ERP into the lakehouse, cleaned and reconciled.
Classify every SKU by ABC value and XYZ volatility so the model fits each segment with an appropriate method.
Train a hierarchical forecasting model that reconciles national, DC, and store-level demand without double-counting.
Layer in external signals that actually earn their keep: public holiday calendars, BOM weather, promotion uplift curves.
Publish the weekly forecast into the ERP replenishment screen with a confidence band and override field.
Route exceptions to the demand planner as a ranked queue, highest cash impact first.
Feed actual sales back into the model each week so forecast accuracy compounds, not decays.
Most forecasting vendors sell a list of exotic inputs. At mid-market scale, half of them cost more than they earn. Here is the honest split.
| Signals we use | Signals we explicitly don't use at mid-market scale |
|---|---|
| Historical sales by SKU and location, 2–5 years where available | Real-time POS streaming. Overkill below $200M revenue; weekly batch is enough. |
| Promotion flags and discount depth from the ERP or trade-promotion system | Third-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 mid-market. |
| BOM weather data for temperature-sensitive categories | Satellite imagery of competitor car parks. Yes, vendors sell this. No, you don't need it. |
| ABC / XYZ classification to fit method to SKU behaviour | Real-time competitor price scraping. Useful for pricing; noise inside a demand forecast. |
| Supplier lead-time variance and inbound shipment status | Generic "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 stores | Bespoke deep-learning models trained from scratch. Your data volume doesn't support them. |
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.
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.
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.
Finance finally has an inventory line in the cash forecast that reflects reality. The CFO stops being surprised in month three of each quarter.
Predictive models, recommendation systems, forecasting, and MLOps, delivered with a ruthless ROI focus.
Learn moreThe operating layer your AI, automation, and analytics run on. Deployed in two weeks.
Learn moreClose the gap between what your data knows and what your business does. The architecture from insight through decision to action.
Learn moreRecover 1-4% of gross margin from the pricing decisions you already make, without losing a single unit of volume.
1-4%
gross margin recovered without volume loss
Handle supplier exceptions at a scale previously reserved for the top of town.
Days to hours
exception response time
Your CMMS already knows which assets are about to fail. Ask it.
15%
Reduction in overstocked spare parts
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.
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