Forecasting demand, and what to stock against it
retail manufacturer · Gulf
The shape of this problem
The same judgment, made differently every time
This is one of our own builds, not a client engagement. It is capability evidence and it is described as such.
The problem
Planning teams needed more reliable forecasts across products and locations, to cut stockouts, excess inventory, and spreadsheet-driven planning cycles. A single model across a whole catalogue is wrong in both directions at once - over-smoothing intermittent items while under-reacting on fast movers. The most damaging error is also the least visible: a period with zero sales because stock was unavailable looks identical in the data to genuine zero demand, so the forecast learns to expect the stockout it caused.
What we built
We built a forecasting pipeline over historical demand, pricing, calendar effects, product attributes, and available external drivers, comparing model families per product series, generating confidence ranges, and publishing forecasts into the planning workflow.
What moved
| Measure | Before | After |
|---|---|---|
| Forecast error | † | down 25% |
| Stockout incidence | † | down 18% |
| Recurring planning effort | † | down 50% |
† Measured against the prior level of the same measure. The source publishes the size of the movement, not the figure it moved from.
Forecast error
Measured against the client's own prior process, indexed to 100. The source publishes the size of the movement, not the absolute figure it moved from.
Stockout incidence
Measured against the client's own prior process, indexed to 100. The source publishes the size of the movement, not the absolute figure it moved from.
Recurring planning effort
Measured against the client's own prior process, indexed to 100. The source publishes the size of the movement, not the absolute figure it moved from.
Figures are drawn from the practice's own delivery records for the engagement named, measured against the process that preceded it. They have not been through third-party audit, and none is presented as an average across clients.
What it turned on
Distinguishing genuine zero demand from unavailable inventory is the specific correction behind the stockout figure. Visible uncertainty and retained planner overrides did the rest - the service improved replenishment decisions without pretending that sparse histories, promotions, and new products can always support a precise point forecast.
- Service line
- Forecasting & Planning AI
The finding matters more than the number here. Both cases repaired data definitions before modelling, which is the practice's whole thesis in miniature.
Start here
Which of the four is yours?
Tell us the documents and the monthly volume and we will send the two closest records, with the proof behind each and the honest note where the match is partial.
You get a reply within one working day, from the engineer who would do the work - not a sales sequence.