Illustrative scenario: forecasting demand across a multi-region retailer

Imagine a retailer with several hundred stores in multiple regions, forecasting weekly demand with spreadsheets owned by regional teams. Forecasts disagree between levels, promotions are handled informally and stock-outs and overstock coexist.

We would begin by profiling sales history and cleaning stock-out periods that distort demand. A hierarchical forecasting model would then produce consistent forecasts at product, store and region levels, with promotion and calendar effects as explicit inputs.

Forecasts would be probabilistic so that replenishment can choose safety stock for a target service level.

Backtests on held-out periods would compare the model with the retailer's current method on forecast error and on the inventory policy outcomes that follow. We would only report improvement demonstrated on the retailer's own data.

This scenario is hypothetical. It does not describe a completed project, and no results are claimed.