ML Demand Forecasting for a Retailer

Fewer stockouts, less overstock, fewer spreadsheets.

THE PROBLEM

A category manager forecasted demand weekly by pasting last year's sales next to 'gut feel' columns. Peaks meant stockouts; lulls meant writedowns.

WHAT WE DID

01
Built a SKU-level forecasting pipeline (hierarchical reconciliation so category totals always match).
02
Mixed a statistical baseline with a trained neural model for the long-tail SKUs.
03
Integrated with the warehouse management system so reorder suggestions land where purchase orders actually get written.
04
Weekly back-tests and accuracy dashboards so the category team sees exactly when the model helped and when it didn't.

OUTCOME

Stockouts down meaningfully on A-tier SKUs. Inventory holding cost down a double-digit percentage, freeing cash.

HAVE A PROJECT LIKE THIS?

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