Demand Forecasting Cuts Inventory Costs for a Retail Chain

A multi-store retail chain was carrying excess inventory in slow categories while constantly running out of best-sellers. Purchasing decisions relied on last year's numbers and manager intuition, tying up cash and losing sales.

We built a machine-learning demand forecasting system using three years of sales history, seasonality, promotions and local events. Forecasts feed directly into weekly purchase recommendations per store and per SKU, delivered through a simple dashboard the buying team checks every Monday.

Within two quarters the chain reduced excess stock significantly while improving on-shelf availability of top sellers. Cash previously locked in dead inventory was redeployed into growth.