A retail chain reduced stockouts by 55% and optimised inventory costs by 30% with AI-powered reordering
Store #12 — Rain jackets (M)
Reorder recommended: 48 units
Store #07 — Wool sweaters
Demand spike — cold snap forecast
Store #19 — Summer sandals
Excess stock, markdown suggested
The challenge
Store managers placed manual reorders based on gut feel and historical averages. Fast-selling items ran out regularly, while slow movers accumulated excess stock. There was no centralised view of inventory across locations. Seasonal demand shifts caught the team off guard every quarter, and markdowns on excess inventory eroded margins.
What we built
We deployed an AI demand forecasting model trained on two years of POS data, seasonal trends, local events, and weather patterns. The system generates reorder recommendations daily for each store and SKU, accounting for lead times and minimum order quantities. A central dashboard gives the merchandising team real-time visibility into stock levels, reorder status, and demand forecasts across all locations. Reorder approvals happen in one click.
How we got there
Train the demand model
Trained forecasting on two years of POS data, seasonal trends, local events, and weather.
Automate reorder recommendations
Generated daily per-store, per-SKU reorder suggestions accounting for lead times and minimum order quantities.
Centralise visibility
Gave merchandising one dashboard for stock levels, reorder status, and forecasts across every location.
Results
Stockouts dropped by 55%. Inventory carrying costs fell by 30%. Markdowns on excess stock decreased significantly. The merchandising team shifted from reactive ordering to strategic planning, using demand forecasts to negotiate better supplier terms.
Services used in this engagement
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