AI-Driven Predictive Replenishment for a Retail Chain

The operations manager was running replenishment on weekly spreadsheet reviews and gut instinct. Stockouts on the top-20 revenue SKUs were running at 14% during peak demand weeks. He knew the number was bad. What he didn't have was a forward-looking signal, a way of knowing on Tuesday that Thursday's demand would spike, before the shelves emptied. The estimated cost of stockouts was ₹78-82 Lakh per quarter, but that number was built on assumptions the team had made up because nobody had a better method.

A demand-sensing model built on 22 months of store-level daily sales data, with the previous two months excluded because they overlapped with an atypical promotions period that would have distorted the training data. The model used XGBoost, integrated with the retailer's Tally-based ERP via a CSV export bridge because the ERP's API limitations made a direct integration impractical. The output was a weekly replenishment recommendation report, not a black-box instruction. Store managers were trained to override with a documented reason, which created a feedback loop that improved the model over time.