ML-powered demand forecasting for a retail chain
91%
Forecast accuracy (WMAPE)
28%
Inventory cost reduction
62%
Fewer stockouts
120+
Stores optimized
The challenge
A mid-size retail chain with 120+ stores was losing millions to overstock and stockouts. Their Excel-based planning couldn't account for seasonal patterns, local events, or cross-store cannibalization.
Our solution
We built an ML-powered demand forecasting system that predicts SKU-level demand across all stores with daily granularity. The model combines gradient boosting with external signals — weather, local events, holidays, promotions. Automated retraining pipeline keeps the model fresh as patterns shift.
Key highlights
SKU-level forecasting
Daily predictions for every product in every store
External signals
Weather, events, holidays, promotions as model inputs
Auto-retraining
Model updates weekly as demand patterns shift
ROI in 90 days
Inventory savings exceeded project cost in Q1
Tech stack
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