Measured outcomes
Stockouts down 41%
Overstock down 27%
Carrying-cost optimization: $14M annual savings
Forecast accuracy at SKU-week level: 78% (was 58%)
A third-party logistics provider managed inventory for 230 client brands across 18 distribution centres. Demand forecasting was driven by spreadsheet rules of thumb. Stockouts and overstock were both stubbornly high.
ML-based forecasting models (Azure ML + Databricks) trained on 4 years of order history, with feature engineering for seasonality, promotion uplift, and weather/event signals. Per-SKU model selection — high-velocity SKUs get GBM models; long-tail SKUs get hierarchical Bayesian. Continuous retraining pipeline.
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