Case study · Retail
Demand Forecasting with ML
Hierarchical forecasting with external signals and bias reduction.
Key results
- Forecast MAPE -19%
- Stockouts -12%
- Inventory turns +9%
Context
An omnichannel retailer ran statistical demand forecasting that worked for steady-state SKUs but failed on new products, promotional periods, and seasonal transitions. Planner overrides were frequent and typically made forecasts worse.
Challenge
The existing forecasting platform did not incorporate external signals (weather, macro indicators, competitive promotions) and did not handle hierarchical consistency (store-level forecasts had to sum to chain-level targets). Planners added noise rather than signal.
Approach
Thoughtwave delivered a hierarchical ML forecasting platform with external-signal integration, automated bias-reduction at the hierarchy levels, and a planner-review workflow that surfaced exceptions rather than asking planners to review every SKU. The 14-week engagement covered discovery, model development, integration, and a full planning cycle.
Outcomes
Forecast MAPE dropped 19% across the SKU base; stockouts dropped 12%; inventory turns improved 9% because the forecast accuracy allowed tighter safety-stock targets. Planner time shifted from SKU-by-SKU review to exception management.
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