ML-powered demand forecasting
A retailer's manual forecasting process caused chronic overstock and stockouts. Buyers spent hours in spreadsheets. We built an ML forecasting system that reduced inventory waste while improving availability.
Context
A mid-size retailer forecasted demand through spreadsheets, leading to systematic overstock on slow-movers and stockouts on fast-movers.
Constraint
Forecasts had to integrate with existing ERP systems, be explainable to non-technical buyers, and improve both overstock and availability metrics.
Intervention
Built a time-series forecasting system using gradient-boosted models. Added a feature store for consistent training and serving. Created an explainability layer showing drivers behind each forecast.
Key decisions
- Time-series models with seasonal adjustment
- Feature store for training/serving consistency
- ERP integration for seamless adoption
- Explainability dashboard for buyers
- Automated retraining pipeline
- A/B testing infrastructure
Outcomes
Overstock reduced 15% in first quarter. Stockouts dropped, improving availability by 8%. Buyers adopted the system without manual overrides.
Why it matters
Better forecasts directly reduce working capital and improve sales—measurable P&L impact from applied ML.
Implementation
Practical technology choices that matched the constraints.
Discuss a similar system
If this resembles your constraints, share a short description of what you run today and what needs to change.