AI-powered demand forecasting for global retail
Hierarchical forecasting across 50K+ SKUs cut excess inventory 23% while reducing stockouts 35% for global retailer
Project Info:
- Industry
- Retail & e-commerce
- Company Size
- $800M retail chain
- Timeline
- 20 weeks
- Investment
- $225K
Challenge:
$45M tied up in excess inventory. Frequent stockouts of items. Manual forecasting couldn't handle 50K+ SKUs across regional variations & seasonal patterns.
Solution:
Hierarchical forecasting models predicting demand at store-SKU level with regional and seasonal adjustments. Automated daily forecasts replacing monthly planning.
Results:
- 23% reduction in inventory holding costs ($10.4M savings)
- 35% fewer stock-outs
- 18% increase in inventory turnover
- Forecast accuracy improved - 65% - 89%
Technical approach:
- Prophet for seasonal decomposition
- XGBoost for hierarchical forecasting
- External data integration
- Snowflake data warehouse
Key takeaway:
Starting with pilot in 50 stores validated approach before full rollout. Regional models outperformed single global model.
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