23% inventory reduction
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.
