Using Predictive Analytics & Machine Learning
| Scenario | Inventory Buffer | Total Waste | Daily Average |
|---|---|---|---|
| Current Practice | 20% Overstock | 1,904,641 units | 3,224 units/day |
| With ML Forecast | 5% Safety Buffer | 757,924 units | 1,285 units/day |
| REDUCTION | -15% | 1,146,718 units | 1,939 units/day |
💡 Key Finding: By using accurate machine learning forecasts with a 5% safety buffer instead of conservative 20% overstock, retailers can reduce food waste by 60.2% while maintaining product availability. This translates to saving over 1.1 million units from being wasted!
Model Accuracy: Random Forest achieved 94.38% accuracy (R² = 0.9438), meaning it explains 94% of the variation in daily sales. This is excellent for real-world demand forecasting.
Waste Reduction: By using predictions instead of conservative estimates, we reduce waste from 1.9 million units to 760,000 units - a 60.2% reduction! This is substantial both financially and environmentally.
Data Volume: The model was trained on 913,000+ daily transaction records spanning 5 years (2013-2017) across 10 stores and 50 products, ensuring it captures real-world patterns and seasonality.
Store-Item Specificity: The model makes individualized predictions for each store-product combination, accounting for location-specific demand patterns and item characteristics.
Business Impact: Reducing waste by 60% directly translates to cost savings through reduced spoilage, improved margins, and increased inventory efficiency.
Sustainability: Saving 1.1 million units from waste has significant environmental benefits through reduced food waste and associated disposal costs.