Inventory Optimization System
Regional Grocery & General Merchandise Retailer — 180 stores across 6 states.
2025
The challenge
What we found.
A regional retailer operating 180 stores across 6 states was losing market share due to inventory issues. They faced an 8.2% stockout rate on high-velocity items, while simultaneously holding $34M in dead stock annually.
Their legacy forecasting system used simple moving averages and couldn't handle local demand variations, weather-driven shifts, or promotional cannibalization. Inventory turnover was lagging at 8.4× vs. industry leaders at 12–14×.
The approach
How we built it.
PRR implemented a multi-echelon inventory optimization system:
- Demand sensing ML models incorporating POS data, weather, local events, and competitor pricing
- Network optimization to determine optimal inventory positioning across 12 DCs and 180 stores
- Automated replenishment with dynamic safety stock and reorder points by SKU-location
- Markdown optimization using price elasticity models to minimize end-of-season losses
- 01SenseMultivariate demand signal
- 02OptimizeMulti-echelon positioning
- 03ActAuto-replenishment
The outcome
The impact.
| Metric | Before | After | Improvement |
|---|---|---|---|
| Stockout rate | 8.2% | 3.1% | -62% |
| Inventory turnover | 8.4× | 11.2× | +33% faster |
| Dead stock value | $34M | $19M | -44% |
| Markdown losses | 22% | 9% | -59% |
Stack
Tools used.
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