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SUPPLY CHAIN
Inventory Optimization System
Regional Grocery & General Merchandise Retailer (180 stores)
18%
Faster Turnover
-62%
Stockout Rate
$28M
Working Capital Freed
-59%
Markdown Losses
The Challenge
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 8.4x compared to industry leaders at 12-14x.
The Approach
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
Optimization Loop
1
Sense
Multivariate Demand Signal
2
Optimize
Multi-Echelon Positioning
3
Act
Auto-Replenishment
The Impact
| Metric | Before | After | Improvement |
|---|---|---|---|
| Stockout rate | 8.2% | 3.1% | -62% |
| Inventory turnover | 8.4x | 11.2x | +33% faster |
| Dead stock value | $34M | $19M | -44% |
| Markdown losses | 22% | 9% | -59% |
Annual impact: $28M in working capital freed + $12M in reduced markdowns
Tech Stack
Azure Machine Learning
Azure Synapse Analytics
Prophet + Custom Models
JDA/Blue Yonder Integration
Power BI
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