Retail
Inventory Forecasting
for a National Retailer
Key Results
32%
Reduced
Stockouts
18%
Lower
Carrying Costs
$8M
Annual
Impact
The Challenge
A national retailer with 400+ locations faced chronic inventory imbalances. Overstock in some regions while others experienced frequent stockouts.
Pain Points
- $12M annual write-offs from overstock
- 8% stockout rate impacting sales
- Manual forecasting taking 40+ hours weekly
- Seasonal patterns poorly predicted
The Solution
We deployed a demand forecasting engine:
1
ML forecasting
Demand prediction incorporating 50+ variables
2
Dynamic reorder
Automated replenishment triggers by SKU/location
3
Anomaly detection
Early warning system for demand spikes
Timeline: 12 weeks to full deployment
The Results
Before
- 8% stockout rate
- $12M write-offs
- 40+ hrs/week manual work
- Poor seasonal accuracy
After
- 5.4% stockout rate
- $7.2M write-offs
- 5 hrs/week oversight
- 94% seasonal accuracy
"We finally have visibility into demand before it happens. The reduction in stockouts directly impacted our bottom line."