Retail Group Optimizes Inventory Across 50 Stores with Demand AI
Our client replaced static replenishment rules with demand-aware inventory forecasting. The result: fewer stockouts on top sellers and less trapped capital in slow-moving SKUs.
The Challenge
Store managers were over-ordering for safety and under-forecasting promotions. HQ teams relied on late spreadsheets, causing working capital drag and uneven in-store availability.
The Solution
We implemented demand forecasting with seasonality, promo signals, and branch-level variance. The system now recommends replenishment, inter-store transfer, and markdown timing decisions.
The Shift (Before vs. After)
Stockout rate (top 200 SKUs)
Before
14.2%
After
9.8%
Inventory holding cost
Before
$8.2M/yr
After
$7.0M/yr
Manual planning hours/week
Before
84h
After
29h
Key Results
Client Perspective
"We finally have one version of inventory truth. Buyers can act on signal instead of intuition."
R. Tan — Head of Merchandising, Our Client
Company Profile
Client
Retail Group
Location
Singapore
Industry
Retail
Business Type
Omnichannel retail network with 50 physical stores
Implementation Timeline
10 weeks across pilot and chain rollout
Primary Impact
15% inventory cost savings
Tech Stack Used
Rollout Plan
- Week 1-3: Data unification + SKU hierarchy cleanup
- Week 4-7: Forecast model calibration by category
- Week 8-10: Pilot to 50-store rollout with guardrails
Ready for similar results?
Let's discuss how we can apply these same strategies to your business.