Context
Pick paths were inefficient and replenishment queues created bottlenecks during peak order windows.
Supply Chain Case Study 3: Warehouse Slotting Intelligence
Innovation
Introduced AI slotting recommendations and heatmap-based picking sequence optimization tied to SKU velocity classes.
Outcome
Order throughput increased by 18%, picker travel distance dropped, and high-volume aisle congestion was reduced.
Executive Analysis
Senior Research Review
Review findings suggest micro-slotting refresh intervals are critical; monthly refresh underperformed compared to fortnightly refresh.
- Method: operational time-and-motion benchmark
- Key KPI shift: fewer queue spikes during top quartile demand days
- Recommendation: automate slotting refresh by demand volatility
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Domain: Supply Chain
Reference: Supply Chain Case Study 3: Warehouse Slotting Intelligence
- Delivery model: Senior-only architecture and implementation team
- Security baseline: AES-256, Zero-Trust IAM, VPC isolation
- SLA baseline: 4-hour critical response pathway