A supply chain rarely has demand spread evenly across its warehouse network. An AI-driven allocation policy can proactively transfer stock from low-demand warehouses toward high-demand regions, cutting regional stockouts — but every inter-warehouse transfer carries its own transportation cost.
The AI Supply Chain Lab models a 6-warehouse network with uneven regional demand. Sliding regional-balancing aggressiveness up reduces stockout events in high-demand regions while increasing the number of costly inter-warehouse transfers needed to get there.
What makes multi-echelon allocation harder than single-warehouse reordering is that every transfer decision ripples across the whole network — moving stock toward one region necessarily means less is available at the warehouse it came from, so the policy has to weigh trade-offs across the entire network at once rather than one location in isolation.
🧪 Try it yourself: the AI Supply Chain Lab simulation lets you move the regional-balancing aggressiveness and watch the monthly outcome update live.
🧪 Try it yourself: the AI Supply Chain Lab simulation lets you experiment with everything described above directly in your browser.