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Impact: Moyen2026-11-02
MIT and Mecalux advance AI-powered warehouse slotting optimisation
Researchers Selene Silvestri and Sarah Schaumann describe a joint MIT–Mecalux solution under development that combines operations research and machine learning to improve product placement. The approach uses order patterns and congestion constraints to determine where items should be stored and when they should move.
L'angle Dataleo
The key decision is not simply where to place an SKU, but whether the expected picking benefit justifies relocating it. AI-driven slotting needs to account for congestion, relocation costs and volatile demand. A good optimisation on historical orders may become a poor operational decision during a demand shift.