MIT’s Centre for Transportation & Logistics, alongside Mecalux, has created GENESIS, an AI-driven simulator designed to enhance inventory distribution across multiple warehouses. This innovative platform employs genetic algorithms and machine-learning models to analyze various inventory and transport strategies, suggesting optimal stock levels and replenishment timings. As multi-warehouse logistics grow increasingly complex amid regional demand fluctuations, GENESIS facilitates network-wide decision-making by considering factors like forecast demand, transport costs, and warehouse capacities.
Users can simulate multiple scenarios to evaluate trade-offs and identify cost-reducing strategies while minimizing stockout risks. The simulator supports both technical teams and business managers, streamlining scenario analyses without needing specialized expertise. A key feature includes inventory rebalancing, optimizing stock transfers between warehouses to avoid shortages and excesses. GENESIS aims to enhance logistical efficiency, enabling swift tactical planning to adapt to changing demands and constraints, while pursuing broader initiatives in warehouse operations through AI.
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