The Massachusetts Institute of Technology (MIT) Center for Transportation & Logistics and Mecalux have developed an artificial intelligence-based simulator capable of optimizing inventory distribution across different warehouses within the same logistics network. The platform, called Genetic Evaluation & Simulation for Inventory Strategy (GENESIS), uses advanced machine learning models to analyze thousands of possible scenarios and determine the optimal stock level at each warehouse and when replenishment should occur.
The AI-based simulator takes into account variables such as forecasted demand in each region, transportation costs, and the operational capacity of each warehouse to test various inventory replenishment policies without affecting real-world operations. “The genetic algorithm enables multiple simulations to be run using different parameters until the most efficient logistics strategy is identified. Companies can compare scenarios and select the one that best fits their operations,” says Dr. Matthias Winkenbach, Director of Research at the MIT Center for Transportation & Logistics and the Intelligent Logistics Systems Lab.
Once data and variables are entered into the system, GENESIS generates the optimal solution along with advanced statistical dashboards. Users can analyze indicators such as consumption patterns, regions with high demand variability, SKUs with a greater risk of stockouts, or warehouses experiencing supply issues.
Redistribute before purchasing
One of the system’s key features is its ability to rebalance inventory across warehouses. Instead of automatically placing new orders with suppliers, the tool analyzes whether it is more efficient to transfer products from another facility within the network where excess inventory is available. In this way, companies can reduce costs and make better use of existing stock.
The system also recommends how to organize transportation. For example, it suggests whether shipments should be consolidated to optimize truckloads or whether specific orders should be fulfilled from a particular location to reduce delivery times and costs.
“The real challenge wasn’t finding the right algorithm — it was making it fast enough to be practical. We developed GENESIS from the ground up to evaluate thousands of scenarios simultaneously rather than sequentially. What used to take days now takes minutes, which means companies can use it for real tactical planning, not just theoretical analysis,” says Rodrigo Hermosilla, Research Engineer at the MIT Intelligent Logistics Systems Lab.
Unlike analytical solutions reserved for specialized users, GENESIS is designed for both technical teams and business decision-makers. “The goal is to help companies minimize the total cost of their logistics network while ensuring the highest service level,” says Javier Carrillo, CEO of Mecalux.
Upcoming AI applications
The AI-powered simulator is one of the first tangible results of the joint initiative between Mecalux and MIT CTL. The collaboration is now entering a new phase focused on expanding the application of AI to other logistics processes, such as internal replenishment, digital twins in high-density automated storage systems, and slotting optimization.
