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Artificial intelligence and simulation could make it easier to manage inventory across a logistics network. Mecalux S.A. and the Massachusetts Institute of Technology, or MIT, today said they have developed an AI-based simulator that can optimize inventory distribution across warehouses.
The platform, called Genetic Evaluation & Simulation for Inventory Strategy (GENESIS), uses advanced machine learning models to analyze thousands of possible scenarios. It can determine the optimal stock level at each warehouse and when replenishment should occur, according to the Intelligent Logistics Systems Lab (ILS) at the MIT Center for Transportation & Logistics (CTL) and Mecalux.
The partners said their simulator takes into account variables such as forecasted demand in each region, transportation costs, and the operational capacity of each warehouse. It tests 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,” said says Dr. Matthias Winkenbach, director of research at ILS. “Companies can compare scenarios and select the one that best fits their operations.”
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.
System redistributes before purchasing
One of the simulator’s key features is its ability to rebalance inventory across warehouses, said MIT and Mecalux. 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. Companies can then reduce costs and make better use of existing stock, the researchers said.
The system also recommends how to organize transportation. For example, it can suggest 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,” said Rodrigo Hermosilla, a research engineer at the MIT lab. “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.”
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,” noted Javier Carrillo, CEO of Mecalux.
MIT, Mecalux look to 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 and retrieval systems (ASRS), and slotting optimization.
The Cambridge, Mass.-based MIT CTL said it is a world leader in supply chain management research and education, with over 50 years of expertise. The center’s work spans industry partnerships, cutting-edge research, and driving innovation into practice through three pillars: R&D, outreach, and education.
Chicago-based Mecalux is a warehouse technology and intralogistics software enterprise. With over 60 years of experience, the company develops ASRS, warehouse management software, and metal racking systems for all sectors.
The Mecalux Group has an extensive worldwide distribution network, 12 manufacturing plants, seven R&D centers, and a workforce of more than 5,500 employees.