Poor product placement within warehouses can be costly for manufacturers, creating bottlenecks and poor routes within warehouses. These mistakes can slow down processes and increase the timeliness and costs of gather and delivering products to customers.
Using Machine Learning to optimize product placement within warehouses can increase the efficiency and reduce the costs associated with picking and the picking route within the warehouse. One can use an association rule algorithm to improve slotting and improve picking. The algorithm can determine where products should be placed so that those most frequently purchased together are near each other. Along with this, the algorithm can help to determine the most efficient picking route in the warehouse, adding dynamic optimization if there are multiple entities picking in the warehouse.