Soothsayer’s client, an animal health distribution company, has multiple networks for distributing their products around the United States and around the world. Their methodology for distribution is conducted manually and requires domain expertise to make decisions about assigning items to a network. The client maintains their service level rate by timely next day deliveries at the expense of paying higher transportation costs, which often causes transportation to be the highest cost associated with an item sold.

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Distribution centers have minimum quantity order requirements with suppliers that creates added constraints for the client’s system. Additionally, some items in the client’s warehouses are overstocked because of lower demands and higher minimum order quantities, leading to various supply-chain related problems like backordering, bullwhip effect, and excess inventory. The client wants to build an AI-based optimization solution to allocate inventory to various distribution centers in order to optimize the total cost incurred by Transportation and Overstocking Inventory.