Trading Inventory Cost for Online Sales Revenue

Sunday, March 27, 2022

In 2021, Professor Simchi-Levi and researchers in the MIT Data Science Lab collaborated with Accenture and Home Depot to study an inventory placement optimization problem, where demand is sensitive to service response time, under the online retail setting. To predict the sensitivity of demand to response time, the team developed a novel demand prediction and elasticity model for different product categories. Such a model may suggest products that need to be positioned closer to market demand. Unfortunately, such a strategy will increase inventory cost. To address this challenge, a new method based on data-driven stochastic programming was devised that optimally trades safety stock for service response time. The efficiency of the approach was demonstrated through data provided by one of the largest e-commerce retailers in North America. The new approach led to more than 10% total profit increment. Our approach offers supply chain managers a general-purpose decision support tool that optimally position inventory in the supply chain and generates recommended stocking levels for stores, distribution centers and warehouses on a daily basis. For more details, see H. Qin, D. Simchi-Levi, R. Ferer, J. Mays, K. Merriam, M. Forrester, A. Hamrick (2022), Trading Safety Stock for Service Response Time in Inventory Positioning. Available at SSRN.