Reducing Waste in Retail: A Mixed Strategy, Cost Optimization Model for Sustainable Dead Stock Management

The retail sector is the most demand-sensitive echelon in the supply chain, where non-moving items accumulate and become dead stock. Existing inventory management studies focus on fast-moving products and income generation. This paper focuses on dead stock management and proposes a mixed strategy solution using a pure integer non-linear programming model that minimizes the dead stock management cost of a retail chain operator. The number of products and volume of product-related data in a retail chain system require big data analysis to ensure sustainable inventory practices that reduce waste generated from dead stock inventory. Through hypothetical data sets, the 3-store, 10-product run showed that discount percentage, expected sales success probability of a product in a store location, and disposition of unsold products were the main drivers of the decisions made by the model. The most significant cost contributors arising from these decisions were the unrecovered product cost (UPC), disposed product cost (PC), and salvage value from the successful sale of dead stock. Inventory managers must balance the effect on these cost components when they choose the strategies to use in managing dead stock.