A Data-Driven Assortment and Inventory Planning Model

In this research article, we investigate a challenge pertaining to shelf-stocking allocation, specifically the scenario where the distribution of demand is unknown. However, a collection of predicted demand figures is at hand. The retailer is required to decide on the products to place on each shelf (ensuring placement) and determine the quantities of those products (ensuring stocking) while abiding by allocation and space limitations. The primary objective is to achieve the highest possible total expected profit. We conduct three experiments and observe how the products react to changes in marginal profit.