Retail markdown price optimization and inventory allocation under demand parameter uncertainty

This paper discusses a prescriptive analytics approach to solving a joint markdown pricing and inventory allocation optimization problem under demand parameter uncertainty. We consider a retailer capable of price differentiation among multiple customer groups with different demand parameters that are supplied from multiple warehouses or fulfillment centers at different costs. In particular, we consider a situation when the retailer has a limited amount of inventory that must be sold by a certain exit date. Since in most practical situations the demand parameters cannot be estimated exactly, we propose an approach to optimize the expected value of the profit based on the given distribution of the demand parameters and analyze the properties of the solution. We also describe a predictive demand model to estimate the distribution of the demand parameters based on the historical sales data. Since the sales data usually include multiple similar products embedded into a hierarchical structure, we suggest an approach to the demand modeling that takes advantage of the merchandise and location hierarchies.