Retail markdown price optimization and inventory allocation under demand parameter uncertainty

29 Pages Posted: 8 Jul 2021

Date Written: June 28, 2021

Abstract

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.

Keywords: markdown optimization, revenue management, network flows, stochastic optimization

Suggested Citation

Vakhutinsky, Andrew, Retail markdown price optimization and inventory allocation under demand parameter uncertainty (June 28, 2021). Available at SSRN: https://ssrn.com/abstract=3875689 or http://dx.doi.org/10.2139/ssrn.3875689

Andrew Vakhutinsky (Contact Author)

Oracle Labs ( email )

MA
United States

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Downloads
33
Abstract Views
104
PlumX Metrics