Predictive and Prescriptive Analytics for Location Selection of Add-on Retail Products
Teng Huang, David Bergman, Ram Gopal
Production and Operations Management · 2019 · Volume 28 · Issue 7
Abstract
In this paper, we study an analytical approach to selecting expansion locations for retailers selling add-on products whose demand is derived from the demand for a separate base product. Demand for the add-on product is realized only as a supplement to the demand for the base product. In our context, either of the two products could be subject to spatial autocorrelation where demand at a given location is impacted by demand at other locations. Using data from an industrial partner selling add-on products, we build predictive models for understanding the derived demand of the add-on product and establish an optimization framework for automating expansion decisions to maximize expected sales. Interestingly, spatial autocorrelation and the complexity of the predictive model impact the complexity and the structure of the prescriptive optimization model. Our results indicate that the formulated models are highly effective in predicting add-on-product sales, and that using the optimization framework built on the predictive model can result in substantial increases in expected sales over baseline policies.
Keywords
add-on products, derived demand, empirical demand estimation, retail expansion optimization, predictive and prescriptive analytics
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