Title: Predictive and Prescriptive Analytics for Location Selection of Add-on Retail Products,
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 of another base product. Demand for the add-on product is realized
only as a supplement to the demand of 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 models formulated 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.