 Predictive and Prescriptive Analytics for Location Selection of Add‐on Retail ProductsTeng Huang, David Bergman and Ram Gopal Production and Operations Management, 2019, vol. 28, issue 7, 1858-1877 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. Date: 2019 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:bla:popmgt:v:28:y:2019:i:7:p:1858-1877 Ordering information: This journal article can be ordered from Access Statistics for this article Production and Operations Management is currently edited by Kalyan Singhal More articles in Production and Operations Management from Production and Operations Management Society |
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