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Predictive and Prescriptive Analytics for Location Selection of Add‐on Retail Products

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  • Teng Huang
  • David Bergman
  • Ram Gopal

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.

Suggested Citation

  • Teng Huang & David Bergman & Ram Gopal, 2019. "Predictive and Prescriptive Analytics for Location Selection of Add‐on Retail Products," Production and Operations Management, Production and Operations Management Society, vol. 28(7), pages 1858-1877, July.
    • Handle: RePEc:bla:popmgt:v:28:y:2019:i:7:p:1858-1877
    DOI: 10.1111/poms.13018
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    Cited by:

    1. Long He & Sheng Liu & Zuo‐Jun Max Shen, 2022. "Smart urban transport and logistics: A business analytics perspective," Production and Operations Management, Production and Operations Management Society, vol. 31(10), pages 3771-3787, October.
    2. Han, Shuihua & Chen, Linlin & Su, Zhaopei & Gupta, Shivam & Sivarajah, Uthayasankar, 2024. "Identifying a good business location using prescriptive analytics: Restaurant location recommendation based on spatial data mining," Journal of Business Research, Elsevier, vol. 179(C).
    3. Stanley Frederick W. T. Lim & Qingchen Wang & Scott Webster, 2023. "Do it right the first time: Vehicle routing with home delivery attempt predictors," Production and Operations Management, Production and Operations Management Society, vol. 32(4), pages 1262-1284, April.
    4. Yu Sun & Feng Lian & Zhong-Zhen Yang, 2022. "Optimizing the location of physical shopping centers under the clicks-and-mortar retail mode," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 24(2), pages 2288-2314, February.
    5. Qian Tang & Mei Lin & Youngsoo Kim, 2021. "Inter‐Retailer Channel Competition: Empirical Analyses of Store Entry Effects on Online Purchases," Production and Operations Management, Production and Operations Management Society, vol. 30(8), pages 2547-2563, August.

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