IDEAS home Printed from https://ideas.repec.org/a/bla/popmgt/v28y2019i7p1858-1877.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/poms.13018
    Download Restriction: no

    File URL: https://libkey.io/10.1111/poms.13018?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bla:popmgt:v:28:y:2019:i:7:p:1858-1877. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: http://onlinelibrary.wiley.com/journal/10.1111/(ISSN)1937-5956 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.