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

arXiv: https://arxiv.org/abs/1804.01182 · HTML: https://mlsanity.com/paper/1804.01182/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Apr 2018.

## Built on by (most cited first, 4 of 4)

- [PySCIPOpt-ML: Embedding Trained Machine Learning Models into Mixed-Integer Programs](https://mlsanity.com/paper/2312.08074/) (2023)
- [Constraint Learning to Define Trust Regions in Predictive-Model Embedded Optimization](https://mlsanity.com/paper/2201.04429/) (2022)
- [Addressing distributional shifts in operations management: The case of order fulfillment in customized production](https://mlsanity.com/paper/2304.11910/) (2023)
- [Learning Decisions Offline from Censored Observations with ε-insensitive Operational Costs](https://mlsanity.com/paper/2408.07305/) (2024)

## Abstract (arXiv)

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.