Attractiveness factors in retail category space location-allocation problem

Abstract

We study the problem of category space location-allocation in the retail industry. We introduce a new attractiveness factor to reflect the product-based visibility level in designing the optimal allocation policy. This factor will be determined for each aisle by the lineup of product categories allocated to that aisle and all other aisles sharing a shopping path with it. We explore how considering the classical location-based attractiveness and the proposed product-based attractiveness can improve a retailer's overall space profitability. We develop a modelling framework that integrates both location-based and product-based attractiveness factors in a mixed-integer nonlinear program. Due to the non-linearity and non-convexity of the proposed model, large-scale instances are computationally challenging to solve using the state-of-the-art commercial solvers. We thus introduce a two-stage heuristic solution method that generates a near-optimal solution in a reasonable amount of time. Using the two-stage model, we explore the optimal store design for an illustrative case study. The results couple the optimal category space allocation to customers' shopping paths and create a profitability-maximising balance between the placement of high-demand and high-impulse product categories. We show that focussing on product-based attractiveness exposes the store to congestion risks, which can be prevented by adding constraints limiting congestion in different aisles of the store.

Keywords:

• Store design
• space planning
• mixed integer programming
• retail analytics
• shopping path consideration

Note

This paper is derived from the first author's Ph.D. dissertation (Babaee 2021). The dissertation investigates three profit maximisation models for coordinating Sales and Operations (S&OP) management. The paper differs from the problem presented in the thesis in some significant factors, including the definition of customers' shopping paths and aisle-visiting probabilities.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The authors confirm that the data supporting the findings of this study are available within the article [and/or] its supplementary materials. A part of the data, as mentioned in Section 6 has been adopted from Ghoniem, Flamand, and Haouari (2016) which is publicly available at the following URL: http://ahmed.ghoniem.info/download/GAPLA.zip.

Additional information

Funding

Financial support for this research was provided by CN through the Centre for Supply Chain Management, Wilfrid Laurier University, and Natural Sciences and Engineering Research Council of Canada under Discovery Grants [RGPIN-2020-05395 and RGPIN-2017-05297].

Notes on contributors

Sara Babaee

Sara Babaee is a lecturer in management at the university of Fraser Valley business school.  She received her Ph.D. in supply chain, operations, and technology management from Lazaridis School of Business and Economics, Wilfrid Laurier University. Dr. Babaee has a mix of both academic research and industry experience. She has experience as a software developer in the aviation industry, developing optimization algorithms to solve various airline scheduling problems such as crew pairing and roster optimization problems. Her research interests include sales and operations management interface, transportation and logistics, mathematical programming, and optimization. Dr Babaee's teaching includes courses in statistics, management science, and operations management.

Mojtaba Araghi

Mojtaba Araghi is an associate professor of operations and decision sciences at the Lazaridis School of Business & Economics, Wilfrid Laurier University. He received his Ph.D. in Operations Management from Rotman School of Management, University of Toronto. Dr. Araghi's primary research interests span coordination in service and healthcare operations management, operations and marketing interface, and incentive theory and mechanism design. Dr Araghi's teaching includes courses in statistics, business decision models, and operations and supply chain management.

Ignacio Castillo

Ignacio Castillo is a professor of operations and decision sciences at the Lazaridis School of Business & Economics, Wilfrid Laurier University. He holds a Ph.D. in industrial engineering from Texas A&M University, an M.S.E. in industrial engineering from Arizona State University, and a B.S. (magna cum laude) in applied sciences from Universidad San Francisco de Quito, Ecuador. His research and teaching interests include business statistics, facility location, facility layout and material handling systems, manufacturing and service operations and logistics, and sustainable and closed-loop supply chain management. Dr. Castillo is a former LASPAU Scholar and Glenn Carroll Teaching Fellow, has served as adjunct faculty at the University of Alberta and the University of Waterloo, and is a member of the Alpha Pi Mu Industrial Engineering Honor Society, The Honor Society of Phi Kappa Phi, and Pinnacle Honor Society.

Borzou Rostami

Borzou Rostami is an Assistant Professor and CPA chair of business analytics in the department of Accounting and Business Analytics at Alberta School of Business, University of Alberta. His research interests include optimization under uncertainty, data analytics in supply chain management and decomposition methods for large-scale mixed-integer nonlinear optimization with application in transportation and logistics, facility location, and hub network design.