Using gravity model to make store closing decisions: A data driven approach.
Expert Syst Appl
; 205: 117703, 2022 Nov 01.
Article
in English
| MEDLINE | ID: covidwho-1889400
ABSTRACT
Many studies propose methods for finding the best location for new stores and facilities, but few studies address the store closing problem. As a result of the recent COVID-19 pandemic, many companies have been facing financial issues. In this situation, one of the most common solutions to prevent loss is to downsize by closing one or more chain stores. Such decisions are usually made based on single-store performance; therefore, the under-performing stores are subject to closures. This study first proposes a multiplicative variation of the well-known Huff gravity model and introduces a new attractiveness factor to the model. Then a forward-backward approach is used to train the model and predict customer response and revenue loss after the hypothetical closure of a particular store from a chain. In this research the department stores in New York City are studied using large-scale spatial, mobility, and spending datasets. The case study results suggest that the stores recommended being closed under the proposed model may not always match the single store performance, and emphasizes the fact that the performance of a chain is a result of interaction among the stores rather than a simple sum of their performance considered as isolated and independent units. The proposed approach provides managers and decision-makers with new insights into store closing decisions and will likely reduce revenue loss due to store closures.
CBG, Census Block Group; COVID-19 pandemic; Closure decision; DDM, Dynamic Decision Modeling; Economic recession; Financial crisis; GIS, Geographical Information Systems; GWR, Geographically Weighted Regression; Huff gravity model; IBLT, Instance Based Learning Theory; MCI, Multiplicative Competitive Interaction; NAICS, North American Industry Classification System; NYC, New York City; OLS, Ordinary Least Squares; PSO, Particle Swarm Optimization; RL, Reinforcement Learning; SME, Small and Medium sized Enterprise; Store closing
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Type of study:
Prognostic study
Language:
English
Journal:
Expert Syst Appl
Year:
2022
Document Type:
Article
Affiliation country:
J.eswa.2022.117703
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