A hybrid stochastic data envelopment analysis and decision tree for performance prediction in retail industry

Abstract

Assessing the retail industry's efficiency is pivotal for economic growth and corporate productivity. This study employs a novel approach, utilizing a regression-based Stochastic Data Envelopment Analysis (SDEA) model, Balanced Scorecard (BSC), and Decision Tree. The integration of these methods is a pioneering effort in the retail sector. This is a data-driven decision-making framework, aiding managers in predicting efficient and inefficient Decision-Making Units (DMUs). Results from a case study in 44 retail store chains in Iran indicate that the accuracy of the SDEA model is 99%. The Decision Tree highlights low branch efficiency due to a low customer count, a unique finding in comparison to prior studies.

Publication DOI: https://doi.org/10.1016/j.jretconser.2024.103908
Divisions: College of Business and Social Sciences > Aston Business School > Operations & Information Management
College of Business and Social Sciences
College of Business and Social Sciences > Aston Business School
Aston University (General)
Additional Information: Copyright © 2024 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (https://creativecommons.org/licenses/by/4.0/).
Uncontrolled Keywords: Stochastic data envelopment analysis,Performance measurement,Balanced scorecard,Decision tree,Retail industry,Chain stores
Publication ISSN: 1873-1384
Data Access Statement: Data will be made available on request.
Last Modified: 02 Dec 2024 09:06
Date Deposited: 23 May 2024 09:59
Full Text Link:
Related URLs: https://linking ... 969698924002042 (Publisher URL)
PURE Output Type: Article
Published Date: 2024-09
Published Online Date: 2024-05-23
Accepted Date: 2024-05-12
Authors: Lagzi, Mohammad Dana
Sajadi, Seyed Mojtaba (ORCID Profile 0000-0002-2139-2053)
Taghizadeh-Yazdi, Mohammadreza

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