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

Mohammad Dana Lagzi, Seyed Mojtaba Sajadi*, Mohammadreza Taghizadeh-Yazdi

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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.
Original languageEnglish
Article number103908
Number of pages12
JournalJournal of Retailing and Consumer Services
Volume80
Early online date23 May 2024
DOIs
Publication statusE-pub ahead of print - 23 May 2024

Bibliographical note

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/).

Data Access Statement

Data will be made available on request.

Keywords

  • Stochastic data envelopment analysis
  • Performance measurement
  • Balanced scorecard
  • Decision tree
  • Retail industry
  • Chain stores

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