Modeling the supply chain sustainability imperatives in the fashion retail industry: Implications...

The resilience of established business strategies has been tested in the wake of recent global supply chain upheavals triggered by events like the COVID-19 pandemic, Russia-Ukraine combat, Hamas-Israel war, and other geopolitical conflicts. Organizations are …
Melany Schultz · 15 days ago · 2 minutes read


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The resilience of established business strategies has been tested in the wake of recent global supply chain upheavals triggered by events like the COVID-19 pandemic, Russia-Ukraine combat, Hamas-Israel war, and other geopolitical conflicts. Organizations are compelled to integrate sustainable practices into their supply chains to navigate the complexities of the post-COVID-19 era and mitigate the far-reaching consequences of such disruptions. However, exploring supply chain imperatives from sustainability dimensions still remains underexplored, particularly in the fashion retail sector. In response, this study aims to pioneer an innovative approach by amalgamating Pareto analysis, Bayes theorem, and the Best-Worst Method to evaluate sustainability imperatives comprehensively. Focusing on emerging economies like Bangladesh and its fashion retail industry, this methodology synthesizes insights from literature reviews, expert feedback, and Pareto analysis to curate a definitive set of influential imperatives. Finally, the Bayesian Best-Worst Method is applied to examine them.

Citation: Imran MTI, Karmaker CL, Karim R, Misbauddin SM, Bari ABMM, Raihan A (2024) Modeling the supply chain sustainability imperatives in the fashion retail industry: Implications for sustainable development. PLoS ONE 19(12):\n e0312671.\n \n https://doi.org/10.1371/journal.pone.0312671

Editor: Afshan Naseem, National University of Sciences and Technology, PAKISTAN

Received: December 18, 2023; Accepted: October 11, 2024; Published: December 31, 2024

Copyright: \u00a9 2024 Imran et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are available within the manuscript and its Supporting information (S1) files.

Funding: The author(s) received no specific funding for this work.

Competing interests: The authors have declared that no competing interests exist.