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, presenting a significant research gap, 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. The results reveal the availability of government support schemes to promote sustainability, developing strategic supply chain interventions to ameliorate the impact of disruptive events, and digitalizing the supply chain as the most monumental imperatives under economic, social, and environmental perspectives, respectively. The study's innovative methodology and its implications for sustainable supply chain management offer valuable insights for both academic research and practical application, presenting a strategic blueprint for the fashion retail industry to navigate and thrive in the post-COVID-19 era. This work can not only advance the theoretical understanding of supply chain sustainability but also provide actionable guidance for industry leaders in developing robust, resilient, and sustainable supply chain strategies.
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| http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0312671 | PLOS |
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11687714 | PMC |
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