QFD-based optimization model for mitigating sustainable supply chain management adoption challenges for Bangladeshi RMG industries

Abstract

In response to heightened pressures from regulatory mandates, global competition, and evolving customer expectations, industries worldwide are compelled to prioritize environmental initiatives, often at the expense of economic considerations. The research gap addressed in this study is the lack of a comprehensive, data-driven optimization model for effectively mitigating sustainable supply chain management adoption challenges specific to the Bangladeshi Readymade Garments (RMG) industry. While previous studies often relied on single techniques, this research proposes a novel AHP integrated QFD-based MILP optimization model. This innovative approach empowers Bangladeshi RMG industries to make data-driven decisions for prioritizing sustainability challenges and selecting cost-effective mitigation strategies to promote the integration of sustainability initiatives within the sector. The study identifies and prioritizes 25 sustainable supply chain management adoption challenges and proposes 16 mitigation strategies. The model emphasizes the critical interplay between sustainability performance and implementation costs, achieving a sustainability performance score of 0.4511 while effectively implementing 12 out of 16 strategies within the expected budget. The optimal solution incorporates green strategies, technology integration, and aspects of Industry 5.0, demonstrating a holistic approach to sustainable supply chain management. The findings are crucial for Bangladeshi RMG industries aiming for global market competitiveness and contribute significantly to the academic field by introducing a robust, data-driven decisions for sustainable supply chain optimization. The implications extend beyond the RMG sector, offering a replicable model for other industries and regions facing similar sustainability challenges.