Exploring the enablers of sustainable supply chain management: A simplified computational intelligence perspective

This chapter applies a cutting-edge machine learning (ML) approach to investigate the relative significance of enablers and barriers of sustainable supply chain management (SSCM). This study primarily focuses on the textile sector, making a noteworthy contribution to the existing body of knowledge. Data for this study were collected from industry experts located across Asia, from seven Asian countries: China, India, Pakistan, Indonesia, Thailand, South Korea, and Bangladesh. All of the experts were working in the supply chain departments of the textile sector. This research represents a pioneering step toward the broader application of ML and AI tools for exploring enablers and barriers of SSCM. The findings from this study highlight the critical role of human capital, supplier integration, relational capital, and structural capital as pivotal drivers or enablers of SSCM within the textile sector. Further, it highlights that a lack of top management support, intrinsic motivation, production constraints, cost considerations, knowledge gaps, and process inefficiencies pose substantial barriers to the successful implementation of SSCM. The study offers a few important recommendations for managers, urging them to strategically employ SSCM drivers as tools for controlling the barriers, thereby fostering a comparatively efficient and effective approach to sustainability integration within the textile industry.