Abstract
In today's rapidly evolving business landscape, the convergence of sustainable supply chain management (SSCM) and machine intelligence, encompassing artificial intelligence (AI) and machine learning (ML), represents a dynamic and transformative nexus. This comprehensive survey paper navigates the intricate terrain of sustainable supply chain practices, delving into its principles, challenges, and the pressing need for organizations to embrace environmental responsibility, ethical sourcing, and social equity. Simultaneously, it explores the disruptive potential of machine intelligence, offering insights into its underlying principles, vast applications, and its pivotal role in optimizing supply chain operations. Through a systematic analysis, this paper uncovers the complex interplay between SSCM and machine intelligence, starting with the foundational principles of each discipline. It then scrutinizes the challenges encountered in integrating machine intelligence with sustainability, including data complexities, ethical dilemmas, and the need for skilled personnel. Conversely, the paper illuminates the myriad opportunities that arise from this synergy, from enhancing demand forecasting and inventory management to fostering sustainable sourcing practices and reducing waste. In closing, the paper anticipates the future landscape of sustainable supply chains in the age of machine intelligence, highlighting emerging trends, technological innovations, and the ethical considerations that will shape the trajectory of this evolving field. It is our hope that this survey serves as a valuable resource for businesses, policymakers, and researchers alike, inspiring the pursuit of environmentally responsible, economically viable, and ethically sound supply chains in an increasingly interconnected world.