Machine learning and data-driven models in sustainable supply chain management: a systematic literature review with content analysis

This paper concentrates on Machine Learning (ML) and data-driven models with applications in Sustainable Supply Chain Management (SSCM) utilising network, bibliometric, and content analyses that can render several innovative insights and perspectives into contemporary research trends in this field. In this work, a comprehensive systematic literature review and bibliometric analysis are undertaken using 324 out of more than 9000 research papers, and accordingly, the decision factors, assumptions, and research objectives for each model are highlighted. The results contribute to both theoretical and practical management elements and give a solid road map for future study in this sector. This paper’s final goal is to provide a thorough overview of applications of ML and data-driven models in SSCM, serving as a source of prospective studies for SSCM scholars and practical insights for SSCM professionals who try to implement their solutions based on ML and AI algorithms.