This study investigates the role of Artificial Intelligence (AI) in bolstering resilience in supply chain management (SCM) and reverse supply chain management (RSCM). A scoping review of 44 peer-reviewed articles (2020–2024) is carried out to explore key themes: AI definitions, its influence on supply chain resilience (SCR) and reverse logistics (RL), prevailing methodologies, and barriers to deployment. The analysis suggests that AI tools, such as machine learning (ML), predictive analytics, and optimization algorithms, are crucial in improving demand forecasting, real-time tracking, risk mitigation, and RL operations, thereby enhancing effectiveness, backing circular economy principles, and aligning with the sustainable development goals (SDGs) adopted by the United Nations. However, challenges persist, including organizational obstacles like conflicting objectives and limited awareness, technological challenges such as subpar data quality and high costs, and ethical concerns related to responsibility and AI decision-making. The study underscores AI's transformative potential in SCM and RSCM, demonstrating its ability to revolutionize logistics while tackling sustainability issues. It advocates for strategic coherence, robust data management, and enhanced AI expertise among stakeholders to overcome implementation challenges. These findings provide valuable insights for scholars and industry professionals, facilitating the progress of adaptive, resilient, and sustainable supply chain systems capable of navigating upcoming trials in a dynamic global context.
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