This study explores the pivotal role of HR analytics in enhancing supply chain management (SCM) efficiency, marking a significant shift from traditional HR practices to data-driven decision-making. The main objective is to bridge the gap between HR analytics and SCM performance, highlighting the transformative potential of integrating advanced data analysis in workforce management to optimize supply chain operations. Using a systematic literature review and content analysis methodology, the research analyzed peer-reviewed articles, conference papers, and scholarly books from prominent databases. Key findings underscore the transformative power of HR analytics in SCM, revealing that data-driven HR practices significantly contribute to operational efficiency, strategic decision-making, and enhanced competitive advantage. Advanced analytical tools, such as AI, machine learning, and predictive analytics, are identified as critical in optimizing workforce performance and aligning human capital with supply chain objectives. The study concludes with actionable recommendations for practitioners, leaders, and policymakers, emphasizing the importance of robust data governance, cross-functional collaboration, and the development of analytics competencies. It also outlines emerging challenges and opportunities in leveraging workforce analytics, suggesting directions for future research, particularly in exploring the strategic impacts of HR analytics on supply chain resilience and sustainability. This research contributes to the growing body of knowledge on the strategic integration of HR analytics in supply chain management, offering a roadmap for organizations seeking to enhance supply chain efficiency through data-driven HR practices.This study explores the pivotal role of HR analytics in enhancing supply chain management (SCM) efficiency, marking a significant shift from traditional HR practices to data-driven decision-making. The main objective is to bridge the gap between HR analytics and SCM performance, highlighting the transformative potential of integrating advanced data analysis in workforce management to optimize supply chain operations. Using a systematic literature review and content analysis methodology, the research analyzed peer-reviewed articles, conference papers, and scholarly books from prominent databases. Key findings underscore the transformative power of HR analytics in SCM, revealing that data-driven HR practices significantly contribute to operational efficiency, strategic decision-making, and enhanced competitive advantage. Advanced analytical tools, such as AI, machine learning, and predictive analytics, are identified as critical in optimizing workforce performance and aligning human capital with supply chain objectives. The study concludes with actionable recommendations for practitioners, leaders, and policymakers, emphasizing the importance of robust data governance, cross-functional collaboration, and the development of analytics competencies. It also outlines emerging challenges and opportunities in leveraging workforce analytics, suggesting directions for future research, particularly in exploring the strategic impacts of HR analytics on supply chain resilience and sustainability. This research contributes to the growing body of knowledge on the strategic integration of HR analytics in supply chain management, offering a roadmap for organizations seeking to enhance supply chain efficiency through data-driven HR practices.