Abstract
This study investigates the transformative impact of predictive analytics on enhancing supply chain resilience (SCR). Employing a systematic literature review and content analysis, the research aims to explore the integration, challenges, and strategic implications of predictive analytics within the supply chain ecosystem. Focusing on literature from 2014 to 2023, the study synthesizes insights from peer-reviewed articles and conference papers, adhering to stringent inclusion and exclusion criteria to ensure relevance and recency. The findings reveal that predictive analytics significantly contributes to supply chain agility, flexibility, and responsiveness, thereby bolstering SCR against disruptions. Key challenges identified include data privacy concerns, the need for skilled personnel, and the integration of predictive analytics into existing supply chain frameworks. Despite these challenges, the future outlook for predictive analytics in SCM is promising, with potential for unprecedented efficiency, sustainability, and competitive advantage. Strategic recommendations for practitioners emphasize the importance of developing predictive analytics capabilities, prioritizing data governance, and continuous staff training. For policymakers, the study suggests the need for standards and regulations that encourage innovation while ensuring the ethical use of predictive analytics. Finally, predictive analytics is pivotal in revolutionizing SCM, offering pathways for innovation, efficiency, and enhanced resilience. Future research should focus on the integration of emerging technologies, ethical data use frameworks, and overcoming adoption barriers, particularly in SMEs and developing economies. This study underscores the critical role of predictive analytics in the future of SCM, driving forward the agenda for research and practice in this evolving field.
 Keywords: Predictive Analytics, Supply Chain Resilience, Systematic Literature Review, Strategic Implications.
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