Abstract

The increasing complexity and volatility of global supply chains, particularly in emerging markets, necessitate the adoption of advanced technologies to enhance resilience and mitigate risks. This paper presents a robust AI-driven supply chain resilience framework, leveraging predictive analytics and risk mitigation strategies to address disruptions. It explores the role of AI-powered models, including machine learning and deep learning, in forecasting potential risks and improving adaptive decision-making. Additionally, the study examines AI applications in proactive risk mitigation, such as demand forecasting, supplier assessment, and logistics optimization, highlighting their impact on supply chain continuity. The proposed framework integrates AI with IoT and cloud computing to enhance real-time visibility, data-driven decision-making, and automated risk response. A structured implementation roadmap is provided to guide businesses in emerging markets in overcoming adoption barriers, with a focus on scalability, interoperability, and cost considerations. Practical implications for businesses, policymakers, and supply chain professionals and potential challenges such as data reliability, algorithmic bias, and cybersecurity risks are discussed. Finally, the paper outlines future research opportunities, emphasizing AI-human collaboration, developing more robust AI models for volatile markets, and cost-effective AI deployment strategies. By adopting this AI-driven resilience framework, businesses can improve supply chain agility, enhance operational efficiency, and navigate the uncertainties inherent in emerging market environments.