Utilizing AI and machine learning algorithms to optimize supplier relationship management and risk mitigation in global supply chains

This research investigates the integration of artificial intelligence (AI) and machine learning (ML) algorithms in revolutionizing supplier relationship management and risk assessment within global supply chains. With supply chain disruptions costing businesses an average of $184 million annually, the need for intelligent solutions has become critical. The study examines the technological foundations of AI-driven supply chain transformation, including machine learning analytics, natural language processing, and federated learning systems. Through analysis of implementation cases across automotive, technology, pharmaceutical, and agricultural sectors, we explore how cognitive computing and autonomous decision-making frameworks are reshaping traditional supply chain operations. The research provides insights into implementation mechanisms focusing on predictive risk modeling, real-time monitoring systems, and supply chain orchestration. Our findings demonstrate the potential of AI technologies to enhance operational efficiency, reduce risks, and create more resilient supply chain ecosystems. The study offers an evidence based perspective on AI's role in transforming supplier relationship management while acknowledging both opportunities and implementation challenges in an increasingly volatile global business environment.