Enhancing Supply Chain Resilience with Machine Learning: Strategies for Risk Mitigation and Adaptation

Abstract

Machine learning (ML) has emerged as a powerful tool for enhancing supply chain resilience by enabling proactive risk mitigation and adaptation strategies. This paper explores various ML-driven approaches for bolstering supply chain resilience, including predictive analytics, anomaly detection, optimization, and simulation. By leveraging ML techniques, organizations can identify potential risks, predict disruptions, optimize inventory levels, and develop agile response strategies. Furthermore, the integration of ML with other technologies such as Internet of Things (IoT) devices and blockchain can further enhance visibility, traceability, and responsiveness across the supply chain. Through case studies and best practices, this paper provides insights into how organizations can harness the potential of ML to build resilient supply chains capable of navigating unforeseen challenges and maintaining competitive advantage in today's volatile business landscape.