LEVERAGING MACHINE LEARNING AND AI FOR RESILIENT AND EFFICIENT SUPPLY CHAIN MANAGEMENT

Abstract

The increasing complexity of world supply chains necessitates the adoption of advanced technology to enhance resilience and performance. Traditional risk manage strategies, which depend on historic records and submit-event assessment, often fail to deal with actual-time disruptions correctly. This check explores the combination of machine getting to know and artificial intelligence (AI) in supply chain manipulate to allow proactive hazard mitigation and operational agility. By leveraging predictive analytics, anomaly detection, time series analysis, and natural language processing, AI-driven models can understand patterns, assume ability dangers, and enhance choice-making. Real-time records processing similarly improves call for forecasting accuracy, optimizes stock control, and enhances operational performance via AI-pushed automation. Additionally, AI-powered collaborative gear guide deliver chain networks via fostering accept as true with and coordination amongst partners. This research affords a conceptual framework that carries AI into hazard manipulate methods, highlighting its capability to enhance visibility, responsiveness, and sustainability. Case studies from various industries show the practical benefits of AI adoption, along with decreased disruptions and higher adaptability. The examine underscores AI’s transformative function in modern deliver chain control, presenting organizations with a strategic method to navigating uncertainty.