AI-driven predictive analytics framework for proactive supply chain disruption management and contingency planning

Abstract

Supply chain disruptions ranging from geopolitical instability and pandemics to natural disasters and cyberattacks have intensified the need for advanced disruption management strategies. Traditional risk management approaches relying on reactive responses are increasingly inadequate in volatile, uncertain, complex, and ambiguous (VUCA) environments. Recent developments in artificial intelligence (AI)-driven predictive analytics present new opportunities for enabling proactive disruption identification, real-time monitoring, and contingency planning in global supply chains. This paper reviews existing literature and proposes a comprehensive conceptual framework integrating machine learning models, big data analytics, digital twins, and scenario-based planning to manage disruptions effectively. By synthesizing over 100 scholarly contributions, we highlight how predictive analytics enables early warning detection, decision-support systems, and automated mitigation strategies. The paper further discusses implementation challenges, including data quality, algorithmic bias, ethical considerations, and interoperability, while offering recommendations for practitioners and researchers. Keywords: AI Predictive Analytics, Supply Chain Disruption, Contingency Planning Framework, Machine Learning Resilience, Digital Twin Integration, Proactive Risk Management.