Predictive Analytics for Supply Chain Resilience: Developing AI Systems to Predict Disruptions

In an era of heightened global interconnectedness and escalating volatility, supply chain resilience has become a strategic imperative for economic stability and national security. Modern supply chains are increasingly exposed to disruptions arising from natural disasters, geopolitical tensions, economic shocks, and global health crises, as starkly demonstrated by the COVID-19 pandemic. These vulnerabilities underscore the urgent need for advanced, predictive mechanisms capable of anticipating disruptions and enabling proactive mitigation. This paper investigates the design and application of artificial intelligence (AI)–driven predictive analytics systems to enhance supply chain resilience. It examines how machine learning, real-time data analytics, and natural language processing (NLP) can be integrated to forecast potential disruptions and support timely decision-making. Building on a comprehensive review of existing literature, the study highlights the evolving role of predictive analytics in supply chain risk management and identifies gaps in current approaches. The proposed framework leverages diverse data sources, including historical supply chain records, weather data, economic indicators, geopolitical developments, and unstructured textual data from news and social media. Multiple machine learning techniques—such as time-series forecasting, classification models, anomaly detection, and NLP—are employed to detect early warning signals and assess disruption severity. Model performance is evaluated using standard metrics to ensure robustness and reliability. Two case studies, focusing on natural disasters and geopolitical risks, demonstrate the practical value of the framework. The findings confirm that AI-driven predictive analytics can significantly improve supply chain resilience by enabling early intervention, risk mitigation, and continuity of operations, while also highlighting implementation challenges and future research directions.