AI-Driven Risk Forecasting for Strengthening the United States Food Supply Chain Resilience: Case Study: A National AI-Enabled Food Supply Chain Risk Forecasting Framework and a Data Analytics Approach to Predicting Disruptions

The United States food supply chain is one of the most complex and interconnected systems in the world, spanning agricultural production, processing, transportation, storage, and retail distribution. While this complexity enables efficiency and scale, it also increases vulnerability to disruptions caused by climate change, labor shortages, geopolitical shocks, transportation failures, cyber threats, and public health crises. Conventional risk management methods, often reactive and siloed, have proven inadequate in predicting and mitigating systemic shocks that have become frequent occurrences in today’s world. This article examines how artificial intelligence (AI) and data analytics can transform risk forecasting in U.S. food supply chains by enabling real-time adaptive, predictive, and prescriptive decision-making. Leveraging machine learning, predictive analytics, and integrated data ecosystems, the paper examines the various stages of the food supply chain, key drivers of disruption, analytical models, data sources, and the benefits of AI-driven risk forecasting. The study concludes that AI-driven risk forecasting offers a powerful pathway toward building a more resilient, transparent, and sustainable U.S. food system.