Early Detection of Food Safety Hazards in Global Supply Chains Using Predictive Analytics

Abstract

The globalization of food supply chains has introduced complex challenges for ensuring food safety across production, transport, and distribution systems. Delays in identifying contamination events can lead to large-scale public health crises and significant economic losses. This paper explores a predictive analytics framework for the early detection of food safety hazards within global supply chains. By integrating historical inspection data, environmental metrics, and transport conditions with machine learning algorithms, the system identifies high-risk nodes and anticipates potential outbreaks before they occur. Results demonstrate that the proposed model achieves high accuracy and lead time advantages in predicting microbial and chemical hazard risks. This approach provides actionable insights for regulators, manufacturers, and logistics providers, enabling timely interventions and enhancing consumer safety across borders.