Optimizing Cold Chain Logistics with Machine Learning to Ensure Temperature Integrity

Abstract

Cold chain logistics is critical for maintaining the safety and efficacy of temperature-sensitive products such as vaccines, pharmaceuticals, and perishable foods. However, the complexity of transportation routes, environmental variability, and real-time monitoring challenges often lead to temperature excursions that compromise product integrity. This paper proposes a machine learning (ML)-driven framework to optimize cold chain logistics by predicting temperature risks, dynamically adjusting routes, and ensuring real-time anomaly detection. By integrating supervised learning for predictive modeling and unsupervised learning for anomaly detection, the framework enhances the responsiveness and reliability of cold chain operations. Evaluations on historical logistics datasets demonstrate improved temperature compliance, reduced spoilage, and increased delivery efficiency, highlighting the potential of ML in transforming cold chain management.