Digital twin-based predictive maintenance in cold chain logistics

In this study, an innovative framework based on digital twin for predictive maintenance in the cold supply chain is presented. By combining real-time data from refrigeration equipment, multi-objective modeling, and intelligent decision-making, this framework enables equipment degradation prediction and dynamic planning for repairs. By continuously linking the real and virtual space, the system is able to adaptively reduce maintenance costs and failure risk and maintain the quality of temperature-sensitive products. The simulation environment developed on the Simulink platform has compared three main situations to evaluate the performance of this model, including a chain without digital twin, a basic digital twin, and a learning digital twin. The results show that using the proposed framework significantly reduces failure rates, economic savings, and operational stability. By providing an intelligent and generalizable model, this research is a new step towards the development of self-learning and decision-support systems in complex and uncertain cold chain environments.