Digital Twin-Based Prediction and Optimization for Dynamic Supply Chain Management

Manufacturing supply chains are becoming increasingly complex due to geopolitical issues, globalization, and market demand uncertainties. These challenges lead to logistics disruptions, inventory shortages, and interruptions in raw materials and spare parts production, resulting in delayed delivery, reduced market share, and lower customer satisfaction. Effective supply chain management is critical for improving operational efficiency and competitiveness. This paper proposes a supply chain digital twin methodology to enhance operational efficiency through real-time monitoring, analysis, and response to disruptions. This methodology defines a supply chain digital twin system architecture and outlines the operational process of digital twin applications. It introduces two key modules: a digital twin module for prediction and monitoring and an optimization module for determining the optimal movement of products. These modules are integrated to align digital simulations with real-world supply chain operations. The proposed approach is validated through a case study of an automobile body production company’s supply chain, demonstrating its effectiveness in reducing inventory and logistics costs while providing countermeasures for abnormal situations.