Global supply chains have become increasingly vulnerable to systemic risks such as pandemics, geopolitical conflicts, and cyberattacks. Traditional supply chain risk management approaches often rely on reactive strategies and lack predictive capabilities to address cascading disruptions. This paper examines how Digital Twin technology, defined as a virtual representation of physical supply chain systems using real-time data, can enhance resilience through risk mapping, scenario planning, and stress testing. By integrating Artificial Intelligence (AI), Internet of Things (IoT), blockchain technologies, and predictive analytics, firms can simulate disruption scenarios and evaluate supply chain robustness using resilience metrics such as Time to Recover (TTR) and Time to Survive (TTS). The study develops a simulation-based framework that models supply chain performance under disruption conditions using exponential recovery functions. Empirical analysis utilizing port traffic and pandemic logistics datasets demonstrates the effectiveness of Digital Twin-based simulations in identifying resilience gaps across industries. Comparative statistical analysis highlights differences in recovery capabilities among sectors such as automotive, healthcare, electronics, and food supply chains. Furthermore, the paper explores the integration of resilience metrics into global governance frameworks, including international trade and sustainability policies. The findings suggest that Digital Twin–enabled supply chains significantly improve disruption preparedness, response coordination, and recovery efficiency, offering a proactive approach to global risk management.