Geopolitical Risk Simulation Using AI-Powered Digital Twin Systems in Global Logistics: A Critical Literature Review

The convergence of artificial intelligence, digital twin technology, and geopolitical risk assessment represents a transformative paradigm in global logistics management. This critical literature review systematically examines the integration of AI-powered digital twin systems for simulating geopolitical risks in supply chain operations, synthesizing 142 peer-reviewed academic papers and 20 industry reports published between 2019 and 2026. The review reveals that while digital twin technology has matured significantly in logistics applications, its integration with AI-driven geopolitical risk simulation remains nascent and fragmented. Key findings indicate that AI-powered digital twins enhance supply chain resilience through real-time monitoring, predictive analytics, and scenario simulation capabilities, with reinforcement learning and deep learning architectures demonstrating particular efficacy. However, critical gaps persist in methodological rigor, empirical validation, data integration frameworks, and theoretical grounding. The review identifies fundamental tensions between model complexity and interpretability, challenges in incorporating exogenous geopolitical signals into predictive frameworks, and limitations in cross-border data harmonization. Future research directions emphasize the need for standardized architectures, multi-modal risk integration frameworks, and longitudinal empirical studies that validate digital twin efficacy under actual geopolitical disruptio