Digital Twins in Supply Chains: Research Hotspots and Future Trajectories

Abstract

Digital twins (DT) transform supply chain management by enabling real-time monitoring, simulation, and enhanced decision-making capabilities. However, the research landscape in this domain remains nascent and fragmented. This study conducts a bibliometric analysis to identify research hotspots and future trajectories in applying DT technology within supply chains, utilising 1,700 scholarly publications indexed in the Web of Science database from 2020 to 2024. The analysis was performed using R programming and the Bibliometrix package, complemented by the Biblioshiny graphical interface. The findings reveal that DT research is primarily concentrated in developed economies, with five critical themes emerging: predictive analytics, supply chain resilience, sustainability, operational efficiency, and integration with emerging technologies. The convergence with AI, blockchain, and the Internet of Things positions DT as a pivotal tool for enhancing transparency, traceability, and dynamic modelling of supply chain networks. This study underscores DT's transformative potential in creating resilient, efficient, and sustainable supply chains. Furthermore, the study maps the current research landscape, identifies knowledge gaps, and highlights The study further maps the current research landscape, identifies knowledge gaps, and underscores the importance of policy support to drive DT adoption. This analysis provides a robust foundation for academics, practitioners, and policymakers to innovate within this critical field.