How digital twin technology may improve safety management: A multi-industry perspective

Abstract

Digital twin (DT) technology, which integrates physical and virtual systems through continuous interaction, enables real-time monitoring, predictive analysis and informed decision-making. This innovative approach is emerging as a transformative method to enhance safety management in high-risk industries. This study identifies key enabling technologies critical to the implementation of digital twin, including virtual modeling, machine learning, Internet of Things (IoT), computer vision, extended reality and robotics. Together, these technologies provide a wide range of functions to improve safety management, including safety simulation analysis, safety state monitoring, predictive safety maintenance, safety risk assessment and safety performance optimization. The research also proposes an application framework for digital twin technology to advance its development and implementation. In addition, this study explores the challenges of application of digital twin technology in the field of safety management, which includes (1) data integration and sharing; (2) integration of different technologies; (3) digital technology versus human centric principle. The paper suggests that future research should focus on: (1) developing verifiable and adaptive functionalities; (2) optimizing technological interoperability; (3) implementing human-centered principle. A unique contribution of this work is its detailed analysis of the research, development and application of digital twin technology for safety management across the five high-risk industries.