Disrupting Maintenance Practices for Enhancing Sustainable Organizational Performance: Examining Digital Twin's Potential

Abstract

Digital Twin (DT) technology has emerged as a transformative approach in enhancing maintenance practices across diverse industries. By creating a virtual replica of physical systems, DT enables real-time monitoring, analysis, and predictive capabilities, fostering improved decision-making and operational efficiency. This study explores the application of DT in maintenance practices, focusing on its role in predictive maintenance, fault diagnosis, and lifecycle management. Leveraging advanced data analytics, machine learning, and IoT, the research demonstrates how DT can optimize maintenance schedules, reduce downtime, and enhance the reliability of critical systems. A comprehensive case study was presented, detailing the integration of DT in a high-maintenance industrial setup, analyzing its impact on system performance and cost-efficiency. The findings reveal that DT not only improves fault detection accuracy but also enables proactive interventions, extending asset lifespan and minimizing operational disruptions. Challenges such as data security, interoperability, and the high initial cost of DT implementation are also discussed, providing a balanced perspective on its adoption. This research underscores the potential of DT as a cornerstone technology in modern maintenance paradigms, bridging the gap between physical assets and digital intelligence. Future work aims to explore scalability and integration with emerging technologies like artificial intelligence and blockchain to further enhance DT capabilities.