The Adoption of Digital Twin Technology to Facilitate Resource and Asset Tracking in Field Service Management

Despite the Digital Twin (DT) technology being thoroughly researched, its integration into Field Service Management (FSM) for condition monitoring and asset tracking is still unclear. Industry 4.0 technologies, including big data, IoT, analytics, and ML, have altered production by boosting flexibility, connection, decision-making, productivity, and efficiency. However, FSM struggles to manage a variety of resources, requiring imaginative solutions. To reap the benefits of Industry 4.0 and improve field operations, Field Service Management (FSM) must efficiently integrate cyber-physical assets like sensors. DT technology, AI, and ML may provide insights into system operations, component links, and future behaviors. Research emphasizes DT integration with asset management systems to improve asset tracking. This study explores DT (Digital Twin) and FSM (Field Service Management) applications to find condition monitoring and asset tracking issues. The proposed system uses loT devices, data analytics, machine learning, AI, and mobile GIS to improve call scheduling, knowledge management, technician collaboration, predictive maintenance, and route optimization. This comprehensive plan intends to speed up and improve service delivery, improving operational efficiency and customer happiness. The theoretical framework is interesting, but more study is needed to demonstrate its efficacy and scalability. Technological complexity and high costs must be overcome to gain acceptability for the model. This study emphasizes the need for continual innovation and adaptation as digital twin (DTs) technology may transform asset management and predictive maintenance in field service management (FSM).