Within Industry 4.0, the concept of digital twins gains significant attention globally as organizations explore their potential in various industries worldwide. International research and development efforts focus on leveraging digital twins to monitor and optimize processes and products' behavior and performance. When applied to automation systems, digital twins primarily support virtual commissioning, enabling the testing of virtual environments before actual processes become operational. However, a research gap exists concerning the utilization of digital twins in maintenance operations post-commissioning. This thesis aims to fill this gap and contribute to the knowledge of how the Real Digital Twin (RDT) can be used to maintain the automation process at Ringhals nuclear power plant. The research methodology involves interviewing maintenance workers to gain insight into their Way-of-Working (WoW) and identify key challenges and requirements. Based on these interviews, various maintenance cases are constructed and subjected to Failure Mode and Effects Analysis (FMEA), a proven method for identifying and evaluating potential failures. The cases are then compared between using and not using RDT to assess its impact on maintenance operations. Emulation is also employed to practically demonstrate the implementation of RDT. The findings highlight the significant benefits of RDT in maintenance operations at Ringhals. RDT enables swift and efficient testing of different operating strategies, leading to the identification of energy-efficient solutions and a reduction in downtime. The comparison of FMEA results demonstrates that RDT enhances the ability to proactively identify and address potential failures, reducing the likelihood of disruptions and ensuring safe and efficient operations. Overall, this thesis showcases the potential of RDT in maintenance operations beyond virtual commissioning, filling the research gap. Integrating RDT into the maintenance workers' Way-of-Working revolutionizes maintenance practices, enhancing efficiency, minimizing downtime, and improving overall operational performance. These findings contribute to the evolving field of Industry 4.0 and digital twin technologies, offering valuable insights for organizations seeking to optimize their maintenance processes and leverage the full potential of digital twins.