Industrial facilities frequently experience significant production losses due to unanticipated failures, sub-optimal maintenance, operational and spare parts logistics challenges. These among other factors directly affect the plant's performance measures such as availability, repair time and costs. Consequently, optimization addresses such challenges. However, a fundamental problem presented here relates to the need for a framework that assists in the determination of critical system to be optimized, variables that significantly impact the performance of such systems, and subsequently undertake optimization. To realistically model such complexities, a framework that applies the discrete simulation model of critical repairable subsystems, undergoing deterioration is proposed. The study utilises empirical maintenance data, where Pareto analysis is employed to identify critical subsystems, while expert input is incorporated to derive model variables. A full factorial Design of Experiment (DOE), is employed to establish the variables with significant main and interaction effects on the total repair time and subsequently employed as decision variables for a simulation-based optimization. The proposed framework is demonstrated in a case study of a thermal power plant. Simulation results highlight the turbocharger as the critical subsystem, while spares availability, the time between overhaul (TBO) and reliance on different maintenance strategies exhibit most significant main and interaction effects. The optimization results obtained demonstrate that TBO, spares availability and reliance on various maintenance strategies, provide a significant impact on the reduction of the repair time. The framework enhances maintenance decision making by optimizing the plants’ operational and maintenance related factors identified.