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Performance measurement is a fundamental instrument of management. For maintenance management, one of the key issues is to ensure the maintenance activities planned and executed have given the expected results. This can be facilitated by effective use of rigorously defined key performance indicators (KPI) that are able to measure important aspects of maintenance function. In this paper, an industrial survey was carried out to explore the use of performance measurement in maintenance management. Based on survey responses, analyses were performed on popularly used KPI's, how these KPI's are sourced or chosen; the influence of manufacturing environment and maintenance objectives on KPI choice and effective use of these KPI's in decision support and performance improvement. It was found that maintenance performance measurement is dominated by lagging indicators (equipment, maintenance cost and safety performance). There is lesser use of leading (maintenance work process) indicators. The results showed no direct correlations between the maintenance objectives pursued and the KPI used. Further analysis showed that only a minority of the companies have high percentage of decisions and changes triggered by KPI use and only a few are satisfied with their performance measurement systems. Correlation analysis showed a strong positive linear relationship between degree of satisfaction and process changes/decisions triggered by KPI use, with the least satisfied people having the least decisions and changes triggered by KPI use. The results indicates some ineffectiveness of performance measurement systems in driving performance improvement in industries.

Due to widespread automation and the high capital tied up in production equipment, the importance of maintenance is ever increasing. This makes maintenance an investment opportunity to be optimized, not a cost to be minimized. Academics have recognized this and many maintenance optimization models have been published over the years. Most of these models focus on one optimization criterion or objective, making multi-objective optimization models an underexplored area of maintenance optimization. Moreover, there is a big gap between academic models and application in practice. It is very difficult for industrial companies to adapt these models to their specific business context. This article reviews the literature on maintenance optimization models, with special focus on the optimization criteria and objectives used. To overcome flaws in present optimization models, a generic classification framework of maintenance optimization models is presented. All factors that have an influence on the optimization model will be made explicit and their links will be established. The framework is a starting point to develop business specific optimization models and enables decision making in e-maintenance. Moreover, it ensures a fit between the business model of a company and the maintenance optimization model right from the beginning. Future research will be on the development of a maintenance optimization model taking into account the most relevant optimization influence factors and criteria for a situation at hand.

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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.