Optimizing life cycle cost of complex machinery with rotable modules using simulation

Abstract

Purpose Maintenance is constantly challenged to increase productivity by maximizing up‐time and reliability while at the same time reducing maintenance expenditure and investment. Traditional reliability models are based on detailed statistical analysis of individual component failures. For complex machinery, especially involving many rotable parts, such analyses are difficult and time‐consuming. This paper aims to propose an alternative method for estimating and improving reliability. Design/methodology/approach The methodology is based on simulating the up‐time of the machine or process as a series of critical modules. Each module is characterized by an empirically derived failure distribution. The simulation model consists of a number of stages including operational up‐time, maintenance down‐time and a user‐interface allowing maintenance and replacement decisions. Findings Initial analysis performed on aircraft gas‐turbine data yielded an optimal combination of modules out of a pool of multiple spares, resulting in an increased machine up‐time of 16 percent. Practical implications The benefits of this methodology are that it is capable of providing reliability trends and forecasts in a short time frame and is based on available data. In addition, it takes into account the rotable nature of many components by tracking the life and service history of individual parts and allowing the user to simulate different combinations of rotables and operating scenarios. Importantly, as more data become available or as greater accuracy is demanded, the model or database can be updated or expanded, thereby approaching the results obtainable by pure statistical reliability analysis. Originality/value The model presented provides senior maintenance managers with a decision tool that optimizes the life cycle maintenance cost of complex machinery in a short time frame by taking into account the rotable nature of modules.