Abstract
Recently, the Predictive Maintenance (PdM) concept has gotten increasing attention in industrial practices and academic research. It is often used with real-time data to monitor the health status, or in the best case, estimate the Remaining Useful Life (RUL) of certain components. Apart from the technical and economic challenges of data acquisition and RUL calculation, estimating RUL is not the final goal in a maintenance management system. As in industry, there are other types of maintenance activities, industrial constraints, and complexities that should be managed together to define overall maintenance planning. In this paper, a simultaneous PdM and Preventive Maintenance (PvM) planning problem of multi-machine and multi-component systems is studied. In this context, a mathematical programming model is proposed to minimize direct and indirect maintenance costs, considering opportunistic grouping of maintenance activities, unused life losses of spare parts, and breakdown costs related to failure risk. To validate the proposed method, a case study from the automotive industry (FPT Powertrain Technologies) is used, and a comprehensive sensitivity analysis is provided. The results indicate that consideration of the mentioned aspects could significantly impact maintenance planning and overall maintenance costs. Finally, the applicability of the proposed approach is discussed in managerial insights.
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