Performance Indicators for Spare Parts and Maintenance Management: An Analytical Study

What is it about?

A properly implemented maintenance management system has an impact at different levels. Maintenance is defined as the set of actions to maintain a property in a specified state. The unavailability of the spare parts required, to carry out the maintenance intervention, causes an extension of the inactivity time of the installation. On the contrary, an excessive stock of spare parts confines enormous capital and entails an enormous cost of ownership. According to the literature already made, we have directed in our work to propose a model of joint management of maintenance and spare parts based on stochastic-deterministic batch Petri networks. We studied this model by simulation using a graphical interface dedicated to the graphical tool used. So, we present, in this paper, the analytical study of the model by defining the performance indicators and viewing the influence of system parameters on these indicators. The main stages of the analytical study are developing the μ-marking graph, the associated Markov process which gives the associated transition matrix, and the definition of performance indicators using the probability distribution of the states. We deal with an application of the analytical evaluation of the proposed model. We end this article with an analysis and synthesis.

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Photo by Alina Grubnyak on Unsplash

Photo by Alina Grubnyak on Unsplash

Why is it important?

In this paper, we have treated the performance evaluation of joint management of spare parts and maintenance through indicators that are developed from our model presented in our previous works. The indicators are evaluated in the form of a presentation of the rate effects (model parameters) on the proposed indicators. This presentation is made in the form of cases by varying two rates that we want to see their effects on the indicators, and the other parameters are fixed. From this analytical study, we were able to determine the parameters that have a great influence on our considered model. Among these parameters, the demand rate influences all the indicators. Preventive maintenance improves joint management. To avoid and eliminate the risks, it is necessary to intervene on the delay rate. Of course, maintenance management is always linked to production in addition to the spare parts’ management. Corrective maintenance will take place when the asset is stopped (broken down). For preventive maintenance, interventions with production must be well planned.

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