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Establishment of maintenance inspection intervals: an application of process mining techniques in manufacturing

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  • Edson Ruschel

    (Pontifical Catholic University of Parana)

  • Eduardo Alves Portela Santos

    (Pontifical Catholic University of Parana)

  • Eduardo de Freitas Rocha Loures

    (Pontifical Catholic University of Parana)

Abstract

Reducing costs and increasing equipment availability (uptime) are among the main goals of industrial ventures. Well defined interval durations between maintenance inspections provide major support in achieving these targets. However, in order to establish the best interval length, process behavior, cycle times and related costs must be clearly known, and future estimates for these parameters must be established. This paper applies process mining techniques in developing a probabilistic model in Bayesian Networks integrated to predictive models. The probability of a given activity occurring in the probabilistic model output establishes the forecast boundaries for predictive models, responsible for estimating process cycle times. Availability (uptime) and cost functions are mathematically defined and an iterative process is performed in the length of intervals between maintenance inspections until the time and costs wasted are minimized and the best interval duration is found. The probabilistic model enables simulating changes in the event occurrence probability, allowing a number of different scenarios to be visualized and providing better support to managers in scheduling maintenance activities. The results show that production losses can be further reduced through optimally defined intervals between maintenance inspections.

Suggested Citation

  • Edson Ruschel & Eduardo Alves Portela Santos & Eduardo de Freitas Rocha Loures, 2020. "Establishment of maintenance inspection intervals: an application of process mining techniques in manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 31(1), pages 53-72, January.
    • Handle: RePEc:spr:joinma:v:31:y:2020:i:1:d:10.1007_s10845-018-1434-7
    DOI: 10.1007/s10845-018-1434-7
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    References listed on IDEAS

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    1. Zerbino, Pierluigi & Stefanini, Alessandro & Aloini, Davide, 2021. "Process Science in Action: A Literature Review on Process Mining in Business Management," Technological Forecasting and Social Change, Elsevier, vol. 172(C).

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