Nidhal Rezg

By Ayoub Chakroun and Nidhal Rezg

Flexible workshops are essential components of modern industry, enabling flexible and efficient production. However, to ensure their proper functioning and prevent unexpected breakdowns, it is crucial to monitor their reliability. Production stoppages caused by unforeseen breakdowns can lead to significant financial losses. This chapter proposes to explore the use of Machine Learning (ML) for predicting the reliability of flexible workshops, thus identifying dates for Preventive Maintenance (PM) interventions and optimizing production management. The objectives of this exploration include the presentation of new predictive model developments and the description of ML models capable of predicting workshop reliability based on real-time data, such as equipment monitoring, production data, and maintenance histories. It also aims to identify optimal times for PM interventions, minimizing production disruptions and optimizing resource utilization. Additionally, the chapter will propose cost optimization models to prevent unplanned breakdowns, extend equipment lifespan, optimize spare parts usage, and maximize productivity by avoiding production interruptions and ensuring the smooth operation of the flexible workshop.

Part of the book: Advances in Logistics Engineering [Working title]