Methodological Framework for Optimizing Inspection Frequency in a Preventive Maintenance Policy

Determining the optimal inspection frequency is a significant challenge, as it requires balancing frequent inspections, which increase operational costs, with infrequent inspections, which can lead to unexpected failures. This paper presents a stochastic simulation model that captures the uncertainty in equipment behavior by defining probabilistic parameters such as maintenance costs and the probability of equipment deterioration. The model uses the Weibull distribution to represent the time to failure and Monte Carlo simulations to evaluate different inspection intervals and their associated costs. The optimal inspection frequency is determined by minimizing the total expected cost, which includes inspection, maintenance, and failure costs. Through the expected cost analysis, a global optimum within the discrete interval is identified, revealing that both excessive inspections and too few inspections lead to increased costs. The sensitivity analysis shows that repair time is the most influential variable, and the CVaR analysis highlights the importance of accurately defining parameters and controlling their fluctuations to manage risk effectively. These results provide valuable insights for decision-making in high-risk and uncertain industrial environments, offering an effective tool for preventive maintenance planning.

Keywords: Stochastic simulation, Preventive maintenance policy, Inspection frequency optimization, Maintenance costs, Risk assessment.