Abstract: Production companies aim to achieve several strategic objectives to remain competitive in the market, including increased productivity, improved profitability, and reduced operational costs. One effective approach to reducing operational costs is implementing a preventive maintenance optimization. This study explores three optimization strategies for reducing maintenance costs: the age replacement model, optimal inspection model, and optimization via Design of Experiment (DOE). The first strategy involves simulating the equipment's aging process and scheduling replacement when it reaches a certain age or usage level. In the second strategy, the goal is to identify the optimal timing for maintenance inspections, which minimizes costs while ensuring equipment reliability. The third strategy involves developing a mathematical model to minimize the expected cost by using ANOVA analysis and response surface methodology to identify the optimal parameter values. The study assesses the efficiency of the three optimization strategies in reducing costs, using the Weibull distribution function as the basis for cost reduction evaluation. The results indicate that shape parameter and replacement time significantly impact the expected cost. Hence, production companies can utilize these findings to determine the most efficient and cost-effective preventive maintenance strategy for their equipment.

Key words: preventive maintenance, optimization, design of experiment, age replacement, inspection model