Maintenance is essential for the sustainable use of heavy service equipment in both the government and private sectors. Therefore, there is a need to adopt an effective and efficient maintenance management system to ensure that machines and equipment in service departments operate in optimal conditions, thus achieving user satisfaction. This research aims to determine the optimal time for the replacement of various heavy service machines and equipment within a particular private company operating in diverse operational environments. For this purpose, an integrated model covering all maintenance expenses and costs was developed and implemented using actual data records. A neural network model was developed to predict the optimal equipment replacement time. The results of this research are expected to guide engineers and shop managers in determining when machinery and equipment in private companies should be replaced.

The primary motivation behind this research was the ignorance and misconceptions of many business owners regarding the optimal time to replace equipment. Hence, this research serves as an excellent guide for both the public and private sectors in determining the critical replacement point. One advantage of this research is that the replacement time is flexible, as it depends on the equipment’s operating data and conditions.