IoT-Integrated Computerized Maintenance Management System (CMMS) for Optimizing Maintenance Efficiency in Smart Manufacturing - Robby Maududy; Alam Alam; Wuslah Raia Maulidiyah; R Reza El Akbar (Universitas Negeri Semarang, 2026)
Purpose: The manufacturing industry continues to face persistent challenges in maintaining equipment reliability and maintenance efficiency, particularly in scheduling, spare parts control, and real-time monitoring of machine conditions. This study aims to develop an Internet of Things (IoT)-based Computerized Maintenance Management System (CMMS) to improve maintenance effectiveness, minimize equipment downtime, and support the realization of smart manufacturing in the automotive sector. Methods: The system was developed using the ADDIE model, consisting of analysis, design, development, Implementation, and Evaluation. and was integrated with IoT sensors to acquire real-time machine temperature and operational status data. and integrated with IoT sensors to collect real-time data on machine temperature and operational status. The collected data were processed in a centralized database and presented through a web-based CMMS application comprising work order management, preventive and corrective maintenance, inventory control, and analytical reporting modules. System functionality was validated using black-box testing, while performance evaluation was conducted by comparing Mean Time Between Failures (MTBF), Mean Time to Repair (MTTR), and maintenance efficiency before and after system implementation. Result: The results indicate that all evaluated equipment experienced performance improvements following CMMS implementation, characterized by increased MTBF and reduced MTTR. On average, overall maintenance efficiency increased by approximately 368%, demonstrating significant reductions in downtime and improvements in maintenance responsiveness supported by real-time condition data. Novelty: The novelty of this study lies in the integration of IoT technology into CMMS that emphasizes the utilization of real-time machine condition data not only for monitoring purposes but also to support maintenance planning, work order management, and data-driven decision-making within a single application. The findings provide empirical evidence that effective data utilization strategies within CMMS implementations can significantly enhance maintenance efficiency and support smart maintenance practices aligned with Industry 4.0 principles.