Abstract
In recent years, the mobile phone manufacturing industry has advanced rapidly in over the world, led by major technology firms such as Samsung Electronics. This study employs Data Envelopment Analysis (DEA) together with Grey theory to build a Total Productive Maintenance (TPM) framework integrated with an Autonomous Maintenance strategy for optimizing the mobile phone production system. The model enables systematic evaluation, monitoring and forecasting of performance at each production stage, allowing managers to detect bottlenecks and prioritize timely improvements that raise operational efficiency and reduce unexpected downtime. Results indicate that the integrated approach delivers stable gains in productivity while minimizing waste, defects and idle time across the line. Data-driven insights highlight practical solutions in equipment maintenance, workforce utilization, material flow, quality control and process optimization. Implementing Autonomous Maintenance also strengthens operator responsibility, improves technical skills, enhances teamwork and sustains consistent product quality over long production cycles. Beyond factory performance, the framework supports long-term competitiveness, cost reduction, operational resilience and sustainable development in the mobile phone sector. Overall, this research demonstrates how combining DEA and Grey analysis within TPM provides a reliable pathway for continuous improvement, smarter decision-making and meaningful economic contribution at both national and global levels, strongly supporting industrial modernization worldwide.
Keywords: Autonomous Maintenance, DEA, Grey Forecasting, Malmquist Index, Production Efficiency, TPM.