This analysis reveals a 75% reduction in downtime in a 1600-ton press machine, suggesting enhanced maintenance strategies. Key metrics like MTBF and KPI highlight significant performance improvements.
This study presents a comprehensive analysis of the reliability and maintenance performance of a 1600-ton press machine in refractory production using an innovative inverse KPI model. Key metrics—including Mean Time Between Failures (MTBF), Mean Time To Repair (MTTR), a specially formulated Key Performance Indicator (KPI), and total downtime—were systematically evaluated over a 24-month observation period (June 2023-May 2025). The methodology involved the meticulous collection of monthly operational data, failure events, and repair logs. Time-series analysis was employed to identify trends and correlations between the implemented proactive maintenance strategies and equipment performance metrics. The inverse KPI, defined as KPI = (MTTR / MTBF) × 100, proved to be a highly effective tool for performance tracking. The results demonstrate a remarkable 75% reduction in total downtime by mid-2024, correlating directly with a significant increase in MTBF and a decrease in the KPI value. A temporary performance anomaly in early 2025 was investigated and linked to an unforeseen component failure, highlighting the importance of continuous monitoring. The findings conclusively demonstrate the efficacy of a data-driven, proactive maintenance approach, providing a practical and transferable framework for enhancing industrial asset management. This study underscores the substantial benefits of applying systematic reliability engineering principles to optimize performance in traditional industrial settings.
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Pejman Moemenishahraki (2025) studied this question.
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