Youngky Odies S (1), Atikha Sidhi Cahyana (2)
General Background Manufacturing continuity in continuous liquid production systems relies heavily on machinery reliability and structured maintenance management. Specific Background PT X faced severe operational disruptions due to high downtime concentrations on the Vacuum Feeder Line 4 machine, reflecting a reactive corrective maintenance approach. Knowledge Gap Although periodic maintenance theories are well established, empirical integration combining Pareto priority analysis, reliability calculations, and software-based scheduling simulations for specific critical liquid-production lines remains limited. Aims This study aims to design a structured preventive maintenance schedule to minimize downtime and improve machine reliability on the Vacuum Feeder Line 4. Results Quantitative analysis of historical downtime data from May 30 to July 1, 2025, revealed a Mean Time Between Failure of 240 hours, a Mean Time To Repair of 345 minutes, and an initial availability of 97.7%, establishing an optimal 8-day preventive maintenance interval that successfully reduced total downtime from 1,036 minutes to 585 minutes (a 43.5% decrease) and increased availability to 98.6%. Novelty This research demonstrates that an 80% Mean Time Between Failure threshold approach integrated with Microsoft Project scheduling effectively transitions reactive maintenance into a proactive reliability-centered strategy. Implications Implementing this structured scheduling framework optimizes plant maintenance resources, reduces unexpected production stoppages, and enhances overall operational efficiency for similar manufacturing facilities.
Key Findings Highlights
Pareto analysis identifies Vacuum Feeder Line 4 as the primary critical contributor, accounting for 52.2% of total production downtime.
Reliability metrics establish a Mean Time Between Failure of 240 hours, setting an optimal preventive maintenance interval of every 8 operating days.
Simulation results indicate a significant 43.5% reduction in total downtime along with an increase in machine availability to 98.6%.
Keywords Downtime, Preventive Maintenance, Pareto Analysis, MTBF, Microsoft Project
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