Analisis Proses Perawatan Preventif Beberapa Komponen dalam Sebuah Mesin dan Pengaruhnya terhadap Kapasitas Produksi (Studi Kasus: Mesin Kiln Putar di Industri Ubin Keramik) - David Andrian (Universitas Sarjanawiyata Tamansiswa, 2026)
Effective preventive maintenance is essential for maintaining machine availability and improving productivity in manufacturing industries. This study aims to analyze preventive maintenance scheduling strategies for multiple components within a single machine and evaluate their impact on downtime and production capacity in rotary kiln machines used in the ceramic tile industry. A quantitative approach was employed using four rotary kiln machines in a ceramic tile manufacturing company located in Gresik, East Java, Indonesia. The analysis was conducted by developing a cost-based model that compares separate and grouped maintenance schedules based on optimal maintenance intervals, opportunity costs, schedule shifting costs, and component failure risks. The results indicate that the optimal maintenance strategy varies among machines. For Kiln IA, Kiln IB, and Kiln IIB, separate maintenance scheduling for each component resulted in lower total maintenance costs, whereas for Kiln IIA, grouping the maintenance schedules of the roll, bearing, and burner produced the minimum total cost of IDR 18,640,607. These findings suggest that grouped maintenance is feasible when the optimal maintenance intervals of different components are relatively close, thereby reducing the frequency of machine downtime. The proposed preventive maintenance schedules also reduced annual downtime by 28.62–77.02%, with the highest efficiency achieved by Kiln IIA at 77.02%. The study demonstrates that the effectiveness of grouped maintenance scheduling is strongly influenced by the proximity of optimal maintenance intervals and the balance between opportunity costs and schedule shifting costs. Therefore, an integrated cost-based preventive maintenance scheduling approach can serve as a decision-making tool for improving machine availability and production capacity.