Optimization of Preventive Replacement of Critical Components in the Komatsu PC2000-8 Excavator to Minimize Downtime at PT XYZ
Downloads
The availability of primary loading equipment, particularly the Komatsu PC2000-8 excavator, is a key determinant of operational efficiency in open-pit mining. Failures of critical components directly affect downtime, maintenance costs, and key performance indicators, including physical availability (PA), mean time between failures (MTBF), and mean time to repair (MTTR). Current preventive maintenance practices, which largely follow generic original equipment manufacturer (OEM) replacement intervals, often fail to prevent actual breakdowns, highlighting the need to optimize preventive replacement intervals based on cost and downtime minimization. This study aims to determine the optimal preventive replacement intervals for the critical components of the Komatsu PC2000-8 excavator to minimize downtime and maintenance costs while improving equipment availability. The study employed a quantitative comparative optimization approach. Critical components were identified using a Pareto diagram based on failure frequency and cumulative downtime. Reliability analysis was performed by selecting the most appropriate time-between-failures distribution using Relyence Weibull software to estimate the distribution parameters. Reliability, hazard rate, MTBF, and cost analyses comparing preventive and corrective replacement—including spare parts, labor, supporting equipment, and lost production or contractual implications—were subsequently performed. An Age Replacement policy was then optimized to determine replacement intervals that minimized the long-run average cost per unit time while satisfying equipment availability constraints. The results indicated that the optimal preventive replacement intervals based on cost minimization were 11,545 hours for the Boom Cylinder, 16,820 hours for the Turbocharger, 7,366 hours for the Water Pump, 20,026 hours for the Final Drive, and 19,624 hours for the Radiator.
Alade, B., Ocheni, E., Onyekachukwu, M., & Olufinmilayo, S. (2024). Big data for predictive maintenance in Industry 4.0: Enhancing operational efficiency and equipment reliability. International Journal of Computer Application Technology Research, 13(10), 37–51.
Answary, A., & Waluyo, B. (2024). Age replacement model for preventive maintenance of finish water pumps: A case study in regional drinking water companies (PDAM). Borobudur Engineering Review, 4(1), 38–55.
Burhannudin, M., & Anshori, M. (2022). Implementasi reliability centered maintenance pada excavator PC-800. JISO: Journal of Industrial and Systems Optimization, 5(2), 143–150.
Karikari, Y. S. (2026). A validated simulation framework for fuel and operational performance analysis in truck-shovel mining systems.
Kim, H., Kim, J., An, B., Song, T., Oh, J., Kim, M., & Lee, S. (2025). The influence of real-time feedback on excavator operator actions in footing excavation: Machine guidance and conventional methods. Applied Sciences, 15(7), 3729.
Kononis, E. P. A., Irawati, D. Y., & Andrian, D. (2026). MTTR, MTBF, and OEE analysis of a blown film extrusion machine for preventive maintenance optimization. Heuristic.
Levious, S. L. (2024). Investigating the flow of information in a surface iron ore mining operation (Master's thesis, University of the Witwatersrand).
Lin, Z. (2026). Open pit loading. In The ECPH encyclopedia of mining and metallurgy (pp. 1477–1480). Springer.
Liu, S. Q., Liu, L., Kozan, E., Corry, P., Masoud, M., Chung, S., & Li, X. (2025). Machine learning for open-pit mining: A systematic review. International Journal of Mining, Reclamation and Environment, 39(1), 1–39.
Liu, W., Luo, X., Zhang, J., Niu, D., Deng, J., Sun, W., & Kang, J. (2022). Review on control systems and control strategies for excavators. Journal of Physics: Conference Series, 2301(1), 012023.
Marinagi, C., Reklitis, P., Trivellas, P., & Sakas, D. (2023). The impact of Industry 4.0 technologies on key performance indicators for a resilient supply chain 4.0. Sustainability, 15(6), 5185.
Moemenishahraki, P. (2025). Reliability and maintenance performance analysis of a 1600-ton press machine using MTBF, MTTR, KPI, and downtime indicators. Industrial Engineering, 9(2), 36–41.
Molaei, A., Kolu, A., Lahtinen, K., & Geimer, M. (2023). Automatic estimation of excavator actual and relative cycle times in loading operations. Automation in Construction, 156, 105080.
Mustofa, F. H., Utomo, R. F., & Soemadi, K. (2018). Filling machine preventive maintenance using age replacement method in PT Lucas Djaja. MATEC Web of Conferences, 154, 01056. https://doi.org/10.1051/matecconf/201815401056
Prabowo, S. A., & Baskoro, G. (2026). Optimizing excavator physical availability using Lean Six Sigma and predictive inventory management: A case study of PC1250 at Samarinda coal mine. ILTEK: Jurnal Teknologi, 21(1), 342–348.
Rediske, G., Michels, L., Siluk, J. C. M., Rigo, P. D., Rosa, C. B., & Bortolini, R. J. F. (2022). Management of operation and maintenance practices in photovoltaic plants: Key performance indicators. International Journal of Energy Research, 46(6), 7118–7136.
Sembiring, N., Tambunan, M., & Devany, J. (2021). Design of preventive maintenance system at PT. Y with reliability engineering approach. IOP Conference Series: Materials Science and Engineering, 1122(1), 012042. https://doi.org/10.1088/1757-899X/1122/1/012042
Silva, A. F., Santos, E. C. S., Luz, R. M. do N., & Fernandes, R. da S. (2023). Analysis of preventive maintenance strategy in off-road trucks. Gestão & Produção, 30. https://doi.org/10.1590/1806-9649-2023v30e5923
Souifi, A., Boulanger, Z. C., Zolghadri, M., Barkallah, M., & Haddar, M. (2022). Uncertainty of key performance indicators for Industry 4.0: A methodology based on the theory of belief functions. Computers in Industry, 140, 103666.
Tasoglu, G., Senol, M. E., Ozfirat, P. M., & Ozfirat, M. K. (2026). Research in underground mining methods considering rock mechanics, multi-criteria decision making, work safety, and economic analysis. Mining, Metallurgy & Exploration, 1–28.
Ulugbek, F., Sheng, B., Xiao, Z., & Ismael, T. (2018). A reliability-based preventive maintenance methodology for the projection spot welding machine. Management Science Letters, 8, 497–506. https://doi.org/10.5267/j.msl.2018.5.005
Wang, K., Deng, C., & Ding, L. (2020). Optimal condition-based maintenance strategy for multi-component systems under degradation failures. Energies, 13(17), 4346. https://doi.org/10.3390/en13174346
Copyright (c) 2026 Imam Nur Soleh, Mokh Suef

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.



