Analisis Efektivitas Predictive Maintenance dalam Mengoptimalkan Cost Avoidance pada Final Drive Komatsu PC200-8 : Studi Kasus di PT. Antareja Mahada Makmur Site PT. Multi Harapan Utama

Esa Cahya Kartika; Mad Yusup; Purbawati Purbawati; Ida Rosanti; Diyaa Aaisyah Salmaa Putri Atmaja

doi:10.61132/venus.v3i4.1059

Authors

Esa Cahya Kartika Universitas Nahdlatul Ulama Kalimantan Timur
Mad Yusup Universitas Nahdlatul Ulama Kalimantan Timur
Purbawati Purbawati Universitas Nahdlatul Ulama Kalimantan Timur
Ida Rosanti Universitas Nahdlatul Ulama Kalimantan Timur
Diyaa Aaisyah Salmaa Putri Atmaja Universitas Nahdlatul Ulama Kalimantan Timur

DOI:

https://doi.org/10.61132/venus.v3i4.1059

Keywords:

predictive maintenance, cost avoidance, final drive, downtime, mining industry

Abstract

This study analyzes the effectiveness of implementing predictive maintenance (PdM) on the final drive components of the Komatsu PC200-8 unit at PT. Antareja Mahada Makmur, Site PT. Multi Harapan Utama, East Kalimantan, in an effort to reduce downtime and operational losses. Before the implementation of PdM in 2022, there were 12 repair cases for the final drive with a total downtime of 772.1 hours, repair costs amounting to IDR 310.6 million, rental income loss of IDR 208.03 million, and total losses of IDR 518.63 million. In 2023, during the PdM transition phase, the number of cases decreased to 4, with a total loss of IDR 252.05 million, although downtime remained high (714.6 hours) due to the limited scope of PdM implementation on certain units and components. In 2024, with full PdM implementation, the number of repair cases decreased to 5, with total downtime of only 96 hours and losses of IDR 45.75 million. The cost of PdM implementation for the year was only IDR 21.9 million. As of July 2025, no further damage to the final drive has been recorded, demonstrating a significant improvement in equipment reliability. The reduction in total losses from 2022 to 2024 amounted to IDR 472.88 million, indicating PdM’s effectiveness in avoiding significant costs through condition monitoring methods such as oil analysis, magnetic plug rating, thermal inspection, and oil leak testing (floating seal). The findings of this study confirm that PdM is effective in reducing downtime, repair costs, and enhancing asset management in the mining sector. It also improves equipment reliability and overall operational efficiency, proving PdM to be a successful strategy in reducing losses, increasing productivity, and supporting the sustainability of company operations.

References

Al-Najjar, B., & Alsyouf, I. (2003). Selecting the most efficient maintenance approach using fuzzy multiple criteria decision making. International Journal of Production Economics, 84(1), 85-100. https://doi.org/10.1016/S0925-5273(02)00380-8

ASTM International. (2017). ASTM D5185-18: Standard test method for multielement determination of used and unused lubricating oils and base oils by inductively coupled plasma atomic emission spectrometry (ICP-AES). ASTM International.

Ben-Daya, M., Kumar, U., & Murthy, D. N. P. (2016). Introduction to maintenance engineering: Modelling, optimization and management. John Wiley & Sons. https://doi.org/10.1002/9781118926581

Bloch, H. P., & Geitner, F. K. (2012). Machinery failure analysis and troubleshooting: Practical machinery management for process plants (4th ed.). Butterworth-Heinemann. https://doi.org/10.1016/B978-0-12-386045-3.00004-0

Brown, A. (2019). Cost avoidance strategies in heavy equipment maintenance. International Journal of Engineering Management, 12(2), 89-102.

Heng, A., Zhang, S., Tan, A. C. C., & Mathew, J. (2009). Rotating machinery prognostics: State of the art, challenges and opportunities. Mechanical Systems and Signal Processing, 23(3), 724-739. https://doi.org/10.1016/j.ymssp.2008.06.009

ISO. (2016). ISO 17359: Condition monitoring and diagnostics of machines - General guidelines. International Organization for Standardization.

Jardine, A. K. S., Lin, D., & Banjevic, D. (2006). A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mechanical Systems and Signal Processing, 20(7), 1483-1510. https://doi.org/10.1016/j.ymssp.2005.09.012

Johnson, R. (2021). Maintenance strategies for heavy equipment: Preventive vs predictive. Journal of Maintenance Engineering, 18(4), 56-70.

Komatsu Ltd. (2010). Shop manual for Komatsu PC200-8 excavator (SEN00084-10).

Li, W., & He, D. (2012). Rotational machine health monitoring and fault detection using EMD-based acoustic emission feature quantification. IEEE Transactions on Instrumentation and Measurement, 61(4), 990-1001. https://doi.org/10.1109/TIM.2011.2179933

Mobley, R. K. (2002). An introduction to predictive maintenance (2nd ed.). Butterworth-Heinemann. https://doi.org/10.1016/B978-075067531-4/50006-3

Peng, Y., Dong, M., & Zuo, M. J. (2010). Current status of machine prognostics in condition-based maintenance: A review. International Journal of Advanced Manufacturing Technology, 50, 297-313. https://doi.org/10.1007/s00170-009-2482-0

PT. Antareja Mahada Makmur. Profil perusahaan. Diakses pada 4 Februari 2025 dari https://amm.id/tentang-kami/

Sharma, A., Yadava, G. S., & Deshmukh, S. G. (2011). A literature review and future perspectives on maintenance optimization. Journal of Quality in Maintenance Engineering, 17(1), 5-25. https://doi.org/10.1108/13552511111116222

Smith, J. (2018). Predictive maintenance in mining industry: A case study. Journal of Industrial Engineering, 45(3), 123-135.

Hilmawan, R., Clark, A. L., & Hermawan, R. (2017). Policy change in Indonesia's coal sector: Domestic market obligation and its impact. Energy Policy, 108, 563-573. https://doi.org/10.1016/j.enpol.2017.06.015

Kumar, R., & Singh, A. (2021). Failure analysis of final drive system in heavy earth moving machinery. Engineering Failure Analysis, 124, 105394. https://doi.org/10.1016/j.engfailanal.2021.105394

Prasetyo, A., & Ardiansyah, H. (2019). Performance evaluation of hydraulic excavators in mining operations. IOP Conference Series: Earth and Environmental Science, 311(1), 012048. https://doi.org/10.1088/1755-1315/311/1/012048

Putra, D. Y., Nugroho, S., & Yuliani, N. (2022). Reliability analysis of heavy equipment components in mining operations. Journal of Mechanical Engineering and Technology, 14(2), 85-94. https://doi.org/10.14710/jmet.14.2.85-94

Rahman, A., Syahrani, S., & Pratama, B. (2020). Maintenance management of heavy equipment in coal mining industry. International Journal of Advanced Science and Technology, 29(5), 11247-11255.

Suryanto, A., & Hidayat, R. (2023). Preventive maintenance strategy for final drive unit in excavators to reduce downtime. Jurnal Teknologi dan Rekayasa, 18(2), 145-156. https://doi.org/10.33510/jtr.v18i2.145-156

Wibowo, H., Setiawan, B., & Susilo, D. (2021). Cost analysis of heavy equipment downtime in Indonesian mining companies. International Journal of Mining, Reclamation and Environment, 35(7), 495-510. https://doi.org/10.1080/17480930.2021.1882089

World Bank. (2023). Indonesia economic prospects: Navigating coal transition. The World Bank. https://www.worldbank.org

Zhang, Y., Li, X., & Chen, H. (2020). Condition monitoring and predictive maintenance of construction equipment using IoT and machine learning. Automation in Construction, 114, 103182. https://doi.org/10.1016/j.autcon.2020.103182