The Effect of Condition-Based Maintenance on Heavy Equipment Performance in the Coal Mining Industry The Moderating Roles of Operational Environmental Conditions and Human Resource Competency
Main Article Content
Keywords
Condition Based Maintenance, Heavy Equipment Performance, Human Resource Competency, Equipment Downtime, Predictive Data Analysis
Abstract
Heavy equipment performance is an important factor that can affect productivity factors in the Indonesian coal mining sector. A major reason behind the high downtime is because they follow time-based maintenance (TBM) methods on their heavy equipment. This has led to the search of new and much better and effective methods such as Condition-Based Maintenance (CBM) method. The use of CBM as a predictive maintenance methodology by coal industry players to minimise equipment downtime is proliferating but, at present, little evidence exists regarding how operational environmental conditions and human resource competencies impact the effectiveness of CBM. This study investigates the impact of Predictive Data Analysis (PDA), Maintenance Actions Proactive (MAP) and Supporting Technology (STE) on heavy equipment performance (HEP) and which include Operational Environment Conditions (OEC) and Human Resource Competency (HRC) as moderating variables. This study collected data from 207 operational and maintenance personnel in Indonesian coal mining companies that have adopted CBM. Analysis of data was conducted through Partial Least Squares Structural Equation Modeling (PLS-SEM) using Smart-PLS 4.0 software. The results showed that PDA, MAP, and STE were positively related to heavy equipment performance, with MAP showing the strongest relationship among the three CBM dimensions. OEC and HRC also had a significant direct effect on performance. However, among the proposed moderating relationships, only the interaction between HRC and MAP was statistically significant. These findings suggest that CBM effectiveness is influenced not only by maintenance-related practices but also by workforce capabilities. This study provides empirical evidence on the relationship between CBM implementation, operational conditions, human resource competencies, and heavy equipment performance in the context of coal mining operations.
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References
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[4] J. Ndhlovu dan P. R. K. Chileshe, "Global mine productivity issues: A review," Int. J. Eng. Res. Technol., vol. 9, no. 5, pp. 319–329, 2020, doi: 10.17577/IJERTV9IS050071.
[5] PT Madhani Talatah Nusantara, "Yearly Report 2023," 2023.
[6] A. Syamsundar, V. N. A. Naikan, dan S. Wu, "Estimating maintenance effectiveness of a repairable system under time-based preventive maintenance," Comput. Ind. Eng., vol. 158, pp. 107278, Agt. 2021, doi: 10.1016/j.cie.2021.107278.
[7] W. Chen dan X. Wang, "Coal Mine Safety Intelligent Monitoring Based on Wireless Sensor Network," IEEE Sensors J., vol. 21, no. 22, pp. 25465-25471, Nov. 2021, doi: 10.1109/JSEN.2020.3046287.
[8] S. Li, M. Wen, T. Zu, dan R. Kang, "Condition-based maintenance optimization method using performance margin," Axioms, vol. 12, no. 2, pp. 168, Feb. 2023, doi: 10.3390/axioms12020168.
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[11] H. Meriem, H. Nora, dan O. Samir, "Predictive maintenance for smart industrial systems: A roadmap," Procedia Comput. Sci., vol. 220, pp. 645–650, 2023, doi: 10.1016/j.procs.2023.03.082.
[12] S. Elkateb et al., "Machine learning and IoT-based predictive maintenance approach for industrial applications," Alexandria Eng. J., vol. 88, pp. 298–309, 2024, doi: 10.1016/j.aej.2023.12.065.
[13] S. Q. Liu, Z. Lin, D. Li, X. Li, dan E. Kozan, "Recent research agendas in mining equipment management: A review," Mining, vol. 2, no. 4, pp. 769–790, 2022, doi: 10.3390/mining2040043.
[14] R. Favari et al., "Reliability and risk centered maintenance: A novel method for supporting maintenance management," Appl. Sci., vol. 13, no. 15, pp. 8827, 2023, doi: 10.3390/app13158827.
[15] J. M. Southgate et al., "Cost-benefit analysis using modular dynamic fault tree analysis and Monte Carlo simulations for condition-based maintenance of unmanned systems," arXiv preprint arXiv:2405.09519, 2024, doi: 10.48550/arXiv.2405.09519.
[16] B. Jakkula, M. G. R., dan N. M. C. S., "Maintenance management of load haul dumper using reliability analysis," J. Qual. Maintenance Eng., vol. 26, no. 4, pp. 621–637, 2020, doi: 10.1108/JQME-10-2018-0083.
[17] M. J. Rahimdel dan B. Ghodrati, "Reliability, maintainability, and availability of mining drilling equipment," in Mining Drilling, IntechOpen, 2024, doi: 10.5772/intechopen.114938.
[18] Caterpillar, "Sustainability Report 2024," 2024. https://www.caterpillar.com
[19] Komatsu, "Annual Report 2021," 2021. https://www.komatsu.jp
[20] E. Quatrini, F. Costantino, G. Di Gravio, dan R. Patriarca, "Condition-based maintenance—An extensive literature review," Machines, vol. 8, no. 2, pp. 31, 2020, doi: 10.3390/machines8020031.
[21] J. Sharma, M. L. Mittal, dan G. Soni, "Condition-based maintenance using machine learning and role of interpretability: A review," Int. J. Syst. Assur. Eng. Manag., vol. 15, no. 4, pp. 1345–1360, 2024, doi: 10.1007/s13198-022-01843-7.
[22] Y. Wang et al., "Condition-based maintenance method for multicomponent system considering maintenance delay based on remaining useful life prediction: Subsea tree system as a case," Ocean Eng., vol. 266, pp. 112616, 2022, doi: 10.1016/j.oceaneng.2022.112616.
[23] J. He, S. Liu, W. Li, W. Qiao, dan Z. Yang, "Operating environment assessment of the coalface in underground coal mining based on analytic hierarchy process (AHP) and matter-element theory (MET)," Geofluids, vol. 2021, pp. 9083547, 2021, doi: 10.1155/2021/9083547.
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[28] Asosiasi Pengusaha Batubara Indonesia (APBI), "Laporan Tahunan 2022," Jakarta, 2022. https://www.apbi-icma.org
[29] H. A. Prabowo, F. Farida, dan E. Y. T. Adesta, "The effect of lean waste reduction technique to business results: A confirmatory study," Manag. Prod. Eng. Rev., vol. 13, no. 2, pp. 92–101, 2022, doi: 10.24425/mper.2022.142058.
[30] J. Hair, W. Black, B. Babin, dan R. Anderson, Multivariate Data Analysis, 8th ed., Cengage Learning, 2018.
[31] C. Li, Y. Zhang, dan M. Xu, "Reliability-based maintenance optimization for multi-component systems considering imperfect maintenance," Chin. J. Mech. Eng., vol. 25, no. 1, pp. 160–168, 2012, doi: 10.3901/CJME.2012.01.160.
[32] F. P. García Márquez, F. Schmid, dan J. C. Collado, "A review of predictive maintenance: Concepts, techniques and applications," Appl. Sci., vol. 15, no. 10, pp. 5465, 2025, doi: 10.3390/app15105465.
[33] A. K. S. Jardine, D. Lin, dan D. Banjevic, "A review on machinery diagnostics and prognostics implementing condition-based maintenance," Mech. Syst. Signal Process., vol. 20, no. 7, pp. 1483–1510, 2006, doi: 10.1016/j.ymssp.2005.09.012.
[34] W. Zhang, D. Yang, dan H. Wang, "Data-driven methods for predictive maintenance of industrial equipment: A survey," IEEE Syst. J., vol. 16, no. 3, pp. 3679–3690, 2022, doi: 10.1109/JSYST.2021.3134604.
[35] T. P. Carvalho et al., "A systematic literature review of machine learning methods applied to predictive maintenance," Comput. Ind. Eng., vol. 137, pp. 106024, 2019, doi: 10.1016/j.cie.2019.106024.
[36] J. Dalzochio et al., "Machine learning and reasoning for predictive maintenance in Industry 4.0: Current status and challenges," Comput. Ind., vol. 123, pp. 10329, 2020, doi: 10.1016/j.compind.2020.10329.
[37] L. Radrigan dan A. S. Morales, "Enhancing Predictive Maintenance in Mining Mobile Machinery through a TinyML-enabled," 2024, pp. 1–22. [Daring]. Tersedia: https://arxiv.org/pdf/2411.07168
[38] Castillo-Perdomo et al., "Strategic management plan for maintenance in mining companies as a form of technological innovation," J. Eng. Compet., vol. 10, no. 2, pp. 395–410, 2023.
[39] S. K. Lodhi, A. Y. Gill, dan I. Hussain, "AI - Powered Innovations in Contemporary Manufacturing Procedures: An Extensive Analysis," Int. J. Multidiscip. Sci. Arts, vol. 3, no. 4, pp. 15–25, 2024.
[40] D. Le, "Working Conditions on Wear of A Ball Screw," J. Appl. Eng. Sci., vol. 20, pp. 372–376, 2022, doi: 10.5937/jaes0-32506.
[41] F. Siraj, M. R. Hasan, dan J. Ali, "Human Resource Development for IT and Technology Adoption in the Industrial Sector," Int. J. Comput. Appl., vol. 82, no. 10, pp. 30–36, 2013, doi: 10.5120/14154-2345.
[42] A. E. Adebayo dan A. Oluleye, "Evaluation of maintenance effectiveness and human factors: Case study of a research institute," J. Eng. Agric. Environ., vol. 10, no. 2, pp. 1–18, 2024, doi: 10.37017/jeae-volume10-no2.2024-3.
[43] M. N. Shergadwala, H. Lakkaraju, dan K. Kenthapadi, "A Human-Centric Perspective on Model Monitoring," dalam Proc. AAAI/ACM Conf. AI Ethics Soc., 2022, doi: 10.1609/hcomp.v10i1.21997
[44] F. Farida et al., "The effect of lean tool on research culture and research performance in Indonesia’s higher education institutions," Knowl. Perform. Manag., vol. 8, no. 1, pp. 91–103, 2024, doi: 10.21511/kpm.08(1).2024.07.
[45] L. Baldo et al., "Condition-based-maintenance for fleet management," Mater. Res. Proc., vol. 33, pp. 57–60, 2023, doi: 10.21741/9781644902677-9.
[46] O. Dayo-Olupona et al., "Adoptable approaches to predictive maintenance in mining industry: An overview," Resour. Policy, vol. 86, pp. 104291, 2023, doi: 10.1016/j.resourpol.2023.104291.
[47] S. R. Simard, M. Gamache, dan P. Doyon-Poulin, "Current practices for preventive maintenance and expectations for predictive maintenance in East Canadian mines," Mining, vol. 3, no. 1, pp. 26–53, 2023, doi: 10.3390/mining3010002.
[48] G. Kharmanda, "Condition-based predictive maintenance as an efficient strategy for industrializing additive manufacturing technology," J. Manuf. Mater. Process., vol. 8, no. 1, pp. 1–12, 2024, doi: 10.53964/jmim.2024008.
[49] J. J. Lin dan J. Shen, "Enhanced digital twin for human-centric and integrated lighting asset management in public libraries: From corrective to predictive maintenance—A demonstration design," arXiv preprint arXiv:2410.03811, 2024, doi: 10.48550/arXiv.2410.03811.
[50] S. Li et al., "Condition-based maintenance optimization method using performance margin," Axioms, vol. 12, no. 2, pp. 168, 2023, doi: 10.3390/axioms12020168.
[51] R. De La Fuente, L. Radrigan, dan A. S. Morales, "Enhancing predictive maintenance in mining mobile machinery through a TinyML-enabled," IEEE Access, pp. 1–22, 2024. https://arxiv.org/pdf/2411.07168
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Jun 30, 2026
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[1]
H. A. Prabowo, “The Effect of Condition-Based Maintenance on Heavy Equipment Performance in the Coal Mining Industry: The Moderating Roles of Operational Environmental Conditions and Human Resource Competency”, J. Optimasi Sist. Ind., vol. 25, no. 1, pp. 191–215, Jun. 2026.
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Author Biography
Andrea Sigit Sulaksono, Universitas Mercu Buana
Magister Industrial Engineering Universitas Mercu Buana Jakarta
Funding data
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Universitas Mercu Buana
Grant numbers 01-1-4/KLN-QS200/024/B-SPK/V/2025