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Abstract

Coal companies have an overburden stripping production target of 95,000 bcm in January 2022, while the realization of production with the Sumitomo SH 350 LHD excavator and Hitachi Zaxis 350 H excavator is only 77,000 bcm or 82% of the production target. The purpose of this study was to obtain an analysis of the productivity of the Sumitomo SH 350 LHD Excavator (40) and Hitachi Zaxis 350 H Excavator (31) in the overburden stripping activity in January 2022, analyze the obstacle factors that caused the available working hours to be reduced by using the Fishbone diagram method, get an analysis of the Overall Equipment Effectiveness (OEE) value of the Sumitomo SH 350 LHD Excavator (40) and Hitachi Zaxis 350 H (31) Excavator before being optimized, and get the analysis and productivity of the Sumitomo SH 350 LHD Excavator (40) and the Hitachi Zaxis 350 H Excavator (31) which has been optimized with the implementation of Overall Equipment Effectiveness (OEE) to achieve the overburden stripping production target. After analysis and improvement efforts, the total overburden stripping production was 149,000 bcm, which means that it has reached the target and even exceeded the production target of 95,000 bcm/month with the OEE value of the digging equipment of 41% and 43%, respectively. However, the OEE value is still very low compared to the world-class standard OEE value, which is 85% and there is still room for improvement.

Keywords

Excavator Fishbone Mining OEE Overburden

Article Details

Creative Commons License

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

Copyright (c): Heri Prabowo, Rahul Hutmi, Indang Dewata (2023)
How to Cite
Prabowo, H., Hutmi, R., & Dewata, I. (2023). Optimizing Digging Equipment Productivity Using Overall Equipment Effectiveness (OEE) Method in Coal Overburden Mining Activities. INVOTEK: Jurnal Inovasi Vokasional Dan Teknologi, 23(2), 99-108. https://doi.org/https://doi.org/10.24036/invotek.v23i2.1097

References

  1. S. Kostic and J. Trivan, “Optimization of Coal Overburden Excavation Considering Variable Geomechanical Properties and States of Excavator Teeth,” Arch. Min. Sci., vol. 67, no. 1, pp. 123–142, 2022, doi: 10.24425/ams.2022.140706.
  2. B. J. Sosantri, D. Yulhendra, and H. Prabowo, “Optimalisasi Peralatan Tambang dengan Metoda Overall Equipment Effectiveness (OEE) di Pit 1 Penambangan Batubara Banko Barat PT Bukit Asam (Persero Terbuka) Tanjung Enim Sumatera Selatan,” J. Bina Tambang, vol. 3 (2), pp. 1–20, 2018.
  3. S. Annamalai and D. Suresh, “Implementation of total productive maintenance for overall equipment effectiveness improvement in machine shop,” Int. J. Recent Technol. Eng., vol. 8, no. 3, pp. 7686–7691, 2019, doi: 10.35940/ijrte.C6212.098319.
  4. S. Hidayat, T. Iskandar, and M. Kudiantoro, F. F. Wijayaningtyas, “Heavy equipment efficiency, productivity and compatibility of coal mine overburden work in east kalimantan,” Int. J. Mech. Eng. Technol., vol. 10, no. 6, pp. 194–202, 2019.
  5. G. G. Dembetembe, “Nchanga Open Pit Fleet Optimization for Productivity Improvement,” no. January, 2017.
  6. L. del C. N. Corrales, M. P. Lambán, M. E. H. Korner, and J. Royo, “applied sciences Overall Equipment E ff ectiveness : Systematic Literature Review and Overview of Di ff erent Approaches,” Appl. Sci., vol. 10, no. 6469, pp. 1–20, 2020.
  7. R. Erwanda, A. Y. Ridwan, and P. S. Muttaqin, “Optimization of Heavy Equipment Costs in Coal Mining Overburden Production Using Match Factor and Linear Programming,” Proc. Conf. Broad Expo. to Sci. Technol. 2021 (BEST 2021), vol. 210, no. Best 2021, pp. 323–331, 2022, doi: 10.2991/aer.k.220131.049.
  8. H. Sandeir, E dan Prabowo, “Evaluasi Kebutuhan dan Estimasi Biaya Alat Muat Kobelco 380 dan Hitachi 350 Dengan Alat Angkut Scania P360 dan Mercedez Actroz 4043,” Bina Tambang, vol. 3, no. 3, pp. 1091–1100, 2018, [Online]. Available: http://ejournal.unp.ac.id/index.php/mining/article/view/101404.
  9. M. Manikandan, M. Adhiyaman, and K. C. Pazhani, “A study and analysis of construction equipment management used in construction projects for improving productivity,” Int. Res. J. Eng. Technol, vol. 5, pp. 1297–1303, 2018.
  10. S. K. Sone, “Performance Evaluation of Surface Mining Equipment with Particular Reference To Shovel-Dumper Mining,” 2016.
  11. K. Aoshima, M. Servin, and E. Wadbro, “Simulation-Based Optimization of High-Performance Wheel Loading,” Proc. Int. Symp. Autom. Robot. Constr., vol. 2021-Novem, pp. 688–695, 2021, doi: 10.22260/isarc2021/0093.
  12. A. M. Ayu Namira, A. V. Anas, R. Amalia, and R. N. S. Tui, “Evaluation of Achievement of Overburden Production Target Using Fishbone Diagram Method at Pit A Site B PT XYZ, South Sumatera Province,” EPI Int. J. Eng., vol. 4, no. 2, pp. 158–167, 2021, doi: 10.25042/10.25042/epi-ije.082021.08.
  13. Ş. M. Aytaç, “Determination of the optimal equipment fleet for overburden stripping operation in a surface coal mine with discrete event simulation,” Middle East Technical University, 2021.
  14. M. A. Kurniawan, P. Dewi, W. T. Adi, and A. Subagio, “Optimization of Heavy Equipment Usage for Railway Track Works,” in International Conference on Railway and Transportation (ICORT 2022), 2023, pp. 146–155. doi: 10.2991/978-94-6463-126-5_16.
  15. R. Singh, D. B. Shah, A. M. Gohil, and M. H. Shah, “Overall equipment effectiveness (OEE) calculation - Automation through hardware & software development,” Procedia Eng., vol. 51, pp. 579–584, 2013, doi: 10.1016/j.proeng.2013.01.082.
  16. M. H. Hagström, K. Gandhi, D. Bergsjö, and A. Skoogh, “Evaluating the effectiveness of machine acquisitions and design by the impact on maintenance cost – A case study,” IFAC-PapersOnLine, vol. 53, no. 3, pp. 25–30, 2020, doi: 10.1016/j.ifacol.2020.11.005.
  17. I. Indah Susanti and M. Ratri Adinda Putri, “Optimization Heavy Equipment Productivity Against Costs on Un-Top Soil and Spreading Work With Linear Programming Simplex Method,” ADRI Int. J. Sci. Eng. Technol., vol. 6, no. 2, pp. 88–97, 2021, doi: 10.29138/ijset.v6i2.33.
  18. B. Denkena, A. Krödel, and O. Pape, “Increasing productivity in heavy machining using a simulation based optimization method for porcupine milling cutters with a modified geometry,” Procedia Manuf., vol. 40, no. 2019, pp. 14–21, 2019, doi: 10.1016/j.promfg.2020.02.004.
  19. Z. Klika, J. Serenčíšová, I. Kolomazník, L. Bartoňová, and P. Baran, “Prediction of CRI and CSR of cokes by two-step correction models for stamp-charged coals – Statistical analysis,” Fuel, vol. 262, no. August, 2020, doi: 10.1016/j.fuel.2019.116623.
  20. L. Van De Ginste, E. H. Aghezzaf, and J. Cottyn, “The role of equipment flexibility in Overall Equipment Effectiveness (OEE)-driven process improvement,” Procedia CIRP, vol. 107, no. March, pp. 289–294, 2022, doi: 10.1016/j.procir.2022.04.047.
  21. A. Moses Obeti, L. Muhwezi, and J. Muhumuza Kakitahi, “Investigating equipment productivity in feeder road maintenance in Uganda,” Transp. Res. Interdiscip. Perspect., vol. 17, no. November 2022, p. 100756, 2023, doi: 10.1016/j.trip.2023.100756.