Effectiveness Analysis Of A Centrifugal Pump In The Wax Ester Preparation Process Of Fatty Alcohol Production Using Overall Equipment Effectiveness
Abstrak
The reliability of production machinery determines process continuity, operational efficiency, and product quality in the oleochemical industry, particularly in the wax ester preparation stage of fatty alcohol production. This study analyzes the effectiveness of a centrifugal pump in the wax ester preparation unit using Overall Equipment Effectiveness (OEE) and identifies the causes of low machine effectiveness using a fishbone diagram. Data were collected from machine operating records for August 2025–January 2026, including machine working time, planned and unplanned downtime, and production volume. The results show an average availability rate of 91.99%, a performance rate of 84.61%, and a quality rate of 100%, yielding an OEE of 77.83%, which is below the world-class standard of the Japan Institute of Plant Maintenance (JIPM) of 85%. Planned downtime (226 hours) reduces loading time and is not a direct cause of the low performance rate, whereas failure and repair time (310 hours) lowered availability in three of the six months. The fishbone analysis identified five main groups of causes, namely man, method, environment, material, and machine, with delayed fault handling, inappropriate maintenance scheduling, and machine condition deterioration as the prominent causes. The study recommends strengthening preventive maintenance and checking component service life to improve the reliability of centrifugal pumps in fatty alcohol production.
Referensi
I. P. S. Ahuja and J. S. Khamba, “Total productive maintenance: literature review and directions,” Int. J. Qual. Reliab. Manag., vol. 25, no. 7, pp. 709–756, Aug. 2008, doi: https://10.1108/02656710810890890.
M. Bengtsson, L.-G. Andersson, and P. Ekström, “Measuring preconceived beliefs on the results of overall equipment effectiveness – A case study in the automotive manufacturing industry,” J. Qual. Maint. Eng., vol. 28, no. 2, pp. 391–410, Mar. 2022, doi: https://10.1108/JQME-03-2020-0016.
F. A. Tonny, A. Maliha, M. I. Chayan, and M. D. Xames, “Optimization of overall equipment effectiveness (OEE) factors: Case study of a vegetable oil manufacturing company,” Manag. Sci. Lett., vol. 13, no. 2, pp. 124–135, 2023, doi: https://10.5267/j.msl.2022.12.002.
S. Raju, H. A. Kamble, R. Srinivasaiah, and D. R. Swamy, “Anatomization of the overall equipment effectiveness (OEE) for various machines in a tool and die shop,” J. Intell. Manuf. Spec. Equip., vol. 3, no. 1, pp. 97–105, May 2022, doi: https://10.1108/JIMSE-01-2022-0004.
M. Zubair et al., “Manufacturing productivity analysis by applying overall equipment effectiveness metric in a pharmaceutical industry,” Cogent Eng., vol. 8, no. 1, Jan. 2021, doi: 10.1080/23311916.2021.1953681.
S. Val, N. Jiménez, and M. P. Lambán, “Overall Equipment Effectiveness as a Strategic KPI in Intelligent Manufacturing: A Case Study in Plastic Injection Moulding,” J. Manuf. Mater. Process., vol. 10, no. 5, p. 159, Apr. 2026, doi: https://10.3390/jmmp10050159.
A. M. Fuad, N. A. Takia, H. A. Zafir, and O. Farrok, “Enhancing operational efficiency through overall equipment efficiency optimization and Kaizen initiatives,” PLoS One, vol. 20, no. 5, p. e0320761, May 2025, doi: https://10.1371/journal.pone.0320761.
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, pp. 289–294, 2022, doi: https://10.1016/j.procir.2022.04.047.
R. Drewniak and Z. Drewniak, “Improving business performance through TPM method: The evidence from the production and processing of crude oil,” PLoS One, vol. 17, no. 9, p. e0274393, Sep. 2022, doi: 10.1371/journal.pone.0274393.
C. A. Porras Guzmán and F. Sierra-Liñan, “Design and Implementation of a Maintenance Plan Based on Total Productive Maintenance in the Packaging of Chemical Products for the Agroindustrial Sector,” Int. J. Eng. Trends Technol., vol. 73, no. 11, pp. 87–106, Nov. 2025, doi: https://10.14445/22315381/IJETT-V73I11P108.
M. Achouch et al., “On Predictive Maintenance in Industry 4.0: Overview, Models, and Challenges,” Appl. Sci., vol. 12, no. 16, p. 8081, Aug. 2022, doi: https://10.3390/app12168081.
M. Molęda, B. Małysiak-Mrozek, W. Ding, V. Sunderam, and D. Mrozek, “From Corrective to Predictive Maintenance—A Review of Maintenance Approaches for the Power Industry,” Sensors, vol. 23, no. 13, p. 5970, Jun. 2023, doi: https://10.3390/s23135970.
L. Bouyaya and R. Chaib, “Maintenance and Reliability of a Motor Pump: A Case Study,” Int. J. Performability Eng., vol. 18, no. 8, p. 559, 2022, doi: https://10.23940/ijpe.22.08.p4.559569.
A.T. Funmilayo and E. G. Saturday, “Cost effective maintenance strategy for centrifugal pumps using reliability centred maintenance,” Int. J. Front. Eng. Technol. Res., vol. 5, no. 2, pp. 001–011, Nov. 2023, doi: https://10.53294/ijfetr.2023.5.2.0031.
Asri, Emil El Faisal, Susilawati, PPG Universitas Sriwijaya, and 2PPG FKIP Universitas Sriwijaya SMP, “dentifikasi Risiko Kerusakan Pada Pompa Cooling Water System Unit Utilitas-2 Dengan Menggunakan Metode Reliability Centered Maintenance (Rcm) Di PT. Pupuk Iskandar Muda,” Live Streaming Commer. Di Indones. Peran Ulasan, Rekom. Dan Komunitas Dalam Membangun Kepercayaan Konsum., vol. 3, no. 5, pp. 200–210, 2025, doi: https://doi.org/10.55377/jis.v6i2.
L. Chen, L. Wei, Y. Wang, J. Wang, and W. Li, “Monitoring and Predictive Maintenance of Centrifugal Pumps Based on Smart Sensors,” Sensors, vol. 22, no. 6, p. 2106, Mar. 2022, doi: https://10.3390/s22062106.
R. F. da Silva, A. H. de A. Melani, M. A. de C. Michalski, and G. F. M. de Souza, “Reliability and Risk Centered Maintenance: A Novel Method for Supporting Maintenance Management,” Appl. Sci., vol. 13, no. 19, p. 10605, Sep. 2023, doi: https://10.3390/app131910605.
S. M. Rizwan, H. Al Nabhani, Y. Al Rahbi, and S. Alagiriswamy, “Reliability Analysis of a Three-unit Pumping System,” Int. J. Eng. Trends Technol., vol. 70, no. 6, pp. 24–31, Jun. 2022, doi: https://10.14445/22315381/IJETT-V70I6P203.
S. Holifahtus Sakdiyah, N. Eltivia, and A. Afandi, “Root Cause Analysis Using Fishbone Diagram: Company Management Decision Making,” J. Appl. Business, Tax. Econ. Res., vol. 1, no. 6, pp. 566–576, Aug. 2022, doi: 10.5440https://8/jabter.v1i6.103.
A. Kumah et al., “Cause-and-Effect (Fishbone) Diagram: A Tool for Generating and Organizing Quality Improvement Ideas,” Glob. J. Qual. Saf. Healthc., vol. 7, no. 2, pp. 85–87, May 2024, doi: https://10.36401/JQSH-23-42.
A. Ghufron and Akmal, “Analisis Kehilangan Minyak ( Oil Losses ) pada Palm Kernel Oil ( PKO ) di Stasiun Press Expeller dengan Metode Fishbone di PT . Perkebunan,” vol. 14, no. 1, 2025, doi: https://doi.org/10.29103/iej.v14i1.20389.
S. Singh, J. S. Khamba, and D. Singh, “Analyzing the Role of Six Big Losses in OEE to Enhance the Performance: Literature Review and Directions,” 2021, pp. 411–421. doi: https://10.1007/978-981-33-4320-7_37.
















