Analysis of Machine Performance Using the Overall Equipment Effectiveness and Six Big Losses on Double Saw Machines
Abstract
PT Sumber Mas Indah Plywood is a manufacturing company engaged in the furniture sector with product specialization in plywood manufacturing furniture and plywood furniture. The research was conducted in January-December 2023 using the Overall Equipment Effectiveness (OEE) method to determine the effectiveness value of the machine.The objective of this study is to improve the performance of the double saw machine and propose improvements at PT Sumber Mas Indah Plywood to improve production quality. This study was conducted to measure the effectiveness of the double saw machine to determine its effectiveness value. the Overall Equipment Effectiveness (OEE) method is used here to measure it. The OEE value is affected by three factors, which are the availability, performance, and quality. The results obtained are that the effectiveness of the machine is still quite high, with an OEE value of 83.54% and still below the OEE standard value of 85%. To improve OEE, additional steps could include the identification of emerging problem. This allows improvement efforts to be directed towards addressing major challenges. The recommendation for the proposed repair of the double saw machine owned by PT Sumber Mas Indah Plywood should be used as a preventive maintenance strategy where the strategy can be carried out to monitor the machine so that it is in prime condition and ready to use and preventive steps can be taken before a problem occurs on the double saw machine.
References
Achouch, M., Dimitrova, M., Ziane, K., Sattarpanah Karganroudi, S., Dhouib, R., Ibrahim, H., & Adda, M. (2022). On predictive maintenance in industry 4.0: Overview, models, and challenges. Applied Sciences, 12(16), 8081. https://doi.org/10.3390/app12168081
Agote-Garrido, A., Martín-Gómez, A. M., & Lama-Ruiz, J. R. (2023). Manufacturing system design in industry 5.0: incorporating sociotechnical systems and social metabolism for human-centered, sustainable, and resilient production. Systems, 11(11), 537. http://dx.doi.org/10.3390/systems11110537
Agustiady, T., & Cudney, E. A. (2023). Total productive maintenance: strategies and implementation guide. CRC press.
Al Hazza, M. H. F., Ali, M. Y., & Razif, N. F. B. M. (2021). Performance improvement using analytical hierarchy process and Overall Equipment Effectiveness (OEE): Case study. Journal of Engineering Science and Technology, 16(3), 2227-2244. http://dx.doi.org/10.36842/jomase.v67i3.351
Andayati, D. (2019). Sistem Informasi Produksi Untuk Meningkatkan Kualitas Sistem Manufaktur Dan Jasa Dina. Jurnal Teknologi Dan Manajemen, 12(1), 87–92. https://doi.org/10.30872/jinv.v20i2.1793
de Assis Dornelles, J., Ayala, N. F., & Frank, A. G. (2022). Smart Working in Industry 4.0: How digital technologies enhance manufacturing workers' activities. Computers & Industrial Engineering, 163, 107804. http://dx.doi.org/10.1016/j.cie.2021.107804
Doyer, I., & Bean, W. L. (2023). As easy as OEE: enabling productivity improvement in schools by using overall equipment effectiveness as framework for classroom data analysis. International Journal of Lean Six Sigma, 14(5), 1055-1074. https://doi.org/10.1108/IJLSS-03-2022-0057
Eswaran, M., & Bahubalendruni, M. R. (2022). Challenges and opportunities on AR/VR technologies for manufacturing systems in the context of industry 4.0: A state of the art review. Journal of Manufacturing Systems, 65, 260-278. http://dx.doi.org/10.1016/j.jmsy.2022.09.016
Garza-Reyes, J. A. (2015). From measuring overall equipment effectiveness (OEE) to overall resource effectiveness (ORE). Journal of Quality in Maintenance Engineering, 21(4), 506-527. http://dx.doi.org/10.1108/JQME-03-2014-0014
Ghafoorpoor Yazdi, P., Azizi, A., & Hashemipour, M. (2018). An empirical investigation of the relationship between overall equipment efficiency (OEE) and manufacturing sustainability in industry 4.0 with time study approach. Sustainability, 10(9), 3031. https://doi.org/10.3390/su10093031
Guo, J., Hao, Z., Wang, C., Tang, Y., Wu, H., Hu, H., ... & Xu, C. (2024). Data-efficient large vision models through sequential autoregression. arXiv preprint arXiv:2402.04841. https://doi.org/10.48550/arXiv.2402.04841
Hamasha, M. M., Bani-Irshid, A. H., Al Mashaqbeh, S., Shwaheen, G., Al Qadri, L., Shbool, M., ... & Al-Bashir, A. (2023). Strategical selection of maintenance type under different conditions. Scientific Reports, 13(1), 15560. http://dx.doi.org/10.1038/s41598-023-42751-5
Jedermann, R., & Lang, W. (2022). Wrapper functions for integrating mathematical models into digital twin event processing. Sensors, 22(20), 7964. https://doi.org/10.3390/s22207964
Lanzilotti, C. O., Pinto, L. F. R., Facchini, F., & Digiesi, S. (2022). Embedding product-service system of cutting tools into the machining process: an eco-efficiency approach toward sustainable development. Sustainability, 14(3), 1100. https://doi.org/10.3390/su14031100
Li, L., & Zhou, M. (2022). Sustainable Manufacturing Systems: An Energy Perspective. John Wiley & Sons.
Li, Z., Sun, Y., Yang, L., Zhao, Z., & Chen, X. (2022). Unsupervised machine anomaly detection using autoencoder and temporal convolutional network. IEEE Transactions on Instrumentation and Measurement, 71, 1-13. http://dx.doi.org/10.1109/TIM.2022.3212547
Melnyk, L. H., Kovalov, B. L., Mykhailov, S. O., Mykhailov, O. O., & Starodub, I. A. (2022). Dynamics of reproduction of economic systems in the transition to digital economy–in the light of synergetic theory of development. Mechanism of an Economic Regulation, (3-4(97-98), 7-14. https://doi.org/10.32782/mer.2022.97-98.01
Mendes, D., Gaspar, P. D., Charrua-Santos, F., & Navas, H. (2023). Integrating TPM and Industry 4.0 to increase the availability of industrial assets: A case study on a conveyor belt. Processes, 11(7), 1956. https://doi.org/10.3390/pr11071956
Mobley, R. K. (2002). An introduction to predictive maintenance. Elsevier.
Moya, M. C. C. (2004). The control of the setting up of a predictive maintenance programme using a system of indicators. Omega, 32(1), 57-75. http://dx.doi.org/10.1016/j.omega.2003.09.009
Muhajir, S. M., & Yuamita, F. (2023). Analisis Total Productive Maintenance Dengan Menggunakan Metode Overall Equipment Effectiveness Pada Mesin Mixing Batching Di PT. Wijaya Karya Beton Tbk Boyolali. Jurnal Inovasi Dan Kreativitas, 3(1), 9. https://dx.doi.org/10.22441/ijiem.v5i2.23832
Nakagawa, T. (2005). Maintenance theory of reliability. Springer Science & Business Media.
Nursanti, E., Avief, R. M. S., Sibut, & Kertaningtyas, M. (2019). Maintenance Capacity Planning. Dream Litera Buana.
Oluyisola, O. E., Bhalla, S., Sgarbossa, F., & Strandhagen, J. O. (2022). Designing and developing smart production planning and control systems in the industry 4.0 era: a methodology and case study. Journal of Intelligent Manufacturing, 33(1), 311-332. https://link.springer.com/article/10.1007/s10845-021-01808-w
Oluyisola, O. E., Bhalla, S., Sgarbossa, F., & Strandhagen, J. O. (2022). Designing and developing smart production planning and control systems in the industry 4.0 era: a methodology and case study. Journal of Intelligent Manufacturing, 33(1), 311-332. https://link.springer.com/article/10.1007/s10845-021-01808-w
Pimenov, D. Y., Mia, M., Gupta, M. K., Machado, Á. R., Pintaude, G., Unune, D. R., ... & Kuntoğlu, M. (2022). Resource saving by optimization and machining environments for sustainable manufacturing: A review and future prospects. Renewable and Sustainable Energy Reviews, 166, 112660. https://doi.org/10.1016/j.rser.2022.112660
Pramula, G., & Hamdy, M. I. (2023). Evaluasi Efektivitas Mesin Ripple Mill Melalui Pendekatan Overall Equipment Effectiveness (OEE). Jurnal Teknologi dan Manajemen Industri Terapan, 2(4), 301-309. https://doi.org/10.55826/tmit.v2i4.281
Pranowo, I. D. (2019). Sistem dan manajemen pemeliharaan. Sleman: Deepublish.
Rafi, F. A., Wahyudin, W., & Nugraha, B. (2023). Analisis Overall Equipment Effectiveness untuk Meningkatkan Efektivitas Mesin Filling Multiline pada PT-XYZ. UNISTEK, 10(2), 126-133. https://doi.org/10.33592/unistek.v10i2.3890
Ramiya, S., & Suresh, M. (2021). Factors influencing lean-sustainable maintenance using TISM approach. International Journal of System Assurance Engineering and Management, 12, 1117-1131. http://dx.doi.org/10.1007/s13198-021-01304-7
Rojek, I., Jasiulewicz-Kaczmarek, M., Piechowski, M., & Mikołajewski, D. (2023). An artificial intelligence approach for improving maintenance to supervise machine failures and support their repair. Applied Sciences, 13(8), 4971. https://doi.org/10.3390/app13084971
Setyawan, W., Sutoni, A., & Munandar, T. (2021, February). Calculation and Analysis of Overall Equipment Effektiveness (OEE) Method and Six Big Losses toward the Production of Corter Manchines in Oni Jaya Motor. In Journal of Physics: Conference Series (Vol. 1764, No. 1, p. 012162). IOP Publishing. http://dx.doi.org/10.1088/1742-6596/1764/1/012162
Simanjuntak, A. W. P. (2020). Studi Penerapan Total Productive Maintenance (TPM) Untuk Peningkatan Efisiensi Pada Pabrik Pupuk Organik PT. AGRO ENERGI INDONESIA.
Singh, R. K., & Gurtu, A. (2022). Prioritizing success factors for implementing total productive maintenance (TPM). Journal of Quality in Maintenance Engineering, 28(4), 810-830. https://doi.org/10.1108/JQME-09-2020-0098
Sobirov, K. (2025). Overall equipment effectiveness (OEE) analysis in Solar Panel Manufacturing (Doctoral dissertation, Politecnico di Torino).
Sultan, K. S., & Moshref, M. E. (2021). Stochastic analysis of a priority standby system under preventive maintenance. Applied Sciences, 11(9), 3861. https://doi.org/10.3390/app11093861
Wahid, A. (2020). Penerapan Total Productive Maintenance (TPM) Produksi Dengan Metode Overall Equipment Effectiveness (OEE) Pada Proses Produksi Botol (PT. XY Pandaan – Pasuruan). Jurnal Teknologi Dan Manajemen Industri, 6(1), 12–16 https://doi.org/10.36040/jtmi.v6i1.2624
Zhang, X., Jie, X., Ning, S., Wang, K., & Li, X. (2022). Coupling and coordinated development of urban land use economic efficiency and green manufacturing systems in the Chengdu-Chongqing Economic Circle. Sustainable Cities and Society, 85, 104012. https://doi.org/10.1016/j.scs.2022.104012
Zhao, J., Gao, C., & Tang, T. (2022). A review of sustainable maintenance strategies for single component and multicomponent equipment. Sustainability, 14(5), 2992. https://doi.org/10.3390/su14052992
Zhou, H., Huang, X., Wen, G., Lei, Z., Dong, S., Zhang, P., & Chen, X. (2022). Construction of health indicators for condition monitoring of rotating machinery: A review of the research. Expert Systems with Applications, 203, 117297. https://doi.org/10.1016/j.eswa.2022.117297
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