ANALISIS NILAI OVERALL EQUIPMENT EFFECTIVENESS (OEE) SEBAGAI DASAR UNTUK PERBAIKAN EFEKTIVITAS KERJA MESIN CUT OFF DI PLANT X PT ABC
Bibliographic record
Abstract
Overall Equipment Effectiveness (OEE) merupakan salah satu aplikasi dari program Total Productive Maintenance (TPM) yang digunakan sebagai alat untuk menentukan tingkat efektivitas mesin. Setelah mengetahui nilai OEE, dilanjutkan dengan mengevaluasi nilai masing-masing faktor six big losses untuk menemukan faktor yang berpengaruh paling dominan. Berdasarkan hasil perhitungan six big losses itulah akan diketahui penyebab utama, yang selanjutnya dianalisis dengan metode Failure Mode Effect and Critical Analysis (FMECA) untuk mengetahui tingkat kekritisannya. Analisis cause and effect diagram juga dilakukan untuk mengetahui akar dari penyebab masalah tersebut. Pada penelitian ini dilakukan pengukuran nilai OEE di salah satu lini produksi PT ABC pada periode tahun 2014. Nilai OEE yang diperoleh adalah 33.54%, masih jauh di bawah nilai ideal OEE yaitu 85%. Hasil penelitian menunjukkan, bahwa faktor utama yang menyebabkan rendahnya nilai OEE adalah nilai availability , dengan nilai 54.27%. Setelah ditelusuri lebih lanjut, ditemukan bahwa yang menjadi penyebab utama adalah breakdown , yang mencapai 24.18%. Dengan menggunakan metode FMECA terhadap breakdown , ditemukan bahwa tingkat kekritisan paling tinggi terletak pada flexible coupling dan clamp , yang akar masalahnya disebabkan oleh unsur-unsur mesin, manusia, metode, material, dan lingkungan. Dalam mengatasi masalah tersebut, disarankan untuk menerapkan autonomous maintenance, salah satu pilar TPM. Kata kunci : TPM, OEE, six big losses , FMECA, cause and effect diagram , autonomous maintenance . Overall Equipment Effectiveness (OEE) is one of the Total Productive Maintenance (TPM) application program that used as a tool to determine the level of effectiveness of the machine. After knowing the value of OEE , it will be followed by evaluating the value of six big losses factor for finding the most dominant influenced factor . Based on the results of the six big losses calculation , the main cause of the problem will be known and then will be analyzed by the method of Failure Mode Effect and Critical Analysis (FMECA) to determine the level of criticality. Cause and effect diagram analysis w as also conducted to determine the root of the problem. This study measured the value of OEE in one of PT ABC line production in period 2014. OEE value was 33.54%, still far below the ideal value of OEE which is 85%. The result showed that the main factor causing low OEE value is the availability, w hich the value was 54.27%. After further exploration, it was found that the main cause is the breakdown, which reached 24.18%. By using FMECA to breakdown, it was found that the highest level of criticality are flexible coupling and clamp, which is the root of the problem is caused by the elements of the machine, man, method, material, and environment. In addressing these issues, it is recommended to apply autonomous maintenance, one of the pillars of TPM. Key words : TPM, OEE, six big losses, FMECA, cause and effect diagram, autonomous maintenance. Daftar Pustaka Besterfield, D. H. (1994). Quality Control. United States of America: Prentice-Hall International, Inc. Evans, J. R., & Lindsay, W. M. (2011). The Management and Control of Quality. Canada: South-Western, Cengage Learning. Gulati, R. (2013). Maintenance and Reliability Best Practice. New York: Industrial Press, Inc. Hasriyono, M. (2009). Evaluasi Efektivitas Mesin dengan Penerapan Total Productive Maintenace (TPM) di PT Hadi Baru. Medan: Departemen Teknik Industri Fakultas Teknik Universitas Sumatera Utara. J, V. (2009, Agustus 3). An Introduction to Total Productive Maintenance (TPM). Retrieved April 1, 2015, from Plant Maintenance Resource Center: http://www.plant-maintenance.com/articles/tpm_intro.shtml Kurniawan, F. (2013). Teknik dan Aplikasi Manajemen Perawatan Industri. Yogyakarta: Graha Ilmu. Nakajima, S. (1984). Introduction to TPM. Cambridge. Nanda, L., Hartanti, L. P., & Runtuk, J. K. (2014). Analisis Risiko Kualitas Produk dalam Proses Produksi Miniatur Bis dengan Metode Failure Mode and Effect Analysis pada Usaha Kecil Menengah Niki Kayoe. Jurnal Gema Aktualita . Nayak, D. M., N, V. K., Naidu, G. S., & Shankar, V. (2013). Evaluation of OEE in a Continuous Process Industry on an Insulation Line in a Cable Manufacturing Unit. International Journal of Innovative Research in Science, Engineering and Technology . Oktaria, S. (2011). Perhitungan dan Analisa Nilai Overall Equipment Effectiveness (OEE) pada Proses Awal Pengolahan Kelapa Sawit (Studi Kasus: PT X). Depok: Program Studi Teknik Industri Fakultas Teknik Universitas Indonesia. Puvanasvaran, P. (2013). Consideration of Demand Rate in Overall Equipment Effectiveness (OEE) on Equipment with Constant Process Time. Jourbal of Industrial Management , 507-524. Reliability Analysis Center. (1993). Failure Mode, Effects and Criticality Analysis (FMECA). Rome, NY: IIT Research Institute. ReliaSoft Corporation. (2004, December). Basic Concepts of FMEA and FMECA. Retrieved February 17, 2015, from weibull.com: http://www.weibull.com/hotwire/issue46/relbasics46.htm Sarjono, H., Santoso, E., Setiawan, E., & Pujadi, A. (2009). Analisis proses Perawatan Mesin dengan Metode Total Productive Maintenance dalam Kaitannya dengan Tingkat Defect dan Breakdown yang Tinggi pada PT FMI Jakarta. Jurnal Riset Manajemen dan Bisnis . Smith, R., & Mobley, R. K. (2007). Rules of Thumb for Maintenance and Reliability Engineers. Sower, V. E. (2011). Essentials of Quality. United States of America: John Wiley & Sons, Inc. Stephens, M. P. (2004). Productivity and Reliability-Based Maintenance Management. New Jersey: Pearson. Subiyanto. (2014). Analisis Efektivitas Mesin/Alat Pabrik Gula Menggunakan Metode Overall Equipment Effectiveness. Jurnal Teknik Industri . Tjahjanto, G. P. (2011). Implementasi Autonomous Maintenance untuk Mengurangi Jumlah Produk Cacat pada Proses Pengemasan Susu Bantal Fleksibel di PT Frisian Flag Indonesia. Depok: Program Teknik Industri Fakultas Teknik Universitas Indonesia. Vorne Industries, Inc. (2012). Six Big Losses. Retrieved January 29, 2015, from OEE: http://www.oee.com/oee-six-big-losses.html
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".