Methodology of overall equipment effectiveness calculation in the context of Industry 4.0 environment

• Autorzy: Aleš Zdeněk , Pavlů Jindřich , Legát Václav , Mošna František , Jurča Vladimír

Identyfikatory

DOI

10.17531/ein.2019.3.7

Warianty tytułu

PL

Metodologia obliczania ogólnej efektywności sprzętu w kontekście środowiska Industry 4.0

• Języki publikacji: EN

Abstrakty

EN

Industry 4.0 and related Maintenance 4.0 demand higher requirement for productivity and maintenance effectiveness. Nakajim’s OEE indicator still plays an important role in measuring effectiveness of production and maintenance. In connection with the current Industry 4.0 challenge, the issue of Industrial Internet of Things (IIoT) is highly accentuated. This topic includes the matter of autonomous management and communication of individual machines and equipment within higher and more complex production units. Authors propose original calculations OEE for the whole production line from OEE knowledge and individual machines, including knowledge of their nominal and actual performance. The presented solution allows a greater depth of analysis of machine efficiency and overall effectiveness calculation of different assembled production lines based on knowledge of individual machines efficiencies.

PL

Industry 4.0 i związana z nią strategia Maintenance 4.0 stawiają wyższe wymagania odnośnie wydajności produkcji i utrzymania ruchu. Wskaźnik ogólnej efektywności urządzeń (OEE) Nakajimy nadal odgrywa ważną rolę w pomiarach efektywności produkcji i utrzymania ruchu. W związku z wyzwaniami stawianymi obecnie przez Industry 4.0, dużą uwagę zwraca się na koncepcję Przemysłowego Internetu Rzeczy. Obejmuje ona kwestię autonomicznego zarządzania i komunikacji pomiędzy poszczególnymi maszynami i urządzeniami w bardziej złożonych jednostkach produkcyjnych wyższego stopnia. Autorzy niniejszej pracy proponują oryginalną metodę obliczania OEE dla całej linii produkcyjnej na podstawie znajomości ogólnej efektywności urządzeń oraz efektywności pojedynczych maszyn, w tym wiedzy o ich nominalnej i rzeczywistej wydajności. Przedstawione rozwiązanie pozwala na głębszą analizę wydajności maszyn oraz obliczanie ogólnej efektywności różnych linii produkcyjnych w oparciu o wiedzę na temat wydajności poszczególnych maszyn.

Słowa kluczowe

EN

overall equipment effectiveness; availability; performance; quality; Industry 4.0

PL

ogólna efektywność sprzętu; gotowość; wydajność; jakość; Industry 4.0

• Wydawca: Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne
• Czasopismo: Eksploatacja i Niezawodność
• Rocznik: 2019
• Tom: Vol. 21, no. 3
• Strony: 411--418
• Opis fizyczny: Bibliogr. 32 poz., rys., tab.

Twórcy

autor

Aleš Zdeněk

• Department for Quality and Dependability of Machines, Faculty of Engineering, Czech University of Life Sciences Prague Kamycka 129, 165 00 Prague 6 – Suchdol, Czech Republic

autor

Pavlů Jindřich

• Department for Quality and Dependability of Machines, Faculty of Engineering, Czech University of Life Sciences Prague Kamycka 129, 165 00 Prague 6 – Suchdol, Czech Republic

autor

Legát Václav

• Department for Quality and Dependability of Machines, Faculty of Engineering, Czech University of Life Sciences Prague Kamycka 129, 165 00 Prague 6 – Suchdol, Czech Republic

autor

Mošna František

• Department of Mathematics Faculty of Engineering, Czech University of Life Sciences Prague Kamycka 129, 165 00 Prague 6 – Suchdol, Czech Republic

autor

Jurča Vladimír

• Department for Quality and Dependability of Machines, Faculty of Engineering, Czech University of Life Sciences Prague Kamycka 129, 165 00 Prague 6 – Suchdol, Czech Republic

Bibliografia

• 1. Al-Najjar B. Condition-based Maintenance: Selection and Improvement of a Cost effective Vibration-based Policy for Rolling Element Bearing, Ph.D. dissertation, Department of Engineering, Lund University, Lund, Sweden 1997.
• 2. Ales Z, Jurca V, Legat V. Effectiveness indicators of food processing lines. In: Conference Proceeding - 5th International Conference Trends in Agricultural Engineering 2013. Prague, Czech Republic: Czech University of Life Sciences Prague; Faculty of Engineering 2013; 56-61.
• 3. Antosz K, Stadnicka D. Evaluation measures of machine operation effectiveness in large enterprises: study results. Eksploatacja i Niezawodnosc – Maintenance and Reliability 2015; 17(1): 107-117, https://doi.org/10.17531/ein.2015.1.15.
• 4. Braglia M, Frosolini M, Zammori, F. Overall equipment effectiveness of a manufacturing line (OEEML): an integrated approach to assess systems performance. Journal of Manufacturing Technology Management 2009; 20 (1): 8–29, https://doi.org/10.1108/17410380910925389.
• 5. Coit W, Smith E A. Reliability Optimization of Series–Parallel Systems Using a Genetic Algorithm. IEEE Transactions on Reliability 1996; 45(2), https://doi.org/10.1109/24.510811.
• 6. Colledani M, Tolio T, Fischer A, Iung B, Lanza G, Schmitt R, Vancza J. Design and management of manufacturing systems for production quality. CIRP Annals 2014; 63(2): 773–796, https://doi.org/10.1016/j.cirp.2014.05.002.
• 7. Ding Z, Yingiie Z, Mingrang Y, Yun C. Reliability evaluation and component importance measure for manufacturing systems based on failure losses. Journal of Intelligent Manufacturing 2017; 28(8): 1859–1869, https://doi.org/10.1007/s10845-015-1073-1.
• 8. Drozyner P, Mikolajczak P. Assessment of the effectiveness of machine and device operation. Eksploatacja i Niezawodnosc – Maintenance and Reliability 2007; 3(35):72-75.
• 9. Duran O, Capaldo A, Acevedo P A D. Sustainable Overall Throughputability Effectiveness (S.O.T.E.) as a Metric for Production Systems. Sustainability 2018; 10(2): 1-15, https://doi.org/10.3390/su10020362.
• 10. Gulati R, Smith R. Maintenance and Reliability Best Practices. New York: Industrial Press, 2009.
• 11. Gola A, Nieoczym A. Application of OEE Coefficient for Manufacturing Lines Reliability Improvement. In Proceedings of the 4th International Conference on Management Science and Management Innovation (MSMI 2017), Advances in Economics, Business and Management Research 2017; 31: 189-194.
• 12. Hartmann E. Successfully installing TPM in a non-Japanese plant: total productive maintenance. Pittsburgh, 1992.
• 13. Kuo W, Wan R. Recent advances in optimal reliability allocation. IEEE Transactions on Systems Man and Cybernetics Part A-Systems and Humans 2007; 37(2): 143–156, https://doi.org/10.1109/TSMCA.2006.889476.
• 14. Legat V, Zaludova A H, Cervenka V, Jurca V, Contribution to optimization of preventive replacement. Reliability Engineering and System Safety 1996; 51: 259 – 266, https://doi.org/10.1016/0951-8320(96)00124-X.
• 15. Malindzak D, Pacana A, Pacaiova H. An effective model for the quality of logistics and improvement of environmental protection in a cement plant. Przemysł Chemiczny 2017; 96(9): 1958–1962.
• 16. Mariano C, Kuri-Morales A. Complex componential approach for redundancy allocation problem solved by simulation optimization framework. Journal of Intelligent Manufacturing 2014; 25(4): 661–680, https://doi.org/10.1007/s10845-012-0712-z.
• 17. Midor K, Kucera M. Improving the service with the servqual method. Management systems in production engineering 2018; 26(1): 60–65.
• 18. Murugaiah U S. The use of 5-WHYs technique to eliminate OEE’s speed loss in a manufacturing firm. Journal of Quality in Maintenance Engineering 2015; 21(4): 419 – 435, https://doi.org/10.1108/JQME-09-2013-0062.
• 19. Muthiah K M N, Huang S H. Overall throughput effectiveness (OTE) metric for factory-level performance monitoring and bottleneck detection. International Journal of Production Research 2007; 45(20): 4753–4769, https://doi.org/10.1080/00207540600786731.
• 20. Nachiappan M, Anantharam N. Evaluation of overall line effectiveness (OLE) in a continuous product line manufacturing system. Journal of Manufacturing Technology Management 2006; 17(7): 987–1008, https://doi.org/10.1108/17410380610688278.
• 21. Nakajima S. Introduction to TPM: total productive maintenance. Cambridge: 1988.
• 22. Oechsner R, Pfeffer M, Pfitzner L, Binder H, Müller E, Vonderstrass T. From overall equipment efficiency (OEE) to overall fab effectiveness (OFE). Material Science in Semiconductor Processing 2003; 5(4): 333-339.
• 23. Pacaiova H, Sinay J, Turisova R, Hajduova Z, Markulik S. Measuring the qualitative factors on copper wire surface. Measurement 2017; 109: 359-365, https://doi.org/10.1016/j.measurement.2017.06.002.
• 24. Pexa M, Muller M, Hloch S. Dynamic measuring of performance parameters for vehicles engines. Measurement 2017; 111: 11-17, https://doi.org/10.1016/j.measurement.2017.07.021.
• 25. Puvanasvaran A, Perumal I, Teruaki T Y S, Yoong S S. Examination of Overall Equipment Effectiveness (OEE) in Term of Maynard's Operation Sequence Technique (MOST). American Journal of Applied Sciences 2016; 13(11): 1214-1220, https://doi.org/10.3844/ ajassp.2016.1214.1220.
• 26. Reyes J A G, et al. Overall equipment effectiveness (OEE) and process capability (PC) measures: A relationship analysis. International Journal of Quality & Reliability Management 2010; 27(1):48–62, https://doi.org/10.1108/02656711011009308.
• 27. Rocco S C M, Ramirez-Marquez J E. Innovative approaches for addressing old challenges in component importance measures. Reliability Engineering and System Safety 2012; 108: 123–130, https://doi.org/10.1016/j.ress.2012.05.009.
• 28. Scott D, Pisa R. Can overall factory effectiveness prolong Moore's Law?,Solid state technology 1998; 41(3): 75–82.
• 29. Sherwin D. A review of overall models for maintenance management. Journal of Quality in Maintenance Engineering 2000; 6(3): 138–164, https://doi.org/10.1108/13552510010341171.
• 30. Sheikh A F. Overall equipment effectiveness of tyre curing press: a case study. Journal of Quality in Maintenance Engineering 2017; 23(1): 39-56, https://doi.org/10.1108/JQME-06-2015-0021.
• 31. Starr A F. Evaluation of overall equipment effectiveness based on market. Journal of Quality in Maintenance Engineering 2010; 16(3): 256–270, https://doi.org/10.1108/13552511011072907.
• 32. Tsarouhas P. Implementation of total productive maintenance in food industry: a case study. Journal of Quality in Maintenance Engineering 2007; 13(1): 5–8, https://doi.org/10.1108/13552510710735087.