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ÜRETİM SİSTEMLERİNDE DARBOĞAZ TESPİTİ: LİTERATÜR ARAŞTIRMASI

Year 2022, Volume: 27 Issue: 3, 1285 - 1304, 31.12.2022

Nagihan Akkurt Servet Hasgül

https://doi.org/10.17482/uumfd.1123981

Abstract

Üretim sistemleri için darboğaz üretim verimliliğini kısıtlayan en etkili faktörlerden biridir. Darboğaza sebep olan bir süreç daha hızlı çalışır ise tüm hattın üretim hızı artacak ve böylelikle üretim süreçlerinin ve tedarik zincirinin devamlılığı sağlanacaktır. Bu sebeple darboğazın tespit edilmesi ve kontrol altına alınması işletmeler için önem kazanmıştır. Literatürde bu konuda çok sayıda yöntem ve çalışma bulunmaktadır. Bu çalışmanın amacı ise literatürde bulunan darboğaz tespiti çalışmalarının incelenmesi, kullanılan yöntemlerin açıklanması ve analiz edilmesidir. Çalışma kapsamında 2007-2022 yıllarına ait toplam 48 makale incelenmiştir. İncelenen çalışmalardan elde edilen sonuçlara göre darboğaz tespitinde en çok benzetim yönteminin kullanıldığı görülmektedir. Aynı zamanda dönüm noktası yöntemi, aktif dönem yöntemi ve matematiksel yöntemler de darboğaz tespitinde diğer yöntemlere göre daha fazla kullanılmaktadır. Son yıllarda ise artan yapay zeka çalışmaları ile birlikte makine öğrenmesi tabanlı yaklaşımlar kullanılmaya başlanmıştır. Literatürde bu kadar sayıda darboğaz tespit yönteminin açıklandığı ve bu konudaki çalışmaların derlenip analiz edildiği bir çalışma bulunmamaktadır. Bu sebeple yapılan çalışmanın ilgili araştırmacılara yol göstermesi hedeflenmektedir.

Keywords

Üretim Sistemleri Darboğaz Darboğaz Tespit Yöntemleri

Thanks

Bu çalışmanın 1. Yazarı TÜBİTAK 2211-A Yurt İçi Doktora Burs Programı tarafından desteklenmektedir. Ancak yayın ile ilgili tüm sorumluluk yayının sahibine aittir. Yayının içeriğinin bilimsel anlamda TÜBİTAK tarafından onaylandığı anlamına gelmez.

References

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Bottleneck Detection in Production Systems: Literature Research

Year 2022, Volume: 27 Issue: 3, 1285 - 1304, 31.12.2022

Nagihan Akkurt Servet Hasgül

https://doi.org/10.17482/uumfd.1123981

Abstract

For production systems, the bottleneck is one of the most effective factors limiting production efficiency. If a process that causes a bottleneck runs faster, the production speed of the entire line will increase, thus ensuring the continuity of the production processes and supply chain. For this reason, it has become important for businesses to detect and control bottlenecks. There are many methods and studies on this subject in the literature. This study aims to examine the bottleneck detection studies in the literature and to explain and analyze the methods used. Within the scope of the study, a total of 48 articles belonging to the years 2007-2022 were examined. According to the results obtained from the studies examined, it is seen that the simulation method is mostly used in bottleneck detection. At the same time, the turning point method, active period method and mathematical methods are also more used in bottleneck detection than other methods. In recent years, machine learning-based approaches have been used in with increasing artificial intelligence studies. There is no study in the literature in which so many bottleneck detection methods are explained and studies on this subject are compiled and analyzed. For this reason, it is aimed that the study will guide the relevant researchers.

Keywords

Production Systems Bottleneck Bottleneck Detection Method

References

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  • 2. Bernedixen, J. (2018) Automated bottleneck analysis of production systems: increasing the applicability of simulation-based multi-objective optimization for bottleneck analysis within industry, Doctoral Thesis, University of Skövde.
  • 3. Betterton, C.E. and Cox, J.F. (2009) Espoused drum-buffer-rope flow control in serial lines: a comparative study of simulation models, International Journal of Production Economics, 117(1), 66–79. doi: 10.1016/j.ijpe.2008.08.050
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  • 15. Kwon, C.M. and Lim, S. (2013) Bottleneck detection based on duration of active periods, Journal of The Korea Society for Simulation, 22(3), 35-41. doi: 10.9709/JKSS.2013.22.3.035
  • 16. Lai, X., Shui, H., Ding, D. and Ni, J. (2021) Data-driven dynamic bottleneck detection in complex manufacturing systems, Journal of Manufacturing Systems, 60, 662-675. doi: 10.1016/j.jmsy.2021.07.016
  • 17. Lawrence, S.R. and Buss, A.H. (1994) Shifting production bottlenecks: causes, cures, and conundrums, Production and Operations Management, 3(1), 21–37. doi: 10.1111/j.1937-5956.1994.tb00107.x
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  • 20. Li, L., Chang, Q., Ni, J., Xiao, G. and Biller, S. (2007) Bottleneck detection of manufacturing systems using data driven method, In 2007 IEEE International Symposium on Assembly and Manufacturing, 76-81. doi: 10.1109/ISAM.2007.4288452
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  • 22. Li, L., Chang, Q. and Ni, J. (2009b) Data driven bottleneck detection of manufacturing systems, IntJ Prod Res, 47, 5019–5036. doi: 10.1080/00207540701881860
  • 23. Li, L. (2018) A systematic-theoretic analysis of data-driven throughput bottleneck detection of production systems, Journal of Manufacturing Systems, 47, 43-52. doi: 10.1016/j.jmsy.2018.03.001
  • 24. Lima, E., Chwif, L. and Barreto, M.R.P. (2008) Metodology for selecting the best suitable bottleneck detection method, In 2008 Winter Simulation Conference, 1746-1751. doi: 10.1109/WSC.2008.4736262
  • 25. Lizarralde-Aiastui, A., Apaolaza-Perez de Eulate, U. and Mediavilla-Guisasola, M. (2020) A strategic approach for bottleneck identification in make-to-order environments: A drum-buffer-rope action research based case study, Journal of Industrial Engineering and Management (JIEM), 13(1), 18-37. doi: 10.3926/jiem.2868
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  • 27. Muthiah, K.M.N. and Huang, S.H. (2007) Overall throughput effectiveness (OTE) metric for factory-level performance monitoring and bottleneck detection, International Journal of Production Research, 45(20), 4753-4769. doi: 10.1080/00207540600786731
  • 28. Nandakumar, N., Saleeshya, P. G. and Harikumar, P. (2020) Bottleneck identification and process improvement by lean six sigma DMAIC methodology, Materials Today: Proceedings, 24, 1217-1224. doi: 10.1016/j.matpr.2020.04.436
  • 29. Ongbali, S.O., Afolalu, S.A. and Igboanugo, A.C. (2018) Bottleneck problem detection in production system using Fourier transform analytics, International Journal of Mechanical Engineering and Technology, 9(12), 113-122.
  • 30. Roh P., Kunz, A. and Netland, T. (2018) Data-driven detection of moving bottlenecks in multi-variant production lines, IFAC-PapersOnLine, 51(11), 158-163. doi: 10.1016/j.ifacol.2018.08.251
  • 31. Roser, C., Lorentzen, K. and Deuse, J. (2014) Reliable shop floor bottleneck detection for flow lines through process and inventory observations, Procedia Cirp, 19, 63-68. doi: 10.1016/j.procir.2014.05.020
  • 32. Roser, C., Nakano, M. and Tanaka, M. (2001) A practical bottleneck detection method, In Proceeding of the 2001 Winter Simulation Conference, 2, 949-953. doi: 10.1109/WSC.2001.977398
  • 33. Roser, C., Nakano, M. and Tanaka, M. (2002) Shifting bottleneck detection, Winter Simulation Conference. doi: 10.1109/WSC.2002.1166360
  • 34. Roser, C., Nakano, M. and Tanaka, M. (2003) Comparison of bottleneck detection methods for AGV systems, In Winter Simulation Conference, 2, 1192-1198. doi: 10.1109/WSC.2003.1261549
  • 35. Roser, C. and Nakano, M. (2015) A quantitative comparison of bottleneck detection methods in manufacturing systems with particular consideration for shifting bottlenecks, In IFIP International Conference on Advances in Production Management Systems, 273-281. doi: 10.1007/978-3-319-22759-7_32
  • 36. Roser, C., Subramaniyan, M., Skoogh, A. and Johansson, B. (2021) An enhanced data-driven algorithm for shifting bottleneck detection. In IFIP International Conference on Advances in Production Management Systems, 683-689. doi: 10.1007/978-3-030-85874-2_74
  • 37. Rudnitckaia, J., Venkatachalam, H. S., Essmann, R., Hruška, T., and Colombo, A. W. (2022) Screening process mining and value stream techniques on industrial manufacturing processes: process modelling and bottleneck analysis. IEEE Access, 10, 24203-24214. doi: 10.1109/ACCESS.2022.3152211
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There are 61 citations in total.

Details

Primary Language Turkish
Subjects Industrial Engineering
Journal Section Survey Articles
Authors

Nagihan Akkurt 0000-0002-8128-2964

Servet Hasgül 0000-0002-9329-6335

Early Pub Date December 9, 2022
Publication Date December 31, 2022
Submission Date June 1, 2022
Acceptance Date October 8, 2022
Published in Issue Year 2022 Volume: 27 Issue: 3

Cite

APA Akkurt, N., & Hasgül, S. (2022). ÜRETİM SİSTEMLERİNDE DARBOĞAZ TESPİTİ: LİTERATÜR ARAŞTIRMASI. Uludağ Üniversitesi Mühendislik Fakültesi Dergisi, 27(3), 1285-1304. https://doi.org/10.17482/uumfd.1123981
AMA Akkurt N, Hasgül S. ÜRETİM SİSTEMLERİNDE DARBOĞAZ TESPİTİ: LİTERATÜR ARAŞTIRMASI. UUJFE. December 2022;27(3):1285-1304. doi:10.17482/uumfd.1123981
Chicago Akkurt, Nagihan, and Servet Hasgül. “ÜRETİM SİSTEMLERİNDE DARBOĞAZ TESPİTİ: LİTERATÜR ARAŞTIRMASI”. Uludağ Üniversitesi Mühendislik Fakültesi Dergisi 27, no. 3 (December 2022): 1285-1304. https://doi.org/10.17482/uumfd.1123981.
EndNote Akkurt N, Hasgül S (December 1, 2022) ÜRETİM SİSTEMLERİNDE DARBOĞAZ TESPİTİ: LİTERATÜR ARAŞTIRMASI. Uludağ Üniversitesi Mühendislik Fakültesi Dergisi 27 3 1285–1304.
IEEE N. Akkurt and S. Hasgül, “ÜRETİM SİSTEMLERİNDE DARBOĞAZ TESPİTİ: LİTERATÜR ARAŞTIRMASI”, UUJFE, vol. 27, no. 3, pp. 1285–1304, 2022, doi: 10.17482/uumfd.1123981.
ISNAD Akkurt, Nagihan - Hasgül, Servet. “ÜRETİM SİSTEMLERİNDE DARBOĞAZ TESPİTİ: LİTERATÜR ARAŞTIRMASI”. Uludağ Üniversitesi Mühendislik Fakültesi Dergisi 27/3 (December 2022), 1285-1304. https://doi.org/10.17482/uumfd.1123981.
JAMA Akkurt N, Hasgül S. ÜRETİM SİSTEMLERİNDE DARBOĞAZ TESPİTİ: LİTERATÜR ARAŞTIRMASI. UUJFE. 2022;27:1285–1304.
MLA Akkurt, Nagihan and Servet Hasgül. “ÜRETİM SİSTEMLERİNDE DARBOĞAZ TESPİTİ: LİTERATÜR ARAŞTIRMASI”. Uludağ Üniversitesi Mühendislik Fakültesi Dergisi, vol. 27, no. 3, 2022, pp. 1285-04, doi:10.17482/uumfd.1123981.
Vancouver Akkurt N, Hasgül S. ÜRETİM SİSTEMLERİNDE DARBOĞAZ TESPİTİ: LİTERATÜR ARAŞTIRMASI. UUJFE. 2022;27(3):1285-304.

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