Bottleneck Detection in Production Systems: Literature Research

Öz

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.

Anahtar Kelimeler

• Production Systems
• Bottleneck
• Bottleneck Detection Method

ÜRETİM SİSTEMLERİNDE DARBOĞAZ TESPİTİ: LİTERATÜR ARAŞTIRMASI

Öz

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.

Anahtar Kelimeler

• Üretim Sistemleri
• Darboğaz
• Darboğaz Tespit Yöntemleri

Kaynakça

1. 1. Alzubi, E., Atieh, A. M., Abu Shgair, K., Damiani, J., Sunna, S. and Madi, A. (2019) Hybrid integrations of value stream mapping, theory of constraints and simulation: application to wooden furniture industry, Processes, 7(11), 816. doi: 10.3390/pr7110816
2. 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. 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
4. 4. Betterton, C.E. and Silver, S.J. (2012) Detecting bottlenecks in serial production lines–a focus on interdeparture time variance, International Journal of Production Research, 50(15), 4158-4174 doi: 10.1080/00207543.2011.596847
5. 5. Biller, S., Li, J., Marin, S. P., Meerkov, S. M. and Zhang, L. (2009) Bottlenecks in Bernoulli serial lines with rework, IEEE Transactions on Automation Science and Engineering, 7(2), 208-217. doi: 10.1109/TASE.2009.2023463
6. 6. Blackstone, J.H. (2008) APICS dictionary, Chicago: APICS The Association for Operations Management.
7. 7. Chiang, S.Y., Kuo, C.T. and Meerkov, S.M. (2001) C-bottlenecks in serial production lines – identification and application, Mathematical Problems in Engineering, 7, 543–578. doi: 10.1155/S1024123X01001776
8. 8. Esmaeeli, H. and Aleahmad, M. (2019) Bottleneck detection in job shop production by high-level Petri nets, In 2019 15th Iran International Industrial Engineering Conference (IIIEC), 178-183. doi: 10.1109/IIIEC.2019.8720639

9. 9. Hao, P.C. and Lin, B.M. (2021) Text mining approach for bottleneck detection and analysis in printed circuit board manufacturing, Computers & Industrial Engineering, 154, 107121. doi: 10.1016/j.cie.2021.107121
10. 10. Hofmann, C., Staehr, T., Cohen, S., Stricker, N., Haefner, B. and Lanza, G. (2019) Augmented go & see: an approach for improved bottleneck identification in production lines, Procedia Manufacturing, 31, 148-154. doi: 10.1016/j.promfg.2019.03.023
11. 11. Hopp, W.J. and Spearman, M.L. (2000) Factory physics, 2nd ed. New York, NY: McGraw-Hill.
12. 12. Kang, Y. and Ju, F. (2017) Identifying bottlenecks in serial production lines with geometric machines: indicators and rules, IFAC-PapersOnLine, 50(1), 13952-13957. doi: 10.1016/j.ifacol.2017.08.2217
13. 13. Kumbhar, M., Ng, A.H. and Bandaru, S. (2022) Bottleneck detection through data ıntegration, process mining and factory physics-based analytics, In 10th Swedish Production Symposium (SPS2022), Skövde, 737-748. doi:10.3233/ATDE220192
14. 14. Kuo, C.T., Lim, J.T. and Meerkov, S.M. (1996) Bottlenecks in serial production lines: a system-theoretic approach, Mathematical Problems in Engineering, 2, 233–276. doi: 10.1155/S1024123X96000348
15. 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. 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. 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
18. 18. Lemessi, M., Rehbein, S., Rehn, G. and Schulze, T. (2012) Semi-automatic simulation-based bottleneck detection approach, In Proceedings of the 2012 Winter Simulation Conference (WSC), 1-12. doi: 10.1109/WSC.2012.6465048
19. 19. Leporis, M. and Králová, Z. (2010) A simulation approach to production line bottleneck analysis, In International Conference Cybernetics and Informatics, 13-22.
20. 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
21. 21. Li, L., Chang, Q., Ni, J. and Biller, S. (2009a) Real time production improvement through bottleneck control, International Journal of Production Research, 47(21), 6145-6158. doi: 10.1080/00207540802244240
22. 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. 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. 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. 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
26. 26. McClelland, G. (2022) Data-driven bottleneck identification for serial production lines, Doctoral Thesis, Queen’s University, Canada.
27. 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. 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. 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. 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. 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. 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. 33. Roser, C., Nakano, M. and Tanaka, M. (2002) Shifting bottleneck detection, Winter Simulation Conference. doi: 10.1109/WSC.2002.1166360
34. 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. 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. 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. 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
38. 38. Sengupta, S., Das, K. and Vantil, R.P. (2008) A new method for bottleneck detection, In 2008 Winter Simulation Conference, 1741-1745. doi: 10.1109/WSC.2008.4736261
39. 39. Singh, M. and Thathia, H. (2019) Analytic tool for identifying bottlenecks using turning point method, Master’s Thesis, Chalmers Unıversıty of Technology.
40. 40. Su, X., Lu, J., Chen, C., Yu, J. and Ji, W. (2022) Dynamic bottleneck identification of manufacturing resources in complex manufacturing system, Applied Sciences, 12(9), 4195. doi: 10.3390/app12094195
41. 41. Subramaniyan, M., Skoogh, A., Gopalakrishnan, M. and Hanna, A. (2016) Real-time data-driven average active period method for bottleneck detection, International Journal of Design & Nature and Ecodynamics, 11(3), 428-437. doi: 10.2495/DNE-V11-N3-428-437
42. 42. Subramaniyan, M., Skoogh, A., Salomonsson, H., Bangalore, P. and Bokrantz, J. (2018a) A data-driven algorithm to predict throughput bottlenecks in a production system based on active periods of the machines, Computers & Industrial Engineering, 125, 533-544. doi: 10.1016/j.cie.2018.04.024
43. 43. Subramaniyan, M., Skoogh, A., Salomonsson, H., Bangalore, P., Gopalakrishnan, M. and Sheikh Muhammad, A. (2018b) Data-driven algorithm for throughput bottleneck analysis of production systems, Production & Manufacturing Research, 6(1), 225-246. doi: 10.1080/21693277.2018.1496491
44. 44. Subramaniyan, M., Skoogh, A., Muhammad, A. S., Bokrantz, J., Johansson, B. and Roser, C. (2020a) A generic hierarchical clustering approach for detecting bottlenecks in manufacturing, Journal of Manufacturing Systems, 55, 143-158. doi: 10.1016/j.jmsy.2020.02.011
45. 45. Subramaniyan, M., Skoogh, A., Muhammad, A. S., Bokrantz, J., Johansson, B. and Roser, C. (2020b) A data-driven approach to diagnosing throughput bottlenecks from a maintenance perspective, Computers & Industrial Engineering, 150, 106851. doi: 10.1016/j.cie.2020.106851
46. 46. Tang, H. (2019) A new method of bottleneck analysis for manufacturing systems, Manufacturing Letters, 19, 21-24. doi: 10.1016/j.mfglet.2019.01.003
47. 47. Thomas, T.E., Koo, J., Chaterji, S. and Bagchi, S. (2018) Minerva: A reinforcement learning-based technique for optimal scheduling and bottleneck detection in distributed factory operations, In 2018 10th International Conference on Communication Systems & Networks (COMSNETS), 129-136. doi: 10.1109/COMSNETS.2018.8328189
48. 48. Thürer, M., Ma, L., Stevenson, M. and Roser, C. (2021) Bottleneck detection in high-variety make-to-order shops with complex routings: an assessment by simulation, Production Planning & Control, 1-12. doi: 10.1080/09537287.2021.1885795
49. 49. Tu, J., Bai, Y., Yang, M., Zhang, L. and Denno, P. (2020) Real-time bottleneck in serial production lines with bernoulli machines: theory and case study, IEEE Transactions on Automation Science and Engineering, 18(4), 1822-1834. doi: 10.1109/TASE.2020.3021346
50. 50. Urban, W., and Rogowska, P. (2020) Methodology for bottleneck identification in a production system when implementing TOC, Engineering Management in Production and Services, 12(2), 74-82. doi: 10.2478/emj-2020-0012
51. 51. Velumani, S., and Tang, H. (2017) Operations status and bottleneck analysis and improvement of a batch process manufacturing line using discrete event simulation, Procedia Manufacturing, 10, 100-111. doi: 10.1016/j.promfg.2017.07.03
52. 52. Wedel, M., Noessler, P. and Metternich, J. (2016) Development of bottleneck detection methods allowing for an effective fault repair prioritization in machining lines of the automobile industry, Production Engineering, 10(3), 329-336. doi: 10.1007/s11740-016-0672-9
53. 53. Wedel, M., Von Hacht, M., Hieber, R., Metternich, J. and Abele, E. (2015) Real-time bottleneck detection and prediction to prioritize fault repair in interlinked production lines, Procedia CIRP, 37, 140-145. doi: 10.1016/j.procir.2015.08.071
54. 54. Yemane, A., Haque, S. and Malfanti, I. S. (2017) Bottleneck identification using time study and simulation modeling of apparel industries, Proceedings of the International Conference on Industrial Engineering and Operations Management Bogota, Colombia.
55. 55. Yu, C. and Matta, A. (2014) Data-driven bottleneck detection in manufacturing systems: A statistical approach, In 2014 IEEE International Conference on Automation Science and Engineering (CASE), 710-715. doi: 10.1109/CoASE.2014.6899406
56. 56. Zhai, Y., Sun, S., Wang, J. and Wang, M. (2010) An effective bottleneck detection method for job shop, In 2010 International Conference on Computing, Control and Industrial Engineering, 2, 198-201. doi: 10.1109/CCIE.2010.168
57. 57. Zhai, Y., Sun, S., Wang, J. and Niu, G. (2011) Job shop bottleneck detection based on orthogonal experiment, Computers & Industrial Engineering, 61(3), 872-880. doi: 10.1016/j.cie.2011.05.021
58. 58. Zhang, M. and Matta, A. (2018) Data-driven downtime bottleneck detection in open flow lines, In 2018 IEEE 14th International Conference on Automation Science and Engineering (CASE), 1513-1518. doi: 10.1016/j.cie.2011.05.021
59. 59. Zhang, Y., Luo, Z., Zeng, L. and Li, C. (2022) Bottleneck detection for discrete manufacturing system based on object-oriented colored petri nets and cloud simulation. In ITM Web of Conferences, EDP Sciences, 45. doi:10.1051/itmconf/20224501015
60. 60. Zhao D., Tian X. and Geng J. (2014) A bottleneck detection algorithm for complex product assembly line based on maximum operation capacity, Mathematical Problems in Engineering. doi: 10.1155/2014/258173
61. 61. Zhou, Z. and Li, L. (2013) Real time electricity demand response for sustainable manufacturing systems considering throughput bottleneck detection, In 2013 IEEE International Conference on Automation Science and Engineering (CASE), 640-644. doi: 10.1109/CoASE.2013.6653942