Bottleneck Identification through Simulation Modelling: A Case of Solid Tire Manufacturing Sector

Authors

  • Edirisinghe Arachchige Dona Dilesha Divyanjali EDIRISINGHE Wayamba University of Sri Lanka
  • Edirisinghe Arachchige Chaminda Prasanna KARUNARATHNE Wayamba University of Sri Lanka

Keywords:

simulation modelling; bottlenecks; productivity; line balancing; solid tire manufacturing

Abstract

Businesses are constantly making productivity improvements to survive in the highly competitive marketplace. Bottlenecks have been identified as one of the main factors limiting the system performance of manufacturing firms. Thus, identifying bottlenecks in the production process is extremely important to increase productivity. Considering its importance, this case study was designed to identify causes for not meeting the tire target and determine the implications of bottlenecks in the tire manufacturing process. For this purpose, simulation analysis was carried out for the solid resilience tire-building process. Through the investigation, the cushion layer-building process was identified as the bottleneck. To validate the identified limitation, Line balancing and Pareto analysis were conducted. Analysis results confirmed the presence of a bottleneck in the cushion layer-building process. Further, to identify the root causes for not reaching the maximum tire target, Cause-and-Effect analysis and 5WHY analysis were adopted. The study revealed that inadequately maintained outdated machines and frequent power failures are the leading causes of not meeting the maximum production. By answering these issues, the target production can be increased, and the results showed the opportunity to increase the efficiency of the manufacturing process by more than 95%.

References

Alzubi, E., Atieh, A. M., Abu Shgair, K., Damiani, J., Sunna, S., & Madi, A. (2019). Hybrid integrations of value stream mapping, theory of constraints and simulation: application to wooden furniture industry. Processes, 7(11), 816. https://doi.org/10.3390/pr7110816

Baldwin, C. Y. (2015, May). Bottlenecks, modules and dynamic architectural capabilities. Harvard Business School Finance Working Paper, (15-028). https://doi.org/10.2139/ssrn.2512209

Bandara, B. N. S., Wijesinghe, H. G. I. M., Senevirathne, A. M. W. K., & Dilrukshi, N. M. D. (2021). Root cause analysis for warming delay of resilient solid tire heel compound during manufacturing. Journal of Agriculture and Value Addition, 4(1), 46-54.

Brito, T. B., dos Santos Silva, R. C., Botter, R. C., Pereira, N. N., & Medina, A. C. (2010, December). Discrete event simulation combined with multi-criteria decision analysis applied to steel plant logistics system planning. In Proceedings of the 2010 Winter Simulation Conference (pp. 2126-2137). IEEE. https://doi.org/10.1109/WSC.2010.5678862

Chetpattananondh, K., Dechwayukul, C., & Thongruang, W. (2008). An applied laser shade vibration measurement technique for rotating imbalance for quality testing of solid tires. Measurement, 41(8), 922-933. https://doi.org/10.1016/j.measurement.2008.01.003

Chiang, S. Y., Kuo, C. T., & Meerkov, S. M. (2000). DT-bottlenecks in serial production lines: theory and application. IEEE Trans Robot Autom, 16, 567–580. https://doi.org/10.1109/70.880806

Chiang, S. Y., Kuo, C. T., & Meerkov, S. M. (2001). Bottlenecks in serial production lines: identification and application. Mathematical Problems in Engineering, 7(6), 543-578. https://doi.org/10.1155/S1024123X01001776

Chuang, K. Y., Lai, C. H., Peng, Y. P., & Yen, T. Y. (2015). Characteristics of particle-bound polychlorinated dibenzo-p-dioxins and dibenzofurans (PCDD/Fs) in atmosphere used in carbon black feeding process at a tire manufacturing plant. Environmental Science and Pollution Research, 22, 19451-19460. https://doi.org/10.1007/ s11356-015-5135-x

Darayi, M., Eskandari, H., & Geiger, C. D. (2013). Using simulation-based optimization to improve performance at a tire manufacturing company. QScience Connect, 2013(1), 13. https://doi.org/10.5339/connect.2013.13

Dechwayukul, C., Kao-ien, W., Chetpattananondh, K., & Thongruang, W. (2010). Measuring service life and evaluating the quality of solid tires. Sonklanakarin Journal of Science and Technology, 32(4), 387.

Gent, A. N. (1992). Engineering with rubber: How to design rubber components. Oxford University Press.

Goldratt, E. M., & Cox, J. (1990). Theory of Constraints. Croton-on-Hudson.

Govender, P., & Dewa, M. (2022). Use of kaizen principle and line balancing technique for process improvement in the assembly of automotive components. South African Journal of Industrial Engineering, 33(3), 69-82. https://dx.doi.org/10.7166/33-3-2790

Gunasekara, H. D. A. (2017). Effect of lignin base antioxidant on natural rubber base solid tyre tread compound (Master thesis, Moratuwa University). http://dl.lib.uom.lk/bitstream/handle/123/12906/TH3434-1.pdf?sequence=2

Gupta, V., Jain, R., Meena, M. L., & Dangayach, G. S. (2018). Six-sigma application in tire-manufacturing company: a case study. Journal of Industrial Engineering International, 14, 511-520. https://link.springer.com/article/10.1007/s40092-017-0234-6

Hao, Q., & Shen, W. (2008). Implementing a hybrid simulation model for a Kanban-based material handling system. Robotics and Computer-Integrated Manufacturing, 24(5), 635-646. https://doi.org/10.1016/j.rcim.2007.09.012

Heshmat, M., El-Sharief, M. A., & El-Sebaie, M. G. (2013). Simulation modeling of production lines: a case study of cement production line. Journal of Engineering Sciences, 41(3),1045-1053. https://doi.org/10.21608/jesaun.2013.114780

Ilie, G., & Ciocoiu, C. N. (2010). Application of fishbone diagram to determine the risk of an event with multiple causes. Management Research and Practice, 2(1), 1-20.

Kahraman, M. M., Rogers, W. P., & Dessureault, S. (2020). Bottleneck identification and ranking model for mine operations. Production Planning & Control, 31(14), 1178-1194. https://doi.org/10.1080/09537287.2019.1701231

Karthikeyan, A. P. (2010). Detection of bottlenecks for multiple products and mitigation using alternative process plans (Doctoral dissertation, Wichita State University).

Karuppusami, G., & Gandhinathan, R. (2006). Pareto analysis of critical success factors of total quality management: A literature review and analysis. The TQM magazine, 18(4), 372-385. https://doi.org/10.1108/09544780610671048

Kasemset, C., Pinmanee, P., & Umarin, P. (2014, October). Application of ECRS and simulation techniques in bottleneck identification and improvement: A paper package factory. In Proceedings of the Asia Pacific Industrial Engineering & Management Systems Conference (pp. 1477-1484). APIEMS.

Kikolski, M. (2016). Identification of production bottlenecks with the use of plant simulation software. Economics and Management, 8(4), 103-112. https://doi.org/10.1515/emj-2016-0038

Kitaw, D., Matebu, A., & Tadesse, S. (2010). Assembly line balancing using simulation technique in a garment manufacturing firm. Zede Journal, 27, 69-80.

Krishnan, S., Dev, A.S., Suresh, R., Sumesh, A., & Rameshkumar, K. (2018). Bottleneck identification in a tyre manufacturing plant using simulation analysis and productivity improvement. Materials Today: Proceedings, 5(11), 24720-24730. https://doi.org/10.1016/j.matpr.2018.10.270

Lai, X., Shui, H., Ding, D., & Ni, J. (2021). Data-driven dynamic bottleneck detection in complex manufacturing systems. Journal of Manufacturing Systems, 60, 662-675. https://doi.org/10.1016/j.jmsy.2021.07.016.

Li, F., Liu, F., Liu, J., Gao, Y., Lu, Y., Chen, J., Yang, H., & Zhang, L. (2018). Thermo-mechanical coupling analysis of transient temperature and rolling resistance for solid rubber tire: numerical simulation and experimental verification. Composites Science and Technology, 167, 04-410. https://doi.org/10.1016/j.compscitech.2018.08.034

Li, L., Chang, Q., & Ni, J. (2008). Data-driven bottleneck detection of manufacturing systems. International Journal of Production Research, 47(18), 5019-5036. https://doi.org/10.1080/00207540701881860

Li, L., Chang, Q., Ni, J., Xiao, G., & Biller, S. (2007, July). Bottleneck detection of manufacturing systems using data driven method. In 2007 IEEE international symposium on assembly and manufacturing (pp. 76-81). IEEE. https://doi.org/10.1109/ISAM.2007.4288452

Li, L. (2018). A systematic-theoretic analysis of data-driven throughput bottleneck detection of production systems. Journal of Manufacturing Systems, 47, 43-52. https://doi.org/10.1016/j.jmsy.2018.03.001

Liong, C. Y., & Loo, C. S. (2009). A simulation study of warehouse loading and unloading systems using Arena. Journal of Quality Measurement and Analysis, 5(2), 45-56.

National Highway Traffic Safety Administration (2006). The Pneumatic Tire. USA Government. https://www.nhtsa.gov/sites/nhtsa.gov/files/pneumatictire_hs-810-561.pdf

Newsmantraa. ( 2022, November 29). Press-on Band Tires (POB Tires) Market Overview, Demand, Size, Growth & Forecast 2030 Analysis. Digital Journal. https://t.ly/-FTtR

Phromjan, J., & Suvanjumrat, C. (2018). A suitable constitutive model for solid tire analysis under quasi-static loads using finite element method. Engineering Journal, 22(2), 141-155. https://doi.org/10.4186/ej.2018.22.2.141

Premarathna, W. A. A. S., Jayasinghe, J. A. S. C., Wijesundara, K. K., Gamage, P., Ranatunga, R. R. M. S. K., & Senanayake, C. D. (2021). Investigation of design and performance improvements on solid resilient tires through numerical simulation. Engineering Failure Analysis, 128. https://doi.org/10.1016/j.engfailanal.2021.105618

Premarathna, W. A .A. S., Jayasinghe, J. A. S. C., Gamage, P., Senanayake, C. D., Wijesundara, K. K., & Ranatunga, R. R. M. S. K. (2022). Analysis of factors influencing on performance of solid tires: combined approach of design of experiments and thermo-mechanical numerical simulation. European Journal of Mechanics - A/Solids, 96, Article 104680, https://doi.org/10.1016/j.euromechsol.2022.104680

Rahman, C., & Sabuj, S. U. (2015). Process flow improvement proposal of a batch manufacturing system using arena simulation modeling. Review of General Management, 21(1), 63-77.

Rasib, A. A. (2021). Production Smoothness Improvement through ARENA Application in the Food Manufacturing Industry. Turkish Journal of Computer and Mathematics Education, 12(3), 3516-3526.

Roser, C., Nakano, M., & Tanaka, M. (2001, December). A practical bottleneck detection method. In Proceeding of the 2001 winter simulation conference (Cat. No. 01CH37304) (Vol. 2, pp. 949-953). IEEE. https://doi.org/10.1109/WSC.2001.977398

Schroer, B. J., & Tseng, F. T. (1987, December). Modeling complex manufacturing systems using simulation. In Proceedings of the 19th conference on Winter simulation (pp. 677-682). https://doi.org/10.1145/318371.318683

Sengupta, S., Das, K., & Vantil, R. P. (2008, December). A new method for bottleneck detection. In 2008 Winter Simulation Conference (pp. 1741-1745). IEEE. https://doi.org/10.1109/WSC.2008.4736261

Sharda, B., & Bury, S. J. (2010, December). Bottleneck analysis of a chemical plant using discrete event simulation. In Proceedings of the 2010 Winter Simulation Conference (pp. 1547-1555). IEEE. https://doi.org/10.1109/WSC.2010.5678916

Siderska, J. (2016). Application of tecnomatix plant simulation for modeling production and logistics processes. Business, Management and Education, 14(1), 64-73. https://doi.org/10.3846/bme.2016.316

Sri Lanka Export Development Board. (2022, 10 8). https://www.srilankabusiness.com/rubber/solid-tyres.html

Srivastava, S. K., & Bhuyan, B. (2018). Rubber Nanocomposites for Tyre Tread Applications. Rubber Nanocomposites: and Nanotextiles. Walter de Gruyter. https://doi.org/10.1515/9783110643879-002

Stîngă, F., Severin, I., Mitrache, I. A., & Lascu, E. (2020). Redesign of the curing area of the tire manufacturing process. Sustainability, 12(17), 6909. https://doi.org/10.3390/su12176909

Su, X., Lu, J., Chen, C., Yu, J., & Ji, W. (2022). Dynamic bottleneck identification of manufacturing resources in complex manufacturing system. Applied Sciences, 12(4195). https://doi.org/10.3390/app12094195

Tague, N. R. (2005). The Quality Toolbox, 600. ASQ Quality Press.

Tang, H. (2019). A new method of bottleneck analysis for manufacturing systems. Manufacturing Letters, 19, 21-24. https://doi.org/10.1016/j.mfglet.2019.01.003

Thombert. (2010). Polyurethane and Rubber Tires: A Comparative Overview. https://rb.gy/fyzdmq

Urban, W., & Rogowska, P. (2018). The case study of bottlenecks identification for practical implementation to the theory of constraints. Multidisciplinary Aspects of Production Engineering, 1(1), 399-405. https://sciendo.com/it/article/10.2478/mape-2018-0051

Urban, W., & Rogowska, P. (2020). Methodology for bottleneck identification in a production system when implementing TOC. Engineering Management in Production and Services, 12(2), 74-82. https://doi.org/10.2478/emj-2020-0012

Üstün, S. (2005). Analysis by simulation of bottleneck problems in a job shop production system (Doctoral dissertation, MSc Thesis, Institute of Science, Karadeniz Technical University, Trabzon).

Velumani, S., & 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. https://doi.org/10.1016/j.promfg.2017.07.033

Wang, T., Guinet, A., Belaidi, A., & Besombes, B. (2009). Modelling and simulation of emergency services with ARIS and Arena. Case study: the emergency department of Saint Joseph and Saint Luc Hospital. Production Planning and Control, 20(6), 484-495. https://doi.org/10.1080/09537280902938605

Wang, Y., Zhao, Q., & Zheng, D. (2005). Bottlenecks in production networks: an overview. Journal of Systems Science and Systems Engineering, 14(3), 347-363. https://doi.org/10.1007/s11518-006-0198-3

Wattegedara, B. M. H. I. B., & Egodage, S. M. (2018). Effect of short nylon fiber loading on high load bearing press-on-band tire tread compound. Annual Sessions of IESL, A1, 731–737.

Xiao, Z., Pramanik, A., Basak, A. K., Prakash, C. & Shankar, S. (2022). Material recovery and recycling of waste tyres: a review. Cleaner Materials, 5, 100115, https://doi.org/10.1016/j.clema.2022.100115.

Yemane, A., Gebremicheal, G., Meraha, T., & Hailemicheal, M. (2020). Productivity improvement through line balancing by using simulation modeling. Journal of Optimization in Industrial Engineering, 13(1), 153-165.

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Published

2023-12-10

How to Cite

EDIRISINGHE, E. A. D. D. D., & KARUNARATHNE, E. A. C. P. (2023). Bottleneck Identification through Simulation Modelling: A Case of Solid Tire Manufacturing Sector. Management Dynamics in the Knowledge Economy, 11(4), 324–337. Retrieved from https://www.managementdynamics.ro/index.php/journal/article/view/534

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