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Bir Üretim İşletmesinde Simülasyon Yöntemi ile Darboğaz Analizi ve Sistem İyileştirmesi

Year 2021, Issue: 28, 917 - 923, 30.11.2021
https://doi.org/10.31590/ejosat.1012124

Abstract

Üretim süreçlerinde iyileştirmelerin yapılabilmesi ve performans artışının sağlanabilmesi için sistemin mevcut durumunun gerçek süreç verileriyle ortaya konması ve süreçte aksaklık yaratan noktaların tespit edilebilmesi gerekmektedir. Üretim sistemlerinde mevcut durumun analiz edilmesi, darboğazların belirlenmesi, çıktı miktarı ve kaynak kullanımını iyileştirecek önerilerin ortaya konması ve değerlendirilebilmesinde simülasyon sıklıkla başvurulan bir yöntemdir. Bu çalışmada elektrik motoru üreten bir firmanın bir stator yarı mamul üretim hattının, AnyLogic yazılımı kullanılarak kesikli olay simülasyon modeli geliştirilmiştir. Ele alınan hatta 8 makine ve 11 operatör kaynağı kullanılarak akış tipi üretim gerçekleştirilmektedir. Model ile darboğaz belirleme ve hatta kaynak kullanım oranlarını maksimize eden ve stok maliyetlerini minimize eden akışın oluşturulması amaçlanmaktadır. Model parametreleri, hatta ilişkin geçmiş bir yıllık veriler kullanılarak yapılan girdi analizi ile hazırlanmıştır. Simülasyon modelinin çalıştırılması ile mevcut durum analiz edilmiştir. Bulgular, hatta yer alan sarım makinesinin darboğaz olduğuna işaret etmektedir. Sistem performansını iyileştirmek için, hattın verimliliği ve kaynakların kullanım oranlarına odaklanılarak, sarım makinesinin ideal üretim miktarı belirlenmiştir. Ayrıca, hat için yeni bir yerleşim önerisinde bulunularak bu önerinin operatör kullanım oranına katkısı değerlendirilmiştir.

Thanks

Bu çalışmaya verdiği destekten dolayı Volt Elektrik Motor San. Tic. A.Ş.’ye teşekkür ederiz.

References

  • Xu, L. D., Xu, E. L., & Li, L. (2018). Industry 4.0: state of the art and future trends. International Journal of Production Research, 56(8), 2941-2962.
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  • 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.
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  • Vazan, P., Znamenak, J., & Juhas, M. (2018, September). Proactive Simulation in Production Line Control. In 2018 IEEE 13th International Scientific and Technical Conference on Computer Sciences and Information Technologies (CSIT) (Vol. 1, pp. 52-55). IEEE.
  • W. Zhou, S. Q. Li, Y. Q. Huang, and J. F. Wang, “Simulation Based Capacity Optimization of a General Assembly Line with Extremely Unbalanced Station Process Time,” In 2019 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM) (pp. 1245-1249), 2019, IEEE.
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  • Kulkarni, R. G., Kulkarni, V. N., & Gaitonde, V. N. (2018). Productivity improvement in assembly workstation of motor winding unit. Materials Today: Proceedings, 5(11), 23518-23525.
  • Heshmat, M., El-Sharief, M., & El-Sebaie, M. (2017). Simulation modelling and analysis of a production line. International Journal of Simulation and Process Modelling, 12(3-4), 369-376.
  • Damiani, L., Demartini, M., Giribone, P., Maggiani, M., Revetria, R., & Tonelli, F. (2018). Simulation and digital twin based design of a production line: A case study. In Proceedings of the International MultiConference of Engineers and Computer Scientists (Vol. 2).
  • Öner-Közen, M., Minner, S., & Steinthaler, F. (2017). Efficiency of paced and unpaced assembly lines under consideration of worker variability–A simulation study. Computers & Industrial Engineering, 111, 516-526.
  • Opacic, L., Sowlati, T., & Mobini, M. (2018). Design and development of a simulation-based decision support tool to improve the production process at an engineered wood products mill. International journal of production economics, 199, 209-219.
  • Singh, N., Herps, K., Martagan, T., & Adan, I. J. (2019, December). > Simulation-Based Performance Evaluation of A Manufacturing Facility with Vertical As/Rs. In 2019 Winter Simulation Conference (WSC) (pp. 2001-2012). IEEE.
  • Allgeier, H., Flechsig, C., Lohmer, J., Lasch, R., Schneider, G., & Zettler, B. (2020, December). Simulation-Based Evaluation of Lot Release Policies in a Power Semiconductor Facility–a Case Study. In 2020 Winter Simulation Conference (WSC) (pp. 1503-1514). IEEE.
  • AnyLogic website. [Online]. Available: https://www.anylogic.com
  • A. Kampa, A., Gołda, G., & Paprocka, I. (2017). Discrete event simulation method as a tool for improvement of manufacturing systems. Computers, 6(1), 10.

Bottleneck Analysis and System Improvement with Simulation Method in a Manufacturing Facility

Year 2021, Issue: 28, 917 - 923, 30.11.2021
https://doi.org/10.31590/ejosat.1012124

Abstract

Achieving improvements in production processes requires analyzing the processes and identifying bottlenecks based on actual process data. Simulation is a widely applied method in analyzing the current state of production systems, identifying bottlenecks, developing alternative scenarios to improve throughput and resource utilization, and evaluating these scenarios’ performances. In this study, a discrete event simulation model of a stator production line of a company that produces electrical motors has been developed using AnyLogic software. The analyzed production line is a flow-type production line that consists of 8 machines and 11 operators. This study aims to detect the bottlenecks and determine a flow that maximizes resource utilization rates and minimizes inventory holding costs. The model parameters have been prepared with input analysis using the actual data from the past year of the line. The current state of the line revealed by running the simulation model has been analyzed. The findings have indicated that the winding machine is the bottleneck of the production line. The ideal production quantity of the winding machine has been determined by focusing on the productivity of the line and the utilization rates of resources to improve the system performance. Moreover, an alternative layout for the line has been suggested. Finally, the projected gains with this layout have been evaluated in terms of operator utilization rate.

References

  • Xu, L. D., Xu, E. L., & Li, L. (2018). Industry 4.0: state of the art and future trends. International Journal of Production Research, 56(8), 2941-2962.
  • Theorin, A., Bengtsson, K., Provost, J., Lieder, M., Johnsson, C., Lundholm, T., & Lennartson, B. (2017). An event-driven manufacturing information system architecture for Industry 4.0. International journal of production research, 55(5), 1297-1311.
  • 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.
  • Pehrsson, L., Ng, A. H., & Bernedixen, J. (2016). Automatic identification of constraints and improvement actions in production systems using multi-objective optimization and post-optimality analysis. Journal of manufacturing systems, 39, 24-37.
  • Goldratt, E. M. (1990). Theory of constraints (pp. 1-159). Croton-on-Hudson: North River.
  • Leporis, M., & Králová, Z. (2010). A simulation approach to production line bottleneck analysis. In International conference cybernetics and informatics (pp. 13-22).
  • Li, L. (2009). Bottleneck detection of complex manufacturing systems using a data-driven method. International Journal of Production Research, 47(24), 6929-6940.
  • Li, L., Chang, Q., & Ni, J. (2009). Data driven bottleneck detection of manufacturing systems. International Journal of production research, 47(18), 5019-5036.
  • Vazan, P., Znamenak, J., & Juhas, M. (2018, September). Proactive Simulation in Production Line Control. In 2018 IEEE 13th International Scientific and Technical Conference on Computer Sciences and Information Technologies (CSIT) (Vol. 1, pp. 52-55). IEEE.
  • W. Zhou, S. Q. Li, Y. Q. Huang, and J. F. Wang, “Simulation Based Capacity Optimization of a General Assembly Line with Extremely Unbalanced Station Process Time,” In 2019 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM) (pp. 1245-1249), 2019, IEEE.
  • Al-Khafaji, S. K., & Al-Rufaifi, H. M. (2012, July). A Case Study of Production Improvement by Using Lean with Simulation Modeling. In Proceedings of the 2012 International Conference on Industrial Engineering and Operations Management. Istanbul, Turkey (pp. 271-279).
  • Kulkarni, R. G., Kulkarni, V. N., & Gaitonde, V. N. (2018). Productivity improvement in assembly workstation of motor winding unit. Materials Today: Proceedings, 5(11), 23518-23525.
  • Heshmat, M., El-Sharief, M., & El-Sebaie, M. (2017). Simulation modelling and analysis of a production line. International Journal of Simulation and Process Modelling, 12(3-4), 369-376.
  • Damiani, L., Demartini, M., Giribone, P., Maggiani, M., Revetria, R., & Tonelli, F. (2018). Simulation and digital twin based design of a production line: A case study. In Proceedings of the International MultiConference of Engineers and Computer Scientists (Vol. 2).
  • Öner-Közen, M., Minner, S., & Steinthaler, F. (2017). Efficiency of paced and unpaced assembly lines under consideration of worker variability–A simulation study. Computers & Industrial Engineering, 111, 516-526.
  • Opacic, L., Sowlati, T., & Mobini, M. (2018). Design and development of a simulation-based decision support tool to improve the production process at an engineered wood products mill. International journal of production economics, 199, 209-219.
  • Singh, N., Herps, K., Martagan, T., & Adan, I. J. (2019, December). > Simulation-Based Performance Evaluation of A Manufacturing Facility with Vertical As/Rs. In 2019 Winter Simulation Conference (WSC) (pp. 2001-2012). IEEE.
  • Allgeier, H., Flechsig, C., Lohmer, J., Lasch, R., Schneider, G., & Zettler, B. (2020, December). Simulation-Based Evaluation of Lot Release Policies in a Power Semiconductor Facility–a Case Study. In 2020 Winter Simulation Conference (WSC) (pp. 1503-1514). IEEE.
  • AnyLogic website. [Online]. Available: https://www.anylogic.com
  • A. Kampa, A., Gołda, G., & Paprocka, I. (2017). Discrete event simulation method as a tool for improvement of manufacturing systems. Computers, 6(1), 10.
There are 20 citations in total.

Details

Primary Language Turkish
Subjects Engineering
Journal Section Articles
Authors

Ege Cihangir 0000-0002-2294-1122

Fatma Demircan Keskin 0000-0002-7000-4731

Ural Gökay Çiçekli 0000-0002-6032-9540

Gökhan Yakan 0000-0002-6619-3138

Publication Date November 30, 2021
Published in Issue Year 2021 Issue: 28

Cite

APA Cihangir, E., Demircan Keskin, F., Çiçekli, U. G., Yakan, G. (2021). Bir Üretim İşletmesinde Simülasyon Yöntemi ile Darboğaz Analizi ve Sistem İyileştirmesi. Avrupa Bilim Ve Teknoloji Dergisi(28), 917-923. https://doi.org/10.31590/ejosat.1012124