1. Proceedings
  2. I3M
  3. 2020
  4. EMSS
  5. 10.46354/i3m.2020.emss.018

Efficiency of increasing of the track speed using simulation in Opentrack

  • a Anas Itani , 
  • b Mohammed Alghamdy , 
  • c Haroon Nazir , 
  • d Saurabh Sharma , 
  • e Rafiq Ahmad 
  • a,b,c,d,e  University of Alberta, Edmonton AB, T6G 1H9, Canada
Cite as
Itani A., Alghamdy M., Nazir H., Sharma S., Ahmad R. (2020). A decision-making tool to integrate lean 4.0 in windows manufacturing using simulation and optimization models. Proceedings of the 32nd European Modeling & Simulation Symposium (EMSS 2020), pp. 137-145. DOI: https://doi.org/10.46354/i3m.2020.emss.018

Abstract

Lean Manufacturing (LM) is known as an effective methodology utilized by manufacturing organizations to increase productivity, improve quality, and decrease costs. On the other hand, Industry 4.0 (I4.0) transforms a traditional factory into a smart one by integrating its machines and processes using advanced information technology and robust communication systems. Simulation and production optimization have proved to be an effective tool to analyze the dynamic nature of the processes and statistically justify modifications paybacks. Currently, there is no decision-making tool that integrates LM and I4.0 using hybrid simulation and production optimization models to benefit the manufacturing processes. For that reason, a framework integrating the LM and I4.0 is developed and implemented on a local window manufacturing plant using simulation and optimization tools to improve the overall productivity of their manufacturing process. The developed framework is able to investigate process improvements and optimize the outputs to identify the bottlenecks and resources utilization, provide a useful tool to test the effectiveness of any proposed process improvements, and identify non-value adding tasks and analyze time breakdown. The framework can be applied to any manufacturing company to not only identify and eliminate the waste but also to statistically forecast the resulting benefits before changes are implemented.

References

  1. Government of Canada. (2019). Canadian manufacturing sector gateway. Retrieved March 15,
    2019, from Canada.ca: https://www.ic.gc.ca/eic/site/mfgfab.nsf/eng/home.
  2. Mourtzis, D., Doukas, M., & Bernidaki, D. (2014). Simulation in manufacturing: Review and
    challenges. Procedia Cirp, 25, 213-229.
  3. Elbert, M. (2012). Lean production for the small company. Crc Press. 
  4. Abdulmalek, F. A., & Rajgopal, J. (2007). Analyzing the benefits of lean manufacturing and value stream mapping via simulation: A process sector case study. International Journal of production economics, 107(1), 223-236.
  5. Pavnaskar, S. J., Gershenson, J. K., & Jambekar, A. B. (2003). Classification scheme for lean
    manufacturing tools. International Journal of Production Research, 41(13), 3075-3090.
  6. Detty, R. B., & Yingling, J. C. (2000). Quantifying benefits of conversion to lean manufacturing with discrete event simulation: a case study. International Journal of Production Research, 38(2), 429-445.
  7. Ahmad, R., Tichadou, S., & Hascoet, J. Y. (2017). A knowledge-based intelligent decision system for production planning. The International Journal of Advanced Manufacturing Technology, 89(5-8), 1717-1729.
  8. Goienetxea Uriarte, A., Ng, A. H., & Urenda Moris, M. (2018). Supporting the lean journey with simulation and optimization in the context of Industry 4.0. In Procedia Manufacturing (Vol. 25, pp. 586-593). 
  9. Dombrowski, U., Richter, T., & Krenkel, P. (2017). Interdependencies of Industrie 4.0 & Lean
    production systems: A use cases analysis. Procedia Manufacturing, 11, 1061-1068.
  10. Heilala, J., & Voho, P. (2001). Modular reconfigurable flexible final assembly systems. Assembly
    Automation.
  11. Thompson, M. B. (1993). The marriage between simulation & real-time control. APICS-The
    performance advantage, 8, 43-46.
  12. Goienetxea Uriarte, A., Ng, A. H., & Urenda Moris, M. (2020). Bringing together Lean and simulation: a comprehensive review. International Journal of Production Research, 58(1), 87-117.
  13. AbouRizk, S. M., Hague, S., & Ekyalimpa, R. (2016). Construction simulation: An introduction using Simphony. Edmonton, Canada: University of Alberta.
  14. Huber, W. (2016). Industrie 4.0 in der Automobilproduktion. Wiesbaden: Springer
    Fachmedien Wiesbaden.
  15. Künzel, H. (2016). Erfolgsfaktor Lean Management 2.0. Springer Berlin Heidelberg.
  16. BearingPoint, G. (2017). Lean 4.0—Schlank durch Digitalisierung. H UND V JOURNAL, 69(1), 30-31.
  17. Staufen, A. G., & der TU Darmstadt, P. T. W. (25). Jahre Lean Management–Lean gestern, heute und morgen. EineStudie der Staufen AG und des Instituts PTW der TechnischenUniversität Darmstadt.
  18. Koether, R., & Meier, K. J. (2017). Lean Production für die variantenreiche Einzelfertigung (p. 25). Wiesbaden: Springer Fachmedien Wiesbaden.
  19. Biao, W. A. N. G., ZHAO, J. Y., WAN, Z. G., Hong, L. I., & Jian, M. A. (2016). Lean intelligent production system and value stream practice. DEStech Transactions on Economics, Business and Management, (icem)
  20. Metternich, J., Müller, M., Meudt, T., & Schaede, C. (2017). Lean 4.0–zwischen Widerspruch und Vision. ZWF Zeitschrift für wirtschaftlichen Fabrikbetrieb, 112(5), 346-348.
  21. Wagner, T., Herrmann, C., & Thiede, S. (2017). Industry 4.0 impacts on lean production systems. Procedia Cirp, 63, 125-131. 
  22. Pokorni, B., Schlund, S., Findeisen, S., Tomm, A., Euper, D., Mehl, D., ... & Palm, D. (2017).
    Produktionsassessment 4.0. ZWF Zeitschrift für wirtschaftlichen Fabrikbetrieb, 112(1-2), 20-24.
  23. Rüttimann, B. G., & Stöckli, M. T. (2016). Lean and Industry 4.0—twins, partners, or contenders? A due clarification regarding the supposed clash of two production systems. Journal of Service Science and Management, 9(6), 485-500.
  24. Kolberg, D., & Zühlke, D. (2015). Lean automation enabled by industry 4.0 technologies. IFACPapersOnLine, 48(3), 1870-1875.
  25. Ganschar, O., Gerlach, S., Hämmerle, M., Krause, T., & Schlund, S. (2013). Produktionsarbeit der ZukunftIndustrie 4.0 (Vol. 150). D. Spath (Ed.). Stuttgart: Fraunhofer Verlag.
  26. Sanders, A., Elangeswaran, C., & Wulfsberg, J. P. (2016). Industry 4.0 implies lean manufacturing: Research activities in industry 4.0 function as enablers for lean manufacturing. Journal of Industrial Engineering and Management (JIEM), 9(3), 811-833.
  27. Vogel - Heuser, B., Bauernhansl, T., & Hompel, M. T. (2017). Handbuch Industrie 4.0 Bd. 4. Allgemeine Grundlagen, 2.
  28. Mrugalska, B., & Wyrwicka, M. K. (2017). Towards lean production in industry 4.0. Procedia Engineering, 182, 466-473.