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A Comparison of the Effects of Worker-Related Variables on Process Efficiency in a Manufacturing System Simulation

  • Lee, Dongjune (Department of Bio-Mechatronic Engineering, College of Biotechnology & Bioengineering, Sungkyunkwan University) ;
  • Park, Hyunjoon (Department of Bio-Mechatronic Engineering, College of Biotechnology & Bioengineering, Sungkyunkwan University) ;
  • Choi, Ahnryul (Department of Bio-Mechatronic Engineering, College of Biotechnology & Bioengineering, Sungkyunkwan University) ;
  • Mun, Joung H. (Department of Bio-Mechatronic Engineering, College of Biotechnology & Bioengineering, Sungkyunkwan University)
  • Received : 2012.10.25
  • Accepted : 2013.02.19
  • Published : 2013.03.01

Abstract

Purpose: The goal of this study was to build an accurate digital factory that evaluates the performance of a factory using computer simulation. To achieve this goal, we evaluated the effect of worker-related variables on production in a simulation model using comparative analysis of two cases. Methods: The overall work process and worker-related variables were determined and used to build a simulation model. Siemens PLM Software's Plant Simulation was used to build a simulation model. Also, two simulation models were built, where the only difference was the use of the worker-related variable, and the total daily production analyzed and compared in terms of the individual process. Additionally, worker efficiency was evaluated based on worker analysis. Results: When the daily production of the two models were compared, a 0.16% error rate was observed for the model where the worker-related variables were applied and error rate was approximately 5.35% for the model where the worker-related variables were not applied. In addition, the production in the individual processes showed lower error rate in the model that included the worker-related variables than the model where the worker-related variables were not used. Also, among the total of 22 workers, only three workers satisfied the IFRS (International Financial Reporting Standards) suggested worker capacity rate (90%). Conclusions: In the daily total production and individual process production, the model that included the worker-related variables produced results that were closer to the real production values. This result indicates the importance of worker elements as input variables, in regards to building accurate simulation models. Also, as suggested in this study, the model that included the worker-related variables can be utilized to analyze in more detail actual production. The results from this study are expected to be utilized to improve the work process and worker efficiency.

Keywords

References

  1. Arndt, F. W. 2006. THE DIGITAL FACTORY Planning and simulation of production in automotive industry. In: Informatics in Control, Automation and Robotics I, eds. J. Braz, H. Araujo, A. Vieira and B. Encarnacao, pp. 27-35. Springer.
  2. Azadeh, A., Z. S. Faiz, S. M. Asadzadeh and R. T. Moghaddam. 2011. An integrated artificial neural network-computer simulation for optimization of complex tandem queue systems. Mathematics and Computers in Simulation 82:666-678. https://doi.org/10.1016/j.matcom.2011.06.009
  3. Baines, T., S. Mason, P. O. Siebers and J. Ladbrook. 2004. Humans: the missing link in manufacturing simulation. Simulation Modelling Practice and Theory 12:515-526. https://doi.org/10.1016/S1569-190X(03)00094-7
  4. Battini, D., M. Faccio, A. Persona and F. Sgarbossa. 2011. New methodological framework to improve productivity and ergonomics in assembly system design. International Journal of Industrial Ergonomics 41:30-42. https://doi.org/10.1016/j.ergon.2010.12.001
  5. Becker, C., M. Faber, K. Hertel and R. Manstetten. 2005. Malthus vs. Wordsworth: Perspectives on humankind, nature and economy. A contribution to the history and the foundations of ecological economics. Ecological Economics 53(3):299-310. https://doi.org/10.1016/j.ecolecon.2005.02.006
  6. Brendan, R., Q. Rong, S. Alex and P. Tony. 2011. Integrating human factors and operational research in a multidisciplinary investigation of road maintenance. Ergonomics 54:436-452. https://doi.org/10.1080/00140139.2011.562983
  7. Carey, E. J. and T. J. Gallwey. 2002. Evaluation of human postures with computer aids and virtual workplace designs. International Journal of Production Research 40(4):825-843. https://doi.org/10.1080/00207540110093927
  8. Cubert, R. M. and P. A. Fishwick. 1998. Software Architecture for Distributed Simulation Multimodels. In: SPIE Aerosense Conference, pp. 154-163.
  9. Dul, J. and W. P. Neumann. 2009. Ergonomics contributions to company strategies. Applied Ergonomics 40:745-752. https://doi.org/10.1016/j.apergo.2008.07.001
  10. International Accounting Standards Committee Foundation. 2012. A Guide through IFRS. United Kingdom: IASCF.
  11. Kim, D. S. and D. H. Moon. 2011. A case study of comparing the measuring methods for workloads of resources in a manufacturing processes of semiconductor-parts. The Korea Society for Simulation 20(3):49-58 (In Korean, with English abstract). https://doi.org/10.9709/JKSS.2011.20.3.049
  12. Kim, Y. J., H. J. Park and J. H. Mun. 2011. The study for optimal layout of the Eleutherococcus Senticosus sap production line analyzed by simulation model. Agricultural and Biosystems Engineering 36(6): 461-466 (In Korean, with English abstract). https://doi.org/10.5307/JBE.2011.36.6.461
  13. Kim, W. K. 2003. Automobile painting process simulation for productivity improvement. The Korean Institute of Plant Engineering 8(1):171-183 (In Korean, with English abstract).
  14. Lee, K. S., H. R. Koo, D. S. Lim, H. C. Kim, H. S. Chae and K.D Min. 2011. The research of job stress and MSDs of small plants with agricultural products. In: 2011 Fall Conference and General Meeting of Ergonomics Society of Korea, pp. 526-529, Cheonan, Chungnam. (In Korean)
  15. Mancini, F., G. Vigano, Z. Liao, M. Sacco and C.R. Boer. 2004. The Virtual Factory: A semi-immersive interactive 3D environment. In: Summer Computer Simulation Conference, pp. 502-506, San Jose, CA.
  16. Neumann, W. P., J. Winkel, L. Medbo, R. Magneberg and S. E. Mathiassen. 2006. Production system design elements influencing productivity and ergonomics. International Journal of Operations & Production Management 26: 904-923. https://doi.org/10.1108/01443570610678666
  17. Otto, A. and A. Scholl. 2011. Incorporating ergonomic risks into assembly line balancing. European Journal of Operational Research 212:277-286. https://doi.org/10.1016/j.ejor.2011.01.056
  18. Pegden, C. D., V. Kachitvichyanukul, J. O. Hendrikson, R.G. Ingallsand and B.W. Schmeiser. 2001. Simulation environment for the new millennium. In: Proceedings of the 2001 Winter Simulation Conference, pp. 541-547.
  19. Siemieniuch, C. E. and M. A. Sinclair. 2002. On complexity, process ownership and organizational learning in manufacturing organizations, from an ergonomics perspective. Applied Ergonomics 33:449-462. https://doi.org/10.1016/S0003-6870(02)00025-X
  20. Siemieniuch, C. E. and M. A. Sinclair. 2006. Systems integration. Applied Ergonomics 37:91-110. https://doi.org/10.1016/j.apergo.2005.06.012
  21. Simpson, R. and A. Abakarov. 2009. Optimal scheduling of canned food plants including simultaneous sterilization. Journal of Food Engineering 90(1):53-59. https://doi.org/10.1016/j.jfoodeng.2008.06.009
  22. Souza, M. C. F., M. Sacco and A. J. V. Porto. 2006. Virtual manufacturing as a way for the factory of the future. Journal of Intelligent Manufacturing 17(6):725-735. https://doi.org/10.1007/s10845-006-0041-1
  23. Zanoelo, E. F., A. Abitante and L. A. C. Meleiro. 2008. Dynamic modeling and feedback control for conveyorsbelt dryers of mate leaves. Journal of Food Engineering 84(3):458-468. https://doi.org/10.1016/j.jfoodeng.2007.06.008
  24. Zhou, M. C. and K. Venkatesh. 1998. Modeling, simulation, and control of flexible manufacturing systems: A Petri net approach. Singapore: World Scientific.