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Manufacturing Complexity Analysis: A Simulation-Based Methodology

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Collaborative Engineering

Abstract

Variability in products is driven by the customer and pushes the manufacturer to offer product variants by mass customization. Companies that offer product variety while maintaining competitive cost and quality will gain a competitive edge over other companies in today's market. As the automobile industry adapts to the mass customization strategy, it would require the ability to conduct early design, development, and manufacturing trade-offs among competing objectives. An analytical approach is then required to manage the complexity and the risk associated with this environment. This chapter will present a set of simulation-based methodologies for measuring complexity. The developed methodologies will assist designers in analyzing and mitigating the risks associated with product variety and its impact on manufacturing complexity.

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References

  1. Aigbedo, H., and Monden, Y. (1996). A simulation analysis for two-level sequence scheduling for just-in-time (JIT) mixed-model assembly lines. International Journal of Production Research , 34(11), 3107–3124.

    Article  MATH  Google Scholar 

  2. Akturk, M. S., and Erhun, F. (1999). An overview of design and operational issues of kanban systems . International Journal of Production Research, 37(17), 3859–3881.

    Article  MATH  Google Scholar 

  3. Anderson, D. M. (1997). Agile Product Development for Mass Customization. How to Develop and Deliver. Second Edition, Chicago, Irwin Professional.

    Google Scholar 

  4. Anwar, F. M., and Nagi, R. (1997). Integrated lot-sizing and scheduling for just-in-time production of complex assemblies with finite setups. International Journal of Production Research, 35(5), 1447–1470.

    Article  MATH  Google Scholar 

  5. Baykoc, O. F., and Erol, S. (1998). Simulation modeling and analysis of a JIT production system. International Journal of Production Economics, 55(7), 203–212.

    Article  Google Scholar 

  6. Benjaafar, S., Kim, J. S., and Vishwanadham, N. (2004). On the effect of product variety in production-inventory systems . Annals of Operations Research, 126(1–4), 71–101.

    Article  MATH  MathSciNet  Google Scholar 

  7. Bukchin, J. (1998). A comparative study of performance measures for throughput of a mixed model assembly line in a JIT environment. International Journal of Production Research, 36(10), 2669–2685.

    Article  MATH  Google Scholar 

  8. Calinescu, A., Efstathiou, J., Bermejo, J., and Schirn, J. (1997). Assessing decision making and process complexity in manufacturing through simulation. Proceedings of the 6th IFAC Symposium on Automated Systems Based on Human Skill, IFAC, Germany, 159–162.

    Google Scholar 

  9. Calinescu, A., Efstathiou, J., Huatuco, L. H., and Sivadasan, S. (2002). Classes of complexity in manufacturing. Research Report, Manufacturing Research Group, Department of Engineering Science, University of Oxford.

    Google Scholar 

  10. Carlson, G. J., and Yao, A. C. (1992). Mixed model assembly simulation. International Journal of Production Economics, 26(4), 161–167.

    Article  Google Scholar 

  11. Chu,C. H., and Shih, W. L. (1992). Simulation studies in JIT production. International Journal of Production Research, 30(1), 2573–2586

    Article  Google Scholar 

  12. Clair, R., Lafrance, J. C., and Hillebrand, D. (1996), The US Automobile Manufacturing Industry. The US Department of Commerce, Office of Technology Policy.

    Google Scholar 

  13. Detty, R. B., and 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.

    Article  MATH  Google Scholar 

  14. Dobson, G., and Yano, C. A. (2002). Product offering, pricing, and make-to-stock/make-to-order decisions with shared capacity. Production and Operations Management, 11(3), 293–306.

    Article  Google Scholar 

  15. Drexl, A., and Kimms, A. (2001). Sequencing JIT mixed-model assembly lines under station-load and part-usage constraints . Management Science, 47(3), 480–491.

    Article  Google Scholar 

  16. Frizelle, G. (1996). An entropic measurement of complexity in manufacturing operations. Research Report, Department of Engineering, University of Cambridge, UK.

    Google Scholar 

  17. Frizelle, G., and Woodcock, E. (1995). Measuring complexity as an aid to developing operational strategy. International Journal of Operations and Production Management, 15(5), 26–39.

    Article  Google Scholar 

  18. Garcia-Sabater, J. P. (2001). The problem of JIT dynamic sequencing. A model and a parametric procedure. ORP 3 , September 26–29.

    Google Scholar 

  19. Ishii, K., and Martin, M. V. (1996). Design for variety. A Methodology for understanding the costs of product proliferation. Proceedings of the 1996 ASME Design Engineering Technical Conferences and Computers in Engineering Conference, August 18–22.

    Google Scholar 

  20. Kamrani, A. K., Adat, A. K., and Rahman, A. (2004). A simulation-based methodology for manufacturing complexity analysis. Proceedings of the National IIE Conference, May 2004.

    Google Scholar 

  21. Li, J. W. (2003). Simulation based comparison of push and pull systems in a job-shop environment considering the context of JIT implementation. International Journal of Production Research, 41(3), 427–447.

    Article  MATH  Google Scholar 

  22. MacDuffie, J. P., Sethuraman, K., and Fisher, M. L. (1996). Product variety and manufacturing performance: Evidence from the International Automotive Assembly Plant Study. Management Science, 42(3), 350–369.

    Article  MATH  Google Scholar 

  23. Malik, S. A., and Sullivan, W. G. (1995). Impact of ABC information on product mix and costing decisions. IEEE Transactions of Engineering Management, 42(2),171–176.

    Article  Google Scholar 

  24. Mane, N., Nahavanadi, S., and Zhang, J. (2002). Sequencing production on an assembly line using goal chasing and user defined algorithm. Proceeding of the 2002 Winter Simulation Conference.

    Google Scholar 

  25. Martinez, F. M., and Bedia, L. M. A. (2002). Modular simulation tool for modeling JIT manufacturing. International Journal of Production Research, 40(7), 1529–1547.

    Article  Google Scholar 

  26. Mejabi, O., and Wasserman, S. (1992). Basic concepts of JIT modeling. International Journal of Production Research, 30(1), 141–149.

    Article  MATH  Google Scholar 

  27. Monden, Y. (1998).Toyota Production Systems—An Integrated Approach to Just in Time. Third Edition, Chapman & Hall.

    Google Scholar 

  28. Muralidhar, K., Swenseth, S. R., and Wilson, R. L. (1992). Describing processing time when simulating JIT environments. International Journal of Production Research, 30(1), 1–11.

    Article  MATH  Google Scholar 

  29. Ozbayrak, M., Akgun, M., and Turker, A. K. (2004). Activity based cost estimation in a push/ pull advanced manufacturing system. Internal Journal of Production Economics, 87, 49–65.

    Article  Google Scholar 

  30. Savsar, M. (1997). Simulation analysis of a pull-push system for an electronic assembly line . International Journal of Production Economics, 51, 205–214.

    Article  Google Scholar 

  31. Spedding, T. A., and Sun, G. Q. (1999). Application of discrete event simulation to the activity based costing of manufacturing systems. International Journal of Production Economics, 58, 289–301.

    Article  Google Scholar 

  32. Sumischrast, R. T., Russell, A., and Taylor, B. W. (1992). A comparative analysis of sequencing procedures for mixed-model assembly line in just-in-time production system. International Journal of Production Research, 30(1), 199–214.

    Article  Google Scholar 

  33. Taylor, L. J., III (1999). A simulation study of WIP inventory drive systems and their effect on financial measurements. Integrated Manufacturing Systems, 10(5), 306–305.

    Article  Google Scholar 

  34. Wang, D., and Xu, C. G. (1997). Hybrid push/pull production control strategy simulation and its applications. Production Planning & Control, 8(2), 142–151.

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Associate Professor, Department of Industrial Engineering, University of Houston, Houston, Texas, 77004, USA

    Ali K. Kamrani

  2. Department of Industrial Engineering, University of Houston, Houston, Texas, 77004, USA

    Arun Adat

Authors
  1. Ali K. Kamrani
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  2. Arun Adat
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Editor information

Editors and Affiliations

  1. Industrial Engineering Department, University of Houston, E206 Engineering Building 2, Houston, TX, 77204-4008

    Ali K. Kamrani  & Emad S. Abouel Nasr  & 

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Kamrani, A.K., Adat, A. (2008). Manufacturing Complexity Analysis: A Simulation-Based Methodology. In: Kamrani, A.K., Nasr, E.S.A. (eds) Collaborative Engineering. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-47321-5_11

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  • DOI: https://doi.org/10.1007/978-0-387-47321-5_11

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-0-387-47319-2

  • Online ISBN: 978-0-387-47321-5

  • eBook Packages: EngineeringEngineering (R0)

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