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Integrated quality and quantity modeling of a production line

  • Jongyoon Kim
  • Stanley B. GershwinEmail author
Chapter

Abstract

During the past three decades, the success of the Toyota Production System has spurred much research in manufacturing systems engineering. Productivity and quality have been extensively studied, but there is little research in their intersection. The goal of this paper is to analyze how production system design, quality, and productivity are inter-related in small production systems. We develop a new Markov process model for machines with both quality and operational failures, and we identify important differences between types of quality failures. We also develop models for two-machine systems, with infinite buffers, buffers of size zero, and finite buffers. We calculate total production rate, effective production rate (ie, the production rate of good parts), and yield. Numerical studies using these models show that when the first machine has quality failures and the inspection occurs only at the second machine, there are cases in which the effective production rate increases as buffer sizes increase, and there are cases in which the effective production rate decreases for larger buffers. We propose extensions to larger systems.

Keywords

Quality Productivity Manufacturing system design 

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References

  1. 1.
    Alles M, Amershi A, Datar S, Sarkar R (2000) Information and incentive effects of inventory in JIT production. Management Science 46(12): 1528–1544CrossRefGoogle Scholar
  2. 2.
    Besterfield DH, Besterfield-Michna C, Besterfield G, Besterfield-Sacre M (2003) Total quality management. Prentice Hall, Englewood CliffsGoogle Scholar
  3. 3.
    Black JT (1991) The design of the factory with a future. McGraw-Hill, New YorkGoogle Scholar
  4. 4.
    Bonvik AM, Couch CE, Gershwin SB (1997) A comparison of production line control mechanisms. International Journal of Production Research 35(3): 789–804Google Scholar
  5. 5.
    Burman M, Gershwin SB, Suyematsu C (1998) Hewlett-Packard uses operations research to improve the design of a printer production line. Interfaces 28(1): 24–26CrossRefGoogle Scholar
  6. 6.
    Buzacott JA, Shantikumar JG (1993) Stochastic models of manufacturing systems. Prentice-Hall, Englewood CliffsGoogle Scholar
  7. 7.
    Cheng CH, Miltenburg J, Motwani J (2000) The effect of straight and U shaped lines on quality. IEEE Transactions on Engineering Management 47(3): 321–334Google Scholar
  8. 8.
    Dallery Y, Gershwin SB (1992) Manufacturing flow line systems: a review of models and analytical results. Queuing Systems Theory and Applications 12: 3–94Google Scholar
  9. 9.
    Fujimoto T (1999) The evolution of a manufacturing systems at Toyota. Oxford University Press, OxfordGoogle Scholar
  10. 10.
    Gershwin SB (1994) Manufacturing systems engineering. Prentice Hall, Englewood CliffsGoogle Scholar
  11. 11.
    Gershwin SB (2000) Design and operation of manufacturing systems — the control-point policy. IIE Transactions 32(2): 891–906Google Scholar
  12. 12.
    Gershwin SB, Schor JE (2000) Efficient algorithms for buffer space allocation. Annals of Operations Research 93: 117–144MathSciNetGoogle Scholar
  13. 13.
    Inman RR, Blumenfeld DE, Huang N, Li J (2003) Designing production systems for quality: research opportunities from an automotive industry perspective. International Journal of Production Research 41(9): 1953–1971CrossRefGoogle Scholar
  14. 14.
    Kim J (2004) Integrated quality and quantity modeling of a production line. Massachusetts Institute of Technology PhD thesis (in preparation)Google Scholar
  15. 15.
    Law AM, Kelton DW, Kelton WD, Kelton DM (1999) Simulation modeling and analysis. McGraw-Hill, New YorkGoogle Scholar
  16. 16.
    Ledolter J, Burrill CW (1999) Statistical quality control. Wiley, New YorkGoogle Scholar
  17. 17.
    Monden Y (1998) Toyota production system — an integrated approach to just-in-time. EMP Books, NorcrossGoogle Scholar
  18. 18.
    Montgomery DC (2001) Introduction to statistical quality control, 4th edn. Wiley, New YorkGoogle Scholar
  19. 19.
    Pande P, Holpp L (2002) What is six sigma? McGraw-Hill, New YorkGoogle Scholar
  20. 20.
    Phadke M (1989) Quality engineering using robust design. Prentice Hall, Englewood CliffsGoogle Scholar
  21. 21.
    Raz T (1986) A survey of models for allocating inspection effort in multistage production systems. Journal of Quality Technology 18(4): 239–246Google Scholar
  22. 22.
    Shin WS, Mart SM, Lee HF (1995) Strategic allocation of inspection stations for a flow assembly line: a hybrid procedure. IIE Transactions 27: 707–715Google Scholar
  23. 23.
    Shingo S (1989) A study of the Toyota production system from an industrial engineering viewpoint. Productivity Press, PortlandGoogle Scholar
  24. 24.
    Toyota Motor Corporation (1996) The Toyota production systemGoogle Scholar
  25. 25.
    Wein L (1988) Scheduling semiconductor wafer fabrication. IEEE Transactions on semiconductor manufacturing 1(3): 115–130MathSciNetCrossRefGoogle Scholar
  26. 26.
    Wooddall WH, Montgomery DC (1999) Research issues and ideas in statistical process control. Journal of Quality Technology 31(4): 376–386Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  1. 1.Department of Mechanical EngineeringMassachusetts Institute of TechnologyCambridgeUSA

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