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Structural Properties and a Heuristic for Kanban-Controlled Serial Lines

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  • Sridhar R. Tayur

    (Graduate School of Industrial Administration, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213)

Abstract

Kanban-controlled serial manufacturing systems have been popular in Japan for many years. The analytical intractibility of such systems makes simulation and heuristics essential in studying them. In this paper we develop some theoretical results---reversibility and dominance---that characterize the dynamics of these systems, provide insight into their behavior and help greatly reduce the simulation effort needed to study them. These structural results also provide the basis for developing heuristics to deal effectively with particular cases. Reversibility deals with certain permutations of the machines; dominance deals with the allocation of kanbans to cells. In addition, we show that if our goal is to maximize the throughput with a given total fixed number of cards, all of the machines should be placed in a single cell. These structural results hold in general because no assumptions are made on the processing time distribution of the machines, the number of cells, the total number of kanbans, or whether or not the machines are identical. Based on the structural results, we also develop a heuristic for the allocation of kanbans to a balanced line. We briefly describe an implementation of this model at a laminate manufacturing plant.

Suggested Citation

  • Sridhar R. Tayur, 1993. "Structural Properties and a Heuristic for Kanban-Controlled Serial Lines," Management Science, INFORMS, vol. 39(11), pages 1347-1368, November.
    • Handle: RePEc:inm:ormnsc:v:39:y:1993:i:11:p:1347-1368
    DOI: 10.1287/mnsc.39.11.1347
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    Cited by:

    1. Ovalle, Oscar Rubiano & Marquez, Adolfo Crespo, 2003. "Exploring the utilization of a CONWIP system for supply chain management. A comparison with fully integrated supply chains," International Journal of Production Economics, Elsevier, vol. 83(2), pages 195-215, February.
    2. Takahashi, Katsuhiko & Morikawa, Katsumi & Nakamura, Nobuto, 2004. "Reactive JIT ordering system for changes in the mean and variance of demand," International Journal of Production Economics, Elsevier, vol. 92(2), pages 181-196, November.
    3. Michael Reiß & Tobias Bernecker, 2006. "Conjoint Controlling: Organisationsgerechte Controllingsysteme zwischen Better Budgeting und komplex-hybrider Steuerung," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 17(1), pages 5-26, March.
    4. Kirkavak, Nureddin & Dincer, Cemal, 1999. "The general behavior of pull production systems: The allocation problems," European Journal of Operational Research, Elsevier, vol. 119(2), pages 479-494, December.
    5. Fernandes, Nuno Octavio & do Carmo-Silva, Silvio, 2006. "Generic POLCA--A production and materials flow control mechanism for quick response manufacturing," International Journal of Production Economics, Elsevier, vol. 104(1), pages 74-84, November.
    6. Kojima, Mitsutoshi & Nakashima, Kenichi & Ohno, Katsuhisa, 2008. "Performance evaluation of SCM in JIT environment," International Journal of Production Economics, Elsevier, vol. 115(2), pages 439-443, October.
    7. Papadopoulos, H. T. & Heavey, C., 1996. "Queueing theory in manufacturing systems analysis and design: A classification of models for production and transfer lines," European Journal of Operational Research, Elsevier, vol. 92(1), pages 1-27, July.
    8. Lage Junior, Muris & Godinho Filho, Moacir, 2010. "Variations of the kanban system: Literature review and classification," International Journal of Production Economics, Elsevier, vol. 125(1), pages 13-21, May.
    9. Kim, Ilhyung & Tang, Christopher S., 1997. "Lead time and response time in a pull production control system," European Journal of Operational Research, Elsevier, vol. 101(3), pages 474-485, September.
    10. Zhao Xiaobo & Qiguo Gong & Kenichi Nakashima, 2001. "Analysis of a production system in a general configuration," Naval Research Logistics (NRL), John Wiley & Sons, vol. 48(2), pages 128-143, March.
    11. Ou, Jihong & Jiang, Jiong, 1997. "Yield comparison of push and pull control methods on production systems with unreliable machines," International Journal of Production Economics, Elsevier, vol. 50(1), pages 1-12, May.
    12. Takahashi, Katsuhiko & Nakamura, Nobuto, 2002. "Decentralized reactive Kanban system," European Journal of Operational Research, Elsevier, vol. 139(2), pages 262-276, June.
    13. Pettersen, Jan-Arne & Segerstedt, Anders, 2009. "Restricted work-in-process: A study of differences between Kanban and CONWIP," International Journal of Production Economics, Elsevier, vol. 118(1), pages 199-207, March.
    14. Askin, Ronald G. & Krishnan, Shravan, 2009. "Defining inventory control points in multiproduct stochastic pull systems," International Journal of Production Economics, Elsevier, vol. 120(2), pages 418-429, August.
    15. Vincent W. Slaugh & Bahar Biller & Sridhar R. Tayur, 2016. "Managing Rentals with Usage-Based Loss," Manufacturing & Service Operations Management, INFORMS, vol. 18(3), pages 429-444, July.
    16. Sridhar Tayur, 2017. "OM Forum—An Essay on Operations Management," Manufacturing & Service Operations Management, INFORMS, vol. 19(4), pages 526-533, October.
    17. Andijani, A. A., 1998. "A multi-criterion approach for Kanban allocations," Omega, Elsevier, vol. 26(4), pages 483-493, August.
    18. Wallace J. Hopp & Mark L. Spearman, 2004. "To Pull or Not to Pull: What Is the Question?," Manufacturing & Service Operations Management, INFORMS, vol. 6(2), pages 133-148, August.
    19. Sridhar Tayur, 2000. "Improving Operations and Quoting Accurate Lead Times in a Laminate Plant," Interfaces, INFORMS, vol. 30(5), pages 1-15, October.

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