An optimal Kanban system in a multi-stage, mixed-model assembly line

Abstract

This paper studies the parameter design and the performance optimization of a Kanban system without stockouts in a multi-stage, mixed-model assembly line. The model of a Kanban system based on production processes is established by examining the relationship among the set-up time, the amount of work in process (WIP), the capacity indicated by a Kanban, and the takt-time ratio. A novel method for optimizing performance on the premise of no stockouts is proposed. Empirical results show that the amount of WIP is reduced remarkably after optimization.

Keywords

Multi-stage / mixed-model assembly line / Kanban management / amount of WIP / one-piece flow

References

[1] Akturk M.S., Erhun F.. An overview of design and operational issues of Kanban systems. International Journal of Production Research, 1999, 37(17): 3859-3882.

[2] Boysen N., Fliedner M., Scholl A.. Sequencing mixed-model assembly lines: Survey, classification and model critique. European Journal of Operational Research, 2009, 192(2): 349-373.

[3] Boysen N., Fliedner M., Scholl A.. The product rate variation problem and its relevance in real world mixed-model assembly lines. European Journal of Operational Research, 2009, 197(2): 818-824.

[4] Bukchin J., Dar-El E.M., Rubinovitz J.. Mixed model assembly line design in a make-to-order environment. Computers & Industrial Engineering, 2002, 41(4): 405-421.

[5] Chen J.. Adaptive scheduling in random flexible manufacturing systems subject to machine breakdowns. International Journal of Production Research, 2003, 41(9): 1927-1951.

[6] Colorni A., Dorigo M., Maniezzo V.. Varela F., Bourgine P.. Distributed optimization by ant colonies. Proceedings of the First European Conference on Artificial Life, 1991, Paris: Elsevier Publishing 134-142.

[7] Dhamala T.N., Kubiak W.. A brief survey of just-in-time sequencing for mixed-model systems. International Journal of Operations Research, 2005, 2(2): 38-42.

[8] Duplaga E.A., Bragg D.J.. Mixed-model assembly line sequencing heuristics for smoothing component parts usage: A comparative analysis. International Journal of Production Research, 1998, 36(8): 2209-2224.

[9] Erhun F., Akturk M.S., Turkcan A.. Interaction of design and operational parameters in periodic review kanban systems. International Journal of Production Research, 2003, 41(14): 3315-3338.

[10] Gokcen H., Erel E.. A goal programming approach to mixed-model assembly line balancing problem. International Journal of Production Economics, 1997, 48(2): 177-185.

[11] Kubiak W.. Minimizing variation of production rates in just-in-time systems: A survey. European Journal of Operational Research, 1993, 66(3): 259-271.

[12] Leitch R.A.. Effect of stochasticity, capacity and lead time cost drivers on WIP and throughput in a pull production environment. Management Accounting Research, 2001, 12(2): 167-196.

[13] Monden, Y. (1983). Toyota Production System: Practical Approach to Production Management. Engineering & Management Press

[14] Monden Y.. Toyota Production System: An Integrated Approach to Just-In-Time, 1998, Norcross, GA: Industrial Engineering and Management Press, Institute of Industrial Engineers

[15] Schonberger, R.J. (1986). World Class Manufacturing: The Lessons of Simplicity Applied. Free Press

[16] Shingo, S. (1989). A Study of the Toyota Production System from an Industrial Engineering Viewpoint. Productivity Press

[17] Takahashi K., Nakamura N.. Decentralized reactive Kanban system. European Journal of Operational Research, 2002, 139(2): 262-276.

[18] Tardif V., Maaseidvaag L.. An adaptive approach to controlling Kanban systems. European Journal of Operational Research, 2001, 132(2): 411-424.

[19] Thomopoulos, N.T. (1967). Line balancing-sequencing for mixed-model assembly. Management Science, 59–75

[20] Zhao X., Katsuhisa O.. Algorithms for sequencing mixed models on an assembly line in a JIT production system. Computers & Industrial Engineering, 1997, 32(1): 47-56.

[21] Zhao X., Zhou Z., Asres A.. A note on Toyota’s goal of sequencing mixed models on an assembly line. Computers & Industrial Engineering, 1999, 36(1): 57-65.