A Markov Chain Analysis of the Effectiveness of Kanban Card with Dynamic Information

Tanhaie, Fahimeh; Jolai, Fariborz; Rabbani, Masoud

doi:10.22034/2017.3.03

A Markov Chain Analysis of the Effectiveness of Kanban Card with Dynamic Information

Document Type : Research Paper

Authors

School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran

10.22034/2017.3.03

Abstract

The pull system produces products based on customer demands. Each station is isolated until a customer order is placed; then a signal or Kanban is sent from downstream station to upstream station and continues until the first station. Most of the papers studied pull system in deterministic environment while many real production lines are subjected to different types of uncertainties. The objective of this paper is to apply a dynamic Kanban system that changes the information on the Kanban cards based on the remained inventory in the buffer. The proposed approach uses a Markov chain analysis to compare effectiveness of the Kanban card with dynamic information with the Kanban card with static information. In this paper the production line of two work stations and two inventory buffer is modeled. Throughput, shortage, work-in-process and cycle time are the model measurement parameters and the results show the advantages of the proposed approach.

Keywords

Main Subjects

operations planning,scheduling & control

References

Bitran, G., and Chang, L. (1987). A Mathematical Programming Approach to a Deterministic Kanban System. Management Science, Vol. 33(4), pp. 427-441.

Framinan, J., González, P., and Ruiz-Usano, R. (2003). The CONWIP production control system: Review and research issues. Production Planning & Control, Vol. 14(3), pp. 255-265.

Framinan, J., González, P., and Ruiz-Usano, R. (2006). Dynamic card controlling in a Conwip system. International Journal of Production Economics, Vol. 99(1-2), pp. 102-116.

Gong, Q., Yang, Y., and Wang, S. (2014). Information and decision-making delays in MRP, KANBAN, and CONWIP. International Journal of Production Economics, Vol. 156, pp. 208-213.

González-R, P., Framinan, J., and Pierreval, H. (2011). Token-based pull production control systems: an introductory overview. Journal of Intelligent Manufacturing, Vol. 23(1), pp. 5-22.

Gupta, S., and Al-Turki, Y. (1997). An algorithm to dynamically adjust the number of Kanbans in stochastic processing times and variable demand environment. Production Planning & Control, Vol. 8(2), pp. 133-141.

Gupta, M. (2003). Constraints management--recent advances and practices. International Journal of Production Research, Vol. 41(4), pp. 647-659.

Hopp, W., and Roof, M. (1998). Setting WIP levels with statistical throughput control (STC) in CONWIP production lines. International Journal of Production Research, Vol. 36(4), pp. 867-882.

Huang, Y., Wan, H., Kuriger, G., and Chen, F. (2013). Simulation Studies of Hybrid Pull Systems of Kanban and CONWIP in an Assembly Line. Advances in Sustainable and Competitive Manufacturing Systems, pp. 1553-1563.

Jou Lin, C., Frank Chen, F., and Min Chen, Y. (2013). Knowledge kanban system for virtual research and development. Robotics and Computer-Integrated Manufacturing, Vol. 29(3), pp. 119-134.

Kemeny, J., & Snell, J. (1960). Finite markov chains. Princeton, N.J.: Van Nostrand.

Khojasteh-Ghamari, Y. (2008). A performance comparison between Kanban and CONWIP controlled assembly systems. Journal of Intelligent Manufacturing, Vol. 20(6), pp. 751-760.

Miltenburg, J. (1997). Comparing JIT, MRP and TOC, and embedding TOC into MRP. International Journal of Production Research, Vol. 35(4), pp. 1147-1169.

Moeeni, F., and Chang, Y. (1990). An Approximate Solution to Deterministic Kanban Systems. Decision Sciences, Vol. 21(3), pp. 596-607.

Renna, P. (2010). Dynamic Control Card in a Production System Controlled by Conwip Approach. Jordan Journal of Mechanical and Industrial Engineering, Vol. 4(4), pp.425-432.

Sato, R., and Khojasteh-Ghamari, Y. (2010). An integrated framework for card-based production control systems. Journal of Intelligent Manufacturing, Vol. 23(3), pp. 717-731.

Schmidbauer, H., and Rösch, A. (1994). A stochastic model of an unreliable Kanban production system. OR Spektrum, Vol. 16(2), pp. 155-160.

Sendil Kumar, C., and Panneerselvam, R. (2006). Literature review of JIT-KANBAN system. Int J Adv Manuf Technol, Vol. 32(3-4), pp. 393-408.

Shahabudeen, P., Krishnaiah, K., and Thulasi Narayanan, M. (2003). Design of a Two-Card Dynamic Kanban System Using a Simulated Annealing Algorithm. The International Journal of Advanced Manufacturing Technology, Vol. 21(10-11), pp. 754-759.

Shahabudeen, P., and Sivakumar, G. (2008). Algorithm for the design of single-stage adaptive kanban system. Computers & Industrial Engineering, Vol. 54(4), pp. 800-820.

Spearman, M., Woodruff, D., and Hopp, W. (1990). CONWIP: a pull alternative to Kanban. Int. J. Of Prodn. Res., Vol. 28(5), pp. 879-894.

Tardif, V., and Maaseidvaag, L. (2001). An adaptive approach to controlling Kanban systems. European Journal of Operational Research, Vol. 132(2), pp. 411-424.