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Comparison among three pull control policies: kanban, base stock, and generalized kanban

Author

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  • C. Duri
  • Y. Frein
  • M. Di Mascolo

Abstract

This paper is concerned with make‐to‐stock pull control policies. A classical policy is the kanban policy. Another policy, very easy to implement, is the base stock policy. These two policies contain one design parameter per stage. A general control policy, known as the generalized kanban policy, can also be used to implement the pull mechanism. The generalized kanban policy includes, as special cases, the kanban and the base stock policies. This policy uses two parameters for each stage of the production system. The aim of this paper is to provide qualitative and quantitative comparisons of these three policies. The results of our study will help to choose the policy to implement in order to control a production system. We give practical rules. We also show that if there is no delay in filling orders, all three policies have similar costs. However, for the systems studied, we show that, if there is a delay in filling orders, generalized kanban systems and base stock systems yield close to optimal costs, which are lower than costs of kanban systems for the same service quality. Copyright Kluwer Academic Publishers 2000

Suggested Citation

  • C. Duri & Y. Frein & M. Di Mascolo, 2000. "Comparison among three pull control policies: kanban, base stock, and generalized kanban," Annals of Operations Research, Springer, vol. 93(1), pages 41-69, January.
    • Handle: RePEc:spr:annopr:v:93:y:2000:i:1:p:41-69:10.1023/a:1018919806139
    DOI: 10.1023/A:1018919806139
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    Cited by:

    1. Nha-Nghi Cruz & Hans Daduna, 2019. "Optimal capacity allocation in a production–inventory system with base stock," Annals of Operations Research, Springer, vol. 277(2), pages 329-344, June.
    2. Manafzadeh Dizbin, Nima & Tan, Barış, 2020. "Optimal control of production-inventory systems with correlated demand inter-arrival and processing times," International Journal of Production Economics, Elsevier, vol. 228(C).
    3. Matta, Andrea & Dallery, Yves & Di Mascolo, Maria, 2005. "Analysis of assembly systems controlled with kanbans," European Journal of Operational Research, Elsevier, vol. 166(2), pages 310-336, October.
    4. Diana Sánchez-Partida & Rodolfo Rodríguez-Méndez & José Luis Martínez-Flores & Santiago-Omar Caballero-Morales, 2018. "Implementation of Continuous Flow in the Cabinet Process at the Schneider Electric Plant in Tlaxcala, Mexico," Interfaces, INFORMS, vol. 48(6), pages 566-577, November.
    5. Ohno, Katsuhisa, 2011. "The optimal control of just-in-time-based production and distribution systems and performance comparisons with optimized pull systems," European Journal of Operational Research, Elsevier, vol. 213(1), pages 124-133, August.
    6. Onyeocha, Chukwunonyelum Emmanuel & Wang, Jiayi & Khoury, Joseph & Geraghty, John, 2015. "A comparison of HK-CONWIP and BK-CONWIP control strategies in a multi-product manufacturing system," Operations Research Perspectives, Elsevier, vol. 2(C), pages 137-149.
    7. Panagiotis D. Paraschos & Georgios K. Koulinas & Dimitrios E. Koulouriotis, 2024. "A reinforcement learning/ad-hoc planning and scheduling mechanism for flexible and sustainable manufacturing systems," Flexible Services and Manufacturing Journal, Springer, vol. 36(3), pages 714-736, September.

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