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Fuzzy knowledge-based token-ordering policies for bullwhip effect management in supply chains

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Abstract

The “Bullwhip Effect” is a well-known example of supply chain inefficiencies and refers to demand amplification as moving up toward upstream echelons in a supply chain. This paper concentrates on representing a robust token-based ordering policy to facilitate information sharing in supply chains in order to manage the bullwhip effect. Takagi–Sugeno–Kang and hybrid multiple-input single-output fuzzy models are proposed to model the mechanism of token ordering in the token-based ordering policy. The main advantage of proposed fuzzy models is that they eliminate the exogenous and constant variables from the procedure of obtaining the optimal amount of tokens which should be ordered in every period. These fuzzy approaches model the mentioned mechanism through a push–pull policy. A four-echelon SC with fuzzy lead time and unlimited production capacity and inventory is considered to survey the outcomes. Numerical experiments confirm the effectiveness of proposed policies in alleviating BWE, inventory costs and variations.

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Notes

  1. Lead times are endogenous variables in supply networks and can be affected mainly by information or transportation delays. Therefore, all delays in providing raw materials, production line, goods delivery, receiving demand information, etc. will be reflected in lead times; these delays may occur because of technical (systematic) issues or just by accident. Thus, lead times are imprecise and unknown in SCs. Fuzzy sets are successful in modeling vague and imprecise variables, so considering fuzzy lead times in supply networks leads to increasing the generality of the supply model [35, 40].

  2. If there is an estimator for MU, based upon the past data in the system (for example seasonal data of the past year, or previous data in automated supply systems), MFs will be driven based on it. Otherwise, the MFs will be calculated based upon the known mean of demand (\(\upmu (t)\)); therefore, according to the fact that \(\upmu (t)\) is more sensitive on demand changes than the MU, using MU in MFs has a controlling effect on the supply system.

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Acknowledgments

Authors would like to thank dear reviewers for their constructive viewpoints that helped to improving the paper.

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Authors and Affiliations

  1. Department of Industrial Engineering, Amir-Kabir University of Technology, Tehran, Iran

    M. H. Fazel Zarandi & F. Shabany Moghadam

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  1. M. H. Fazel Zarandi
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  2. F. Shabany Moghadam
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Correspondence to M. H. Fazel Zarandi.

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Zarandi, M.H.F., Moghadam, F.S. Fuzzy knowledge-based token-ordering policies for bullwhip effect management in supply chains. Knowl Inf Syst 50, 607–631 (2017). https://doi.org/10.1007/s10115-016-0954-8

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