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Modeling of a Fuzzy Expert System for Choosing an Appropriate Supply Chain Collaboration Strategy

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Industrial Engineering Department, Beykent University, Istanbul, Turkey

* Corresponding Author: Kazim Sari, email

Intelligent Automation & Soft Computing 2018, 24(2), 405-412. https://doi.org/10.1080/10798587.2017.1352258

Abstract

Nowadays, there has been a great interest for business enterprises to work together or collaborate in the supply chain. It is thus possible for them to gain a competitive advantage in the marketplace. However, determining the right collaboration strategy is not an easy task. Namely, there are several factors that need to be considered at the same time. In this regard, an expert system based on fuzzy rules is proposed to choose an appropriate collaboration strategy for a given supply chain. To this end, firstly, the factors that are significant for supply chain collaboration are extracted via an extensive review of literature. Then, a simulation model of a supply chain is constructed to reveal the performance of collaborative practices under various scenarios. Thereby, it is made possible to establish fuzzy rules for the expert system. Finally, feasibility and practicability of our proposed model is verified with an illustrative case.

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Cite This Article

APA Style
Kazim Sari, (2018). Modeling of a fuzzy expert system for choosing an appropriate supply chain collaboration strategy. Intelligent Automation & Soft Computing, 24(2), 405-412. https://doi.org/10.1080/10798587.2017.1352258
Vancouver Style
Kazim Sari . Modeling of a fuzzy expert system for choosing an appropriate supply chain collaboration strategy. Intell Automat Soft Comput . 2018;24(2):405-412 https://doi.org/10.1080/10798587.2017.1352258
IEEE Style
Kazim Sari, “Modeling of a Fuzzy Expert System for Choosing an Appropriate Supply Chain Collaboration Strategy,” Intell. Automat. Soft Comput. , vol. 24, no. 2, pp. 405-412, 2018. https://doi.org/10.1080/10798587.2017.1352258



cc Copyright © 2018 The Author(s). Published by Tech Science Press.
This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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