Supplier Segmentation using Fuzzy Linguistic Preference Relations and Fuzzy Clustering

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Pegah Sagheb Haghighi 1,* Mahmoud Moradi 2 Maziar Salahi 3

1. Dept. of Computer Engineering, University of Guilan, Rasht, Iran

2. Dept. of Management, University of Guilan, Rasht, Iran

3. Dept. of Applied Mathematics, University of Guilan, Rasht, Iran

* Corresponding author.


Received: 27 Aug. 2013 / Revised: 5 Dec. 2013 / Accepted: 16 Jan. 2014 / Published: 8 Apr. 2014

Index Terms

Supply Chain Management (SCM), Supplier Segmentation, Fuzzy Set, Fuzzy Linguistic Preference Relations, Fuzzy C-Means


In an environment characterized by its competitiveness, managing and monitoring relationships with suppliers are of the essence. Supplier management includes supplier segmentation. Existing literature demonstrates that suppliers are mostly segmented by computing their aggregated scores, without taking each supplier’s criterion value into account. The principle aim of this paper is to propose a supplier segmentation method that compares each supplier’s criterion value with exactly the same criterion of other suppliers. The Fuzzy Linguistic Preference Relations (LinPreRa) based Analytic Hierarchy Process (AHP) is first used to find the weight of each criterion. Then, Fuzzy c-means algorithm is employed to cluster suppliers based on their membership degrees. The obtained results show that the proposed method enhances the quality of the previous findings.

Cite This Paper

Pegah Sagheb Haghighi, Mahmoud Moradi, Maziar Salahi, "Supplier Segmentation using Fuzzy Linguistic Preference Relations and Fuzzy Clustering", International Journal of Intelligent Systems and Applications(IJISA), vol.6, no.5, pp.76-82, 2014. DOI:10.5815/ijisa.2014.05.08


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