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Palgo Journal Of Business Management, Vol. 1(2) pp. 16-39, October, 2014. Copyright © 2014 Palgo Journals

CASE STUDY

COMPARATIVES DATA MINING TECHNIQUES FOR CLUSTERING ITEMS IN A MULTI-LAYER AND MULTI-PRODUCT SUPPLY CHAIN

Ermia Aghasi and Mansour Momeni

Faculty of Management, University of Tehran, Iran

Email: ermia.aghasi@gmail.com    

Accepted 18 July, 2014

This paper concerns with clustering elements in a multi-layer and multi-product supply chain aiming purification of interactions using data mining. The goal is to improve the performance of the supply chain and preventing the bottlenecks. Thus, data mining techniques are applied for clustering elements configure the proposed supply chain using operational specifications related to each layer of the supply chain. Here, we propose a multi-clustering system to cluster the elements of a supply network based on the similarity in information flow.We apply data mining techniques as decision aid in our supply chain.

 Keywords: Multi-Layer; Multi-Product; Supply Chain; Clustering Techniques

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October: Vol. 1(2) pp.16-39

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Palgo Journal of Business Management