NDLI: Recognizing Real Customers in E-Supply Chain Based on SOFM Neural Network and Corresponding Marketing Strategies

Content Provider	IEEE Xplore Digital Library
Author	Li-Juan Huang
Copyright Year	2006
Description	Author affiliation: Jiangxi Univ. of Fin. & Econ., Nanchang (Li-Juan Huang)
Abstract	In today's buyers' market, "the customer is God", the competition is no longer enterprise to enterprise, but supply chain to supply chain. So exactly to recognize real customer in e-supply chain is the key factor to success for assuring the profit of the whole e-supply chain. There is a large amount of consuming data stored in the e-supply chain everyday. If one e-supply chain can make full use of these valuable data on the Internet to recognize its real customers and target these customers' features of consuming behaviors to produce, sell, distribute, and offer a integrated individualized service from the whole e-supply chain, this greatly increases the loyalty of customers of the e-supply chain, maximize the profit of the whole e-supply chain, and make the e-supply chain more competitive in the world. In this paper, SOFM neural network is employed as a tool to mine these valuable consuming data in the e-supply chain for recognizing its real customers. The SOFM neural network is a novel and very interesting approach that can resemble recognition function of the brain. By mean of the SOFM neural network's approach, these most-downstream customers in e-supply chain can be objectively, scientifically and automatically clustered and divided into different groups. Besides, based on the scientific recognition and analysis of customers' consuming behaviors from these different groups, there are some corresponding marketing strategies to be suggested just for references
Sponsorship	IEEE Syst., Man and Cybernetics Hebei Univ.
Starting Page	1592
Ending Page	1597
File Size	246399
Page Count	6
File Format	PDF
ISBN	1424400619
DOI	10.1109/ICMLC.2006.258835
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-08-13
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Neural networks Supply chains Neurons Biological neural networks Finance Electronic mail Web and internet services Target recognition IP networks Clustering algorithms strategies Recognition customer SOFM e-supply chain marketing
Content Type	Text
Resource Type	Article

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