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Using Artificial Neural Networks to Determine the Qualification of Suppliers for Automobile Manufactures

Many parts used by the automobile manufacturers are provided by outside suppliers. Hence, the chain between the automobile manufacturers and their suppliers has been considered very important for the purchasing department of an automobile factory. Finding qualified suppliers that can meet the demands of the automobile manufacturers is thus an important issue.
With the application of neural networks, this thesis develops an approach to help determining the qualification of the suppliers. By using data of the known qualified and unqualified suppliers and by setting a number of features to characterize the capability of the suppliers, neural networks are trained to determine the qualification of the suppliers. In training the neural networks, the features are incrementally removed until optimal classification accuracy is reached. It is hoped that this system can become an effective decision-supporting system in screening the potential suppliers for the automobile manufacturers.

Identiferoai:union.ndltd.org:NSYSU/oai:NSYSU:etd-0208107-140050
Date08 February 2007
CreatorsSu, Yi-Ting
ContributorsShiuh-kuang Yang, Inn-chyn Her, Chen-wen Yen
PublisherNSYSU
Source SetsNSYSU Electronic Thesis and Dissertation Archive
LanguageCholon
Detected LanguageEnglish
Typetext
Formatapplication/pdf
Sourcehttp://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0208107-140050
Rightsrestricted, Copyright information available at source archive

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