Determining supply chain flexibility using statistics and neural networks: a comparative study
|dc.contributor.editor||Yang Xiang, Javier Lopez and Haining Wang|
|dc.identifier.citation||Jeeva, Ananda and Guo, William. 2009. Determining supply chain flexibility using statistics and neural networks: a comparative study, in Yang Xiang, Javier Lopez and Haining Wang (ed), International Conference on Network and System Security, Oct 19 2009, pp. 506-509. Gold Coast, Queensland, Australia: Conference Publishing Services.|
The purpose of this paper is to examine the application of neural networks as a flexibility andperformance measure in supplier-manufacturer activities. The dimensions of information exchange,supplier integration, product delivery, logistics, and organisational structure are used as determinantsfactors affecting this supply chain flexibility. The data set was collected from more than 200 Australianmanufacturing firms evaluating their suppliers. Our study shows that neural networks can accuratelydetermine a supplier's flexibility with an error within 1%, which is more accurate than the conventional multivariate regression can.
|dc.publisher||Conference Publishing Services|
|dc.title||Determining supply chain flexibility using statistics and neural networks: a comparative study|
|dcterms.source.title||Proceedings of the network and system security 2009|
|dcterms.source.series||Proceedings of the network and system security 2009|
|dcterms.source.conference||Network and system security 2009|
|dcterms.source.conference-start-date||Oct 19 2009|
|dcterms.source.conferencelocation||Gold Coast, Queensland, Australia|
|dcterms.source.place||Los Alamitos, California USA|
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|curtin.faculty||Curtin Business School|
|curtin.faculty||School of Information Systems|