Show simple item record

dc.contributor.authorJeeva, Ananda
dc.contributor.authorGuo, W.
dc.contributor.editorZhigang Zeng
dc.contributor.editorJun Wang
dc.date.accessioned2017-01-30T15:21:39Z
dc.date.available2017-01-30T15:21:39Z
dc.date.created2010-06-29T20:02:37Z
dc.date.issued2010
dc.identifier.citationJeeva, Ananda S. and Guo, William W. 2010. Supply chain flexibility assessment by multivariate regression and neural networks, in Zhigang Zeng and Jun Wang (ed), Advances in neural network research and applications. pp. 845-852. Berlin Heidelberg: Springer-Verlag.
dc.identifier.urihttp://hdl.handle.net/20.500.11937/45545
dc.identifier.doi10.1007/978-3-642-12990-2_98
dc.description.abstract

This paper compares two vastly different methods of analysis - multiple regression and neural networks, in supply chain flexibility assessment. Data of manufacturing firms evaluating their prominent suppliers were analysed by multiple regression and simulated using three-layer multilayer perceptron(MLP) neural networks. Our study shows that NN can accurately determine a supplier's flexibility capability within an error of 1% The incorporation of these two methods can lead to better understanding and dynamic prediction of supply chain flexibility for buyers.

dc.publisherSpringer-Verlag
dc.subjectsupply chain
dc.subjectmultivariate regression
dc.subjectflexibility
dc.subjectNeural network
dc.titleSupply chain flexibility assessment by multivariate regression and neural networks
dc.typeBook Chapter
dcterms.source.startPage845
dcterms.source.endPage852
dcterms.source.titleAdvances in neural network research and applications
dcterms.source.isbn9783642129902
dcterms.source.placeBerlin Heidelberg
dcterms.source.chapter67
curtin.note

The original publication is available at : http://www.springerlink.com

curtin.accessStatusFulltext not available
curtin.facultyCurtin Business School
curtin.facultySchool of Information Systems


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record