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    Determining supply chain flexibility using statistics and neural networks: a comparative study

    134557_134557.pdf (358.5Kb)
    Access Status
    Open access
    Authors
    Jeeva, Ananda
    Guo, W.
    Date
    2009
    Type
    Conference Paper
    
    Metadata
    Show full item record
    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.
    Source Title
    Proceedings of the network and system security 2009
    Source Conference
    Network and system security 2009
    DOI
    10.1109/NSS.2009.87
    ISBN
    9780769538389
    Faculty
    Curtin Business School
    School of Information Systems
    Remarks

    Copyright © 2009 IEEE This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.

    URI
    http://hdl.handle.net/20.500.11937/9696
    Collection
    • Curtin Research Publications
    Abstract

    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.

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