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    Supply chain flexibility assessment by multivariate regression and neural networks

    Access Status
    Fulltext not available
    Authors
    Jeeva, Ananda
    Guo, W.
    Date
    2010
    Type
    Book Chapter
    
    Metadata
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    Citation
    Jeeva, 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.
    Source Title
    Advances in neural network research and applications
    DOI
    10.1007/978-3-642-12990-2_98
    ISBN
    9783642129902
    Faculty
    Curtin Business School
    School of Information Systems
    Remarks

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

    URI
    http://hdl.handle.net/20.500.11937/45545
    Collection
    • Curtin Research Publications
    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.

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