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    Think Exogenous to Excel: Alternative Supply Chain Data to Improve Transparency and Decisions

    246820_246820.pdf (282.4Kb)
    Access Status
    Open access
    Authors
    Wood, Lincoln
    Reiners, Torsten
    Srivastava, H.
    Date
    2016
    Type
    Journal Article
    
    Metadata
    Show full item record
    Citation
    Wood, L. and Reiners, T. and Srivastava, H. 2016. Think Exogenous to Excel: Alternative Supply Chain Data to Improve Transparency and Decisions. International Journal of Logistics: Research and Applications. 20 (5): pp. 426-443.
    Source Title
    International Journal of Logistics: Research and Applications
    DOI
    10.1080/13675567.2016.1267126
    School
    School of Information Systems
    Remarks

    This is an Author's Original Manuscript of an article published by Taylor & Francis in International Journal of Logistics: Research and Applications on 15/12/2016 available online at http://www.tandfonline.com/10.1080/13675567.2016.1267126

    URI
    http://hdl.handle.net/20.500.11937/8383
    Collection
    • Curtin Research Publications
    Abstract

    Efficient decisions along the supply chain have traditionally demanded sophisticated information sharing processes. Even with decades of research on theoretical and practical developments on integrating systems and stakeholders, in practice, we still seem to struggle to achieve full transparency and mitigate inefficiency challenges. We explore the emerging sentiment analysis technique to augment sales and operations planning (S&OP) with currently unavailable exogenous information. Even though sentiment analysis has gained traction, a comprehensive application in supply chains has not yet been attempted. Relevant topics are reviewed to allow an examination of the key relationships in a process framework, grounded in dual-process and bullwhip effect theory. Our proposed conceptual framework extends our conception of sentiment analysis integration to improve supply chain decisions and performance. The framework addresses managers interested in developing additional analytical capabilities and researchers to initiate further empirical research on the potential held by sentiment analysis in supply chain research.

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