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    Smart data and business analytics: A theoretical framework for managing rework risks in mega-projects

    Access Status
    Fulltext not available
    Embargo Lift Date
    2024-02-13
    Authors
    Matthews, Jane
    Love, Peter
    Porter, Stuart R.
    Fang, W.
    Date
    2022
    Type
    Journal Article
    
    Metadata
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    Citation
    Matthews, J. and Love, P.E.D. and Porter, S.R. and Fang, W. 2022. Smart data and business analytics: A theoretical framework for managing rework risks in mega-projects. International Journal of Information Management. 65: ARTN 102495.
    Source Title
    International Journal of Information Management
    DOI
    10.1016/j.ijinfomgt.2022.102495
    ISSN
    0268-4012
    Faculty
    Faculty of Humanities
    Faculty of Science and Engineering
    School
    School of Design and the Built Environment
    School of Civil and Mechanical Engineering
    Funding and Sponsorship
    http://purl.org/au-research/grants/arc/DP210101281
    URI
    http://hdl.handle.net/20.500.11937/90139
    Collection
    • Curtin Research Publications
    Abstract

    Within construction, we have become increasingly accustomed to relying on the benefits of digital technologies, such as Building Information Modelling, to improve the performance and productivity of projects. We have, however, overlooked the problems that technology is unable to redress. One such problem is rework, which has become so embedded in practice that technology adoption alone can not resolve the issue without fundamental changes in how information is managed for decision-making. Hence, the motivation of this paper is to bring to the fore the challenges of classifying and creating an ontology for rework that can be used to understand its patterns of occurrence and risks and provide a much-needed structure for decision-making in transport mega-projects. Using an exploratory case study approach, we examine ‘how’ rework information is currently being managed by an alliance that contributes significantly to delivering a multi-billion dollar mega-transport project. We reveal the challenges around location, format, structure, granularity and redundancy hindering the alliance's ability to classify and manage rework data. We use the generative machine learning technique of Correlation Explanation to illustrate how we can make headway toward classifying and then creating an ontology for rework. We propose a theoretical framework utilising a smart data approach to generate an ontology that can effectively use business analytics (i.e., descriptive, predictive and prescriptive) to manage rework risks.

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