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    Data Mining in MRO

    Centre for Applied Research Technology

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    Author(s)
    Pelt, Maurice
    Apostolidis, Asteris
    de Boer, Robert J.
    Borst, Maaik
    Broodbakker, Jonno
    Jansen, Ruud
    Helwani, Lorance
    Patron, Roberto
    Stamoulis, Konstantinos
    Collection
    Dutch Research Council (NWO)
    Language
    English
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    Abstract
    Data mining seems to be a promising way to tackle the problem of unpredictability in MRO organizations. The Amsterdam University of Applied Sciences therefore cooperated with the aviation industry for a two-year applied research project exploring the possibilities of data mining in this area. Researchers studied more than 25 cases at eight different MRO enterprises, applying a CRISP-DM methodology as a structural guideline throughout the project. They explored, prepared and combined MRO data, flight data and external data, and used statistical and machine learning methods to visualize, analyse and predict maintenance. They also used the individual case studies to make predictions about the duration and costs of planned maintenance tasks, turnaround time and useful life of parts. Challenges presented by the case studies included time-consuming data preparation, access restrictions to external data-sources and the still-limited data science skills in companies. Recommendations were made in terms of ways to implement data mining – and ways to overcome the related challenges – in MRO. Overall, the research project has delivered promising proofs of concept and pilot implementations
    URI
    http://library.oapen.org/handle/20.500.12657/39481
    Keywords
    data mining
    Publisher
    Amsterdam University of Applied Sciences
    Publication date and place
    2019
    Grantor
    • Nederlandse Organisatie voor Wetenschappelijk Onderzoek
    Classification
    Computing and Information Technology
    Pages
    53
    Public remark
    21-7-2020 - No DOI registered in CrossRef for ISBN 9789492644114
    Rights
    https://creativecommons.org/licenses/by-nc-nd/4.0/
    • Imported or submitted locally

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    License

    • If not noted otherwise all contents are available under Attribution 4.0 International (CC BY 4.0)

    Credits

    • logo EU
    • This project received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 683680, 810640, 871069 and 964352.

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