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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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        • 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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