Data mining in MRO

Maurice Pelt, Asteris Apostolidis, Robert J. de Boer, Maaik Borst, Jonno Broodbakker, Ruud Jansen, Lorance Helwani, Roberto Felix Patron, Konstantinos Stamoulis

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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
Original languageEnglish
Place of PublicationAmsterdam
PublisherHogeschool van Amsterdam, Faculteit Techniek
Number of pages104
ISBN (Print)9789492644114
Publication statusPublished - Feb 2019


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