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dc.contributor.authorPelt, Maurice
dc.contributor.authorApostolidis, Asteris
dc.contributor.authorde Boer, Robert J.
dc.contributor.authorBorst, Maaik
dc.contributor.authorBroodbakker, Jonno
dc.contributor.authorJansen, Ruud
dc.contributor.authorHelwani, Lorance
dc.contributor.authorPatron, Roberto
dc.contributor.authorStamoulis, Konstantinos
dc.date.accessioned2020-06-09T10:19:12Z
dc.date.available2020-06-09T10:19:12Z
dc.date.issued2019
dc.identifier.urihttp://library.oapen.org/handle/20.500.12657/39481
dc.description.abstractData 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 implementationsen_US
dc.languageEnglishen_US
dc.subject.classificationUen_US
dc.subject.classificationbic Book Industry Communication::U Computing & information technology
dc.subject.otherdata miningen_US
dc.titleData Mining in MROen_US
dc.title.alternativeCentre for Applied Research Technologyen_US
dc.typebook
oapen.relation.isPublishedByc3051bc5-324a-4e1b-aa48-b1f6b58fe5a1en_US
virtual.oapen_relation_isPublishedBy.publisher_nameAmsterdam University of Applied Sciences
oapen.relation.isFundedByda087c60-8432-4f58-b2dd-747fc1a60025en_US
virtual.oapen_relation_isFundedBy.grantor_name Nederlandse Organisatie voor Wetenschappelijk Onderzoek
oapen.collectionDutch Research Council (NWO)
oapen.pages53en_US
oapen.remark.public21-7-2020 - No DOI registered in CrossRef for ISBN 9789492644114


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