| Περίληψη: | 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
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