Abstract
Data analytics seems a promising approach to address the problem of unpredictability in MRO organizations. The Amsterdam University of Applied Sciences in cooperation with the aviation industry has initiated a two-year applied research project to explore the possibilities of data mining. More than 25 cases have been studied at eight different MRO enterprises. The CRISP-DM methodology is applied to have a structural guideline throughout the project. The data within MROs were explored and prepared. Individual case studies conducted with statistical and machine learning methods, were successfully to predict among others, the duration of planned maintenance tasks as well as the optimal maintenance intervals, the probability of the occurrence of findings during maintenance tasks.
Highlights
The aircraft maintenance process is often characterized by unpredictable process times and material requirements
The current paper presents the results from representative case studies that reflect the typical challenges of MRO companies and show the tested data analytics methods
In general data analytics at MROs is in an early maturity stage
Summary
The aircraft maintenance process is often characterized by unpredictable process times and material requirements. This problem is compensated for by large buffers in terms of time, personnel and parts, resulting in higher cost. Traditional preventive maintenance policies often result in components replacements before the end of life. This increases part costs and conflicts with the growing need for sustainable operations. The main research objective was to decrease maintenance costs and aircraft downtime at medium sized MROs using fragmented historical maintenance (and other) data. The current paper presents the results from representative case studies that reflect the typical challenges of MRO companies and show the tested data analytics methods
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