Data-driven Decision Support for Component Flow Turnaround Time Reduction in Aircraft Maintenance

Case Study at KLM Engineering & Maintenance

Master thesis (2018)

Authors

T.F. Spaan Aerospace Engineering

Contributors

W.J.C. Verhagen (supervisor 1)
Faculty
Aerospace Engineering
Copyright: © 2018 Tessa Spaan
Supply chain Greedy algorithm Multi-criteria decision-making Capacity optimization Aircraft maintenance Data-driven decision support Component flow Turnaround time reduction
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Published Date

31-08-2018

Language

English

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Faculty

Aerospace Engineering

Abstract

Aircraft maintenance is inherently unpredictable due to differences in failure rates between components. This results in high variance inflow in repair shops, which in case of insufficient capacity yields long turnaround times and thus the need for additional components in stock. The aim of this research project is to determine the effect of service level optimisation in the presence of highly variable demand from an aircraft maintenance supply chain perspective. The first approach focuses on determining the optimal capacity in the repair shop using a greedy algorithm, while the second aims at controlling demand by assisting in in- or outsourcing decision-making by means of a multi-criteria decision-making model. A case study at KLM Engineering & Maintenance is performed to test the applicability of the models in the aircraft maintenance industry.