Digital twin for dynamic coordination of systems in complex and variable environments

A case study at KLM Engineering & Maintenance

Master Thesis (2024)
Author(s)

J.B. Niers (TU Delft - Mechanical Engineering)

Contributor(s)

A. Napoleone – Mentor (TU Delft - Transport Engineering and Logistics)

R. R. Negenborn – Graduation committee member (TU Delft - Transport Engineering and Logistics)

B.O.M.N. Noble – Graduation committee member (TU Delft - Ship Design, Production and Operations)

Faculty
Mechanical Engineering
Copyright
© 2024 Jesse Niers
More Info
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Publication Year
2024
Language
English
Copyright
© 2024 Jesse Niers
Graduation Date
26-02-2024
Awarding Institution
Delft University of Technology
Programme
['Mechanical Engineering | Multi-Machine Engineering']
Faculty
Mechanical Engineering
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Abstract

This research investigates the application of a digital twin in managing challenges of variability and complexity within systems by using a case study at KLM Engineering & Maintenance. Using the DMADV (define, measure, analyse, design and verify) methodology, the research evaluates the problem using a literature review, measures and analyses the current state of the Logistic Handling Area (LHA), designs a digital twin concept and verifies its value. The literature review highlights the variability in the Maintenance, Repair and Overhaul (MRO) industry, which enables the investigation of digital twin applications for dynamic coordination. The analysis phase reveals significant operational challenges arising from variability in inflows and processing times, enhanced by system complexity and integral coordination issues between departments. To address these challenges, a digital twin is designed that enables real-time monitoring of KPIs, testing of dynamic resource allocation and integral operational target setting. The value assessment shows that the digital twin can support process operators by managing variability through continuous monitoring and evaluation of resource allocation, ultimately achieving predictable and stable system performance in a complex and variable environment.

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