Technology Roadmap for Aircraft Maintenance, Repair and Overhaul

doi: 10.32560/rk.2022.3.2


Nowadays, the demand for aircraft Maintenance, Repair and Overhaul (MRO) is constantly growing. The market size of the European MRO segment is estimated to be USD 206.13 billion in 2022, growing at a Compound Annual Growth Rate (CAGR) of 2.8% between 2022 and 2030 [1]. This forecast is a good indication of the growth in the number of incoming assignments. As a result, airlines and aircraft operators will increasingly rely on companies with experience in the MRO field to perform maintenance and repair work. Furthermore, as many airlines now choose to outsource maintenance and repair, this will further increase the load on MRO companies. As the number of incoming jobs increases, the companies concerned are constantly looking for and implementing new and better methods and technologies, with another aim of gaining a larger market share. Moreover, as there is still scope for the development and introduction of new technologies and processes in this area, a significant number of research and development projects are underway or in the pipeline. Therefore, the main objective of this study is to use the available information to present a generalised technology roadmap for the companies involved in MRO activities and, on this basis, to collect, present and categorise the state-of-the-art developments in the MRO sector, highlighting what the future will hold for companies that incorporate these revolutionary innovations into their daily work processes.


Repülőgép-karbantartás, -javítás és -felújítás Stratégia és ütemterv Kutatási és fejlesztési tevékenységek Az MRO-innovációk kategorizálása

Hogyan kell idézni

S. Ichou és Árpád . Veress, „Technology Roadmap for Aircraft Maintenance, Repair and Overhaul”, RepTudKoz, köt. 34, sz. 3, o. 19–30, júl. 2023.


Market Analysis Report, Europe MRO Distribution Market Report, 2022–2030. Online:

H. Löfsten, ‘Measuring Maintenance Performance – In Search for a Maintenance Productivity Index’. International Journal of Production Economics, Vol. 63, no 1. pp. 47–58. 2000. Online:

W. Jacobyansky, Maintenance vs. Production: How to Mend the Relationship. Online: https://www.

B. Pearce, IATA Forecasts Solid Long-term Aviation Recovery; Urges Digitisation of Passenger Processing’. The Moodie Davitt Report, 27 May 2021. Online:

C. G. Drury, K. P. Guy and C. A. Wenner, ‘Outsourcing Aviation Maintenance: Human Factors Implications, Specifically for Communications'. The International Journal of Aviation Psychology, Vol. 20, no 2. pp. 124–143. 2010. Online:

H. Al-kaabi, A. Potter and M. Naim, ‘An Outsourcing Decision Model for Airlines’ MRO Activities’. Journal of Quality in Maintenance Engineering, Vol. 13, no 3. pp. 217–227. 2007. Online:

M. Norkhairunnisa, T. Chai Hua, S. M. Sapuan and R. A. Ilyas, ‘Evolution of Aerospace Composite Materials’. In Advanced Composites in Aerospace Engineering Applications, M. Norkhairunnisa, S. M. Sapuan and R. A. Ilyas, eds. Cham, Springer. pp. 367–385. 2022. Online:

D. Sziroczak, I. Jankovics, I. Gal and D. Rohacs, ‘Conceptual Design of Small Aircraft with Hybrid-electric Propulsion Systems’, Energy, Vol. 204, no 2. 2020. Online:

J. Rohacs and D. Rohacs, ‘Energy Coefficients for Comparison of Aircraft Supported by Different Propulsion Systems’. Energy, Vol. 191, no 3. 2020. Online:

J. Rohacs, U. Kale and D. Rohacs, ‘Radically New Solutions for Reducing the Energy Use by Future Aircraft and their Operations’. Energy, Vol. 239. 2022. Online:

J. Bera and L. Pokorádi, ‘Monte-Carlo Simulation of Helicopter Noise’. Acta Polytechnica Hungarica, Vol. 12, no 2. pp. 21–32. 2015. Online:

K. Beneda, ‘Investigation of Novel Thrust Parameters to Variable Geometry Turbojet Engines’, in 2021 IEEE 19th World Symposium on Applied Machine Intelligence and Informatics (SAMI), pp. 000339–000342. 2021. Online:

K. Beneda, ‘Development of an Advanced Pressure Signal Acquisition Card for a Modular Turbojet Fadec System’. Online:

D. C. Nagel, ‘Human Error in Aviation Operations’, in Human Factors in Aviation, E. L. Wiener and D. C. Nagel, eds. Amsterdam, Academic Press. pp. 263–303. 1988. Online:

EASA, ‘Artificial Intelligence Roadmap’, EASA, 2020. Online:

F. Ansari, R. Glawar and W. Sihn, ‘Prescriptive Maintenance of CPPS by Integrating Multimodal Data with Dynamic Bayesian Networks’ in Machine Learning for Cyber Physical Systems. Selected papers from the International Conference ML4CPS 2017, J. Beyerer, A. Maier and O. Niggemann, eds. Heidelberg, Springer Vieweg Berlin. pp. 1–8. 2020. Online:

D. Dinis, A. Barbosa-Póvoa and Â. P. Teixeira, ‘A Supporting Framework for Maintenance Capacity Planning and Scheduling: Development and Application in the Aircraft MRO Industry’. International Journal of Production Economics, Vol. 218. pp. 1–15. 2019. Online:

S. Albakkoush, E. Pagone and K. Salonitis, ‘An Approach to Airline MRO Operators Planning and Scheduling during Aircraft Line Maintenance Checks Using Discrete Event Simulation’. Procedia Manufacturing, Vol. 54. pp. 160–165. 2021. Online: https://doi. org/10.1016/j.promfg.2021.07.024

M. Esposito, M. Lazoi, A. Margarito and L. Quarta, ‘Innovating the Maintenance Repair and Overhaul Phase through Digitalization'. Aerospace, Vol. 6, no 5. p. 53. 2019. Online:

J. Ordieres-Meré, T. Prieto Remon and J. Rubio, ‘Digitalization: An Opportunity for Contributing to Sustainability From Knowledge Creation'. Sustainability, Vol. 12, no 4. p. 1460. 2020. Online:

A. Siyaev and G.-S. Jo, ‘Towards Aircraft Maintenance Metaverse Using Speech Interactions with Virtual Objects in Mixed Reality'. Sensors, Vol. 21, no 6. p. 2066. 2021. Online:

A. Siyaev and G.-S. Jo, ‘Neuro-Symbolic Speech Understanding in Aircraft Maintenance Metaverse’. IEEE Access, Vol. 9. pp. 154484–154499. 2021. Online:

H. M. Shakir and B. Iqbal, ‘Application of Lean Principles and Software Solutions for Maintenance Records in Continuing Airworthiness Management Organisations’. The Aeronautical Journal, Vol. 122, no 1254. pp. 1263–1274. 2018. Online:

S. Hongli, W. Qingmiao, Y. Weixuan, L. Yuan, C. Yihui and W. Hongchao, ‘Application of AR Technology in Aircraft Maintenance Manual’. Journal of Physics: Conference Series, vol. 1738, no 1. 2021. Online:

D. Leca, V. Cadenat, T. Sentenac, A. Durand-Petiteville, F. Gouaisbaut and E. Le Flecher, ‘Sensor-based Obstacles Avoidance Using Spiral Controllers for an Aircraft Maintenance Inspection Robot', in 2019 18th European Control Conference (ECC), pp. 2083–2089. 2019. Online:

F. Donadio, J. Frejaville, S. Larnier and S. Vetault, ‘Human-robot Collaboration to Perform Aircraft Inspection in Working Environment’, 2016.

M. Hrúz, M. Bugaj, A. Novák, B. Kandera and B. Badánik, ‘‘The Use of UAV with Infrared Camera and RFID for Airframe Condition Monitoring'. Applied Sciences, Vol. 11, no 9. 2021. Online:

B. Brandoli et al., ‘Aircraft Fuselage Corrosion Detection Using Artificial Intelligence’. Sensors, Vol. 21, no 12. 2021. Online: ; DOI:

F. Heilemann, A. Dadashi and K. Wicke, ‘Eeloscope – Towards a Novel Endoscopic System Enabling Digital Aircraft Fuel Tank Maintenance’. Aerospace, Vol. 8, no 5. 2021. Online:

T. Tyncherov and L. Rozkova, ‘Predictive Maintenance Model of Refined Aircraft Tires Replacement’, in International Conference on Reliability and Statistics in Transportation and Communication, pp. 164–173. 2020. Online:

L. Rozhkova and T. Tyncherov, ‘Remaining Useful Life for Tires on Aircraft’s Main Wheels: Prediction Based on Quick Access Recorder Data’, in International Conference on Reliability and Statistics in Transportation and Communication, pp. 140–150. 2020. Online:

C. Boller, ‘Ways and Options for Aircraft Structural Health Management’. Smart Materials and Structures, Vol. 10, no 3. p. 432. 2001. Online:

T. Tyncherov and L. Rozkova, ‘Aircraft Lifecycle Digital Twin for Defects Prediction Accuracy Improvement', in International Conference on Reliability and Statistics in Transportation and Communication, pp. 54–63. 2019. Online:

A. Y. Yurin, Y. V. Kotlov and V. M. Popov, ‘The Conception of an Intelligent System for Troubleshooting an Aircraft’, 2021.

A. Nasiri Pour, B. Rostami-Tabar and A. Rahimzadeh, ‘A hybrid Neural Network and Traditional Approach for Forecasting Lumpy Demand’. Proceedings of the World Academy of Science, Engineering and Technology, Vol. 2, no 4. 2008.

M. M. Gyazova and I. D. Vlaznev, ‘Mathematical Method of Artificial Neural Networks in Aircraft Maintenance, Repair and Overhaul’. TEM Journal, Vol. 9, no 4. p. 1372. 2020. Online:

S. Schmid, U. Martens, W. K. Schomburg and K.-U. Schröder, ‘Integration of Eddy Current Sensors into Repair Patches for Fatigue Reinforcement at Rivet Holes’. Strain, Vol. 57, no 5. 2021. Online:


Letölthető adat még nem áll rendelkezésre.