ADCM-360: Airline Digital Core Modernization 360° Framework

SeyyedAbdolHojjat  MoghadasNian

ПРЕПРИНТ

Эта статья является препринтом и не была отрецензирована.
О результатах, изложенных в препринтах, не следует сообщать в СМИ как о проверенной информации.

ADCM-360: Airline Digital Core Modernization 360° Framework

S. MoghadasNian

2026-06-10

https://doi.org/10.24108/preprints-3115488

Airlines increasingly compete through the maturity of their digital core, not only through fleet size, routes, pricing, and service quality. Yet many airline digital initiatives remain fragmented across enterprise resource planning, passenger service systems, departure control, maintenance and engineering, operational control, customer relationship management, finance, procurement, business intelligence, cloud platforms, and artificial intelligence pilots. ADCM-360 is a Design Science method and technical reference model for transforming fragmented legacy airline environments into intelligent, connected, predictive, and human-governed autonomous operating models. The model integrates seven layers: systems integration; data governance and intelligence; process modernization; AI and predictive operations; governance, risk, compliance and AI assurance; performance and value realization; and leadership, transformation, capability and adoption. Its core principle is Universal Core + Adaptive Execution: all airlines require the same modernization logic, but the implementation pathway must be calibrated to airline type, fleet complexity, digital maturity, budget, regulatory environment, vendor dependency, workforce capability, and strategic priorities. The method does not claim completed empirical validation. Instead, it provides a structured artifact, KPI architecture, maturity trajectory, pilot-to-production governance logic, and validation protocol for expert review, case-based demonstration, maturity assessment, and future empirical testing.

Ссылка для цитирования:

MoghadasNian S. 2026. ADCM-360: Airline Digital Core Modernization 360° Framework. PREPRINTS.RU. https://doi.org/10.24108/preprints-3115488

Список литературы

1. Boston Consulting Group. (2025, September 30). Are you generating value from AI? The widening gap. Boston Consulting Group. https://www.bcg.com/publications/2025/are-you-generating-value-from-ai-the-widening-gap

2. Daft, J., & Albers, S. (2013). A conceptual framework for measuring airline business model convergence. Journal of Air Transport Management, 28, 47-54. https://doi.org/10.1016/j.jairtraman.2012.12.010

3. European Union Aviation Safety Agency. (2023). EASA Artificial Intelligence Roadmap 2.0: A human-centric approach to AI in aviation. https://www.easa.europa.eu/en/document-library/general-publications/easa-artificial-intelligence-roadmap-20

4. European Union Aviation Safety Agency. (2024). EASA Artificial Intelligence (AI) Concept Paper Issue 2: Guidance for Level 1 and Level 2 machine-learning applications. https://www.easa.europa.eu/en/document-library/general-publications/easa-artificial-intelligence-concept-paper-issue-2

5. Geske, A. M., Herold, D. M., & Kummer, S. (2024). Integrating AI support into a framework for collaborative decision-making (CDM) for airline disruption management. Journal of the Air Transport Research Society, 3, Article 100026. https://doi.org/10.1016/j.jatrs.2024.100026

6. Gregor, S., & Hevner, A. R. (2013). Positioning and presenting design science research for maximum impact. MIS Quarterly, 37(2), 337-355.

7. Heiets, I., La, J., Zhou, W., Xu, S., Wang, X., & Xu, Y. (2022). Digital transformation of airline industry. Research in Transportation Economics, 92, Article 101186. https://doi.org/10.1016/j.retrec.2022.101186

8. Hevner, A. R., March, S. T., Park, J., & Ram, S. (2004). Design science in information systems research. MIS Quarterly, 28(1), 75-105.

9. Higginbotham, J. (2021). Principles of web API design: Delivering value with APIs and microservices. Addison-Wesley Professional.

10. International Air Transport Association. (2021). Digital & Data Think Tank White Paper. https://www.iata.org/globalassets/iata/programs/innovation-hub/2021-ddtt-wp---final.pdf

11. International Air Transport Association. (2022). Digital Think Tank White Paper 2022. https://www.iata.org/contentassets/a46387f9bc6b42368c0a72664f6f930f/digital_think-tank_white-paper_2022.pdf

12. International Air Transport Association. (n.d.). Airline retailing: A world of Offers and Orders. Retrieved June 10, 2026, from https://www.iata.org/en/programs/airline-distribution/retailing/

13. International Civil Aviation Organization. (2019). Aviation Cybersecurity Strategy. https://www.icao.int/aviation-cybersecurity/strategy

14. International Organization for Standardization. (2023). ISO/IEC 42001:2023: Information technology - Artificial intelligence - Management system. https://www.iso.org/standard/42001

15. Krol, F., Saeed, M. A., & Kersten, W. (2020). A holistic digitalization KPI framework for the aerospace industry. In W. Kersten, T. Blecker, & C. M. Ringle (Eds.), Data Science and Innovation in Supply Chain Management: How Data Transforms the Value Chain. Hamburg International Conference of Logistics. https://www.econstor.eu/handle/10419/228940

16. Maslej, N., Fattorini, L., Perrault, R., Gil, Y., Parli, V., Kariuki, N., Capstick, E., Reuel, A., Brynjolfsson, E., Etchemendy, J., Ligett, K., Lyons, T., Manyika, J., Niebles, J. C., Shoham, Y., Wald, R., Walsh, T., Hamrah, A., Santarlasci, L., Betts Lotufo, J., & Shi, A. (2025). Artificial Intelligence Index Report 2025. Stanford Institute for Human-Centered Artificial Intelligence. https://hai.stanford.edu/ai-index

17. MoghadasNian, S. A. H. (2025a). Digital transformation and AI in airline management: Aligning agility, innovation, and data-driven strategies. SSRN. https://doi.org/10.2139/ssrn.6137987

18. MoghadasNian, S. A. H. (2025b). AI-powered predictive maintenance in aviation operations. SSRN. https://doi.org/10.2139/ssrn.6117408

19. Moghadasnian, S. A. H., & Kashian, H. (2025, August 1). Agentic AI in Airline Management: A KPI-Governed Architecture for Trust-Based Autonomy, Strategic Co-Leadership, and Operational Excellence. SSRN. https://doi.org/10.2139/ssrn.6033194

20. MoghadasNian, S. A. H., & Manafi, F. (2024, September 29). Elevating airline performance with data analytics: A strategic guide to key performance indicators. SSRN. https://doi.org/10.2139/ssrn.6236539

21. National Institute of Standards and Technology. (2023). Artificial Intelligence Risk Management Framework (AI RMF 1.0) (NIST AI 100-1). https://doi.org/10.6028/NIST.AI.100-1

22. National Institute of Standards and Technology. (2024). Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile (NIST AI 600-1). https://doi.org/10.6028/NIST.AI.600-1

23. Peffers, K., Tuunanen, T., Rothenberger, M. A., & Chatterjee, S. (2007). A design science research methodology for information systems research. Journal of Management Information Systems, 24(3), 45-77. https://doi.org/10.2753/MIS0742-1222240302

24. Pilon, R. V. (2023). Artificial intelligence in commercial aviation: Use cases and emerging strategies. Routledge. https://doi.org/10.4324/9781003018810

25. Sultanow, E., Brockmann, C., Schroeder, K., & Breithaupt, C. (2016). Lufthansa aviation standard: Developing an Open Group reference architecture for the aviation industry. In H. C. Mayr & M. Pinzger (Eds.), INFORMATIK 2016, Lecture Notes in Informatics, 259, 825-836. Gesellschaft für Informatik.

26. Taneja, N. K. (2017). 21st century airlines: Connecting the dots. Routledge. https://doi.org/10.4324/9781315107028

27. Taneja, N. K. (2019). Re-platforming the airline business: To meet travelers' total mobility needs. Routledge. https://doi.org/10.4324/9780429429101

ПРЕПРИНТ

ИНФОРМАЦИЯ

Использование куки-файлов