Airline Logistics AI Performance Framework A 360-Degree, Multi-Layered KPI Approach for Safety, Sustainability, Efficiency, and Innovation

SeyyedAbdolHojjat  MoghadasNian

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Эта статья является препринтом и не была отрецензирована.
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Airline Logistics AI Performance Framework A 360-Degree, Multi-Layered KPI Approach for Safety, Sustainability, Efficiency, and Innovation

S. MoghadasNian

2026-01-15

https://doi.org/10.24108/preprints-3114281

The integration of Artificial Intelligence (AI) in airline logistics is a transformative force, enhancing operational efficiency, safety, sustainability, and innovation. This paper introduces a 360-degree, multi-layered Key Performance Indicator (KPI) framework designed to assess and optimize AI performance within the airline logistics sector. The framework addresses key dimensions such as safety, sustainability, operational efficiency, and ethical AI, providing a comprehensive tool for evaluating AI systems’ contributions to airline operations. By offering insights into AI integration challenges and the importance of strategic alignment, the proposed framework serves as a valuable resource for airline managers, regulators, and technology providers in fostering continuous improvement, regulatory compliance, and innovation in the logistics ecosystem.

Ссылка для цитирования:

MoghadasNian S. 2026. Airline Logistics AI Performance Framework A 360-Degree, Multi-Layered KPI Approach for Safety, Sustainability, Efficiency, and Innovation. PREPRINTS.RU. https://doi.org/10.24108/preprints-3114281

Список литературы

1. Bati, F., Lee, F., Bollschweiler, R., Withington, L., Khorrami, N., & et al. (2021). Risk Metrics to Measure Safety Performance of the National Airspace System: Implementation Using Machine Learning. Symposium on Dependable Autonomic and Secure Computing.

2. Göçmen, E. (2021). Smart Airport: Evaluation of Performance Standards and Technologies for a Smart Logistics Zone. Transportation Research Record.

3. Moghadasnian, S. & Mirfaizi, S. (2024). Enhancing Airline Logistics Efficiency: KPI-Driven Strategies. Presented at the 4th International Conference on Advanced Research in Management and Humanities, Munich, Germany, 11 July.

4. Moghadasnian, S. & EbrahimNezhad, M. (2024). Optimizing Airline Logistics Stock Control: The Strategic Role of Key Performance Indicators (KPIs). Presented at the 9th National Conference on Interdisciplinary Research in Engineering and Management, Tehran, Iran, 20 November.

5. Thums, J., Künzel, L., Klumpp, M., Bardmann, M.-M., & Ruiner, C. (2023). Future air transportation and digital work at airports – Review and developments. Transportation Research Interdisciplinary Perspectives.

6. Mgbachi, V. (2024). AI and Blockchain Integration in Business Aviation: Securing Supply Chain and Enhancing Traceability. Journal of Business and Strategic Management.

7. Sridhar, B., & Bell, D. (2022). Sustainable Aviation Operations and the Role of Information Technology and Data Science: Background, Current Status and Future Directions. AIAA AVIATION 2022 Forum.

8. Ogunsina, K. E., & DeLaurentis, D. (2021). Enabling Integration and Interaction for Decentralized Artificial Intelligence in Airline Disruption Management. Engineering Applications of Artificial Intelligence.

9. Thai, P., Alam, S., Lilith, N., Tran, P. N., & Binh, N. T. (2020). Deep4Air: A Novel Deep Learning Framework for Airport Airside Surveillance. IEEE International Conference on Multimedia & Expo Workshops (ICMEW).

10. Kabashkin, I. (2024). Integration of Foundation Models and Federated Learning in AIoT-Based Aircraft Health Monitoring Systems. Mathematics.

11. Ellis, K. K., Prinzel, L., Krois, P., Davies, M., Oza, N., & et al. (2022). A Future In-time Aviation Safety Management System (IASMS) Perspective for Commercial Air Carriers. AIAA AVIATION 2022 Forum.

12. Moghadasnian, S. (2022). Flight to Excellence: A Comprehensive Guide to Key Performance Indicators in the Airline Industry: Unlocking Success Through Data-Driven Strategies and Performance Metrics. Aviation and Tourism Research and Innovation Center (ATRIC). Digital Publication. Tehran, Iran & Milan, Italy.

13. Moghadasnian, S. (2020). Soaring Above Boundaries: A Comprehensive Guide to KPIs for the Chief Logistics Officer in the Airline Industry: Leveraging Metrics to Optimize Airline Logistics and Streamline Operations. Aviation and Tourism Research and Innovation Center (ATRIC). Digital Publication. Tehran, Iran & Milan, Italy.

14. Tsakalerou, M., Nurmaganbetov, D., & Beltenov, N. (2021). Aircraft Maintenance 4.0 in an Era of Disruptions. IEEE International Symposium on Multimedia.

15. Xie, H., Li, Y., Pu, Y., Zhang, C., & Huang, J. (2022). Evaluating airline service quality through a comprehensive text-mining and multi-criteria decision-making analysis. Journal of Air Transport Management.

16. Vevere, K., Dange, K., Linina, I., & Zvirgzdiņa, R. (2024). Integration of Artificial Intelligence (AI) within SmartLynx Airlines to Increase Operational Efficiency. WSEAS Transactions on Business and Economics.

17. Miller, M., Holley, S., & Halawi, L. (2023). The Evolution of AI on the Commercial Flight Deck: Finding Balance between Efficiency and Safety While Maintaining the Integrity of Operator Trust. Artificial Intelligence, Social Computing and Wearable Technologies.

18. Puranik, T. G., Rodriguez, N., & Mavris, D. (2020). Towards Online Prediction of Safety-Critical Landing Metrics in Aviation Using Supervised Machine Learning. Aerospace.

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