Otonom Teknolojilerin Hava Kargo Operasyonlarında Kullanımına İlişkin Riskler: Bulanık Tabanlı FMEA ile Bir Değerlendirme

Abstract

Hava kargo operasyonları, yük çeşitliliği, hacim farklılığı ve uçak tipine göre sürekli değişen gereksinimler nedeniyle standardizasyonun sınırlı, hata toleransının düşük olduğu karmaşık faaliyetlerden oluşmaktadır. Bununla birlikte küresel ölçekte artan kargo hacmi ve çeşitlenen müşteri beklentileri, operasyonları daha yoğun ve yönetilmesi daha güç bir yapıya dönüştürmektedir. Otonom teknolojiler, bu zorlu koşullarda süreçleri daha standart ve izlenebilir hale getirerek hava kargo operasyonlarının yönetilmesini kolaylaştırmakta ve operasyonel verimliliğin artırılmasına katkı sağlamaktadır. Ancak bu teknolojilerin hava kargo operasyonlarında kullanımı, operasyonel süreklilik ve emniyet üzerinde etkili olabilecek yeni riskleri de beraberinde getirmektedir. Bu çalışmada söz konusu riskler bulanık FMEA ile analiz edilerek önem düzeyleri belirlenmiş ve müdahale önceliği gerektiren alanlara yönelik öneriler geliştirilmiştir. Bu çerçevede çalışma kapsamında ele alınan on iki risk, İGA İstanbul Havalimanı’nda A sınıfı çalışma ruhsatına sahip olan bir yer hizmeti şirketinin hava kargo operasyonlarında görev yapan 6 uzmanın görüşleri üzerinden analiz edilmiştir. Analizler sonucunda risklerin düşük ve orta düzeyde kümelendiği, en yüksek riskin acil duruma uyumsuzluk (RÖS= 210,48), en düşük riskin ise görev çakışması (RÖS= 135,11) riski olduğu saptanmıştır. Ayrıca tedarikçi bağımlılığı (RÖS= 199,82) ve çarpışma (RÖS= 190,00) gibi riskler üst sıralarda yer almış, bu durum otonom sistemlere ilişkin risklerin hava kargo operasyonlarında normal işletim koşullarından ziyade acil durumlarda kritikleştiğini göstermiştir.

Keywords

• Hava kargo operasyonları
• Operasyonel risk yönetimi
• Otonom teknoloji riskleri
• Havacılıkta emniyet yönetimi
• Bulanık FMEA

Risks Associated with the Use of Autonomous Technologies in Air Cargo Operations: An Assessment Using Fuzzy Based FMEA

Abstract

Air cargo operations consist of complex activities with limited standardization and low tolerance for error due to continuously changing requirements arising from cargo diversity, volume variations, and differences in aircraft types. Moreover, the global increase in cargo volumes and the diversification of customer expectations are making operations more intensive and more difficult to manage. Under these challenging conditions, autonomous technologies facilitate the management of air cargo operations by standardizing and tracing processes and improving operational efficiency. However, the use of these technologies in air cargo operations also introduces new risks that may affect operational continuity and safety. In this study, the risks were analyzed using fuzzy FMEA to determine their importance levels and clarify the areas requiring intervention priority. In this regard, the twelve risks addressed in the study were analyzed based on the opinions of six experts working in air cargo operations at a ground handling company holding a Class A operating license at IGA Istanbul Airport. The analysis indicated that the risks cluster at low and medium levels, with the highest risk being incompatibility with emergency situations (RPN=210.48) and the lowest risk being task conflict (RPN=135.11). In addition, risks such as supplier dependency (RPN=199.82) and collision (RPN=190.00) ranked among the top, indicating that risks associated with autonomous systems are most critical in emergency scenarios rather than under normal operating conditions.

Keywords

• Air cargo operations
• Operational risk management
• Autonomous technology risks
• Safety Management in Aviation
• Fuzzy FMEA

References

1. Ajakwe, S. O., & Kim, D. S. (2024). Facets of security and safety problems and paradigms for smart aerial mobility and intelligent logistics. IET Intelligent Transport Systems, 18, 2827-2855. doi:10.1049/itr2.12579
2. Anderson, E., Fannin, T., & Nelson, B. (2018). Levels of aviation autonomy. In 2018 IEEE/AIAA 37th Digital Avionics Systems Conference (DASC) (pp. 1-8). London, England, 23-27 September, IEEE. doi:10.1109/DASC.2018.8569280
3. Arslan, Ö., Karakurt, N., Cem, E., & Cebi, S. (2023). Risk analysis in the food cold chain using decomposed fuzzy set-based FMEA approach. Sustainability, 15(17), 13169. doi: 10.3390/su151713169
4. Aylak, B. L., Kayıkcı, Y., & Taş, M. A. (2020). Türkiye’de lojistik sektöründe faaliyet gösteren işletmelerin dijital trendlerinin incelenmesi. Yaşar Üniversitesi E-Dergisi, 15(57), 98-116. doi:10.19168/jyasar.569599
5. Balaraju, J., Raj, M. G., & Murthy, C. S. (2019). Fuzzy-FMEA risk evaluation approach for LHD machine- A case study. Journal of Sustainable Mining, 18(4), 257-268. doi:10.1016/j.jsm.2019.08.002
6. Baykasoğlu, A., & Gölcük, İ. (2020). Comprehensive fuzzy FMEA model: a case study of ERP implementation risks. Operational Research, 20(2), 795-826. doi:10.1007/s12351-017-0338-1
7. Bierwirth, B., Schwanecke, U., Gietzen, T., Lopéz, D. A., & Brylka, R. (2019). SmartAirCargoTrailer: Autonomous short distance transports in air cargo. In Artificial Intelligence and Digital Transformation in Supply Chain Management: Innovative Approaches for Supply Chains. Proceedings of the Hamburg International Conference of Logistics (HICL), Vol. 27 (pp. 151-185). Berlin: epubli GmbH. doi:10.15480/882.2468
8. Bilici, S., & Kosanoğlu, F. (2021). Değer akış haritalama ve FMEA yöntemleri kullanılarak Tekstil Sektöründe Yalın Üretim Uygulaması. International Journal of Advances in Engineering and Pure Sciences, 33(1), 131-142. doi: 10.7240/jeps.784530

9. Cardiel-Ortega, J. J., & Baeza-Serrato, R. (2023). Failure mode and effect analysis with a Fuzzy Logic approach. Systems, 11(7), 348. doi:10.3390/systems11070348
10. Caristi, G., Barilla, D., Morabito, M., & Ferrara, M. (2025). Industrial processes and energy storage: modelling and optimizing risk priority number and recovery time objective. Decisions in Economics and Finance, 1-35. doi:10.1007/s10203-025-00544-7
11. Chakhrit, A., Bougofa, M., Guetarni, I. H. M., Bouafia, A., Kharzi, R., Nehal, N., & Chennoufi, M. (2024). A hybrid integrated multi-criteria decision-making approach for risk assessment: a study of automotive parts industry. International Journal of Quality & Reliability Management, 41(4), 1020-1043. doi:10.1108/IJQRM-01-2023-0010
12. Chanamool, N., & Naenna, T. (2016). Fuzzy FMEA application to improve decision-making process in an emergency department. Applied Soft Computing, 43, 441-453. doi:10.1016/j.asoc.2016.01.007
13. Chang, K. H., Cheng, C. H., & Chang, Y. C. (2010). Reprioritization of failures in a silane supply system using an intuitionistic fuzzy set ranking technique. Soft Computing, 14(3), 285-298. doi:/10.1007/s00500-009-0403-7
14. Chang, Y. S., Son, M. G., & Oh, C. H. (2011). Design and implementation of RFID based air-cargo monitoring system. Advanced Engineering Informatics, 25(1), 41-52. doi:10.1016/j.aei.2010.05.004
15. Chen, E., Luo, X., Ma, X., Bai, X., & Luo, J. (2025). Weight-embedded fuzzy FMEA framework for enhanced risk prioritization of offshore wind turbines. Ocean Engineering, 341, 122649. doi:10.1016/j.oceaneng.2025.122649
16. Dağıdır, B. D., & Özkan, B. (2025). Risk management in maintenance processes: A spherical fuzzy-based failure mode and effect analysis approach in the glass processing industry. Engineering Applications of Artificial Intelligence, 161, 112076. doi:10.1016/j.engappai.2025.112076
17. Dağsuyu, C., Göçmen, E., Narlı, M., & Kokangül, A. (2016). Classical and fuzzy FMEA risk analysis in a sterilization unit. Computers & Industrial Engineering, 101, 286-294. doi:10.1016/j.cie.2016.09.015
18. de Aguiar, J., Scalice, R. K., & Bond, D. (2018). Using fuzzy logic to reduce risk uncertainty in failure modes and effects analysis. Journal of the Brazilian Society of Mechanical Sciences and Engineering, 40(11), 516. doi:10.1007/s40430-018-1437-5
19. Demiral, D. G. (2021). Endüstri 4.0'ın lojistik boyutu: Lojistik 4.0. IBAD Sosyal Bilimler Dergisi, (9), 231-251. doi:10.21733/ibad.838751
20. DeVries, P. D. (2008). The state of RFID for effective baggage tracking in the airline industry. International Journal of Mobile Communications, 6(2), 151-164. doi:10.1504/IJMC.2008.016574
21. Eitrheim, M. H. R., Nordfjærn, T., Log, M. M., & Tørset, T. (2025). Risk perception and trust in autonomous vehicles: A case study of ground handling employees at an international airport. Journal of Risk Research, 28(9-10), 1146-1162. doi:10.1080/13669877.2024.2423202
22. Florido-Benítez, L. (2023). The Role of the Top 50 US cargo airports and 25 air cargo airlines in the logistics of e-commerce companies. Logistics, 7(1), 8. doi:/10.3390/logistics7010008
23. Godina, R., Silva, B. G. R., & Espadinha-Cruz, P. (2021). A DMAIC integrated fuzzy FMEA model: a case study in the Automotive Industry. Applied Sciences, 11(8), 3726. doi:10.3390/app11083726
24. Gupta, G., Ghasemian, H., & Janvekar, A. A. (2021). A novel failure mode effect and criticality analysis (FMECA) using fuzzy rule-based method: A case study of industrial centrifugal pump. Engineering Failure Analysis, 123, 105305. doi:10.1016/j.engfailanal.2021.105305
25. Han, P., Yang, X., Zhao, Y., Guan, X., & Wang, S. (2022). Quantitative ground risk assessment for urban logistical unmanned aerial vehicle (UAV) based on bayesian network. Sustainability, 14(9), 5733. doi:10.3390/su14095733
26. Ilbahar, E., Karaşan, A., Cebi, S., & Kahraman, C. (2018). A novel approach to risk assessment for occupational health and safety using Pythagorean fuzzy AHP & fuzzy inference system. Safety Science, 103, 124-136. doi:10.1016/j.ssci.2017.10.025
27. International Air Transport Association. (2025). 2025 vision for the future of air cargo facilities: Reimagining logistics through technology and innovation.
28. Jin, G., Meng, Q., & Feng, W. (2022). Optimization of logistics system with fuzzy FMEA-AHP methodology. Processes, 10(10), 1973. doi:10.3390/pr10101973
29. Kahraman, C., Kaya, İ., & Şenvar, Ö. (2013). Healthcare failure mode and effects analysis under fuzziness. Human and Ecological Risk Assessment: An International Journal, 19(2), 538-552. doi:10.1080/10807039.2012.737753
30. Kostrubiec, B., Kwasiborska, A., & Owczarek, P. (2025). Automation of cargo handling on the example of Warsaw Chopin Airport. WUT Journal of Transportation Engineering, 140, 23-45. doi:10.5604/01.3001.0055.2412
31. Kuroda, Y., Hanaoka, S., Sato, S., & Horiguchi, R. (2025). Traffic simulation of automated-driving ground support equipment at Tokyo International Airport. Aerospace, 12(10), 896. doi:10.3390/aerospace12100896
32. Kurzweil, L., & Kolářová, L. (2026). Shifting readiness of Prague Airport for safe operations of autonomous tow tractors. Transportation Research Procedia. 94, 101-110. doi:10.1016/j.trpro.2026.01.011
33. Kütahya, C. K., & Kanmaz, U. (2025). Prioritizing risk mitigation strategies in air cargo freight operations: a fuzzy TOPSIS approach. Journal of Aviation, 9(1), 181-195. doi:10.30518/jav.1599331
34. Li, J., Zou, M., Lv, Y., & Sun, D. (2024). AGV scheduling for optimizing irregular air cargo containers handling at airport transshipment centers. Mathematics, 12(19), 3045. doi:10.3390/math12193045
35. Min, K. J., Chung, B. D., & Cho, G. S. (2025). Agent-based modelling for human–robot collaborative order picking system considering workers’ performance. International Journal of Production Research, 1-24. doi:10.1080/00207543.2024.2440795
36. Muecklich, N., & Wehner, M. (2025). Testing autonomous cargo vehicles at airports: An analysis of the basic requirements focusing on staff-related human factors. Journal of Air Transport Studies, 14(1), 1-35. doi:10.38008/jats.v14i1.203
37. Muecklich, N., Sikora, I., Paraskevas, A., & Padhra, A. (2023). The role of human factors in aviation ground operation-related accidents/incidents: A human error analysis approach. Transportation Engineering, 13, 100184. doi:10.1016/j.treng.2023.100184
38. Perić, I., Klarić, K., Barčić, A. P., Vukman, K., Sedlar, T., & Grošelj, P. (2025). Optimising risk management in wood-based manufacturing: A fuzzy AHP-FMEA framework approach. BioResources, 20(2). 2979-3001. doi: 10.15376/biores.20.2.2979-3001
39. Rahnamay Bonab, S., & Osgooei, E. (2025). Environment risk assessment of wastewater treatment using FMEA method based on Pythagorean fuzzy multiple-criteria decision-making. Environment, Development and Sustainability, 27(9), 22185-22215. doi:10.1007/s10668-022-02555-5
40. Sachdeva, A., Kumar, P., & Kumar, D. (2009). Maintenance criticality analysis using TOPSIS. In 2009 IEEE International Conference on Industrial Engineering and Engineering Management (pp. 199-203). Hong Kong, 8-11 December, IEEE.
41. Shao, P. C. (2020). Risk assessment for UAS logistic delivery under UAS traffic management environment. Aerospace, 7(10), 140. doi: 10.3390/aerospace7100140
42. Sharma, R. K., Kumar, D., & Kumar, P. (2005). Systematic failure mode effect analysis (FMEA) using fuzzy linguistic modelling. International Journal of Quality & Reliability Management, 22(9), 986-1004. doi:/10.1108/02656710510625248
43. Shevchuk, D. O., & Steniakin, I. A. (2023). A holistic approach to ensuring safety and cybersecurity in the use of intelligent technologies in air transport. Electronics & Control Systems, 75(1). 97-101. doi: 10.18372/1990-5548.75.17563
44. Spandonidis, C., Sedikos, E., Giannopoulos, F., Petsa, A., Theodoropoulos, P., Chatzis, K., & Galiatsatos, N. (2022). A novel intelligent IOT system for improving the safety and planning of air cargo operations. Signals, 3(1), 95-112. doi:10.3390/signals3010008
45. Sun, J., Tang, X., & Shao, Q. (2024). A collision risk assessment method for aircraft on the apron based on Petri nets. Applied Sciences, 14(19), 9128. doi:10.3390/app14199128
46. Tabares, D. A., & F. Mora-Camino. (2017). Aircraft ground handling: Analysis for automation. In 17th AIAA AviationTechnology, Integration, and Operations Conference. Denver, Colorado: American Institute of Aeronautics and Astronautics, 5-9 June 2017. doi:10.2514/6.2017-3425
47. Tadić, S., Krstić, M., Savić, D., & Vasov, L. (2024). Assessing the applicability of Industry 4.0 technologies in optimizing air cargo terminal operations. Journal of Organizations, Technology and Entrepreneurship, 2(2), 97-112. doi:10.56578/jote020203
48. Testik, O. M., & Unlu, E. T. (2023). Fuzzy FMEA in risk assessment for test and calibration laboratories. Quality and Reliability Engineering International, 39(2), 575-589. doi:10.1002/qre.3198
49. van der Zwan, M., Ermiş, G., & Sharpanskykh, A. (2025). Multi-agent task allocation and path planning for autonomous ground support equipment. Journal of Air Transport Management, 129, 102855. doi:10.1016/j.jairtraman.2025.102855
50. Wang, S., Che, Y., Zhao, H., & Lim, A. (2021). Accurate tracking, collision detection, and optimal scheduling of airport ground support equipment. IEEE Internet of Things Journal, 8(1), 572-584. doi:10.1109/JIOT.2020.3004874
51. Wehner, M., Blesing, C., Ullrich, N., Gössner, M., & Klumpp, M. (2025). Air cargo logistics automation and digital airport process management: comprehensive empirical insights from Germany. Logistics Research, 1-25. doi:10.1108/LORE-04-2025-0054
52. Zheng, B., Zhou, W., & Ma, X. (2025). Quantitative study on uav operational risk based on specific operation risk assessment model. Quality and Reliability Engineering International, 41(5), 1689-1713. doi:10.1002/qre.3746
53. Zhu, J., Shuai, B., Wang, R., & Chin, K. S. (2019). Risk assessment for failure mode and effects analysis using the Bonferroni mean and TODIM method. Mathematics, 7(6), 536. doi:10.3390/math7060536