Yapay Zekâ Destekli Lojistik Otonomi: Performans, Sürdürülebilirlik ve Dayanıklılık Üzerine Kuramsal ve Kavramsal Bir Model Önerisi

Abstract

Bu çalışma, yapay zekâ (YZ) destekli lojistik otonominin tedarik zinciri performansı, sürdürülebilirlik ve dayanıklılık hedefleri arasındaki ilişkileri nasıl yeniden şekillendirdiğini kavramsal ve kuramsal bir perspektiften incelemektedir. Mevcut literatür incelendiğinde YZ destekli lojistik sistemlerin çoğunlukla verimlilik ve optimizasyon ekseninde ele alındığı, söz konusu hedefler arasındaki yapısal gerilimlerin ise sınırlı düzeyde tartışıldığı görülmektedir. Bu çalışmada YZ destekli lojistik otonomi, doğrusal bir teknolojik ilerleme aracı olarak değil, çok boyutlu gerilimler üreten sosyo-teknik bir dönüşüm olarak değerlendirilmiştir. Bu kapsamda çalışmada, performans, sürdürülebilirlik ve dayanıklılık arasındaki ilişkileri açıklamak amacıyla “üçlü gerilim modeli” geliştirilmiş ve model doğrultusunda 15 adet test edilebilir önerme oluşturulmuştur. Çalışma, Paradoks Kuramı, Sosyo-Teknik Sistemler Kuramı, Örgütsel Bilgi İşleme Kuramı, Dinamik Yetkinlikler Kuramı, Normal Kazalar Kuramı ve Kurumsal Kuram çerçevesinde teorik olarak temellendirilmiştir. Ayrıca Türkiye bağlamı üzerinden yapılan değerlendirmeler, dijital olgunluk, veri altyapısı, yönetişim kapasitesi, kurumsal yapı ve jeopolitik kırılganlıkların otonomi uygulamalarının etkilerini belirgin biçimde şekillendirdiğini ortaya koymaktadır. Çalışma, YZ destekli lojistik literatürüne teknoloji iyimserliğinin ötesinde, gerilim yönetimi, algoritmik yönetişim ve bağlamsal farklılıklar perspektifi kazandırarak gelecekte yapılacak ampirik araştırmalar için bütüncül bir kuramsal zemin sunmayı amaçlamaktadır. Çalışma, kuramsal çerçeve ve önermeler aracılığıyla YZ destekli lojistik otonominin farklı sektörel ve kurumsal bağlamlarda nasıl sonuçlar üretebileceğine ilişkin kavramsal bir tartışma alanı da oluşturmaktadır.

Keywords

• Yapay zeka
• Sürdürülebilirlik
• Performans
• Dayanıklılık
• Agoritmik Yönetişim

AI-Powered Logistics Autonomy: A Theoretical and Conceptual Model Proposal on Performance, Sustainability, and Resilience

Abstract

This study examines, from a conceptual and theoretical perspective, how AI-powered logistics autonomy reshapes the relationships between supply chain performance, sustainability, and resilience goals. A review of the existing literature reveals that AI-powered logistics systems are mostly discussed in terms of efficiency and optimization, while the structural tensions between these goals are only discussed to a limited extent. In this study, AI-powered logistics autonomy is considered not as a linear technological advancement tool, but as a socio-technical transformation generating multidimensional tensions. Within this scope, a "triple tension model" is developed to explain the relationships between performance, sustainability, and resilience, and 15 testable propositions are formulated based on this model. This study is theoretically grounded in Paradox Theory, Socio-Technical Systems Theory, Organizational Information Processing Theory, Dynamic Capabilities Theory, Normal Accidents Theory, and Institutional Theory. Furthermore, evaluations conducted within the Turkish context reveal that digital maturity, data infrastructure, governance capacity, institutional structure, and geopolitical vulnerabilities significantly shape the impact of autonomy applications. Beyond technological optimism, this study aims to provide a holistic theoretical framework for future empirical research in AI-assisted logistics by introducing perspectives on stress management, algorithmic governance, and contextual differences. Through its theoretical framework and propositions, the study also creates a conceptual discussion space regarding how AI-assisted logistics autonomy can produce results in different sectoral and institutional contexts.

Keywords

• Artificial intelligence
• sustainability
• performance
• resilience
• algorithmic governance

References

1. Amodei, D., Olah, C., Steinhardt, J., Christiano, P., Schulman, J., & Mané, D. (2016). Concrete problems in AI safety. arXiv preprint arXiv:1606.06565
2. Andrei, N., Scarlat, C., & Ioanid, A. (2024). Transforming E-commerce logistics: sustainable practices through autonomous maritime and last-mile transportation solutions. Logistics, 8(3), 71, https://doi.org/10.3390/logistics8030071
3. Arvis, J.-F., Ojala, L., Shepherd, B., Ulybina, D., & Wiederer, C. (2023). Connecting to compete 2023: Trade logistics in an uncertain global economy – The logistics performance index and its indicators. World Bank. https://lpi.worldbank.org/sites/default/files/2023-04/LPI_2023_report.pdf
4. Atkinson, R. (1999). Project management: Cost, time and quality, two best guesses and a phenomenon, its time to accept other success criteria. International Journal of Project Management, 17(6), 337-342, https://doi.org/10.1016/S0263-7863(98)00069-6
5. Bainbridge, L. (1983). Ironies of automation. In Analysis, design and evaluation of man–machine systems. Pergamon, 129-135, https://doi.org/10.1016/B978-0-08-029348-6.50026-9
6. Bechtsis, D., Tsolakis, N., Vlachos, D., & Iakovou, E. (2017). Sustainable supply chain management in the digitalisation era: The impact of Automated Guided Vehicles. Journal of Cleaner Production, 142, 3970-3984, https://doi.org/10.1016/j.jclepro.2016.10.057
7. Boysen, N., De Koster, R., & Weidinger, F. (2019). Warehouse in the e-commerce era: A survey. European Journal of Operational Research, 277(2), 396-411, https://doi.org/10.1016/j.ejor.2018.08.023
8. Bozma, G., & Künü, S. (2020). Türkiye’nin ekonomi politikasının üçlü açmaz bağlamında incelenmesi. Uluslararası Ekonomi ve Yenilik Dergisi, 6(2), 331-341, https://doi.org/10.20979/ueyd.792532

9. Buhmann, A., & Fieseler, C. (2023). Deep learning meets deep democracy: Deliberative governance and responsible innovation in artificial intelligence. Business Ethics Quarterly, 33(1), 146-179.
10. Burrell, J. (2016). How the machine ‘thinks’: Understanding opacity in machine learning algorithms. Big Data & Society, 3(1), 1-12, https://doi.org/10.1177/2053951715622512.
11. Cagnin, C., Amanatidou, E., & Keenan, M. (2012). Orienting European innovation systems towards grand challenges and the roles that FTA can play. Science and Public Policy, 39(2), 140-152. https://doi.org/10.1093/scipol/scs014
12. Carter, C. R., & Rogers, D. S. (2008). A framework of sustainable supply chain management: moving toward new theory. International Journal of Physical Distribution & Logistics Management, 38(5), 360-387, https://doi.org/10.1108/09600030810882816
13. Chen, H., Wen, Y., Zhu, M., Huang, Y., Xiao, C., Wei, T., & Hahn, A. (2021). From automation system to autonomous system: An architecture perspective. Journal of Marine Science and Engineering, 9(6), 1-24, https://doi.org/10.3390/jmse9060645
14. Christopher, M., & Peck, H. (2004)., Building the resilient supply chain. International Journal of Logistics Management, 15 (2), 1-13, https://doi.org/10.1108/09574090410700275
15. Choi, T. M., Wallace, S. W., & Wang, Y. (2018). Big data analytics in operations management. Production and Operations Management, 27(10), 1868-1883, https://doi.org/10.1111/poms.12838
16. Cömert, H. (2016). İmkânsız üçleme’den imkânsız ikilem’e: Bretton Woods dönemi ve sonrası para politikası. Hacettepe Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 34(1), 115-136, https://doi.org/10.17065/huniibf.309287
17. Cobbe, J., Veale, M., & Singh, J. (2023). Understanding accountability in algorithmic supply chains. In Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’23), 1186-1197, https://doi.org/10.1145/3593013.3594073
18. Çelik, E. N. (2025). Yapay zekâ sistemlerinin küresel tedarik zinciri entegrasyonu ve performansına etkileri üzerine bir araştırma. Ege Üniversitesi Ulaştırma Yönetimi Araştırmaları Dergisi, 2(1), 86-99.
19. Crainic, T. G., Perboli, G., & Rosano, M. (2018). Simulation of intermodal freight transportation systems: a taxonomy. European Journal of Operational Research, 270(2), 401-418, https://doi.org/10.1016/j.ejor.2017.11.061
20. Culot, G., Podrecca, M., & Nassimbeni, G. (2024). Artificial intelligence in supply chain management: A systematic literature review of empirical studies and research directions. Computers in Industry, 162, 104132, https://doi.org/10.1016/j.compind.2024.104132
21. Demir, E., Bektaş, T., & Laporte, G. (2014). A review of recent research on green road freight transportation. European Journal of Operational Research, 237(3), 775-793, https://doi.org/10.1016/j.ejor.2013.12.033
22. DiMaggio, P. J., & Powell, W. W. (1983). The iron cage revisited: Institutional isomorphism and collective rationality in organizational fields. American Sociological Review, 48(2), 147-160, doi: 10.2307/2095101
23. Elkington, J. (1998). Elkington, J. (1998). Partnerships from cannibals with forks: The triple bottom line of 21st‐century business. Environmental quality management, 8(1), 37-51. https://doi.org/10.1002/tqem.3310080106
24. Endsley, MR ve Kiris, EO (1995). The Out-of-the-Loop Performance Problem and Level of Control in Automation. Human Factors, 37 (2), 381-394, https://doi.org/10.1518/001872095779064555
25. Endsley, M. R. (2017). Toward a theory of situation awareness in dynamic systems. In Situational awareness. Routledge.
26. Endsley, M. R. (2017). From here to autonomy: lessons learned from human–automation research. Human Factors, 59(1), 5-27, https://doi.org/10.1177/0018720816681350
27. Fagnant, D. J., & Kockelman, K. (2015). Preparing a nation for autonomous vehicles: opportunities, barriers and policy recommendations. Transportation Research Part A: Policy and Practice, 77, 167-181, https://doi.org/10.1016/j.tra.2015.04.003
28. Floridi, L., Cowls, J., Beltrametti, M., Chatila, R., Chazerand, P., Dignum, V., & Vayena, E. (2018). AI4People—An ethical framework for a good AI society: Opportunities, risks, principles, and recommendations. Minds and Machines, 28(4), 689-707.
29. Galbraith, J. (1973). Designing complex organizations. Reading, Mass.
30. Göz, M. M. (2025). Yapay zekânın demokrasiye etkileri: fırsatlar ve tehditler. Akademik İzdüşüm Dergisi, 10(2), 629-660. https://izlik.org/JA46AX99HH
31. Gunasekaran, A., Patel, C., & Tirtiroglu, E. (2001). Performance measures and metrics in a supply chain environment. International Journal of Operations & Production Management, 21(1/2), 71-87, doi: 10.1108/01443570110358468
32. Hosseini Shekarabi, S. A., Kiani Mavi, R., & Romero Macau, F. (2025). Supply chain resilience: A critical review of risk mitigation, robust optimisation, and technological solutions and future research directions. Global Journal of Flexible Systems Management, 26(3), 681-735, https://doi.org/10.1007/s40171-025-00458-8
33. ILO (2021). Should developing countries fear the impact of automation on jobs? https://www.ilo.org/resource/article/should-developing-countries-fear-impact-automation-jobs.
34. Ivanov, D., & Dolgui, A. (2020). Viability of intertwined supply networks: extending the supply chain resilience angles towards survivability. A position paper motivated by COVID-19 outbreak. International Journal of Production Research, 58(10), 2904-2915, https://doi.org/10.1080/00207543.2020.1750727
35. Ivanov, D. (2021). Supply chain viability and the COVID-19 pandemic: A conceptual and formal generalisation of four major adaptation strategies. International Journal of Production Research, 59(12), 3535-3552, https://doi.org/10.1080/00207543.2021.1890852
36. Judijanto, L., Rustiyana, R., & Rusdi, R. (2026). AI-governance and algorithmic accountability: Rethinking legal standards in the age of autonomous decision-making. International Journal of Humanities, Social Sciences and Business (INJOSS), 5(1), 11-25.
37. Kamble, S. S., Gunasekaran, A., & Gawankar, S. A. (2020). Achieving sustainable performance in a data-driven agriculture supply chain: A review for research and applications. International Journal of Production Economics, 219, 179-194, https://doi.org/10.1016/j.ijpe.2019.05.022
38. Kane, G. C., Palmer, D., Phillips, A. N., Kiron, D., & Buckley, N. (2015). Strategy, not technology, drives digital transformation. MIT Sloan Management Review.
39. Kareem, S., Fehrer, J. A., Shalpegin, T., & Stringer, C. (2025). Navigating tensions of sustainable supply chains in times of multiple crises: A systematic literature review. Business Strategy and the Environment, 34(1), 316-337, https://doi.org/10.1002/bse.3990
40. Kesgin, K. (2025). Yapay Zekâ Tabanlı Karar Destek Sistemlerinde Teknik, Etik ve Yönetişimsel Yaklaşımlar: Çok Sektörlü Bir Derleme Çalışması. International Journal of Management Information Systems and Computer Science, 9(1), 29-48.
41. Kou, H., Tang, R., & Chen, N. (2025). Enterprise digitalization and ESG performance: Evidence from interpretable AI large language models. Systems, 13(9), 832, https://doi.org/10.3390/systems13090832
42. Liu, Z., Zhou, J., & Li, J. (2023). How do family firms respond strategically to the digital transformation trend: Disclosing symbolic cues or making substantive changes? Journal of Business Research, 155, 113395, https://doi.org/10.1016/j.jbusres.2022.113395
43. Long, C., Simchi-Levi, D., Calmon, A. P., & Calmon, F. P. (2025). When supply chains become autonomous. Harvard Business Review,. https://hbr.org/2025/12/when-supply-chains-become-autonomous
44. Luccioni, A. S., Strubell, E., & Crawford, K. (2025). From efficiency gains to rebound effects: The problem of Jevons’ paradox in AI’s polarized environmental debate. In Proceedings of the 2025 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’25), , 76-88, https://doi.org/10.1145/3715275.3732007
45. Makri, C., Gürdür Broo, D., & Neely, A. (2022). Human-in-loop decision-making and autonomy: Lessons learnt from the aviation industry transferred to cyber-physical systems. Technologies, 10(6), 1-21, https://doi.org/10.3390/technologies10060120
46. Marchant, G. E. (2011). The growing gap between emerging technologies and the law. The growing gap between emerging technologies and legal-ethical oversight: The Pacing Problem, 19-33.
47. Mckinnon, A. C. (2016). The possible impact of 3D printing and drones on last-mile logistics: An exploratory study. Built Environment, 42(4), 617-629, https://doi.org/10.2148/benv.42.4.617
48. McKinnon, A., Cullinane, S., Browne, M., & Whiteing, A. (Eds.). (2010). Green Logistics: Improving the Environmental Sustainability of Logistics (2nd ed.). Kogan Page. London.
49. Mentzer, J. T., Flint, D. J., & Hult, G. T. M. (2001). Logistics service quality as a segment-customized process. Journal of marketing, 65(4), 82-104, https://doi.org/10.1509/jmkg.65.4.82.18390
50. Meredith, J. (1993). Theory building through conceptual methods. International Journal of Operations & Production Management, 13(5), 3-11, https://doi.org/10.1108/01443579310028120
51. Mohammad, A. A. S., Khanfar, I. A., Al Oraini, B., Vasudevan, A., Suleiman, I. M., & Fei, Z. (2024). Predictive analytics on artificial intelligence in supply chain optimization. Data and Metadata, 3, 395-395, doi: 10.56294/dm2024395
52. Ng, K. K., Chen, C. H., Lee, C. K., Jiao, J. R., & Yang, Z. X. (2021). A systematic literature review on intelligent automation: Aligning concepts from theory, practice, and future perspectives. Advanced Engineering Informatics, 47, 1-15, https://doi.org/10.1016/j.aei.2021.101246
53. Nitsche, B. (2021). Exploring the potentials of automation in logistics and supply chain management: Paving the way for autonomous supply chains. Logistics, 5(3), 51, https://doi.org/10.3390/logistics5030051
54. Notteboom, T., Pallis, A., & Rodrigue, J. P. (2022). Port economics, management and policy. Routledge.
55. OECD (2021). The Digital Transformation of SMEs. https://www.oecd.org/en/publications/the-digital-transformation-of-smes_bdb9256a-en.html.
56. OECD (2023). OECD Regional Development Papers. https://www.oecd.org/content/dam/oecd/en/publications/reports/2023/06/the-territorial-impact-of-the-earthquakes-in-turkiye_3e1731ee/bb5c07e6-en.pdf.
57. OECD (2023). Realising the Potential of the Middle Corridorhttps://www.oecd.org/content/dam/oecd/en/publications/reports/2023/12/realising-the-potential-of-the-middle-corridor_c458041c/635ad854-en.pdf.
58. Perrow, C. (2011). Normal accidents: Living with high risk Technologies. Princeton University Press.
59. Pettit, T. J., Fiksel, J., & Croxton, K. L. (2010). Ensuring supply chain resilience: development of a conceptual framework. Journal of Business Logistics, 31(1), 1-21, https://doi.org/10.1002/j.2158-1592.2010.tb00125.x
60. Parasuraman, R., & Riley, V. (1997). Humans and automation: Use, misuse, disuse, abuse. Human Factors, 39(2), 230-253, https://doi.org/10.1518/001872097778543886
61. Parasuraman, R., Sheridan, T. B., & Wickens, C. D. (2000). A model for types and levels of human interaction with automation. IEEE Transactions on systems, man, and cybernetics-Part A: Systems and Humans, 30(3), 286-297, doi: 10.1109/3468.844354
62. Ponomarov, S. Y., & Holcomb, M. C. (2009). Understanding the concept of supply chain resilience. The İnternational Journal of Logistics Management, 20(1), 124-143, https://doi.org/10.1108/09574090910954873
63. Richardson, L. S., Fidock, J., & Gunawan, I. (2025). Systematic Literature Review of Levels of Automation (Autonomy) Taxonomy: Critiques and Recommendations. International Journal of Human–Computer Interaction, 1-20, https://doi.org/10.1080/10447318.2025.2502978
64. Rodrigue, J. P. (2020). The geography of transport systems. Routledge.
65. Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nature Machine İntelligence, 1(5), 206-215.
66. Russell, S., Norvig, P., & Intelligence, A. (1995). A modern approach. Artificial Intelligence. Prentice-Hall, Egnlewood Cliffs, 25(27), 79-80.
67. Smith, W. K., & Lewis, M. W. (2011). Toward a theory of paradox: A dynamic equilibrium model of organizing. Academy of Management Review, 36(2), 381-403, https://doi.org/10.5465/amr.2009.0223
68. Snyder, H. (2019). Literature review as a research methodology: An overview and guidelines. Journal of Business Research, 104, 333-339, https://doi.org/10.1016/j.jbusres.2019.07.039
69. Sivil Havacılık Genel Müdürlüğü (2016). İnsansız Hava Aracı Sistemleri Talimatı (sht-iha). https://web.shgm.gov.tr/documents/sivilhavacilik/files/mevzuat/sektorel/talimatlar/2020/SHT-IHA_Rev-04.pdf.
70. Taleb, N. N. (2012). Antifragile: Things that gain from disorder. Penguin UK.
71. Tang, C. S. (2006). Robust strategies for mitigating supply chain disruptions. International Journal of Logistics: Research and Applications, 9(1), 33-45, https://doi.org/10.1080/13675560500405584
72. T.C. Cumhurbaşkanlığı Strateji ve Bütçe Başkanlığı. (2025). Lojistik hizmetlerinin geliştirilmesi özel ihtisas komisyonu raporu. https://www.sbb.gov.tr/wp-content/uploads/2025/08/Lojistik-Ozel-Ihtisas-Komisyonu-Raporu_01082025.pdf
73. T.C. Ticaret Bakanlığı. (2024). Dış Ticaret Lojistiği 2024 Raporu. https://ticaret.gov.tr/data/5b87bf9113b8761160fa1258/D%C4%B1%C5%9F%20Ticaret%20Lojisti%C4%9Fi%202024.pdf
74. Teece, D. J. (2007). Explicating dynamic capabilities: the nature and microfoundations of (sustainable) enterprise performance. Strategic Management Journal, 28(13), 1319-1350, https://doi.org/10.1002/smj
75. Tundys, B., & Wiśniewski, T. (2026). Who wins the energy race? Artificial intelligence for smarter energy use in logistics and supply chain management. Energies, 19(2), 305, https://doi.org/10.3390/en19020305
76. Trist, E. L., & Bamforth, K. W. (1951). Some social and psychological consequences of the longwall method of coal-getting: An examination of the psychological situation and defences of a work group in relation to the social structure and technological content of the work system. Human Relations, 4(1), 3-38, https://doi.org/10.1177/001872675100400101
77. Tunahan, H., Karataş, A., Garip, B., & Atakan, G. (2023). Avrupa Yeşil Mutabakat ı 55’e Uyum: Türk Lojistik Sektörü Üzerine İnceleme. Ankara Avrupa Çalışmaları Dergisi, 22(1), 225-255, https://doi.org/10.32450/aacd.1327083
78. UNCTAD, (2024). Navigating troubled waters impact to global trade of disruption of shipping routes in the red sea, black sea and panama canal. https://unctad.org/system/files/official-document/osginf2024d2_en.pdf.
79. UTİKAD (Uluslararası Taşımacılık ve Lojistik Hizmet Üretenleri Derneği). (2022). Lojistik sektörü raporu 2022. https://www.utikad.org.tr/images/HizmetRapor/utikadlojistiksektoruraporu2022-857.pdf
80. Wacker, J. G. (1998). A definition of theory: research guidelines for different theory-building research methods in operations management. Journal of Operations Management, 16(4), 361-385, https://doi.org/10.1016/S0272-6963(98)00019-9
81. Wadud, Z., MacKenzie, D., & Leiby, P. (2016). Help or hindrance? The travel, energy and carbon impacts of highly automated vehicles. Transportation Research Part A: Policy and Practice, 86, 1-18, https://doi.org/10.1016/j.tra.2015.12.001
82. Wamba, S. F., Gunasekaran, A., Akter, S., Ren, S. J. F., Dubey, R., & Childe, S. J. (2017). Big data analytics and firm performance: Effects of dynamic capabilities. Journal of Business Research, 70, 356-365, https://doi.org/10.1016/j.jbusres.2016.08.009
83. Wei, S., & Ma, Y. (2025). The impact of artificial intelligence on supply chain resilience in manufacturing firms: The moderating role of internationalization degree. In Proceedings of the 2025 2nd International Conference on Digital Economy and Computer Science (DECS ’25), 587-596, https://doi.org/10.1145/3785706.3785799
84. Whetten, D. A. (1989). What constitutes a theoretical contribution? Academy of Management Review, 14(4), 490-495, https://doi.org/10.5465/AMR.1989.4308371
85. Wiener, M., Mähring, M., Remus, U., & Saunders, C. (2016). Control configuration and control enactment in information systems projects. Mis Quarterly, 40(3), 741-774.
86. Wu, L., Yue, X., Jin, A., & Yen, D. C. (2016). Smart supply chain management: a review and implications for future research. The İnternational Journal of Logistics Management, 27(2), 395-417, https://doi.org/10.1108/IJLM-02-2014-0035