HİPEROTOMASYON LİTERATÜRÜNÜN EVRİMİ VE LOJİSTİK ALANINDAKİ YANSIMALARI: BİBLİYOMETRİK BİR ANALİZ

Öz

Bu çalışmanın amacı, hiperotomasyon literatürünün lojistik ve tedarik zinciri yönetimi alanındaki bilimsel gelişimini bibliyometrik analiz yöntemiyle incelemektir. Araştırmada hiperotomasyon, robotik süreç otomasyonu (RPA), süreç madenciliği ve akıllı otomasyon teknolojilerinin lojistik ve tedarik zinciri süreçlerindeki akademik yansımaları değerlendirilmiştir. Araştırma kapsamında veri kaynağı olarak Web of Science veri tabanı kullanılmıştır. Tarama sonucunda elde edilen 543 yayından, makale türündeki 317 çalışma analiz edilmiştir. Araştırmada bibliyometrik analiz yöntemi kullanılmış, veri analiz sürecinde R programlama dili ile Bibliometrix/Biblioshiny paketlerinden yararlanılmıştır. Çalışma kapsamında yıllık bilimsel üretim analizi, atıf analizi, Bradford Yasası analizi, Lotka Yasası analizi, ülke üretim analizi, anahtar kelime analizi ve yaşam döngüsü analizi gerçekleştirilmiştir. Araştırma bulgularına göre hiperotomasyon literatürünün özellikle 2018 yılı sonrasında hızlı bir gelişim gösterdiği belirlenmiştir. Analiz sonuçları, yayınların büyük ölçüde otomasyon, yapay zeka ve bilgi sistemleri odaklı dergilerde yoğunlaştığını ortaya koymuştur. Çin’in hem yayın sayısı hem de toplam atıf bakımından en etkili ülke olduğu görülürken, Hollanda ve İsveç gibi ülkelerin makale başına yüksek atıf değerleriyle öne çıktığı belirlenmiştir. Anahtar kelime analizinde “process mining”, “robotic process automation”, “supply chain” ve “logistics” kavramlarının araştırma alanının temel odak noktalarını oluşturduğu tespit edilmiştir. Yaşam döngüsü analizi sonuçları ise literatürün olgunlaşma ve doygunluk aşamasına yaklaştığını göstermiştir. Sonuç olarak, hiperotomasyonun lojistik ve tedarik zinciri yönetiminde süreç optimizasyonu, operasyonel verimlilik ve dijital dönüşümü destekleyen stratejik bir yaklaşım olarak öne çıktığı belirlenmiştir. Bulgular, araştırma alanının veri odaklı, yapay zekâ destekli ve sürdürülebilir uygulamalar doğrultusunda gelişimini sürdüreceğine işaret etmektedir. Çalışma, hiperotomasyon literatürünün gelişim dinamiklerini ortaya koyarak gelecekteki araştırmalar için stratejik bir çerçeve sunmaktadır.

Anahtar Kelimeler

• Hiperotomasyon
• Lojistik
• Tedarik Zinciri Yönetimi
• Yapay Zeka
• Bibliyometrik Analiz

Etik Beyan

Bu çalışma, yalnızca Web of Science veri tabanından elde edilen ikincil verilerin bibliyometrik analizine dayandığından, insan katılımcılardan veri toplanmasını veya kişisel veri işlenmesini içermemektedir. Bu nedenle çalışma kapsamında Etik Kurul İzni gerekmemektedir.

THE EVOLUTION OF HYPERAUTOMATION LITERATURE AND ITS REFLECTIONS ON THE LOGISTICS FIELD: BIBLIOMETRIC ANALYSIS

Öz

The aim of this study is to examine the scientific development of the hyperautomation literature in the field of logistics and supply chain management through a bibliometric analysis. The study evaluates the academic reflections of hyperautomation, robotic process automation (RPA), process mining, and intelligent automation technologies within logistics and supply chain processes. The Web of Science database was used as the data source. Of the 543 publications identified through the search process, 317 journal articles were included in the analysis. Bibliometric analysis was conducted using the Bibliometrix/Biblioshiny packages in the R programming environment. The analysis encompassed annual scientific production, citation analysis, Bradford’s Law, Lotka’s Law, country productivity, keyword analysis, and life cycle analysis. The findings indicate that the hyperautomation literature has experienced significant growth since 2018. The results reveal that publications are predominantly concentrated in journals focusing on automation, artificial intelligence, and information systems. China emerged as the most influential country in terms of both publication output and total citations, while the Netherlands and Sweden stood out with the highest average citations per article. Keyword analysis identified process mining, robotic process automation, supply chain, and logistics as the core themes of the research field. Life cycle analysis further suggests that the literature is approaching the maturity and saturation stage. Overall, the findings demonstrate that hyperautomation has become a strategic approach supporting process optimization, operational efficiency, and digital transformation in logistics and supply chain management. Moreover, the field is expected to continue evolving toward data-driven, artificial intelligence-enabled, and sustainable applications. This study provides a strategic framework for future research by revealing the developmental dynamics of the hyperautomation literature.

Anahtar Kelimeler

• Hyperautomation
• Logistics
• Supply Chain Management
• Artificial Intelligence
• Bibliometric Analysis

Etik Beyan

This study is based solely on the bibliometric analysis of secondary data obtained from the Web of Science database and does not involve data collection from human participants or the processing of personal data. Therefore, Ethics Committee Approval is not required for this study.

Kaynakça

1. Aria, M., & Cuccurullo, C. (2017). Bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959-975.
2. Balasubramaniam, S., Prasanth, A., Kumar, K. S., & Kadry, S. (2024). Artificial intelligence‐based hyperautomation for smart factory process automation. Hyperautomation for next‐generation industries, 55-89.
3. Bhatt, J., & Singh, S. (2025). Hyper-automation in Supply Chain and Quality Management: A Cross-Industry Perspective. ESP International Journal of Advancements in Computational Technology. 3 (2).18-24. 10.56472/25838628/IJACT-V3I2P103
4. Birkbeck, A., & Weston, P. (2024). Seeking ambidexterity through hyperautomation: What next for GBA logistics in their evolution from local labour entrepreneurs to global players in logistics. Journal of Information Technology Teaching Cases, 20438869241261074.
5. Bradford, S. C. (1976). Classic paper: Sources of information on specific subjects. Collection Management, 1(3-4), 95-104.
6. Burnham, Kenneth P., and David R. Anderson. (2004) Multimodel inference: understanding AIC and BIC in model selection. Sociological methods & research. 33(2). 261-304.
7. Büyüközkan, G., & Göçer, F. (2018). Digital Supply Chain: Literature review and a proposed framework for future research. Computers in industry. 97.157-177.
8. Călin, M. (2025). The Evolution of Business Process Automation from Industry 1.0 to Hyperautomation: A Literature Review. In International Conference on Informatics in Economy (129-143). Singapore: Springer Nature Singapore.

9. Chen, D. Q., Preston, D. S., & Swink, M. (2015). How the use of big data analytics affects value creation in supply chain management. Journal of management information systems, 32(4), 4-39.
10. Donthu, N., Kumar, S., Mukherjee, D., Pandey, N., & Lim, W. M. (2021). How to conduct a bibliometric analysis: An overview and guidelines. Journal of business research, 133, 285-296.
11. Fedosovsky, M. E., Uvarov, M. M., Aleksanin, S. A., Pyrkin, A. A., Colombo, A. W., & Prattichizzo, D. (2022). Sustainable Hyperautomation in high-tech manufacturing industries: a case of linear Electromechanical Actuators. IEEE Access, 10, 98204-98219.
12. Frank, A. G., Dalenogare, L. S., & Ayala, N. F. (2019). Industry 4.0 technologies: Implementation patterns in manufacturing companies. International journal of production economics, 210, 15-26.
13. Geethanjali, S. (2026). Case Study: Hyper Automation in Manufacturing Industry. In Implementing Hyper Automation: Strategies, Best Practices and Case Studies (181-210). Bentham Science Publishers.
14. Geethanjali, S. (2026). Case Study: Hyper Automation in Manufacturing Industry. In Implementing Hyper Automation: Strategies, Best Practices and Case Studies (pp. 181-210). Bentham Science Publishers
15. George, A. S., George, A. H., Baskar, T., & Sujatha, V. (2023). The rise of hyperautomation: a new frontier for business process automation. Partners Universal International Research Journal, 2(4), 13-35.
16. Haleem, A., Javaid, M., Singh, R. P., Rab, S., & Suman, R. (2021). Hyperautomation for the enhancement of automation in industries. Sensors International, 2, 100124.
17. Hasan, M. K., Latif, Z., Hlali, A., Xunping, L., & Aka, S. A. B. (2026). Hyperautomation: The Next Frontier in Supply Chain (SC) and Manufacturing. In Emerging Trends in Smart Logistics Technologies (1-44). IGI Global Scientific Publishing.
18. High, P. (2019). Gartner Announces Top 10 Strategic Technology Trends For 2020. https://www.forbes.com/sites/peterhigh/2019/10/21/breaking-gartner-announces-top-10-strategic-technology-trends-for-2020/
19. Hofmann, P., Samp, C., & Urbach, N. (2020). Robotic process automation. Electronic markets, 30(1), 99-106. Ivanov, D., & Dolgui, A. (2021). A digital supply chain twin for managing the disruption risks and resilience in the era of Industry 4.0. Production Planning & Control, 32(9), 775-788.
20. Ivanov, D., Dolgui, A., & Sokolov, B. (2019). The impact of digital technology and Industry 4.0 on the ripple effect and supply chain risk analytics. International journal of production research, 57(3), 829-846.
21. Koeppen, M., Chen, M. Y., & Wu, H. T. (2025). Omnipresent AI and intelligent hyperautomation: methodologies, interactivity and applications. Enterprise Information Systems, 19(11), 2551727.
22. Marković, E. (2026). From Robotic Process Automation to Hyperautomation: A Comprehensive Theoretical and Empirical Examination of Intelligent Process Automation in Contemporary Organizations. Emerging Frontiers Library for The American Journal of Engineering and Technology, 8(01), 20-25.
23. Qadeem, S. E. Y., & Mahmood, T. (2025). From Data to Decisions: Hyper Automation and Real-Time Analytics in Building Resilient Operations and Supply Chain Management. Sarhad Journal of Management Sciences, 11(2), 173-188.
24. Qadeem, S. E. Y., & Mahmood, T. (2025). From Data to Decisions: Hyper Automation and Real-Time Analytics in Building Resilient Operations and Supply Chain Management. Sarhad Journal of Management Sciences, 11(2), 173-188.
25. Queiroz, M. M., Pereira, S. C. F., Telles, R., & Machado, M. C. (2021). Industry 4.0 and digital supply chain capabilities: A framework for understanding digitalisation challenges and opportunities. Benchmarking: an international journal, 28(5), 1761-1782.
26. Sharmili, K. C. (2026). Strategic Considerations for Hyperautomation. In Implementing Hyper Automation: Strategies, Best Practices and Case Studies (pp. 31-59). Bentham Science Publishers.
27. Sobczak, A., Doligalski, T., & Goliński, M. (Eds.). (2026). Hyper-Automation, AI and Business Processes. Taylor & Francis.
28. Tathavadekar, V. P., & Mahankale, N. R. (2025). Developing human capital for sustainable supply chain hyper automation: an action learning framework. Development and Learning in Organizations: An International Journal, 1-4.
29. Van Der Aalst, W. (2012). Process mining: Overview and opportunities. ACM Transactions on Management Information Systems (TMIS), 3(2), 1-17.
30. Zhao, X., Oseni, T., & Medishetty, B. T. (2022, October). Overview of business hyper-automation. In 2022 IEEE International Conference on e-Business Engineering (ICEBE) (pp. 100-105). IEEE.
31. Zizic, M. C., Mladineo, M., Gjeldum, N., & Celent, L. (2022). From industry 4.0 towards industry 5.0: A review and analysis of paradigm shift for the people, organization and technology. Energies, 15(14), 5221.
32. Zrelli, I., & Rejeb, A. (2024). A bibliometric analysis of IoT applications in logistics and supply chain management. Heliyon, 10(16). https://doi.org/10.1016/j.heliyon.2024.e36578
33. Zupic, I., & Čater, T. (2015). Bibliometric methods in management and organization. Organizational research methods, 18(3), 429-472.