Bibliometric Analysis of Logistics and Artificial Intelligence Research Trends in the Last 10 Years
DOI:
https://doi.org/10.58190/ijamec.2024.112Keywords:
Artificial Intelligence (AI), Artificial Intelligence in Logistics, Bibliometric Analysis, Logistics, Artificial Intelligence (AI), Artificial Intelligence in Logistics, Bibliometric Analysis, Logistics, Research TrendsAbstract
In recent years, the integration of logistics and artificial intelligence has become increasingly important across various industries, fostering innovation and progress. This study seeks to uncover key contributors, prominent keywords, influential journals, and leading countries at the crossroads of logistics and AI to provide direction for future research. By analyzing 1118 articles from the past decade (2015–2024) using the Web of Science (WoS) database and VOSviewer software, several critical insights were derived. The analysis included co-occurrence of keywords, citation patterns (articles, sources, institutions, and countries), and co-authorship networks. Results from the keyword analysis reveal that “artificial intelligence” and “logistics” dominate, followed by terms such as “machine learning,” “deep learning,” “blockchain,” “optimization,” and “internet of things.” Citation analysis identified the study by Dwivedi et al. (2021) as the most cited work, with 1009 citations. Among journals, Engineering Applications of Artificial Intelligence stands out, featuring 58 papers and 894 citations. In co-authorship analysis, Angappa Gunasekaran emerges as the most impactful author with six publications and 330 citations. Institutionally, the Chinese Academy of Sciences leads with 342 citations, while China ranks first among countries with 3979 citations, followed by India and the United Kingdom. This bibliometric analysis highlights pivotal resources, influential studies, and leading contributors in the field of logistics and artificial intelligence, serving as a foundational guide and valuable reference for future researchers in this domain.
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[1] W. Chen, Y. Men, N. Fuster, C. Osorio, and A. A. Juan, "Artificial intelligence in logistics optimization with sustainable criteria: A review," Sustainability, vol. 16, no. 21, p. 9145, 2024, doi: 10.3390/su16219145.
[2] N. Rane, P. Desai, J. Rane, and M. Paramesha, "Artificial intelligence, machine learning, and deep learning for sustainable and resilient supply chain and logistics management," Trustworthy Artificial Intelligence in Industry and Society, pp. 156-184, 2024, doi: 10.70593/978-81-981367-4-9_5.
[3] Q. Liu, Y. Ma, L. Chen, W. Pedrycz, M. J. Skibniewski, and Z.-S. Chen, "Artificial intelligence for production, operations and logistics management in modular construction industry: A systematic literature review," Information Fusion, p. 102423, 2024, doi: 10.1016/j.inffus.2024.102423.
[4] S. Mukherjee, R. Nagariya, K. Mathiyazhagan, M. M. Baral, M. Pavithra, and A. Appolloni, "Artificial intelligence-based reverse logistics for improving circular economy performance: a developing country perspective," The International Journal of Logistics Management, vol. 35, no. 6, 2024, doi: 10.1108/IJLM-03-2023-0102.
[5] G. R. Sarria et al., "Artificial Intelligence–Based Autosegmentation: Advantages in Delineation, Absorbed Dose-Distribution, and Logistics," Advances in Radiation Oncology, vol. 9, no. 3, p. 101394, 2024, doi: 10.1016/j.adro.2023.101394.
[6] X. Xie, W. Zhang, and L. Wang, "4W1H in Resource Distribution in Artificial Intelligence for Emergency Logistics," IT Professional, vol. 26, no. 4, pp. 55-61, 2024, doi: 10.1109/MITP.2024.3421944.
[7] C. Yaiprasert and A. N. Hidayanto, "AI-powered ensemble machine learning to optimize cost strategies in logistics business," International Journal of Information Management Data Insights, vol. 4, no. 1, p. 100209, 2024, doi: 10.1016/j.jjimei.2023.100209.
[8] A. C. Odimarha, S. A. Ayodeji, and E. A. Abaku, "Machine learning's influence on supply chain and logistics optimization in the oil and gas sector: a comprehensive analysis," Computer Science & IT Research Journal, vol. 5, no. 3, pp. 725-740, 2024, doi: 10.51594/csitrj.v5i3.976.
[9] E. O. Sodiya, U. J. Umoga, O. O. Amoo, and A. Atadoga, "AI-driven warehouse automation: A comprehensive review of systems," GSC Advanced Research and Reviews, vol. 18, no. 2, pp. 272-282, 2024, doi: 10.30574/gscarr.2024.18.2.0063.
[10] R. G. Richey Jr, S. Chowdhury, B. Davis‐Sramek, M. Giannakis, and Y. K. Dwivedi, "Artificial intelligence in logistics and supply chain management: A primer and roadmap for research," vol. 44, ed: Wiley Online Library, 2023, pp. 532-549.
[11] B. Ferreira and J. Reis, "A systematic literature review on the application of automation in logistics," Logistics, vol. 7, no. 4, p. 80, 2023, doi: 10.3390/logistics7040080.
[12] E. T. Yasin, M. Erturk, M. T. Bulut, and M. Koklu, "Bibliometric analysis of deep learning applications in dentistry," International Dental Journal, vol. 74, p. S216, 2024, doi: 10.1016/j.identj.2024.07.044.
[13] W. Jia and D. Bin, "Exploring Applications of Artificial Intelligence Technology in Modern Intelligent Logistics Development," in 2024 IEEE 7th Information Technology, Networking, Electronic and Automation Control Conference (ITNEC), 2024, vol. 7: IEEE, pp. 1359-1364, doi: 10.1109/ITNEC60942.2024.10733205.
[14] A. Li, S. Zhuang, T. Yang, W. Lu, and J. Xu, "Optimization of logistics cargo tracking and transportation efficiency based on data science deep learning models," 2024, doi: 10.20944/preprints202407.1428.v1
[15] O. S. Joel, A. T. Oyewole, O. G. Odunaiya, and O. T. Soyombo, "Leveraging artificial intelligence for enhanced supply chain optimization: a comprehensive review of current practices and future potentials," International Journal of Management & Entrepreneurship Research, vol. 6, no. 3, pp. 707-721, 2024, doi: 10.51594/ijmer.v6i3.882.
[16] Q. Liu, "Logistics Distribution Route Optimization in Artificial Intelligence and Internet of Things Environment," Decision Making: Applications in Management and Engineering, vol. 7, no. 2, pp. 221-239, 2024, doi: 10.31181/dmame7220241072.
[17] J. Rana and Y. Daultani, "Mapping the role and impact of artificial intelligence and machine learning applications in supply chain digital transformation: a bibliometric analysis," Operations Management Research, vol. 16, no. 4, pp. 1641-1666, 2023, doi: 10.1007/s12063-022-00335-y.
[18] M. Hajizadeh, M. Alaeddini, and P. Reaidy, "Bibliometric analysis on the convergence of artificial intelligence and blockchain," in International Congress on Blockchain and Applications, 2023: Springer, pp. 334-344, doi: 10.1007/978-3-031-21229-1_31.
[19] R. E. Bawack, S. F. Wamba, K. D. A. Carillo, and S. Akter, "Artificial intelligence in E-Commerce: a bibliometric study and literature review," Electronic markets, vol. 32, no. 1, pp. 297-338, 2022, doi: 10.1007/s12525-022-00537-z.
[20] X. Zhu, N. Liu, and Y. Shi, "Artificial intelligence technology in modern logistics system," International Journal of Technology, Policy and Management, vol. 22, no. 1-2, pp. 66-81, 2022, doi: 10.1504/IJTPM.2022.122537.
[21] I. Ponomarenko and D. Ponomarenko, "AI-Powered Logistics and Digital Marketing for Business Optimisation," Economics & Education, vol. 8, no. 4, pp. 27-33, 2023, doi: 10.30525/2500-946X/2023-4-4.
[22] S. S. Bahar, M. B. Yildiz, S. Gerz, E. T. Yasin, A. Goktas, and M. Koklu, Biometric Analysis Of Research Trends In Logistics And Artificial Intelligence For 2015-2024. 2nd International Conference on Trends in Advanced Research ICTAR 2024: All Sciences Academy, 2024.
[23] Y. K. Dwivedi et al., "Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy," International journal of information management, vol. 57, p. 101994, 2021, doi: 10.1016/j.ijinfomgt.2019.08.002.
[24] E. Kristoffersen, F. Blomsma, P. Mikalef, and J. Li, "The smart circular economy: A digital-enabled circular strategies framework for manufacturing companies," Journal of business research, vol. 120, pp. 241-261, 2020, doi: 10.1016/j.jbusres.2020.07.044.
[25] C. S. Tang and L. P. Veelenturf, "The strategic role of logistics in the industry 4.0 era," Transportation Research Part E: Logistics and Transportation Review, vol. 129, pp. 1-11, 2019, doi: 10.1016/j.tre.2019.06.004.
[26] S. Benzidia, N. Makaoui, and O. Bentahar, "The impact of big data analytics and artificial intelligence on green supply chain process integration and hospital environmental performance," Technological forecasting and social change, vol. 165, p. 120557, 2021, doi: 10.1016/j.techfore.2020.120557.
[27] M. A. Ahad, S. Paiva, G. Tripathi, and N. Feroz, "Enabling technologies and sustainable smart cities," Sustainable cities and society, vol. 61, p. 102301, 2020, doi: 10.1016/j.scs.2020.102301.
[28] N. Koklu and S. A. Sulak, "Recent Developments in Educational Data Mining: A Four-Year Bibliometric Analysis" Advances in Education Sciences, M. Dalkılıç and O. Soslu, Eds. Platanus Publishing, 2024, ch. 1, pp. 5–29. ISBN: 978-625-6176-53-6, doi: 10.5281/zenodo.14582661.
[29] N. Koklu and S. A. Sulak, "Bibliometric Analysis of Publications Related to Augmented Reality in Education in The Last 20 Years", Advances in Education Sciences, M. Dalkılıç and O. Soslu, Eds. Platanus Publishing, 2024, ch. 2, pp. 31-58. ISBN: 978-625-6176-53-6, doi: 10.5281/zenodo.14582661.
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