Unveiling the AI–SCM Nexus: A Bibliometric and PRISMA-Based Review of Trends, Technologies, and Future Directions (2021–2025)
Article Sidebar
Main Article Content
The integration of Artificial Intelligence (AI) into Supply Chain Management (SCM) has become a key driver of digital transformation, transforming industry practices and academic discourse. As global supply chains grow more complex and data-driven, AI’s role in improving efficiency, responsiveness, and sustainability has gained significant attention. This study conducts a bibliometric review of 499 peer-reviewed articles published between 2021 and 2025, sourced from Scopus and Web of Science, to trace the changing intellectual and thematic dimensions of AI–SCM research. Quantitative analysis using R Studio and network visualization via VOSviewer indicate key trends, prolific contributors, and dominant research clusters. The result indicates a significant rise in scholarly output over the past five years, with China, India, and the United States leading in publications. Institutions such as the Indian Institutes of Management and Penn State University emerge as major knowledge centers. The research identifies thematic focal points such as AI-assisted decision-making, supply chain resilience, risk management, and sustainability, closely associated to technologies such as machine learning, big data analytics, and Industry 4.0 frameworks. Network analysis reveals strong keyword co-occurrence around automation, optimization, digital supply chains, and predictive analytics, reflecting the convergence of AI capabilities with the fundamental SCM functions. Despite substantial progress, challenges remain—high implementation costs, shortages in skilled talent, data privacy issues, and ethical implications of AI application. These gaps underscore the need for more inclusive and responsible adoption strategies. This bibliometric paper provides a structured overview of the current research landscape, aiding scholars and practitioners in understanding key developments, influential authors, and emerging opportunities. The paper ends with a recommendation to organize future research on the topic of human-AI collaboration, governance systems, and socio-economic effects of AI on global supply-chains
Downloads
References
Belu, M. G., & Marinoiu, A. M. (2025). AI-Enabled Supply Chain Management: A Bibliometric Analysis UsingVOSviewer and RStudio Bibliometrix Software Tools. SUSTAINABILITY, 17(5). https://doi.org/10.3390/su17052092
Braganza, A., Chen, W., Canhoto, A., & Sap, S. (2022). Gigification, job engagement and satisfaction: the moderating role of AIenabled system automation in operations management. PRODUCTION PLANNING & CONTROL, 33(16, SI), 1534–1547. https://doi.org/10.1080/09537287.2021.1882692
Chee, M. L., Chee, M. L., Huang, H., Mazzochi, K., Taylor, K., Wang, H., Feng, M., Ho, A. F. W., Siddiqui, F. J., Ong, M. E. H., & Liu, N. (2023). Artificial intelligence and machine learning in prehospital emergency care: A scoping review. ISCIENCE, 26(8). https://doi.org/10.1016/j.isci.2023.107407
Chilicaus, G. C. F., Licapa-Redolfo, G. S., Ballesteros, M. A. A., Otazu, C. D. C., Miranda, S. J. A., Castillo, M. M. F., Ijiri, G. L. C., Valle, M. D. L. A. G., & Castillo, J. C. A. (2025). Digital Transformation and Sustainability in Post-Pandemic Supply Chains: A Global Bibliometric Analysis of Technological Evolution and Research Patterns (2020-2024). SUSTAINABILITY, 17(7). https://doi.org/10.3390/su17073009
D, A., M, K., A, K., & H, G. (2025). AJMERA D, 2025, INT J LOGIST MANAG. INTERNATIONAL JOURNAL OF LOGISTICS MANAGEMENT. https://doi.org/10.1108/IJLM-09-2023-0408
Daios, A., Kladovasilakis, N., Kelemis, A., & Kostavelis, I. (2025). AI Applications in Supply Chain Management: A Survey. APPLIED SCIENCES-BASEL, 15(5). https://doi.org/10.3390/app15052775
Dosdogru, A. T., Boru Ipek, A., & Gocken, M. (2021). A novel hybrid artificial intelligence-based decision support frameworkto predict lead time. INTERNATIONAL JOURNAL OF LOGISTICS-RESEARCH AND APPLICATIONS, 24(3), 261–279. https://doi.org/10.1080/13675567.2020.1749249
Feng, Y., Lai, K., & Zhu, Q. (2022). Green supply chain innovation: Emergence, adoption, and challenges. INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS, 248. https://doi.org/10.1016/j.ijpe.2022.108497
Ghouati, S., Oulfarsi, S., & El Amri, A. (2025). Evolution and Impact of Artificial Intelligence in Sustainable Supply Chain Management: Systematic Review and Bibliometric Analysis. Corporate Governance and Sustainability Review, 9(3 (special issue)), 217–230. https://doi.org/10.22495/cgsrv9i3sip3
Godinho Filho, M., de Almeida, S. V. Q., Lage Junior, M., Osiro, L., Lima, B., & Callefi, M. H. (2025). A path to follow to overcome foundational barriers to the adoption of artificial intelligence within the manufacturing industry: a conceptual framework. ENTERPRISE INFORMATION SYSTEMS, 19(1–2). https://doi.org/10.1080/17517575.2025.2458685
Gupta, S., Modgil, S., Kumar, A., Sivarajah, U., & Irani, Z. (2022). Artificial intelligence and cloud-based Collaborative Platforms for Managing Disaster, extreme weather and emergency operations. INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS, 254. https://doi.org/10.1016/j.ijpe.2022.108642
Gupta, S., Modgil, S., Meissonier, R., & Dwivedi, Y. K. (2024). Artificial Intelligence and Information System Resilience to Cope WithSupply Chain Disruption. IEEE TRANSACTIONS ON ENGINEERING MANAGEMENT, 71, 10496–10506. https://doi.org/10.1109/TEM.2021.3116770
HANGL, J., KRAUSE, S., & BEHRENS, V. (n.d.). DRIVERS, BARRIERS AND SOCIAL CONSIDERATIONS FOR AI ADOPTION IN SCM.
Helo, P., & Hao, Y. (2022). Artificial intelligence in operations management and supply chainmanagement: an exploratory case study. PRODUCTION PLANNING & CONTROL, 33(16, SI), 1573–1590. https://doi.org/10.1080/09537287.2021.1882690
Hendriksen, C. (2023). Artificial intelligence for supply chain management: Disruptiveinnovation or innovative disruption? JOURNAL OF SUPPLY CHAIN MANAGEMENT, 59(3), 65–76. https://doi.org/10.1111/jscm.12304
Hezam, I. M., Ali, A. M., Alshamrani, A. M., Gao, X., & Abdel-Basset, M. (2024). Artificial intelligence’s impact on drug delivery in healthcare supplychain management: data, techniques, analysis, and managerialimplications. JOURNAL OF BIG DATA, 11(1). https://doi.org/10.1186/s40537-024-01049-7
Hosseinnia Shavaki, F., & Ebrahimi Ghahnavieh, A. (2023). Applications of deep learning into supply chain management: a systematicliterature review and a framework for future research. ARTIFICIAL INTELLIGENCE REVIEW, 56(5), 4447–4489. https://doi.org/10.1007/s10462-022-10289-z
Hwang, R.-H., Chou, T.-Y., Lin, J.-Y., Sudyana, D., Lai, Y.-C., & Lin, Y.-D. (2025). Optimal Resource Allocation for AIoT as a Service Under Various Service Scenarios and Architectures. IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT, 22(2), 1420–1436. https://doi.org/10.1109/TNSM.2024.3503575
Jahani, N., Sepehri, A., Vandchali, H. R., & Tirkolaee, E. B. (2021). Application of Industry 4.0 in the Procurement Processes of SupplyChains: A Systematic Literature Review. SUSTAINABILITY, 13(14). https://doi.org/10.3390/su13147520
JAHANI, N., SEPEHRI, A., VANDCHALI, H., & TIRKOLAEE, E. (n.d.). APPLICATION OF INDUSTRY 4.0 IN THE PROCUREMENT PROCESSES OF SUPPLY CHAINS: A SYSTEMATIC LITERATURE REVIEW.
Joshi, S., & Sharma, M. (2022). Sustainable Performance through Digital Supply Chains in Industry 4.0Era: Amidst the Pandemic Experience. SUSTAINABILITY, 14(24). https://doi.org/10.3390/su142416726
Li, Y., Hu, Y., Min, K., Park, H., Yang, H., Wang, T., Sung, J., Seol, J.-Y., & Zhang, C. J. (2023). Artificial Intelligence Augmentation for Channel State Information in 5Gand 6G. IEEE WIRELESS COMMUNICATIONS, 30(1), 104–110. https://doi.org/10.1109/MWC.005.2200245
Lin, Y., Tang, J., Guo, J., Wu, S., & Li, Z. (2025). Advancing AI-Enabled Techniques in Energy System Modeling: A Review ofData-Driven, Mechanism-Driven, and Hybrid Modeling Approaches. ENERGIES, 18(4). https://doi.org/10.3390/en18040845
Liu, B. (2023). Integration of novel uncertainty model construction of green supplychain management for small and medium-sized enterprises using artificialintelligence. OPTIK, 273. https://doi.org/10.1016/j.ijleo.2022.170411
Liu, K.-S., & Lin, M.-H. (2021). Performance Assessment on the Application of Artificial Intelligence toSustainable Supply Chain Management in the Construction MaterialIndustry. SUSTAINABILITY, 13(22). https://doi.org/10.3390/su132212767
Luković, M., Cvetić, B., Vasiljević, D., & Danilović, M. (2025). Exploring blockchain adoption for supply chain transformation in industry 4.0. Journal of Decision Analytics and Intelligent Computing, 5(1), 111–121. https://doi.org/10.31181/jdaic10005072025l
Mahroof, K. (2019). A human-centric perspective exploring the readiness towards smart warehousing: The case of a large retail distribution warehouse. INTERNATIONAL JOURNAL OF INFORMATION MANAGEMENT, 45, 176–190. https://doi.org/10.1016/j.ijinfomgt.2018.11.008
Mitrović, D., Demir, G., Badi, I., & Bouraima, M. B. (2025). Balancing Efficiency and Risk in Public Sector Artificial Intelligence with Data Envelopment Analysis and Portfolio Approaches. 1(1), 15–35.
Nayal, K., Raut, R. D., Queiroz, M. M., Yadav, V. S., & Narkhede, B. E. (2023). Are artificial intelligence and machine learning suitable to tackle theCOVID-19 impacts? An agriculture supply chain perspective. INTERNATIONAL JOURNAL OF LOGISTICS MANAGEMENT, 34(2, SI), 304–335. https://doi.org/10.1108/IJLM-01-2021-0002
Naz, F., Kumar, A., Majumdar, A., & Agrawal, R. (2022). Is artificial intelligence an enabler of supply chain resiliency postCOVID-19? An exploratory state-of-the-art review for future research. OPERATIONS MANAGEMENT RESEARCH, 15(1–2), 378–398. https://doi.org/10.1007/s12063-021-00208-w
NAZ, F., KUMAR, A., MAJUMDAR, A., & AGRAWAL, R. (n.d.). IS ARTIFICIAL INTELLIGENCE AN ENABLER OF SUPPLY CHAIN RESILIENCY POST COVID-19? AN EXPLORATORY STATE-OF-THE-ART REVIEW FOR FUTURE RESEARCH.
Ressi, D., Romanello, R., Piazza, C., & Rossi, S. (2024). AI-enhanced blockchain technology: A review of advancements and opportunities. JOURNAL OF NETWORK AND COMPUTER APPLICATIONS, 225. https://doi.org/10.1016/j.jnca.2024.103858
Richey, R. G., Chowdhury, S., Davis-Sramek, B., Giannakis, M., & Dwivedi, Y. K. (2023). Artificial intelligence in logistics and supply chain management: A primer and roadmap for research. Journal of Business Logistics, 44(4), 532–549. https://doi.org/10.1111/jbl.12364
Rolf, B., Beier, A., Jackson, I., Mueller, M., Reggelin, T., Stuckenschmidt, H., & Lang, S. (2025). A review on unsupervised learning algorithms and applications in supplychain management. INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH, 63(5), 1933–1983. https://doi.org/10.1080/00207543.2024.2390968
Salimimoghadam, S., Ghanbaripour, A. N., Tumpa, R. J., Rahimi, A. K., Golmoradi, M., Rashidian, S., & Skitmore, M. (2025). The Rise of Artificial Intelligence in Project Management: A Systematic Literature Review of Current Opportunities, Enablers, and Barriers. BUILDINGS, 15(7). https://doi.org/10.3390/buildings15071130
Serrano-Torres, G. J., Lopez-Naranjo, A. L., Larrea-Cuadrado, P. L., & Mazon-Fierro, G. (2025). Transformation of the Dairy Supply Chain Through ArtificialIntelligence: A Systematic Review. SUSTAINABILITY, 17(3). https://doi.org/10.3390/su17030982
Sharma, M., Antony, R., Sehrawat, R., Cruz, A. C., & Daim, T. U. (2022). Exploring post-adoption behaviors of e-service users: Evidence from the hospitality sector /online travel services. Technology in Society, 68. https://doi.org/10.1016/j.techsoc.2021.101781
Sharma, M., & Firoz, M. (2022). Delineating investors’ rationality and behavioural biases - evidence from the Indian stock market. International Journal of Management Practice, 15(1), 59 – 86. https://doi.org/10.1504/IJMP.2022.119925
Sharma, R., Shishodia, A., Gunasekaran, A., Min, H., & Munim, Z. H. (2022a). The role of artificial intelligence in supply chain management: mappingthe territory. INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH, 60(24, SI), 7527–7550. https://doi.org/10.1080/00207543.2022.2029611
Sharma, R., Shishodia, A., Gunasekaran, A., Min, H., & Munim, Z. H. (2022b). The role of artificial intelligence in supply chain management: mapping the territory. International Journal of Production Research, 60(24), 7527–7550. https://doi.org/10.1080/00207543.2022.2029611
Shrivastav, M. (2021). Barriers Related to AI Implementation in Supply Chain Management. Journal of Global Information Management, 30(8), 1–19. https://doi.org/10.4018/JGIM.296725
Shrivastav, M. (2022). Barriers Related to AI Implementation in Supply Chain Management. JOURNAL OF GLOBAL INFORMATION MANAGEMENT, 30(8). https://doi.org/10.4018/JGIM.296725
Sohrabpour, V., Oghazi, P., Toorajipour, R., & Nazarpour, A. (2021). Export sales forecasting using artificial intelligence. TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE, 163. https://doi.org/10.1016/j.techfore.2020.120480
VLACHOS, I., & REDDY, P. (n.d.). MACHINE LEARNING IN SUPPLY CHAIN MANAGEMENT: SYSTEMATIC LITERATURE REVIEW AND FUTURE RESEARCH AGENDA.
Wang, S., Jia, C., Khan, A., Khan, N. H., Hsieh, C.-H., Hung, C.-W., & Chen, S.-C. (2024). BIG DATAANALYTICS-ARTIFICIAL INTELLIGENCE, AMBIDEXTERITY, AND GREENSUPPLY CHAIN MANAGEMENT: IMPLICATIONS ON RESPONSIBLE ECONOMY. RAE-REVISTA DE ADMINISTRACAO DE EMPRESAS, 65(1). https://doi.org/10.1590/S0034-759020250101
Wang, Y., Gao, J., Cheng, T. C. E., Jin, M., Yue, X., & Wang, H. (2025). Is it necessary for the supply chain to implement artificialintelligence-driven sales services at both the front-end and back-endstages? TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW, 194. https://doi.org/10.1016/j.tre.2024.103923
Wei, C., Shan, Y., & Zhen, M. (2025). Deep learning-based anomaly detection for precision field crop protection. FRONTIERS IN PLANT SCIENCE, 16. https://doi.org/10.3389/fpls.2025.1576756
Wu, B., Chen, H., & Shi, Y. (2025). Influence of artificial intelligence development on supply chaindiversification. FINANCE RESEARCH LETTERS, 78. https://doi.org/10.1016/j.frl.2025.107210
Yao, Y., Li, S., Shi, P., Huang, Y., Jiang, X., Zheng, Y., & Wang, Z. (2024). Leveraging hybrid machine learning for sustainable development through recycled waste in landscape design. ADVANCES IN CONCRETE CONSTRUCTION, 18(2), 105–113. https://doi.org/10.12989/acc.2024.18.2.105
This work is licensed under a Creative Commons Attribution 4.0 International License.
All articles published in our journal are licensed under CC-BY 4.0, which permits authors to retain copyright of their work. This license allows for unrestricted use, sharing, and reproduction of the articles, provided that proper credit is given to the original authors and the source.