Research status of artificial intelligence in the era of Industry 4.0: A bibliometric analysis
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Abstract
The rapid evolution of Industry 4.0 has had a profound impact on modern industrial ecosystems, with Artificial Intelligence (AI) emerging as a key enabler of intelligent, data-driven operations. This present study presents a large-scale, data-driven bibliometric mapping of AI research in the Industry 4.0 domain, offering novel insights into its intellectual structure and emerging directions. The Biblioshiny framework was utilized to systematically analyze 6,856 Scopus-indexed publications from 2013-2026 through the utilization of RStudio and Microsoft Excel. The findings reveal a conference-driven research landscape, with conference papers contributing 48.25% of total output, followed by journal articles (32%) and book chapters (10%). A distinct geographical shift is observed, with India emerging as the leading contributor, alongside strong participation from the United States, the United Kingdom, and Germany. Keyword density and co-occurrence analysis reveal that “Industry 4.0” is the central knowledge hub, with a high degree of interconnectedness with machine learning, AI, deep learning, digital twin, and cloud computing. These technologies form the technological backbone of the domain. Additionally, the study identifies a transition from core industrial applications towards interdisciplinary frontiers, including smart farming, precision agriculture, and education 4.0, thereby signalling the diffusion of AI-driven Industry 4.0 concepts into broader societal contexts. By quantitatively uncovering research concentration, thematic evolution, and emerging application domains, this study proposes a novel perspective on the dynamic knowledge architecture of AI in Industry 4.0. The findings provide policymakers with actionable insights for designing targeted research funding and innovation strategies that are aligned with emerging interdisciplinary domains. For industry practitioners, the results highlight key technological priorities and application areas that have the potential to drive competitive advantage and operational efficiency. Furthermore, the study offers a strategic roadmap for researchers by identifying themes that have received inadequate concern and future research directions. This fosters more impactful and collaborative advancements in AI-driven Industry 4.0.
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References
1. E. W. Solikhah, H. M. Asih, F. H. Astuti, I. Ghazali and E. B. Mohammad, Industry 4.0 readiness trends: a bibliometric and visualisation analysis, Int. J. Robot. Control Syst. 4 (2024) 105–124.
2. A. Tatarczak, Mapping the landscape of AI in supply chain management: a bibliometric analysis, Mod. Manage. Rev. 29 (2024) 43–57.
3. J. L. Ruiz-Real, J. Uribe-Toril, J. A. Torres and J. De Pablo, AI in business and economics research: trends and future, J. Bus. Econ. Manage. 22 (2021) 98–117.
4. M. Woschank, E. Rauch and H. Zsifkovits, A review of further directions for AI, machine learning, and deep learning in smart logistics, Sustainability 12 (2020) 3760.
5. W. M. R. W. Ibrahim and R. Hassan, Recruitment trends in the era of Industry 4.0 using AI: pros and cons, Asian J. Res. Bus. Manage. 1 (2019) 16–21.
6. K. Chi-Hsien and S. Nagasawa, Applying machine learning to market analysis: knowing your luxury consumer, J. Manage. Anal. 6 (2019) 404–419.
7. C. Zhang and Y. Lu, Study on AI: the state of the art and prospects, J. Ind. Inf. Integr. 23 (2021) 100224.
8. M. Javaid, A. Haleem, R. P. Singh and R. Suman, AI applications for Industry 4.0: a literature-based study, J. Ind. Integr. Manage. 7 (2022) 83–111.
9. S. Luthra and S. K. Mangla, Evaluating challenges to Industry 4.0 initiatives for supply chain sustainability in emerging economies, Process Saf. Environ. Prot. 117 (2018) 168–179.
10. D. Kiel, J. M. Müller, C. Arnold and K. I. Voigt, Sustainable industrial value creation: benefits and challenges of Industry 4.0, Int. J. Innov. Manage. 21 (2017) 1740015.
11. T. Stock and G. Seliger, Opportunities of sustainable manufacturing in Industry 4.0, Procedia CIRP 40 (2016) 536–541.
12. L. S. Dalenogare, G. B. Benitez, N. F. Ayala and A. G. Frank, The expected contribution of Industry 4.0 technologies for industrial performance, Int. J. Prod. Econ. 204 (2018) 383–394.
13. K. Witkowski, IoT, big data, industry 4.0–innovative solutions in logistics and supply chain management, Procedia Eng. 182 (2017) 763–769.
14. M. Rüßmann, M. Lorenz, P. Gerbert, M. Waldner, J. Justus, P. Engel and M. Harnisch, Industry 4.0: the future of productivity and growth in manufacturing industries, Boston Consulting Group 9 (2015) 54–89.
15. F. G. B. Flores, V. Y. R. Chacón, A. C. A. Caqui, M. L. Palacios-Núñez and L. G. P. Alva, AI and Industry: a bibliometrics analysis, in: 2024 IEEE XXXI International Conference on Electronics, Electrical Engineering and Computing (INTERCON), IEEE, 2024, pp. 1–6.
16. A. Zahid, P. Leclaire, L. Hammadi, R. C. Affonso and A. El Ballouti, Mapping the evolution of production chains in the era of Industry 4.0: ascientometric perspective, Multidisciplinary Reviews 8 (2025) 2025152.
17. L. Kumar and R. K. Sharma, Exploring research issues and potential future directions in Industry 4.0 adoption in SMEs: a comprehensive analysis and integration, FIIB Bus. Rev. (2023) 23197145231203512.
18. D. Kushwaha and F. Talib, A bibliometric analysis of Quality 4.0: current status, trends and future research directions, Int. J. Qual. Reliabil. Manage. 42 (2025) 474–503.
19. M. Elahi, S. O. Afolaranmi, J. L. Martinez Lastra and J. A. Perez Garcia, A comprehensive literature review of the applications of AI techniques through the lifecycle of industrial equipment, Discover AI 3 (2023) 43.
20. T. Ahmad, H. Zhu, D. Zhang, R. Tariq, A. Bassam, F. Ullah and S. S. Alshamrani, Energetics systems and AI: applications of Industry 4.0, Energy Rep. 8 (2022) 334–361.
21. A. T. Rosário and J. C. Dias, Sustainability and the digital transition: a literature review, Sustainability 14 (2022) 4072.
22. M. A. Amani and S. A. Sarkodie, Mitigating the spread of contamination in the meat supply chain management using deep learning, Sci. Rep. 12 (2022) 5037.
23. L. Liu, W. Song and Y. Liu, Leveraging digital capabilities toward a circular economy: reinforcing sustainable supply chain management with Industry 4.0 technologies, Comput. Ind. Eng. 178 (2023) 109113.
24. R. Agrawal, V. A. Wankhede, A. Kumar, S. Luthra and D. Huisingh, Progress and trends in integrating Industry 4.0 within Circular Economy: A comprehensive literature review and future research propositions, Bus. Strategy Environ. 31 (2022) 559–579.
25. A. R. Santhi and P. Muthuswamy, Pandemic, war, natural calamities, and sustainability: Industry 4.0 technologies to overcome traditional and contemporary supply chain challenges, Logistics 6 (2022) 81.
26. V. Sima, I. G. Gheorghe, J. Subi? and D. Nancu, Influence of the Industry 4.0 revolution on human capital development and consumer behaviour: a systematic review, Sustainability 12 (2020) 4035.
27. R. S. Peres, X. Jia, J. Lee, K. Sun, A. W. Colombo and J. Barata, Industrial AI in industry 4.0-systematic review, challenges and outlook, IEEE Access 8 (2020) 220121–220139.
28. Z. Huang, Y. Shen, J. Li, M. Fey and C. Brecher, A survey on AI-driven digital twins in Industry 4.0: smart manufacturing and advanced robotics, Sensors 21 (2021) 6340.
29. A. Jamwal, R. Agrawal and M. Sharma, Deep learning for manufacturing sustainability: models, applications in Industry 4.0 and implications, Int. J. Inf. Manage. Data Insights 2 (2022) 100107.
30. M. D. Bormida, The big data world: benefits, threats and ethical challenges, in: Ethical Issues in Covert, Security and Surveillance Research, Emerald Publishing Limited, 2021, pp. 71–91.
31. R. Morrar, H. Arman and S. Mousa, The fourth industrial revolution (Industry 4.0): a social innovation perspective, Technol. Innov. Manage. Rev. 7 (2017) 12–20.
32. Y. Liao, F. Deschamps, E. D. F. R. Loures and L. F. P. Ramos, Past, present and future of Industry 4.0: a systematic literature review and research agenda proposal, Int. J. Prod. Res. 55 (2017) 3609–3629.
33. S. Nakamoto and A. Bitcoin, A peer-to-peer electronic cash system, Bitcoin. 4 (2008) 15.
34. T. N. Dinh and Y. Kim, Blockchain-based secure firmware update for IoT devices, Electronics 9 (2020) 161.
35. K. Korpela, J. Hallikas and T. Dahlberg, Digital supply chain transformation toward blockchain integration, (2017).
36. T. Wohlers, Additive manufacturing and 3D printing state of the industry, Wohlers Associates, Fort Collins, CO 6 (2013) 269–375.
37. G. Rasiya, A. Shukla and K. Saran, Additive manufacturing: a review, Mater. Today Proc. 47 (2021) 6896–6901.
38. A. Martínez-García, M. Monzón and R. Paz, Standards for additivemanufacturing technologies: structure and impact, in: Additive Manufacturing, Elsevier, 2021, pp. 395–408.
39. R. Bogue, The role of AI in robotics, Ind. Robot Int. J. 41 (2014) 119–123.
40. N. Gupta, A. Tiwari, S. T. Bukkapatnam and R. Karri, Additive manufacturing cyber-physical system: supply chain cybersecurity and risks, IEEE Access 8 (2020) 47322–47333.
41. Y. Yokokohji, Y. Kawai, M. Shibata, Y. Aiyama, S. Kotosaka, W. Uemura and K. Yokoi, Assembly Challenge: a robot competition of the Industrial Robotics Category, World Robot Summit–summary of the pre-competition in 2018, Adv. Robot. 33 (2019) 876–899.
42. A. Kusiak, Smart manufacturing, Int. J. Prod. Res. 56 (2018) 508–517.
43. J. Bughin, J. Manyika, J. Woetzel, F. M. Mattern, S. Chui, A. Lund and S. Nyquist, A future that works: automation, employment, and productivity, McKinsey Global Institute (2017) 1–28.
44. H. Kagermann, J. Helbig, A. Hellinger and W. Wahlster, Recommendations for implementing the strategic initiative INDUSTRIE 4.0: securing the future of German manufacturing industry; final report of the Industrie 4.0 Working Group, Forschungsunion (2013).
45. L. D. Sturm, C. B. Williams, J. A. Camelio, J. White and R. Parker, Cyber-physical vulnerabilities in additive manufacturing systems: a case study attack on the .STL file with human subjects, J. Manuf. Syst. 44 (2017) 154–164.
46. S. Wang, J. Wan, D. Zhang, D. Li and C. Zhang, Towards smart factory for industry 4.0: a self-organized multi-agent system with big data based feedback and coordination, Comput. Netw. 101 (2016) 158–168.
47. R. Srinivasan, M. Kumar and S. Narayanan, Human resource management in an Industry 4.0 era, in: The Oxford Handbook of Supply Chain Management, Oxford University Press, 2020.
48. P. Russom, Big data analytics, TDWI Best Practices Report 19 (2011) 1–34.
49. F. Doil, W. Schreiber, T. Alt and C. Patron, Augmented reality for manufacturing planning, in: Proceedings of the Workshop on Virtual Environments 2003, 2003, pp. 71–76.
50. H. Cloud, The nist definition of cloud computing, Natl. Inst. Stand. Technol. Spec. Publ. 800 (2011) 145.
51. R. Rai, M. K. Tiwari, D. Ivanov and A. Dolgui, Machine learning in manufacturing and Industry 4.0 applications, Int. J. Prod. Res. 59 (2021) 4773–4778.
52. E. Glaessgen and D. Stargel, The digital twin paradigm for future NASA and US Air Force vehicles, in: 53rd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference, 2012, p. 1818.
53. M. M. Gunal, Simulation for industry 4.0. Past, Present, and Future, Springer (2019) 3.
54. A. Haibi, K. Oufaska, K. El Yassini, M. Boulmalf and M. Bouya, Systematic mapping study on RFID technology, IEEE Access 10 (2022) 6363–6380.
55. M. Aria and C. Cuccurullo, Bibliometrix: an R-tool for comprehensive science mapping analysis, J. Informetr. 11 (2017) 959–975.
56. J. Lee, B. Bagheri and H. A. Kao, A cyber-physical systems architecture for Industry 4.0-based manufacturing systems, Manuf. Lett. 3 (2015) 18–23.
57. M. N. Akhtar, A. Haleem and M. Javaid, Exploring the advent of Medical 4.0: a systematic review of bibliometric analysis and technology adoption insights, Informatics Health 1 (2024) 16–28.
58. Y. Yang, T. Gai, M. Cao, Z. Zhang, H. Zhang and J. Wu, Application of group decision making in shipping industry 4.0: bibliometric analysis, trends, and future directions, Systems 11 (2023) 69.
59. M. Naeem, W. Ozuem, K. Howell and S. Ranfagni, A step-by-step process of thematic analysis to develop a conceptual model in qualitative research, Int. J. Qual. Methods 22 (2023) 16094069231205789.
60. L. S. Nowell, J. M. Norris, D. E. White and N. J. Moules, Thematic analysis: striving to meet the trustworthiness criteria, Int. J. Qual. Methods 16 (2017) 1609406917733847.
61. M. O. Arshad, A. Haleem and M. Nadeem, Understanding the energy retrofit strategies towards net zero energy building in sync with the SDGs: a review and bibliometric analysis, J. Build. Eng. (2025) 112574.
62. H. Xie, Y. Zhang and K. Duan, Evolutionary overview of urban expansion based on bibliometric analysis in Web of Science from 1990 to 2019, Habitat Int. 95 (2020) 102100.
63. N. Donthu, S. Kumar, D. Mukherjee, N. Pandey and W. M. Lim, How to conduct a bibliometric analysis: an overview and guidelines, J. Bus. Res. 133 (2021) 285–296.
64. A. Sharifi, Urban sustainability assessment: an overview and bibliometric analysis, Ecol. Indic. 121 (2021) 107102.
65. L. D. Xu, E. L. Xu and L. Li, Industry 4.0: state of the art and future trends, Int. J. Prod. Res. 56 (2018) 2941–2962.
66. West Science Information System and Technology, AI in Industry 4.0: a bibliometric analysis of research trends, West Sci. Inf. Syst. Technol. 3 (2024) 75–83.
67. S. Tiwari, P. C. Bahuguna and R. Srivastava, Smart manufacturing and sustainability: a bibliometric analysis, Benchmarking Int. J. 30 (2023) 3281–3301.
68. P. Ponce, B. Anthony, R. Bradley, W. Xu, J. I. Méndez and A. Molina, A comprehensive framework integrating ML, automation pyramid, and KPIs for Industry 5.0, in: International Conference on Innovative Intelligent Industrial Production and Logistics, Springer Nature Switzerland, 2024, pp. 385–409.
69. A. W. Al-Khatib, The impact of Industry 4.0 capabilities on operational performance: the mediating effect of supply chain ambidexterity, TQM J. 37 (2025) 125–149.
70. P. K. Ambadekar, S. Ambadekar, C. M. Choudhari, S. A. Patil and S. H. Gawande, AI and its relevance in mechanical engineering from Industry 4.0 perspective, Aust. J. Mech. Eng. 23 (2025) 110–130.
71. V. P. Sharma, Prakash, S. Singh and C. Malik, Analysing industry 4.0 key performance indicators for sustainability of smart warehouses, in: Recent Advances in Green Technologies and Sustainable Development, CRC Press, pp. 1–12.