Industry 4.0 Research in Emerging Economies: A Bibliometric Comparison of Brazil and Globally Leading Countries

Leandro Scala da Rocha; Ítalo De Oliveira Matias

doi:10.4067/s0718-27242026000100112

Authors

Leandro Scala da Rocha Universidade Federal Rural do Rio de Janeiro , Seropédica, Brazil
Ítalo De Oliveira Matias Universidade Candido Mendes , Campos dos Goytacazes, Brazil.

DOI:

https://doi.org/10.4067/s0718-27242026000100112

Keywords:

Manufacturing, Industrial revolution, Artificial intelligence, Decision making, Smart factory

Abstract

Objective: This study aims to map and analyze the Industry 4.0 research landscape in Brazil, comparing it with leading countries in order to identify dominant trends, research gaps, and Brazil’s position and potential contributions to the global knowledge base. Methods: A bibliometric approach was adopted based on scientific publications indexed in the Scopus database. Text mining and network analysis techniques were applied using the VOSviewer software to examine publication growth, co-authorship collaboration patterns, and thematic clusters. Comparative analyses between Brazil and leading countries were conducted to identify convergences and divergences in research focus over time. Results: The findings indicate strong global growth in Industry 4.0 research, while Brazil exhibits a comparatively slower expansion. Brazilian publications largely align with international research themes, particularly those related to the Internet of Things (IoT), Big Data, and Machine Learning. However, Brazilian studies show a distinctive emphasis on applications aimed at operational efficiency and sustainability. Conclusions: The study concludes that, although Brazil follows global Industry 4.0 research trends, differences remain in the scale and pace of scientific production. The results highlight opportunities to strengthen Brazil’s position through increased investment in research and development and expanded international collaboration. These insights are relevant for researchers, policymakers, and industry professionals seeking to promote strategic actions that enhance the impact and global integration of Brazilian Industry 4.0 research.

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References

Adadi, A., & Berrada, M. (2018). Peeking inside the black-box: A survey on explainable artificial intelligence (XAI). IEEE Access, 6, 52138-52160. https://doi.org/10.1109/ACCESS.2018.2870052

Antony, J., Sony, M., Garza-Reyes, J. A., Mcdermott, O., Tortorella, G., Jayaraman, R., Sucharitha, R. S., Salentijin, W., & Maalouf, M. (2023). Industry 4.0 benefits, challenges and critical success factors: A comparative analysis through the lens of resource dependence theory across continents and economies. Journal of Manufacturing Technology Management, 34(7), 1073-1097. https://doi.org/10.1108/JMTM-10-2022-0371

Ayala, N. F., Gerstlberger, W., & Frank, A. G. (2019). Managing servitization in product companies: The moderating role of service suppliers. International Journal of Operations and Production Management, 39(1), 43-74. https://doi.org/10.1108/IJOPM-08-2017-0484

Baas, J., Schotten, M., Plume, A., Côté, G., & Karimi, R. (2020). Scopus as a curated, high-quality bibliometric data source for academic research in quantitative science studies. Quantitative Science Studies, 1(1), 377-386. https://doi.org/10.1162/qss_a_00019

Chadegani, A. A., Salehi, H., Yunus, M. M., Farhadi, H., Fooladi, M., Farhadi, M., & Ebrahim, N. A. (2013). A comparison between two main academic literature collections: Web of Science and Scopus databases. Asian Social Science, 9(5), 18-26. https://doi.org/10.5539/ass.v9n5p18

Chen, T., & Tsai, H. R. (2017). Ubiquitous manufacturing: Current practices, challenges, and opportunities. Robotics and Computer-Integrated Manufacturing, 45, 126-132. https://doi.org/10.1016/j.rcim.2016.01.001

Cobo, M. J., Chiclana, F., Collop, A., De Oña, J., & Herrera-Viedma, E. (2014). A bibliometric analysis of the intelligent transportation systems research based on science mapping. IEEE Transactions on Intelligent Transportation Systems, 15(2), 901-908. https://doi.org/10.1109/TITS.2013.2284756

Cobo, M. J., López-Herrera, A. G., Herrera-Viedma, E., & Herrera, F. (2012). SciMAT: A new science mapping analysis software tool. Journal of the American Society for Information Science and Technology, 63(8), 1609-1630. https://doi.org/10.1002/asi.22688

Dalenogare, L. S., Benitez, G. B., Ayala, N. F., & Frank, A. G. (2018). The expected contribution of Industry 4.0 technologies for industrial performance. International Journal of Production Economics, 204, 383-394. https://doi.org/10.1016/j.ijpe.2018.08.019

Feng, S., Li, L. X., & Cen, L. (2001). An object-oriented intelligent design tool to aid the design of manufacturing systems. Knowledge-Based Systems, 14(5-6), 225-232. https://doi.org/10.1016/S0950-7051(01)00100-9

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. https://doi.org/10.1016/j.ijpe.2019.01.004

Gavel, Y., & Iselid, L. (2008). Web of Science and Scopus: A journal title overlap study. Online Information Review, 32(1), 8-21. https://doi.org/10.1108/14684520810865958

Gawer, A., & Cusumano, M. A. (2014). Industry platforms and ecosystem innovation. Journal of Product Innovation Management, 31(3), 417-433. https://doi.org/10.1111/jpim.12105

Hermann, M., Pentek, T., & Otto, B. (2016). Design principles for Industrie 4.0 scenarios. In Proceedings of the Annual Hawaii International Conference on System Sciences (pp. 3928-3937). IEEE Computer Society. https://doi.org/10.1109/HICSS.2016.488

Hofmann, E., & Rüsch, M. (2017). Industry 4.0 and the current status as well as future prospects on logistics. Computers in Industry, 89, 23-34. https://doi.org/10.1016/j.compind.2017.04.002

IEEE. (2024). January 2024 IEEE Thesaurus. IEEE.

Ivanov, D., Dolgui, A., Sokolov, B., Werner, F., & Ivanova, M. (2016). A dynamic model and an algorithm for short-term supply chain scheduling in the smart factory Industry 4.0. International Journal of Production Research, 54(2), 386-402. https://doi.org/10.1080/00207543.2014.999958

Jabbour, A. B. L. D. S., Jabbour, C. J. C., Foropon, C., & Godinho Filho, M. (2018). When titans meet: Can Industry 4.0 revolutionise the environmentally-sustainable manufacturing wave? The role of critical success factors. Technological Forecasting and Social Change, 132, 18-25. https://doi.org/10.1016/j.techfore.2018.01.017

Kagermann, H., Wahlster, W., & Helbig, J. (2013). Recommendations for implementing the strategic initiative Industrie 4.0. Acatech. https://en.acatech.de/publication/recommendations-for-implementing-the-strategic-initiative-industrie-4-0-final-report-of-the-industrie-4-0-working-group/

Kortmann, S., & Piller, F. (2016). Open business models and closed-loop value chains: Redefining the firm-consumer relationship. California Management Review, 58(3), 88-108. https://doi.org/10.1525/cmr.2016.58.3.88

Lasi, H., Fettke, P., Kemper, H.-G., Feld, T., & Hoffmann, M. (2014). Industry 4.0. Business and Information Systems Engineering, 6(4), 239-242. https://doi.org/10.1007/s12599-014-0334-4

Lee, J., Bagheri, B., & Kao, H. A. (2015). A cyber-physical systems architecture for Industry 4.0-based manufacturing systems. Manufacturing Letters, 3, 18-23. https://doi.org/10.1016/j.mfglet.2014.12.001

Longo, F., Nicoletti, L., & Padovano, A. (2017). Smart operators in Industry 4.0: A human-centered approach to enhance operators' capabilities and competencies within the new smart factory context. Computers and Industrial Engineering, 113, 144-159. https://doi.org/10.1016/j.cie.2017.09.016

Monostori, L. (2014). Cyber-physical production systems: Roots, expectations and R&D challenges. Procedia CIRP, 17, 9-13. https://doi.org/10.1016/j.procir.2014.03.115

Morris, S. A., & Van Der Veer Martens, B. (2008). Mapping research specialties. Annual Review of Information Science and Technology, 42(1), 213-295. https://doi.org/10.1002/aris.2008.1440420113

Muhuri, P. K., Shukla, A. K., & Abraham, A. (2019). Industry 4.0: A bibliometric analysis and detailed overview. Engineering Applications of Artificial Intelligence, 78, 218-235. https://doi.org/10.1016/j.engappai.2018.11.007

Porter, M., & Heppelmann, J. E. (2015). How smart, connected products are transforming companies. Harvard Business Review, 1-19.

Raguseo, E., Gastaldi, L., & Neirotti, P. (2016). Smart work: Supporting employees' flexibility through ICT, HR practices and office layout. Evidence-Based HRM, 4(3), 240-256. https://doi.org/10.1108/EBHRM-01-2016-0004

Schuh, G., Anderl, R., Dumitrescu, R., Krüger, A., & Ten Hompel, M. (2020). Industrie 4.0 Maturity Index: Managing the digital transformation of companies (Update 2020). Acatech Study. https://en.acatech.de/publication/industrie-4-0-maturity-index-update-2020/

Singh, V. K., Singh, P., Karmakar, M., Leta, J., & Mayr, P. (2021). The journal coverage of Web of Science, Scopus and Dimensions: A comparative analysis. Scientometrics, 126, 5113-5142. https://doi.org/10.1007/s11192-021-03948-5

Stock, T., Obenaus, M., Kunz, S., & Kohl, H. (2018). Industry 4.0 as enabler for a sustainable development: A qualitative assessment of its ecological and social potential. Process Safety and Environmental Protection, 118, 254-267. https://doi.org/10.1016/j.psep.2018.06.026

Thames, L., & Schaefer, D. (2016). Software-defined cloud manufacturing for Industry 4.0. Procedia CIRP, 52, 12-17. https://doi.org/10.1016/j.procir.2016.07.041

Tortorella, G. L., & Fettermann, D. (2017). Implementation of Industry 4.0 and lean production in Brazilian manufacturing companies. International Journal of Production Research, 56(8), 2975-2987. https://doi.org/10.1080/00207543.2017.1391420

Van Eck, N. J., & Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84, 523-538. https://doi.org/10.1007/s11192-009-0146-3

Vieira, E. S., & Gomes, J. A. (2009). A comparison of Scopus and Web of Science for a typical university. Scientometrics, 81, 587-600. https://doi.org/10.1007/s11192-009-2178-0

Wang, S., Wan, J., Li, D., & Zhang, C. (2016a). Implementing smart factory of Industrie 4.0: An outlook. International Journal of Distributed Sensor Networks, 12(1), 1-10. https://doi.org/10.1155/2016/3159805

Wang, S., Wan, J., Zhang, D., Li, D., & Zhang, C. (2016b). Towards smart factory for Industry 4.0: A self-organized multi-agent system with big data based feedback and coordination. Computer Networks, 101, 158-168. https://doi.org/10.1016/j.comnet.2015.12.017

Wollschlaeger, M., Sauter, T., & Jasperneite, J. (2017). The future of industrial communication. IEEE Industrial Electronics Magazine, 11(1), 17-27. https://doi.org/10.1109/MIE.2017.2649104

Xu, L. D., Xu, E. L., & Li, L. (2018). Industry 4.0: State of the art and future trends. International Journal of Production Research, 56(8), 2941-2962. https://doi.org/10.1080/00207543.2018.1444806

Zhong, R. Y., Xu, X., Klotz, E., & Newman, S. T. (2017). Intelligent manufacturing in the context of Industry 4.0: A review. Engineering, 3(5), 616-630. https://doi.org/10.1016/J.ENG.2017.05.015

Zupic, I., & Čater, T. (2015). Bibliometric methods in management and organization. Organizational Research Methods, 18(3), 429-472. https://doi.org/10.1177/1094428114562629