The DOI matches its Crossref record; 147 of the 147 references it cites were found in a registry or an index.
Industry 4.0: A bibliometric analysis and detailed overview
Abstract
Abstract With the arrival of Industry 4.0, the overall transformation using digital integration and intelligent engineering has taken a giant leap towards futuristic technology. All devices today are equipped with machine learning, automation has become a priority and thus another industrial revolution is in the making. In this state-of-the-art paper, we have performed bibliometric analysis and an extensive survey on recent developments in the field of “Industry 4.0”. In bibliometric analysis, different performance metrics are extracted, such as: total papers, total citations, and citation per paper. Further, top 10 of the most productive and highly cited authors, major subject areas, sources or journals, countries, and institutions are evaluated. A list of highly influential papers is also assessed. Later on, a detailed discussion of the most cited papers is analysed and a sectional classification is provided. This paper summarizes the growth structure of Industry 4.0 during the last 5 years and provides the concise background overview of Industry 4.0 related works and various application areas.
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Citations
Cites
A Cyber-Physical Systems architecture for Industry 4.0-based manufacturing systems
Lee, J., Bagheri, B., & Kao, H.-A. (2015) · Manufacturing Letters · doi:10.1016/j.mfglet.2014.12.001 · Cited by 5,113
Lasi, H., Fettke, P., Kemper, H.-G., Feld, T., & Hoffmann, M. (2014) · Business & Information Systems Engineering · doi:10.1007/s12599-014-0334-4 · Cited by 4,276
Statistical bibliography or bibliometrics
Pritchard, A. E. (1969) · Journal of Documentation · Cited by 3,298
Design Principles for Industrie 4.0 Scenarios
Hermann, M., Pentek, T., & Otto, B. (2016) · 2016 49th Hawaii International Conference on System Sciences (HICSS) · doi:10.1109/hicss.2016.488 · Cited by 2,974
Past, present and future of Industry 4.0—A systematic literature review and research agenda proposal
Liao, Y., Deschamps, F., Loures, E. D. F. R., & Ramos, L. F. P. (2017) · International Journal of Production Research · doi:10.1080/00207543.2017.1308576 · Cited by 2,036
Service Innovation and Smart Analytics for Industry 4.0 and Big Data Environment
Lee, J., Kao, H.-A., & Yang, S. (2014) · Procedia CIRP · doi:10.1016/j.procir.2014.02.001 · Cited by 1,965
Opportunities of Sustainable Manufacturing in Industry 4.0
Stock, T., & Seliger, G. (2016) · Procedia CIRP · doi:10.1016/j.procir.2016.01.129 · Cited by 1,893
Industry 4.0 and the current status as well as future prospects on logistics
Hofmann, E., & Rüsch, M. (2017) · Computers in Industry · doi:10.1016/j.compind.2017.04.002 · Cited by 1,841
Cyber-physical systems in manufacturing
Monostori, L., Kádár, B., Bauernhansl, T., Kondoh, S., Kumara, S., Reinhart, G., Sauer, O., Schuh, G., Sihn, W., & Ueda, K. (2016) · CIRP Annals · doi:10.1016/j.cirp.2016.06.005 · Cited by 1,697
Toward a definition of “bibliometrics”
Broadus, R. N. (1987) · Scientometrics · doi:10.1007/bf02016680 · Cited by 1,646
Cited by
Dwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., Duan, Y., Dwivedi, R., Edwards, J., Eirug, A., et al. (2021) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2019.08.002 · Cited by 4,611
Predictive maintenance in the Industry 4.0: A systematic literature review
Zonta, T., da Costa, C. A., da Rosa Righi, R., de Lima, M. J., da Trindade, E. S., & Li, G. P. (2020) · Computers & Industrial Engineering · doi:10.1016/j.cie.2020.106889 · Cited by 1,302
Sony, M., & Naik, S. (2020) · Production Planning & Control · doi:10.1080/09537287.2019.1691278 · Cited by 401
Tackling Faults in the Industry 4.0 Era—A Survey of Machine-Learning Solutions and Key Aspects
Angelopoulos, A., Michailidis, E. T., Nomikos, N., Trakadas, P., Hatziefremidis, A., Voliotis, S., & Zahariadis, T. (2019) · Sensors · doi:10.3390/s20010109 · Cited by 365
Zhang, D., Pee, L. G., & Cui, L. (2021) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2020.102304 · Cited by 228
Metadata
- Authors
- Muhuri, P. K., Shukla, A. K., & Abraham, A.
- Venue
- Engineering Applications of Artificial Intelligence
- Publisher
- Elsevier BV
- Published
- 2019-02
- Volume
- 78
- Pages
- 218-235
- ISSN
- 0952-1976 (ISSN-L)
- OpenAlex
- W2904029666
- Cited by
- 549
- References
- 147
- FWCI
- 49.01
- Type
- journal article
- Language
- en
- Open access
- closed
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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- This page
- CoChat. (2026). Citation check: Industry 4.0: A bibliometric analysis and detailed overview. https://app.cochat.ai/papers/10.1016/j.engappai.2018.11.007
- The paper
- Muhuri, P. K., Shukla, A. K., & Abraham, A. (2019). Industry 4.0: A bibliometric analysis and detailed overview. Engineering Applications of Artificial Intelligence. https://doi.org/10.1016/j.engappai.2018.11.007
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
