The DOI matches its Crossref record; 63 of the 63 references it cites were found in a registry or an index.
Sustainable industrial and operation engineering trends and challenges Toward Industry 4.0: a data driven analysis
Open accessAbstract
This study supplies contributions to the existing literature with a state-of-the-art bibliometric review of sustainable industrial and operation engineering as the field moves toward Industry 4.0, and guidance for future studies and practical achievements. Although industrial and operation engineering is being promoted forward to sustainability, the systematization of the knowledge that forms firms’ manufacturing and operations and encompasses their wide concepts and abundant complementary elements is still absent. This study aims to analyze contemporary sustainable industrial and operations engineering in Industry 4.0 context. The bibliometric analysis and fuzzy Delphi method are proposed. Resulting in a total of 30 indicators that are criticized and clustered into eight study groups, including lean manufacturing in Industry 4.0, cyber-physical production system, big data-driven and smart communications, safety and security, artificial intelligence for sustainability, the circular economy in a digital environment, business intelligence and virtual reality, and environmental sustainability.
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Citations
Cites
Bibliometric Methods in Management and Organization
Zupic, I., & Čater, T. (2015) · Organizational Research Methods · doi:10.1177/1094428114562629 · Cited by 7,495
Industry 4.0 technologies: Implementation patterns in manufacturing companies
Frank, A. G., Dalenogare, L. S., & Ayala, N. F. (2019) · International Journal of Production Economics · doi:10.1016/j.ijpe.2019.01.004 · Cited by 2,958
Literature review of Industry 4.0 and related technologies
Oztemel, E., & Gursev, S. (2020) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-018-1433-8 · Cited by 2,143
Industry 4.0, digitization, and opportunities for sustainability
Ghobakhloo, M. (2020) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2019.119869 · Cited by 2,115
Deep learning for smart manufacturing: Methods and applications
Wang, J., Ma, Y., Zhang, L., Gao, R. X., & Wu, D. (2018) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2018.01.003 · Cited by 1,740
Silva, B. N., Khan, M., & Han, K. (2018) · Sustainable Cities and Society · doi:10.1016/j.scs.2018.01.053 · Cited by 1,668
Tao, F., Qi, Q., Wang, L., & Nee, A. Y. C. (2019) · Engineering · doi:10.1016/j.eng.2019.01.014 · Cited by 1,487
Artificial intelligence in sustainable energy industry: Status Quo, challenges and opportunities
Ahmad, T., Zhang, D., Huang, C., Zhang, H., Dai, N., Song, Y., & Chen, H. (2021) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2021.125834 · Cited by 1,258
Artificial intelligence for sustainability: Challenges, opportunities, and a research agenda
Nishant, R., Kennedy, M., & Corbett, J. (2020) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2020.102104 · Cited by 1,203
Scanning the Industry 4.0: A Literature Review on Technologies for Manufacturing Systems
Alcácer, V., & Cruz-Machado, V. (2019) · Engineering Science and Technology, an International Journal · doi:10.1016/j.jestch.2019.01.006 · Cited by 1,063
Cited by
A framework of digital technologies for the circular economy: digital functions and mechanisms
Liu, Q., Trevisan, A. H., Yang, M., & Mascarenhas, J. (2022) · Business Strategy and the Environment · doi:10.1002/bse.3015 · Cited by 324
Achieving a data-driven risk assessment methodology for ethical AI
Felländer, A., Rebane, J., Larsson, S., Wiggberg, M., & Heintz, F. (2022) · Digital Society · doi:10.1007/s44206-022-00016-0 · Cited by 36
Mangalore, P., Gamit, J. S., & Kanchan, M. (2025) · Discover Sustainability · doi:10.1007/s43621-025-02146-0 · Cited by 4
Metadata
- Authors
- Tseng, M.-L., Tran, T. P. T., Ha, H. M., Bui, T.-D., & Lim, M. K.
- Venue
- Journal of Industrial and Production Engineering
- Publisher
- Informa UK Limited
- Published
- 2021-07-11
- Volume
- 38
- Issue
- 8
- Pages
- 581-598
- ISSN
- 2168-1015 (ISSN-L)
- 2168-1023
- OpenAlex
- W3195662367
- Cited by
- 341
- References
- 63
- FWCI
- 32.40
- Type
- journal article
- Language
- en
- Open access
- green
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- Tseng, M.-L., Tran, T. P. T., Ha, H. M., Bui, T.-D., & Lim, M. K. (2021). Sustainable industrial and operation engineering trends and challenges Toward Industry 4.0: a data driven analysis. Journal of Industrial and Production Engineering. https://doi.org/10.1080/21681015.2021.1950227
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.
