The DOI matches its Crossref record; 152 of the 152 references it cites were found in a registry or an index.
Industry 4.0, digitization, and opportunities for sustainability
Abstract
Abstract The fourth industrial revolution and the underlying digital transformation, known as Industry 4.0, is progressing exponentially. The digital revolution is reshaping the way individuals live and work fundamentally, and the public remains optimistic regarding the opportunities Industry 4.0 may offer for sustainability. The present study contributes to the sustainability literature by systematically identifying the sustainability functions of Industry 4.0. In doing so, the study first reviews the fundamental design principles and technology trends of Industry 4.0 and introduces the architectural design of Industry 4.0. The study further draws on the interpretive structural modelling technique to model the contextual relationships among the Industry 4.0 sustainability functions. Results indicate that sophisticated precedence relationships exist among various sustainability functions of Industry 4.0. ‘Matrice d’Impacts Croises Multiplication Appliquee aun Classement’ (MICMAC) analysis reveals that economic sustainability functions such as production efficiency and business model innovation tend to be the more immediate outcome of Industry 4.0, which pays the way for development of more remote socioenvironmental sustainability functions of Industry 4.0 such as energy sustainability, harmful emission reduction, and social welfare improvement. This study can serve Industry 4.0 stakeholders – leaders in the public and private sectors, industrialists, and academicians – to better understand the opportunities that the digital revolution may offer for sustainability, and work together more closely to ensure that Industry 4.0 delivers the intended sustainability functions around the world as effectively, equally, and fairly as possible.
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Cites
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Cited by
Ahmad, T., Madonski, R., Zhang, D., Huang, C., & Mujeeb, A. (2022) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2022.112128 · Cited by 711
Kumar, S., Raut, R. D., Nayal, K., Kraus, S., Yadav, V. S., & Narkhede, B. E. (2021) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2021.126023 · Cited by 412
Digital transformation success under Industry 4.0: a strategic guideline for manufacturing SMEs
Ghobakhloo, M., & Iranmanesh, M. (2021) · Journal of Manufacturing Technology Management · doi:10.1108/jmtm-11-2020-0455 · Cited by 379
Self-citationArtificial Intelligence as a Disruptive Technology—A Systematic Literature Review
Păvăloaia, V.-D., & Necula, S.-C. (2023) · Electronics · doi:10.3390/electronics12051102 · Cited by 290
Ferreira, J. J., Lopes, J. M., Gomes, S., & Rammal, H. G. (2023) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2023.136841 · Cited by 167
Metadata
- Authors
- Ghobakhloo, M.
- Venue
- Journal of Cleaner Production
- Publisher
- Elsevier BV
- Published
- 2020-04
- Volume
- 252
- Pages
- 119869
- ISSN
- 0959-6526 (ISSN-L)
- OpenAlex
- W2997638503
- Cited by
- 2,115
- References
- 152
- FWCI
- 102.40
- Type
- journal article
- Language
- en
- Open access
- closed
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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.