The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Sustainable manufacturing in Industry 4.0: an emerging research agenda
Open accessAbstract
This systematic review intends to identify how sustainable manufacturing research is contributing to the development of the Industry 4.0 agenda and for a broader understanding about the links between the Industry 4.0 and Sustainable Manufacturing by mapping and summarising existing research efforts, identifying research agendas, as well as gaps and opportunities for research development. A conceptual framework formed by the principles and technological pillars of Industry 4.0, sustainable manufacturing scope, opportunities previously identified, and sustainability dimensions, guided analysis of 35 papers from 2008–2018, selected by a systematic approach. Bibliometrics data and social network analysis complement results identifying how research is being organised and its respective research agendas, relevant publications, and status of the research lifecycle. Results point to that the current research is aligned with the goals defined by different national industrial programs. There are, however, research gaps and opportunities for field development, becoming more mature and having a significant contribution to fully developing the agenda of Industry 4.0.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
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
Kusiak, A. (2018) · International Journal of Production Research · doi:10.1080/00207543.2017.1351644 · Cited by 1,330
Recent advances and trends in predictive manufacturing systems in big data environment
Lee, J., Lapira, E., Bagheri, B., & Kao, H.-A. (2013) · Manufacturing Letters · doi:10.1016/j.mfglet.2013.09.005 · Cited by 1,090
More Sustainability in Industry through Industrial Internet of Things?
Beier, G., Niehoff, S., & Xue, B. (2018) · Applied Sciences · doi:10.3390/app8020219 · Cited by 195
Cited by
Rodríguez-Espíndola, O., Cuevas-Romo, A., Chowdhury, S., Díaz-Acevedo, N., Albores, P., Despoudi, S., Malesios, C., & Dey, P. (2022) · International Journal of Production Economics · doi:10.1016/j.ijpe.2022.108495 · Cited by 459
Ivanov, D., Tang, C. S., Dolgui, A., Battini, D., & Das, A. (2021) · International Journal of Production Research · doi:10.1080/00207543.2020.1798035 · Cited by 452
Alkaraan, F., Albitar, K., Hussainey, K., & Venkatesh, V. (2022) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2021.121423 · Cited by 422
Palomares, I., Martínez-Cámara, E., Montes, R., García-Moral, P., Chiachio, M., Chiachio, J., Alonso, S., Melero, F. J., Molina, D., Fernández, B., et al. (2021) · Applied Intelligence · doi:10.1007/s10489-021-02264-y · Cited by 276
Chari, A., Niedenzu, D., Despeisse, M., Machado, C. G., Azevedo, J. D., Boavida‐Dias, R., & Johansson, B. (2022) · Business Strategy and the Environment · doi:10.1002/bse.3040 · Cited by 259
Self-citation
Metadata
- Authors
- Machado, C. G., Winroth, M. P., & Ribeiro da Silva, E. H. D.
- Venue
- International Journal of Production Research
- Publisher
- Informa UK Limited
- Published
- 2019-08-20
- Volume
- 58
- Issue
- 5
- Pages
- 1462-1484
- ISSN
- 0020-7543 (ISSN-L)
- 1366-588X
- OpenAlex
- W2969750664
- Cited by
- 829
- References
- 4
- FWCI
- 59.10
- Type
- journal article
- Language
- en
- Open access
- hybrid
About these checks
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.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Sustainable manufacturing in Industry 4.0: an emerging research agenda. https://app.cochat.ai/papers/10.1080/00207543.2019.1652777
- The paper
- Machado, C. G., Winroth, M. P., & Ribeiro da Silva, E. H. D. (2020). Sustainable manufacturing in Industry 4.0: an emerging research agenda. International Journal of Production Research. https://doi.org/10.1080/00207543.2019.1652777
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.
