The DOI matches its Crossref record; 62 of the 62 references it cites were found in a registry or an index.
Smart manufacturing: Characteristics, technologies and enabling factors
Open accessAbstract
The purpose of this article is to collect and structure the various characteristics, technologies and enabling factors available in the current body of knowledge that are associated with smart manufacturing. Eventually, it is expected that this selection of characteristics, technologies and enabling factors will help compare and distinguish other initiatives such as Industry 4.0, cyber-physical production systems, smart factory, intelligent manufacturing and advanced manufacturing, which are frequently used synonymously with smart manufacturing. The result of this article is a comprehensive list of such characteristics, technologies and enabling factors that are regularly associated with smart manufacturing. This article also considers principles of “semantic similarity” to establish the basis for a future smart manufacturing ontology, since it was found that many of the listed items show varying overlaps; therefore, certain characteristics and technologies are merged and/or clustered. This results in a set of five defining characteristics, 11 technologies and three enabling factors that are considered relevant for the smart manufacturing scope. This article then evaluates the derived structure by matching the characteristics and technology clusters of smart manufacturing with the design principles of Industry 4.0 and cyber-physical systems. The authors aim to provide a solid basis to start a broad and interdisciplinary discussion within the research and industrial community about the defining characteristics, technologies and enabling factors of smart manufacturing.
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Cites
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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
The Internet of Things (IoT): Applications, investments, and challenges for enterprises
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Probabilistic machine learning and artificial intelligence
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Smart manufacturing: Past research, present findings, and future directions
Kang, H. S., Lee, J. Y., Choi, S., Kim, H., Park, J. H., Son, J. Y., Kim, B. H., & Noh, S. D. (2016) · International Journal of Precision Engineering and Manufacturing-Green Technology · doi:10.1007/s40684-016-0015-5 · Cited by 1,362
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Cited by
Mittal, S., Khan, M. A., Romero, D., & Wuest, T. (2018) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2018.10.005 · Cited by 1,121
Self-citationXia, K., Sacco, C., Kirkpatrick, M., Saidy, C., Nguyen, L., Kircaliali, A., & Harik, R. (2021) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2020.06.012 · Cited by 406
Smart industrial robot control trends, challenges and opportunities within manufacturing
Arents, J., & Greitans, M. (2022) · Applied Sciences · doi:10.3390/app12020937 · Cited by 338
The degree of readiness for the implementation of Industry 4.0
Pacchini, A. P. T., Lucato, W. C., Facchini, F., & Mummolo, G. (2019) · Computers in Industry · doi:10.1016/j.compind.2019.103125 · Cited by 268
Metadata
- Authors
- Mittal, S., Khan, M. A., Romero, D., & Wuest, T.
- Venue
- Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture
- Publisher
- SAGE Publications
- Published
- 2017-10-26
- Volume
- 233
- Issue
- 5
- Pages
- 1342-1361
- ISSN
- 0954-4054 (ISSN-L)
- 2041-2975
- OpenAlex
- W2766196346
- Cited by
- 560
- References
- 62
- FWCI
- 39.59
- Type
- journal article
- Language
- en
- Open access
- hybrid
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- Mittal, S., Khan, M. A., Romero, D., & Wuest, T. (2019). Smart manufacturing: Characteristics, technologies and enabling factors. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture. https://doi.org/10.1177/0954405417736547
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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.
