The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Data-driven smart manufacturing
Abstract
Abstract The advances in the internet technology, internet of things, cloud computing, big data, and artificial intelligence have profoundly impacted manufacturing. The volume of data collected in manufacturing is growing. Big data offers a tremendous opportunity in the transformation of today’s manufacturing paradigm to smart manufacturing. Big data empowers companies to adopt data-driven strategies to become more competitive. In this paper, the role of big data in supporting smart manufacturing is discussed. A historical perspective to data lifecycle in manufacturing is overviewed. The big data perspective is supported by a conceptual framework proposed in the paper. Typical application scenarios of the proposed framework are outlined.
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Citations
Cites
Beyond the hype: Big data concepts, methods, and analytics
Gandomi, A., & Haider, M. (2015) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2014.10.007 · Cited by 4,226
Digital twin-driven product design, manufacturing and service with big data
Tao, F., Cheng, J., Qi, Q., Zhang, M., Zhang, H., & Sui, F. (2018) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-017-0233-1 · Cited by 2,920
Self-citationChen, M., Mao, S., & Liu, Y. (2014) · Mobile Networks and Applications · doi:10.1007/s11036-013-0489-0 · Cited by 2,913
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
Cited by
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
The expected contribution of Industry 4.0 technologies for industrial performance
Dalenogare, L. S., Benitez, G. B., Ayala, N. F., & Frank, A. G. (2018) · International Journal of Production Economics · doi:10.1016/j.ijpe.2018.08.019 · Cited by 2,201
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
Enabling technologies and tools for digital twin
Qi, Q., Tao, F., Hu, T., Anwer, N., Liu, A., Wei, Y., Wang, L., & Nee, A. Y. C. (2021) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2019.10.001 · Cited by 1,579
Self-citationTao, F., Xiao, B., Qi, Q., Cheng, J., & Ji, P. (2022) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2022.06.015 · Cited by 1,255
Metadata
- Authors
- Tao, F., Qi, Q., Liu, A., & Kusiak, A.
- Venue
- Journal of Manufacturing Systems
- Publisher
- Elsevier BV
- Published
- 2018-07
- Volume
- 48
- Pages
- 157-169
- ISSN
- 0278-6125 (ISSN-L)
- OpenAlex
- W2788276261
- Cited by
- 1,806
- References
- 4
- FWCI
- 127.72
- Type
- journal article
- Language
- en
- Open access
- closed
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- Tao, F., Qi, Q., Liu, A., & Kusiak, A. (2018). Data-driven smart manufacturing. Journal of Manufacturing Systems. https://doi.org/10.1016/j.jmsy.2018.01.006
Data sources
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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