The DOI matches its Crossref record; 41 of the 41 references it cites were found in a registry or an index.
New IT Driven Service-Oriented Smart Manufacturing: Framework and Characteristics
Abstract
Recently, along with the wide application of new generation information technologies (New IT) in manufacturing, many countries issued their national advanced manufacturing development strategies, such as Industrial Internet, Industry 4.0, and Made in China 2025. One common aim of these strategies is to achieve smart manufacturing, which demands the interoperation, integration, and fusion of the physical world and the cyber world of manufacturing. As well, New IT [such as Internet of Things (IoT), cloud computing, big data, mobile Internet, and cyber-physical systems (CPS)] have played pivotal roles in promoting smart manufacturing. Data generated in the physical world can be sensed and transfered to the cyber world through IoT and the Internet, and be processed and analyzed by cloud computing, big data technologies to adjust the physical world. The physical world and the cyber world of manufacturing are integrated based on CPS. On the other hand, servitization has become a prominent trend in the manufacturing. Embracing the concept of “Manufacturing-as-a-Service,” manufacturing is provided as service for users. Because of the characteristics of interoperability and platform independence, services pave the way for large-scale smart applications and manufacturing collaboration. Combining New IT and services, this paper proposes a framework-New IT driven service-oriented smart manufacturing (SoSM). SoSM aims at facilitating the visions of smart manufacturing by making full use of New IT and services. Complementary to the framework of SoSM, the New IT driven typical characteristics of SoSM are also investigated and discussed, respectively.
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
The Internet of Things: A survey
Atzori, L., Iera, A., & Morabito, G. (2010) · Computer Networks · doi:10.1016/j.comnet.2010.05.010 · Cited by 15,613
A Cyber-Physical Systems architecture for Industry 4.0-based manufacturing systems
Lee, J., Bagheri, B., & Kao, H.-A. (2015) · Manufacturing Letters · doi:10.1016/j.mfglet.2014.12.001 · Cited by 5,113
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
Cyber-physical systems: the next computing revolution
Rajkumar, R. (., Lee, I., Sha, L., & Stankovic, J. (2010) · Proceedings of the 47th Design Automation Conference · doi:10.1145/1837274.1837461 · Cited by 1,986
From cloud computing to cloud manufacturing
Xu, X. (2012) · Robotics and Computer-Integrated Manufacturing · doi:10.1016/j.rcim.2011.07.002 · Cited by 1,780
The servitization of manufacturing: A review of literature and reflection on future challenges
Baines, T. S., Lightfoot, H. W., Benedettini, O., & Kay, J. M. (2009) · Journal of Manufacturing Technology Management · doi:10.1108/17410380910960984 · Cited by 1,541
Brettel, M., Friederichsen, N., Keller, M., & Rosenberg, M. (2014) · RWTH Publications (RWTH Aachen) · doi:10.5281/zenodo.1336426 · Cited by 1,362
Industrial Product-Service Systems—IPS 2
Meier, H., Roy, R., & Seliger, G. (2010) · CIRP Annals · doi:10.1016/j.cirp.2010.05.004 · Cited by 994
Current status and advancement of cyber-physical systems in manufacturing
Wang, L., Törngren, M., & Onori, M. (2015) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2015.04.008 · Cited by 948
Design Techniques and Applications of Cyberphysical Systems: A Survey
Khaitan, S. K., & McCalley, J. D. (2015) · IEEE Systems Journal · doi:10.1109/jsyst.2014.2322503 · Cited by 784
Research works printed but not linked: 11
- 10.1007/978-3-642-23330-2_11 · doi:10.1007/978-3-642-23330-2_11
- evans, 2012, Industrial Internet Pushing the Boundaries of Minds and Machines
- 10.1080/09511920802139875 · doi:10.1080/09511920802139875
- leiva, 2016, On the Journey to a Smart Manufacturing Revolution
- 10.1109/TSMC.2015.2438780 · doi:10.1109/tsmc.2015.2438780
- 10.1109/JPROC.2004.832955 · doi:10.1109/jproc.2004.832955
- tao, 2008, Study on manufacturing grid & its resource service optimal-selection system, Int J Adv Manuf Technol, 37, 1022, 10.1007/s00170-007-1033-9 · doi:10.1007/s00170-007-1033-9
- 2016, German Standardization Roadmap—Industry 4 0 (Version 2)
- 10.1109/ICMA.2011.5985626 · doi:10.1109/icma.2011.5985626
- 2011, Implementing 21st Century Smart Manufacturing
- lee, 2006, Cyber-physical systems—Are computing foundations adequate?, Proc Position Paper NSF Workshop Cyber-Phys Syst Res Motivat Techn Roadmap, 2, 6
Cited by
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
Digital twin and big data towards smart manufacturing and Industry 4.0: 360 degree comparison
Qi, Q., & Tao, F. (2018) · IEEE Access · doi:10.1109/access.2018.2793265 · Cited by 1,716
Self-citationEnabling 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-citationThe future of manufacturing industry: a strategic roadmap toward Industry 4.0
Ghobakhloo, M. (2018) · Journal of Manufacturing Technology Management · doi:10.1108/jmtm-02-2018-0057 · Cited by 1,381
Blockchain for the future of sustainable supply chain management in Industry 4.0
Esmaeilian, B., Sarkis, J., Lewis, K., & Behdad, S. (2020) · Resources, Conservation and Recycling · doi:10.1016/j.resconrec.2020.105064 · Cited by 855
Metadata
- Authors
- Tao, F., & Qi, Q.
- Venue
- IEEE Transactions on Systems, Man, and Cybernetics: Systems
- Published
- 2019-01
- Volume
- 49
- Issue
- 1
- Pages
- 81-91
- ISSN
- 2168-2216 (ISSN-L)
- 2168-2232
- OpenAlex
- W2736552576
- Cited by
- 432
- References
- 41
- FWCI
- 45.58
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Funders
- National Natural Science Foundation of China (51522501)
- Beijing Nova Program (Z161100004916063)
- National Key Research and Development Program of China (2016YFB1101700)
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: New IT Driven Service-Oriented Smart Manufacturing: Framework and Characteristics. https://app.cochat.ai/papers/10.1109/tsmc.2017.2723764
- The paper
- Tao, F., & Qi, Q. (2019). New IT Driven Service-Oriented Smart Manufacturing: Framework and Characteristics. IEEE Transactions on Systems, Man, and Cybernetics: Systems. https://doi.org/10.1109/tsmc.2017.2723764
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.
