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Smart manufacturing, manufacturing intelligence and demand-dynamic performance
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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
A Review of the Roles of Digital Twin in CPS-based Production Systems
Negri, E., Fumagalli, L., & Macchi, M. (2017) · Procedia Manufacturing · doi:10.1016/j.promfg.2017.07.198 · Cited by 1,669
A survey on digital twin: definitions, characteristics, applications, and design implications
Barricelli, B. R., Casiraghi, E., & Fogli, D. (2019) · IEEE Access · doi:10.1109/access.2019.2953499 · Cited by 1,480
Kamble, S. S., Gunasekaran, A., & Gawankar, S. A. (2018) · Process Safety and Environmental Protection · doi:10.1016/j.psep.2018.05.009 · Cited by 1,440
Fortune favors the prepared: How SMEs approach business model innovations in Industry 4.0
Müller, J. M., Buliga, O., & Voigt, K.-I. (2018) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2017.12.019 · Cited by 1,262
Lim, K. Y. H., Zheng, P., & Chen, C.-H. (2020) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-019-01512-w · Cited by 700
Digital twin for CNC machine tool: modeling and using strategy
Luo, W., Hu, T., Zhang, C., & Wei, Y. (2019) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-018-0946-5 · Cited by 350
Managerial challenges of Industry 4.0: an empirically backed research agenda for a nascent field
Schneider, P. (2018) · Review of Managerial Science · doi:10.1007/s11846-018-0283-2 · Cited by 345
Exploring the role of digital twin for asset lifecycle management
Macchi, M., Roda, I., Negri, E., & Fumagalli, L. (2018) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2018.08.415 · Cited by 216
Known unknowns: Indirect energy effects of information and communication technology
Horner, N. C., Shehabi, A., & Azevedo, I. L. (2016) · Environmental Research Letters · doi:10.1088/1748-9326/11/10/103001 · Cited by 177
Smart manufacturing standardization: Architectures, reference models and standards framework
Li, Q., Tang, Q., Chan, I., Wei, H., Pu, Y., Jiang, H., Li, J., & Zhou, J. (2018) · Computers in Industry · doi:10.1016/j.compind.2018.06.005 · Cited by 139
Digital Twin-Based Cyber-Attack Detection Framework for Cyber-Physical Manufacturing Systems
Balta, E. C., Pease, M., Moyne, J., Barton, K., & Tilbury, D. M. (2024) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2023.3243147 · Cited by 125
Standards landscape and directions for smart manufacturing systems
Lu, Y., Morris, K. C., & Frechette, S. (2015) · 2015 IEEE International Conference on Automation Science and Engineering (CASE) · doi:10.1109/coase.2015.7294229 · Cited by 66
Data analytics for quality management in Industry 4.0 from a MSME perspective
Sariyer, G., Mangla, S. K., Kazancoglu, Y., Ocal Tasar, C., & Luthra, S. (2025) · Annals of Operations Research · doi:10.1007/s10479-021-04215-9 · Cited by 50
Wu, Z., Meng, Z., & Gray, J. (2017) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2017.2704613 · Cited by 40
Cimini, C., Pinto, R., & Cavalieri, S. (2017) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2017.08.2548 · Cited by 40
Digital Twins in the IIoT: Current Practices and Future Directions Toward Industry 5.0
Mon, B. F., Hayajneh, M., Ali, N. A., Ullah, F., Ullah, H., & Alkobaisi, S. (2025) · Computers, Materials & Continua · doi:10.32604/cmc.2025.061411 · Cited by 26
“Smart manufacturing, as defined by Davis et al. [89], is the widespread, intensive use of “manufacturing intelligence” in all aspects of industrial manufacturing and supply chain.”
Metadata
- Authors
- Davis, J., Edgar, T., Porter, J., Bernaden, J., & Sarli, M.
- Venue
- Computers & Chemical Engineering
- Publisher
- Elsevier BV
- Published
- 2012-12
- Volume
- 47
- Pages
- 145-156
- ISSN
- 0098-1354 (ISSN-L)
- OpenAlex
- W2032614950
- Cited by
- 762
- References
- 0
- FWCI
- 37.78
- 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.
