Standards landscape and directions for smart manufacturing systems
Open accessAbstract
The future of manufacturing lies in being able to optimize the use of resources to produce high quality product and adapt quickly to changing conditions. From smaller lot sizes, to more customization, to sudden changes in supply chain; the variability that manufacturers face is rapidly increasing. A key to enabling adaptive and smart manufacturing systems is the appropriate definition and use of information. Standards are fundamental 1) to facilitate the delivery of the right information at the right time, 2) to enable actions based on that information and 3) to reduce risk of technology adoption and development. This paper provides a review of the standards - a standards landscape - in which future smart manufacturing systems will operate. The landscape focuses on standards used to integrate within and across three manufacturing lifecycle dimensions: product, production system, and business. Opportunities and challenges for new standards are discussed. Emerging activities addressing these opportunities are presented. This paper will allow manufacturing practitioners to better understand the standards useful to integrate smart manufacturing technologies within their areas of expertise.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Fourth generation SCADA systems emerged from smart manufacturing target capabilities such as agility, quality, productivity and sustainability and Smart Manufacturing Ecosystem derived from ISO/IEC 62264, implementing digital transformation under Smart factory and I4.0.
“4) The fourth generation SCADA systems emerged from the smart manufacturing target capabilities such as agility, quality, productivity and sustainability [59] and Smart Manufacturing Ecosystem (SME) [56] derived from the functional and equipment hierarchy and the physical asset equipment model from the ISO/IEC 62264 standard, as introduced by the National Institute of Standards and Technology (N”
From SCADA Systems With Focus on Continuous Manufacturing and Steel Industry: A Survey on Architectures, Standards, Challenges and Industry 5.0 (2022) · cites this paper as [59]
Cites
Smart manufacturing, manufacturing intelligence and demand-dynamic performance
Davis, J., Edgar, T. F., Porter, J. B., Bernaden, J., & Sarli, M. (2012) · Computers & Chemical Engineering · doi:10.1016/j.compchemeng.2012.06.037 · Cited by 762 · see citing works
Supply chain excellence : a handbook for dramatic improvement using the SCOR model
Bolstorff, P., & Rosenbaum, R. (2003) · Thammasat University Digital Collections · Cited by 333 · see citing works
Agile Manufacturing: The 21st Century Competitive Strategy
Gunasekaran, A. (2001) · Elsevier eBooks · doi:10.1016/b978-0-08-043567-1.x5000-1 · Cited by 138 · see citing works
Global Information Architecture for Industrial Automation
Vogel‐Heuser, B., Kegel, G., & Wucherer, K. (2013) · atp magazin · doi:10.17560/atp.v51i01-02.1948 · Cited by 123 · see citing works
Emerging Global Trends in Advanced Manufacturing
Shipp, S., Gupta, N., Lal, B., Scott, J. A., Weber, C. L., Finnin, M. S., Blake, M., Newsome, S., & Thomas, S. (2012) · Cited by 80 · see citing works
AutomationML: From data exchange to system planning and simulation
Faltinski, S., Niggemann, O., Moriz, N., & Mankowski, A. (2012) · IEEE International Conference on Industrial Technology (ICIT) · doi:10.1109/icit.2012.6209967 · Cited by 38 · see citing works
Towards the Synthesis of Product Knowledge Across the Lifecycle
Witherell, P., Kulvatunyou, B., & Rachuri, S. (2013) · doi:10.1115/imece2013-65220 · Cited by 7 · see citing works
Sustainable manufacturing program workshop report
Rachuri, S., Morris, K. C., Roy, U., Dornfeld, D. A., & Kumara, S. R. T. (2013) · doi:10.6028/nist.ir.7975 · Cited by 5 · see citing works
Self-citationSustainable Manufacturing Program Workshop Report
Rachuri, S., Morris, K. C., Roy, U. K., Dornfeld, D. A., & Kumara, S. R. T. (2013) · doi:10.1002/https://dx.doi.org/10.6028/nist.ir.7975 · Cited by 3 · see citing works
Self-citation
Flagged first, then by citations
Cited by
Sorted by citations
Industrial IoT, Cyber Threats, and Standards Landscape: Evaluation and Roadmap
Dhirani, L. L., Armstrong, E., & Newe, T. (2021) · Sensors · doi:10.3390/s21113901 · Cited by 172 · see citing works
“The papers [75,76] discuss how implementing security standards and controls increase trust, visibility, and integrity of the industrial environment.”
BackgroundIndustry 4.0 reference architectures: State of the art and future trends
Nakagawa, E. Y., Antonino, P. O., Schnicke, F., Capilla, R., Kuhn, T., & Liggesmeyer, P. (2021) · Computers & Industrial Engineering · doi:10.1016/j.cie.2021.107241 · Cited by 167 · see citing works
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 · see citing works
Sverko, M., Grbac, T. G., & Mikuc, M. (2022) · IEEE Access · doi:10.1109/access.2022.3211288 · Cited by 71 · see citing works
“…fourth generation SCADA systems emerged from the smart manufacturing target capabilities such as agility, quality, productivity and sustainability [59] and Smart Manufacturing Ecosystem (SME) [56] derived from the functional and equipment hierarchy and the physical asset equipment model from the…”
BackgroundA Role-Based Access Control Model in Modbus SCADA Systems. A Centralized Model Approach
Figueroa-Lorenzo, S., Añorga, J., & Arrizabalaga, S. (2019) · Sensors · doi:10.3390/s19204455 · Cited by 58 · see citing works
- Authors
- Lu, Y., Morris, K. C., & Frechette, S.
- Affiliations
- National Institute of Standards and Technology (US)
- ORCID
- Venue
- 2015 IEEE International Conference on Automation Science and Engineering (CASE)
- Publisher
- IEEE
- Published
- 2015-08
- Pages
- 998-1005
- ISSN
- 2161-8070 (ISSN-L)
- OpenAlex
- W1609904810
- Cited by
- 66
- References
- 9
- Citations by year
- 2025: 4 · 2024: 5 · 2023: 7 · 2022: 9 · 2021: 10 · 2020: 7 · 2019: 9 · 2018: 4 · 2017: 5 · 2016: 6
- FWCI
- 11.04
- Type
- conference paper
- Language
- en
- Open access
- green
- zenodo.org
- Licence
- public-domain
- Locations
- IEEE International Conference on Automation Science and Engineering (CASE) (publishedVersion)
- Zenodo (CERN European Organization for Nuclear Research) (submittedVersion, public-domain)
- Funders
- National Institute of Standards and Technology
- Topics
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Digital Transformation in Industry
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Flexible and Reconfigurable Manufacturing Systems
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Manufacturing Process and Optimization
- Keywords
- smart manufacturing, production standards, standards development, product life cycle, production systems, business integration, technology adoption, supply chain variability, customization
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