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Current Standards Landscape for Smart Manufacturing Systems
Open accessAbstract
Today's manufacturers face ever-increasing demands of variability-greater customization, smaller lot sizes, sudden supply-chain changes and disruptions.Successful manufacturers will have to choose and incorporate technologies that help them quickly adapt to rapid change and to elevate product quality while optimizing use of energy and resources.These technologies form the core of an emerging, information-centric, Smart Manufacturing System that maximizes the flow and re-use of data throughout the enterprise.The ability of disparate systems, however, to exchange, understand, and exploit product, production, and business data rests critically on information standards.This report provides a review of the body of pertinent standards-a standards landscape-upon which future smart manufacturing systems will rely.This landscape comprises integration standards within and across three manufacturing lifecycle dimensions: product, production system, and business.We discuss opportunities and challenges for new standards, and present emerging activities addressing these opportunities.This report will allow manufacturing practitioners to better understand those standards useful to integration of smart manufacturing technologies.
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“As a result of the smart factory concept implementation, Lean, Flexible, Sustainable, Digital, Cloud, Intelligent, Holonic, Additive and Agile manufacturing models have been defined [3], [4], based on utilization of specific groups of I4.0 enabling technologies and their impact on the entire industrial value chain.”
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 [4]
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“As constructive as these architectural alignments are, they also bring to the surface the fundamental differences in some aspects that prevent higher degrees of convergence between the reference architectures.Apart from the starting viewpoints and approaches, these differences are partly the result of the application of different basic and industry-specific standards [4], [108]”
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 [4]
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Standard connections for IIoT empowered smart manufacturing
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Self-citationFeature-based product modelling: an ontological approach
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Smart Manufacturing for High-Performance Materials: Advances, Challenges, and Future Directions
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Metadata
- Authors
- Lu, Y., Morris, K., & Frechette, S.
- ORCID
- Lu, Yan
- Published
- 2016-02
- OpenAlex
- W2623083822
- Cited by
- 360
- References
- 21
- Citations by year
- 2026: 21 · 2025: 20 · 2024: 34 · 2023: 41 · 2022: 35 · 2021: 46 · 2020: 48 · 2019: 40 · 2018: 42 · 2017: 26 · 2016: 7
- Type
- report
- Language
- en
- Open access
- gold
- doi.org
- Licence
- public-domain
- 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 systems, information standards, manufacturing integration, product life cycle, production systems, data reuse, manufacturing variability, supply chain disruption, 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.
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