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Cited by 360Digital Transformation in IndustryFlexible and Reconfigurable Manufacturing SystemsManufacturing Process and Optimization

Current Standards Landscape for Smart Manufacturing Systems

Open access
Lu, Yan, Morris, KC, Frechette, Simon·2016
DOI 10.6028/nist.ir.8107
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Abstract

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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Crossref · 10.6028/nist.ir.8107

Cites

21 of 21 references verified0 citation errors · 0 not checkable

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  • As a result of smart factory concept implementation, Lean, Flexible, Sustainable, Digital, Cloud, Intelligent, Holonic, Additive and Agile manufacturing models have been defined.

    “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]

    The Introduction says smart manufacturing combines features of earlier manufacturing paradigms; Table 1 presents those paradigms as preceding smart manufacturing. This reverses the claimed sequence: the paper does not say the listed models were defined as a result of implementing the smart factory concept. (LLM, conf=0.88)

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  • Architectural alignments bring to the surface fundamental differences in some aspects that prevent higher degrees of convergence between the reference architectures, partly due to the application of different basic and industry-specific standards.

    “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]

    Section 3.6 (page not provided) says standards developed independently for different industry sectors can be inconsistent and calls for greater harmonization. That supports the general point that differing standards can impede convergence, but the cited paper does not connect those differences to alignments or convergence between reference architectures. (LLM, conf=0.78)

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Cites

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Research works printed but not linked: 5

  1. J. Davis , T. Edgar, J. Porter, J. Bernaden, M. Sarli, Smart Manufacturing, manufacturing intelligence and demand-dynamics performance. Computers & Chemical Engineering, 2012, 47,145-156.
  2. SMLC, Implementing 21 st Century Smart Manufacturing: Workshop Summary Report, June 2011, http://www.energetics.com /resourcecenter/products/roadmaps/Documents/SMLC_Smart_Manufacturing.pdf, Access date March 8, 2015.
  3. P. Witherell, B. Kulvatunyou, and S. Rachu, Toward the Synthesis of Product Knowledge across the Lifecycle, Proceedings of the ASME 2013 International Mechanical Engineering Congress & Exposition IMECE2013, November 13-21, 2013, San Diego, California, USA
  4. NIST Workshop on Open Cloud Architectures for Smart Manufacturing, http://www.nist.gov/el/msid/opencloudarch.cfm
  5. A. V. Euler-Chelpin, Information modelling for the manufacturing system life cycle, Doctoral Thesis, KTH -Production Engineering, Stockholm, Sweden, 2008, ISSN 1650-1888

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Related

Authors
Lu, Y., Morris, K., & Frechette, S.
ORCID
Lu, Yan
Publisher
National Institute of Standards and Technology
Published
2016-02
DOI
10.6028/nist.ir.8107
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
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

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