The DOI matches its Crossref record; 21 of the 21 references it cites were found in a registry or an index.
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.
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.
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?
The cited source doesn’t support the claim as written.
In the citing paper
“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]
Checked by a reasoning model — may be wrong.
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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]
Experimental — accuracy not yet measured
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
An Introduction to Efficiency and Productivity Analysis
Coelli, T. J., Rao, D. S. P., O’Donnell, C. J., & Battese, G. E. (2005) · Springer eBooks · doi:10.1007/b136381 · Cited by 6,926
World Wide Web Consortium (W3C)
(2017) · CRC Press eBooks · doi:10.1081/e-elis4-120044744 · Cited by 539
Supply chain excellence : a handbook for dramatic improvement using the SCOR model
Bolstorff, P., & Rosenbaum, R. (2003) · Thammasat University Digital Collections · Cited by 333
Amoako‐Gyampah, K., & Acquaah, M. (2007) · International Journal of Production Economics · doi:10.1016/j.ijpe.2007.02.030 · Cited by 305
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
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
A Portrait of an ISO STEP Tolerancing Standard as an Enabler of Smart Manufacturing Systems
Feeney, A. B., Frechette, S., & Srinivasan, V. (2014) · Journal of Computing and Information Science in Engineering · doi:10.1115/1.4029050 · Cited by 113
Self-citationEncyclopedia Britannica online
(2003) · Choice Reviews Online · doi:10.5860/choice.40sup-0010 · Cited by 97
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
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
Research works printed but not linked: 5
- 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.
- 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.
- 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
- NIST Workshop on Open Cloud Architectures for Smart Manufacturing, http://www.nist.gov/el/msid/opencloudarch.cfm
- A. V. Euler-Chelpin, Information modelling for the manufacturing system life cycle, Doctoral Thesis, KTH -Production Engineering, Stockholm, Sweden, 2008, ISSN 1650-1888
Cited by
Sorted by citations
Items 301–360 of 360
Bacelar, G. R., da Silva, R. N. A., & Ataíde, D. L. (2019) · doi:10.5935/978-85-7042-043-5.cap.11
Sprock, T., Brundage, M. P., Bernstein, W. Z., Sexton, R., & Sharp, M. E. (2020) · Smart and Sustainable Manufacturing Systems · doi:10.1520/ssms20200051
Huang, C., & Wranér, H. (2018) · KTH Publication Database DiVA (KTH Royal Institute of Technology)
Williams, G. T., O’Brien, J. F., Mezghani, A., Eachus, M. J., Meisel, N. A., & McComb, C. C. (2019) · Texas Digital Library (University of Texas) · doi:10.26153/tsw/17348
Analyzing a Knowledge Graph of Industry4.0 StandardsPreprint
Grangel-González, I., & Vidal, M.-E. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2107.01910
An Ontological Model to Integrate and Assist Virtualization of Automation Systems for Industry 4.0
Gil, S., Zapata, G., & Giraldo-Gómez, G. L. (2021) · Smart and Sustainable Manufacturing Systems · doi:10.1520/ssms20210010
The Evolution of Efficiency Indicators in the Economies of European Countries
Stoenoiu, C. E. (2021) · International Conference on Electromechanical and Energy Systems (SIELMEN) · doi:10.1109/sielmen53755.2021.9600418
(2021) · Dunod eBooks · doi:10.3917/dunod.julie.2021.01.0249
Kabanov, A. (2022) · Spacecrafts & Technologies · doi:10.26732/j.st.2022.3.05
PIS: IoT & Industry 4.0 Challenges
Mouël, F. L., & Carrillo, O. (2022) · Springer eBooks · doi:10.1007/978-3-031-18176-4_6
Mazak‐Huemer, A., Wimmer, M., Huemer, C., Wally, B., Frühwirth, T., & Kästner, W. (2020) · Springer eBooks · doi:10.1007/978-3-662-45537-1_94-2
A Benchmarking of Reference Models for Digital Manufacturing Platforms
Fraile, F., Anaya, V., Sanchís, R., Ortíz, Á., & Poler, R. (2023) · Proceedings of the I-ESA Conference · doi:10.1007/978-3-030-90387-9_9
The Application of ArchiMate for Planning the Implementation of Manufacturing Management Systems
Gibbons, D. W., & Af, V. D. M. (2023) · Lecture notes in production engineering · doi:10.1007/978-3-031-15602-1_21
Nogradi, C., & Goff, L. (2023) · Annual Reliability and Maintainability Symposium (RAMS) · doi:10.1109/rams51473.2023.10088256
Szejka, A. L., Canciglieri, O., & Mas, F. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4517534
Recent Developments on Smart Manufacturing
Patra, S. P., Agrawal, R., & Wankhede, V. A. (2023) · Environmental footprints and eco-design of products and processes · doi:10.1007/978-981-99-4894-9_2
Entry Barriers for Industry 4.0 in Argentina
Gutnisky, J., Montilla, D., & Salimbeni, S. (2022) · Cadernos de Educação Tecnologia e Sociedade · doi:10.14571/brajets.v15.nse2.95-109
ÜRETİM ENDÜSTRİSİNİ DÖNÜŞTÜREN TEKNOLOJİ TRENDLERİNE GENEL BİR BAKIŞ
Gür, Y. E., & GÜNDÜZ, K. (2023) · Fırat Üniversitesi Sosyal Bilimler Dergisi · doi:10.18069/firatsbed.1297867
Toward Smart Manufacturing and Supply Chain Logistics
Marin, E. G. C. (2023) · doi:10.1002/9781119987635.ch10
Emerging Trends in Smart Manufacturing: A Bibliometric Analysis
Kaur, R., Goyal, D., Gupta, D., & Dang, R. K. (2023) · International Conference on IoT, Communication and Automation Technology (ICICAT) · doi:10.1109/icicat57735.2023.10263597
BENEFÍCIOS E DESAFIOS DA ADOÇÃO DAS TECNOLOGIAS DA I4.0 NAS INDÚSTRIAS MOVELEIRAS
LIMA, A. A. M. D., Feil, A. A., Froehlich, C., & Schreiber, D. (2023) · Anais ... Encontro Nacional de Engenharia de Produção/Anais do Encontro Nacional de Engenharia de Produção · doi:10.14488/enegep2023_tn_st_407_2001_46582
Advanced manufacturing of biopharmaceuticals
Rashid, M. M., Parulekar, S. J., & Çınar, A. (2024) · Elsevier eBooks · doi:10.1016/b978-0-323-99135-3.00001-4
(2024) · Elsevier eBooks · doi:10.1016/b978-0-323-99029-5.00025-x
Advanced System Diagnostics Tools: Innovations and Applications
Suhail, A. H., Guangul, F. M., & Nazeer, A. (2024) · Industrial engineering and management. · doi:10.5772/intechopen.114378 · Cited by 2
Mazak‐Huemer, A., Wimmer, M., Huemer, C., Wally, B., Frühwirth, T., & Kästner, W. (2024) · Springer eBooks · doi:10.1007/978-3-662-58528-3_94
The New Industry 5.0 Paradigm: An Investigation in the Italian Automotive Sector
Murmura, F., Bravi, L., & Pierli, G. (2024) · Lecture notes in information systems and organisation · doi:10.1007/978-3-031-66801-2_13 · Cited by 1
Digitalization, Communications, and Mobilities
Shaban, A. (2024) · Springer eBooks · doi:10.1007/978-981-97-9278-8_7
Modern Approaches of Adaptive Planning and Management in Discrete Production
Antonov, A. (2025) · Lecture notes in networks and systems · doi:10.1007/978-3-031-80710-7_15
Panyaram, S. (2022) · International Journal of Innovative Research in Engineering & Multidisciplinary Physical Sciences · doi:10.37082/ijirmps.v10.i3.232554
Cognitive Architecture of Aware System of Systems
Bernus, P., Cardier, B., Noran, O. S., & Smith, G. A. (2025) · Springer eBooks · doi:10.1007/978-3-031-82790-7_11
Gigante, F., Navarro, J. D. A., & Ariño, M. J. N. (2025) · Proceedings of the I-ESA Conference · doi:10.1007/978-3-031-93995-2_11
Leveraging Blockchain, IoT, AI, and Robotics in Lean Six Sigma Processes
Arora, M. M. L., & Saini, J. R. (2025) · IGI Global eBooks · doi:10.4018/979-8-3373-0943-9.ch004
Wu, Y., Kao, C., Hsiang, S., & Hsu, A. (2025) · International Microsystems, Packaging, Assembly and Circuits Technology Conference · doi:10.1109/impact67645.2025.11281667
Edge Computing and Smart Data Processing in Ultrasound-Based Manufacturing
Deshapaga, M. (2025) · IGI Global eBooks · doi:10.4018/979-8-3373-2205-6.ch007
A Maturity Level Evaluation Method on R&D and Design Software Product
Liu, X., Li, P., Rong, W., Lin, J., & Wu, Z. (2025) · International Conference on Management Science and Computer Engineering (MSCE) · doi:10.1145/3760023.3760111
Design of a Dual-Protocol Embedded EtherCAT Master Station for Real-Time Motion Control
Wu, C., Tang, H., Guo, R., Ma, J., Bhatti, U. A., & Tong, C. W. (2025) · Asia-Pacific Conference on Intelligent Robot Systems (ACIRS) · doi:10.1109/acirs66343.2025.11360865
A Comprehensive System Architecture Model for Telematics Systems of Multimodal Transport Hubs
Mamedova, N. A., Afanasev, M. A., Urintsov, A. I., & Haritonov, S. (2026) · WSEAS Transactions on Information Science and Applications archive · doi:10.37394/23209.2026.23.10
Ştirbu, C., Stirbu, E.-L., Ionescu, N., Ionescu, L. M., Lazar, M., Bogatu, A.-M., Ronțescu, C., & Bondoc, M.-D. (2026) · Sustainability · doi:10.3390/su18062988
Product configuration system complexity: Exploring sustainable customization modelling
Jakobsen, A. M. S. Ø., & Tambo, T. (2026) · AIP conference proceedings · doi:10.1063/5.0308579
Esparza, V., Cabezas, D., García, L., Quezada, D., & Rohten, J. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-07333-4_15
Rouidi, I., Serrou, D., & Lagrat, S. (2025) · International Conference on Advanced Technologies and Interdisciplinary Innovation (ICAT2I) · doi:10.1109/icat2i69744.2025.11472640
Cyber-Physical Systems (CPS) and the Industrial Control System (ICS) Ecosystem
Iqbal, M., Goel, P. K., Rimmy, Sharma, A. K., Kumar, A., & Tyagi, H. (2026) · IGI Global eBooks · doi:10.4018/979-8-2600-1498-1.ch006
Conformalized multi-step forecasting for high-rate dynamic systems
Chua, Y. K., Li, T., Navidi, S., Razmarashooli, A., Mundiwala, M., Martinez, D. A. S., Okumu, M., Hu, C., Laflamme, S., & Schrader, P. T. (2026) · Structural and Multidisciplinary Optimization · doi:10.1007/s00158-026-04325-7 · Cited by 1
STEP-NC-Based Framework for Additive Manufacturing Systems
Nhassa, G., & Snoussi, A. (2026) · IGI Global eBooks · doi:10.4018/979-8-3373-6214-4.ch003
Digital thread complexity and key requirements for circularity
Nordholm, A. K., & Sakao, T. (2026) · Procedia CIRP · doi:10.1016/j.procir.2026.05.113
A proposal of a modular and customizable digital architecture for additive manufacturing
Kelly, K., Saldaña, C., & Saleeby, K. S. (2026) · Manufacturing Letters · doi:10.1016/j.mfglet.2026.06.008
Ojubanire, O. A., Sebti, H., Berbain, S., Marhraoui, M. A., & Oke, O. I. (2026) · Innovation & Management Review · doi:10.1108/inmr-01-2024-0010
Evolution to Digital Twin-Driven Manufacturing System
Liu, S., Zheng, P., & Bao, J. (2026) · Springer eBooks · doi:10.1007/978-981-95-8236-5_2
The Smart Manufacturing: Revolution Technology, Workforce, and Sustainability
Maladzhi, R. W., & Bello, K. A. (2025) · IEEE Connecting the Unconnected Europe, Middle East, and Africa Regional Summit (CTUS-EMEA) · doi:10.1109/ctus-emea68989.2025.11592206
A Next Generation Framework for IoT-Enabled Industrial Automation and Predictive Operations
Pote, Y., Nishane, O. P., Totewar, P., Sarode, S., Ambhore, S., & Madireddy, L. (2026) · IEEE International Conference on Intelligent Technologies for a Sustainable and Inclusive Future (ICITSIF) · doi:10.1109/icitsif69060.2026.11608862
A Review of Digital Twin Architectures in IIoT-Driven Manufacturing
Muka, E., Marinova, G. I., & Mankolli, E. (2026) · Communications in computer and information science · doi:10.1007/978-3-032-24513-7_26
Subhash, P., Gattu, T. R., Thakur, S. S., Karadgi, S., & Hiremath, P. S. (2026) · Production Engineering · doi:10.1007/s11740-026-01462-2
Pekarčíková, M., Trebuňa, P., Kliment, M., & Kronová, J. (2026) · Electronics · doi:10.3390/electronics15173872
Mining Industry 5.0: A 6S Framework for Sustainable and Human-Centric Mining Systems
Yadav, U., Agarwal, S., Obracaj, D., Sinha, A., Ranjit, K., András, J., & Masoudi, P. (2026) · Mining · doi:10.3390/mining6030075
Manufacturing in a Circular Economy: Research Needs in Design, Systems Modeling, and Digital Thread
Last, N., Schumacher, K. A., Mathur, N., Ferrero, V. J., Triebe, M. J., Hapuwatte, B. M., Alqseer, N., Reslan, M., & Morris, K. C. (2026) · Smart and Sustainable Manufacturing Systems · doi:10.1520/ssms20250023
Self-citationIndustrial Automation Using Internet of Things: A Vision for Smart Industry
Nishane, O. P., Nishane, D., Sagane, I., & Dagadkar, S. (2026) · International Conference Computational Vision and Bio Inspired Computing · doi:10.1109/iccvbic71195.2026.11688841
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.
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
