From Monitoring to Supervised Control: A Capability- Maturity Perspective on Digital Twins in Smart Manufacturing

Main Article Content

Yilin Lu
Shanghai University of Engineering Science, Shanghai, China

DOI: https://doi.org/10.70267/aitia.2026326335

Keywords

digital twin, smart manufacturing, ISO 23247, capability maturity, physics-informed modelling, digital thread

Abstract

Manufacturing digital twins are increasingly promoted as a route from shop-floor visibility to data-driven operational improvement. In practice, however, many implementations remain descriptive dashboards and do not demonstrate measurable gains in quality, throughput, maintenance, or energy performance. Existing reviews also tend to classify studies by algorithm family rather than by the manufacturing capability delivered. This paper develops a capability-maturity lens for AI-enabled manufacturing digital twins by aligning the NIST progression from descriptive to intelligent capabilities with the ISO 23247 reference architecture and the digital thread. Based on PRISMA-guided screening of 34 studies published between 2018 and 2026, the review shows that current literature is concentrated in descriptive monitoring and predictive analytics, while prescriptive decision support and supervised intelligent control remain comparatively limited. More importantly, many studies do not report standard-compliant integration pathways or KPI-validated shop-floor outcomes. The paper therefore synthesises scenario-level evidence, identifies transition barriers between maturity bands, and proposes an engineering agenda for trustworthy progression toward supervised, auditable control.

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Published
Sep 13, 2026
Conference Proceedings Volume
Vol. 16 (2026): Proceedings of the 2026 International Conference on AI and Technological Innovation Applications (AITIA 2026)
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Articles
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How to Cite

Lu, Yilin. “From Monitoring to Supervised Control: A Capability- Maturity Perspective on Digital Twins in Smart Manufacturing”. Exploring Science Academic Conference Series, vol. 16, Sept. 2026, pp. 326-35, https://doi.org/10.70267/aitia.2026326335.