AI-Enabled Digital Twins for Sustainable Manufacturing: A Systematic Review of Technological Functions, Sustainability Outcomes, and Managerial Implications

Jacobus Rico Kuntag, Simon Siamsa, Mega Suteki, Alfarizi Alfarizi

Abstract


Sustainable manufacturing increasingly depends on data-driven technologies to improve productivity, cost efficiency, quality, and sustainability performance. This study reviews literature on AI-enabled digital twins in sustainable manufacturing, focusing on research trends, technological functions, sustainability outcomes, managerial implications, barriers, and future directions. Using a PRISMA-based systematic literature review of Web of Science and Scopus records, the study synthesizes 88 publications: 66 full-text studies for critical synthesis and 22 abstract-based records for descriptive mapping. Analysis combined descriptive, thematic, and integrative synthesis with framework-based coding. The findings indicate that real-time monitoring, simulation, prediction/prognostics, and optimization are the most established functions, whereas control, decision support, and autonomous adjustment remain emerging. Reported outcomes concentrate on energy and resource efficiency, waste and emission reduction, quality, downtime, and operational efficiency. The study highlights governance as a core implementation condition and calls for stronger empirical validation, standardized metrics, and robust data-model governance.

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References


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