Digital twin-enabled smart industrial systems: a bibliometric review

Ciano, Maria Pia; Pozzi, Rossella; Rossi, Tommaso; Strozzi, Fernanda

Abstract

The aim of this study is to investigate the body of literature on digital twins, exploring, in particular, their role in enabling smart industrial systems. This review adopts a dynamic and quantitative bibliometric method including works citations, keywords co-occurrence networks and keywords burst detection with the aim of clarifying the main contributions to this research area and highlighting prevalent topics and trends over time. The analysis performed on citations traces the backbone of contributions to the topic, visible within the main path. Keywords co-occurrence networks depict the prevalent issues addressed, tools implemented and application areas. The burst detection completes the analysis identifying the trends and most recent research areas characterizing research on the digital twin topic.
Decision-making, process design and life cycle as well as the enabling role in the adoption of the latest industrial paradigms emerge as the prevalent issues addressed by the body of literature on digital twins. In particular, the up-to-date issues of real-time systems and industry 4.0 technologies, closely related to the concept of smart industrial systems, characterize the latest research trajectories identified in the literature on digital twins. In this context, the digital twin can find new opportunities for application in manufacturing, control and services.

Citation

Ciano, M. P., Pozzi, R., Rossi, T., & Strozzi, F. (2021). Digital twin-enabled smart industrial systems: a bibliometric review. International Journal of Computer Integrated Manufacturing, 34(7-8), 690-708. https://doi.org/10.1080/0951192X.2020.1852600

• Journal Article Type: Article
• Acceptance Date: Nov 5, 2020
• Online Publication Date: Dec 16, 2020
• Publication Date: Aug 3, 2021
• Deposit Date: Dec 3, 2021
• Publicly Available Date: Dec 17, 2021
• Journal: International Journal of Computer Integrated Manufacturing
• Print ISSN: 0951-192X
• Electronic ISSN: 1362-3052
• Publisher: Taylor and Francis
• Peer Reviewed: Peer Reviewed
• Volume: 34
• Issue: 7-8
• Pages: 690-708
• DOI: https://doi.org/10.1080/0951192X.2020.1852600
• Keywords: Digital twin, smart industrial systems, literature review, co-occurrence network, burst detection, main path
• Public URL: https://nottingham-repository.worktribe.com/output/6848671
• Publisher URL: https://www.tandfonline.com/doi/full/10.1080/0951192X.2020.1852600
• Additional Information: Peer Review Statement: The publishing and review policy for this title is described in its Aims & Scope.; Aim & Scope: http://www.tandfonline.com/action/journalInformation?show=aimsScope&journalCode=tcim20; Received: 2019-03-29; Accepted: 2020-11-05; Published: 2020-12-16