ABSTRACT. In this article, I cumulate previous research findings indicating that the digital twin of manufacturing system can monitor and enhance the product development process. I contribute to the literature on digital twin modeling in virtual enterprises and autonomous manufacturing systems by showing that machine learning algorithms can assist in smart process manufacturing by harnessing visualization capabilities and data mining tools. Throughout March 2022, I performed a quantitative literature review of the Web of Science, Scopus, and ProQuest databases, with search terms including “digital twin modeling” + “virtual enterprises,” “autonomous manufacturing systems,” “deep learning and neural network algorithms,” “immersive visualization tools,” and “cognitive data fusion techniques.” As I inspected research published in 2022, only 148 articles satisfied the eligibility criteria. By eliminating controversial findings, outcomes unsubstantiated by replication, too imprecise material, or having similar titles, I decided upon 21, generally empirical, sources. Data visualization tools: Dimensions (bibliometric mapping) and VOSviewer (layout algorithms). Reporting quality assessment tool: PRISMA. Methodological quality assessment tools include: AXIS, Dedoose, ROBIS, and SRDR.
JEL codes: D53; E22; E32; E44; G01; G41

Keywords: digital twin; virtual enterprise; autonomous manufacturing system

How to cite: Robinson, R. (2022). “Digital Twin Modeling in Virtual Enterprises and Autonomous Manufacturing Systems: Deep Learning and Neural Network Algorithms, Immersive Visualization Tools, and Cognitive Data Fusion Techniques,” Economics, Management, and Financial Markets 17(2): 52–66. doi: 10.22381/emfm17220223.

Received 26 March 2022 • Received in revised form 21 June 2022
Accepted 25 June 2022 • Available online 30 June 2022

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