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ABSTRACT. Despite the relevance of digital twin-based product development and manufacturing processes in virtual space, only limited research has been conducted on this topic. In this article, we cumulate previous research findings indicating that digital twin-based product development and manufacturing processes in virtual space require performance optimization and maintenance scheduling. We contribute to the literature on digital twin-based smart manufacturing technologies and tools by showing that sensor-based data acquisition and analysis are pivotal in diagnosis and simulation of digital twin-based product development. Throughout February 2022, we performed a quantitative literature review of the Web of Science, Scopus, and ProQuest databases, with search terms including “digital twin” + “product development,” “manufacturing processes,” “data visualization tools and techniques,” “cloud computing technologies,” and “cyber-physical production systems.” As we inspected research published in 2022, only 154 articles satisfied the eligibility criteria. By eliminating controversial findings, outcomes unsubstantiated by replication, too imprecise material, or having similar titles, we decided upon 23, 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, Distiller SR, and MMAT.
JEL codes: D53; E22; E32; E44; G01; G41

Keywords: digital twin; cloud computing; cyber-physical production system

How to cite: Michalkova, L., Machova, V., and Carter, D. (2022). “Digital Twin-based Product Development and Manufacturing Processes in Virtual Space: Data Visualization Tools and Techniques, Cloud Computing Technologies, and Cyber-Physical Production Systems,” Economics, Management, and Financial Markets 17(2): 37–51. doi: 10.22381/emfm17220222.

Received 25 February 2022 • Received in revised form 22 June 2022
Accepted 27 June 2022 • Available online 30 June 2022

1Faculty of Operation and Economics of Transport and Communications, Department of Economics, University of Zilina, Zilina, Slovak Republic, This email address is being protected from spambots. You need JavaScript enabled to view it..
2The School of Expertness and Valuation, The Institute of Technology and Business in Ceske Budejovice, Czech Republic, This email address is being protected from spambots. You need JavaScript enabled to view it..
3The Center for Data-driven Automated Decision-Making at ISBDA, Detroit, MI, USA, This email address is being protected from spambots. You need JavaScript enabled to view it. (corresponding author).

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