Analysis of Visual Modeling Tools Development for Complex Production Systems

Andrey Vlasov () and Alexander Naumenko
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Andrey Vlasov: Bauman Moscow State Technical University
Alexander Naumenko: Bauman Moscow State Technical University

A chapter in Digital Transformation in Industry, 2022, pp 335-346 from Springer

Abstract: Abstract The paper reviews the software tools for visual system analysis of complex production systems in the context of digital industry transformation. The main trends in the visual modeling development of complex systems and the problems of their implementation are outlined. The groups of tools for visual system analysis of complex systems are heterogeneous. There are diagrammatic, iconic, and parametric visual models. The analyzed tools differ in the depth of coverage of the studied processes, their detailing, and the integration of blocks of different levels of expertise into one model. Based on the results of a comparative analysis of individual classes of visual modeling tools, recommendations for their use are given. The market analysis of visual instruments is based on the Interest Index. According to the data, in Russia, there is a growing interest in solutions that focus on the modeling of active systems and opportunities to integrate the modeling of business processes with the modeling of information system components. One of the major trends in the development and implementation of modern visual business process modeling solutions is the use of simulation modeling as an integral component. Based on this, it is concluded that the visual model of the business process in digital transformation should be considered a digital twin. The analysis of the dynamics of the Interest Index allows stating the prevalence of ARIS-based tool solutions in the market. UML-based solutions in demand increased their share, but not significantly, indicating the conservatism and a certain constancy of the potential group of its users.

Keywords: Visual modeling tools; Production systems; Business process; Industry 4.0 (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:spr:lnichp:978-3-030-94617-3_24

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DOI: 10.1007/978-3-030-94617-3_24

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