ISSN 2587-814X (print),
ISSN 2587-8158 (online)

Russian version: ISSN 1998-0663 (print),
ISSN 2587-8166 (online)

Valery Makarov  1, Albert Bakhtizin  1, Gayane Beklaryan 1
  • 1 Central Economics and Mathematics Institute, Russian Academy of Sciences, 47, Nakhimovsky Prospect, Moscow 117418, Russia

Developing digital twins for production enterprises

2019. No. 4 Vol.13. P. 7–16 [issue contents]

      This article presents a new approach to developing digital twins of production companies with the use of simulation methods. It describes the concept of digital twins as an integrated system that aggregates simulation models, databases and intelligent software modules of the class of genetic optimization algorithms, subsystems of data mining, etc. The article presents examples of simulation models of different production companies, in particular, a typical assembly plant and a typical oil production enterprise. The first company carries out activities to assembly products from individual components with its own individual characteristics. To describe the behavior of such an enterprise, methods of agent and discrete-event modeling are used. The second enterprise produces raw carbohydrate materials at existing fields with individual characteristics. The integrated simulation models thus developed are integrated with a subject-oriented database and optimization modules that facilitate providing a control of the technological and resource characteristics of the respective production enterprises. The development of these models was performed using AnyLogic and Powersim simulation systems that support agent-based modeling and system dynamics methods. We demonstrate here the possibility of creating ‘digital twins’ for production companies using modern simulation tools.

Graphical abstract

Citation: Makarov V.L., Bakhtizin A.R., Beklaryan G.L. (2019) Developing digital twins for production enterprises. Business Informatics , vol. 13, no 4, pp. 7–16. DOI: 10.17323/1998-0663.2019.4.7.16
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