Enterprise performance management based on digital twin technology in the fifth-generation industry
Abstract
In the context of the increasing need to improve the management efficiency of enterprises that support the implementation of the principles of digital transformation based on the concept of the fifth-generation industry, the relevance of research on the development of appropriate systems in terms of ensuring continuous targeted and sustainable development, customer-centricity and social orientation of production is increasing. Digital twin technology and its multi-agent implementation act as effective means of building enterprise performance management systems. At the same time, the lack of scientific research in this area determines the purpose of the article, which is to develop a product-resource approach to enterprise performance management based on digital twins in the fifth-generation industry. A distinctive feature of the proposed approach developed by the authors is the use of dynamic enterprise performance management technology based on digital twins, which ensures the integration of business processes and resources used at the level of not only one enterprise, but also at the level of network value chains based on a common digital platform of the business ecosystem. The paper analyzes approaches to the intellectualization of enterprise management, on the basis of which the requirements for an enterprise performance management system are formulated, ensuring the solution of interrelated tasks of targeted enterprise development, the formation of flexible value chains, and the rational and sustainable use of enterprise resources. The possibilities and disadvantages of the efficiency management process in EPC class systems are analyzed. The paper substantiates the use of digital twin technology and its multi-agent implementation to build an enterprise performance management system in the context of mass customization and the network nature of value chains in the fifth-generation industry. A process for managing the efficiency of enterprises at all stages of the life cycle based on the technology of digital twins of products and resources has been developed, dynamically ensuring the targeting, adaptability and sustainability of the functioning and development of the enterprise.
Downloads
References
Scheer A.-V. (2020). Industry 4.0: from a breakthrough business model to business process automation. Moscow: Delo Publishing House (in Russian).
Federal Ministry for Economic Affairs and Energy of the Federal Republic of Germany. (2018). Industrie 4.0. Reference Architectural Model Industrie 4.0 (RAMI4.0) – An Introduction. https://www.plattform-i40.de/PI40/Redaktion/EN/Downloads/Publikation/rami40-an-introduction.html
Technet Association. (2025). The multilevel structure of the factory of the future (in Russian). https://technet-nti.ru/article/fabriki-buducshego
Business ecosystem management / Ed. K. H. Abdurakhmanov. (2024). Moscow: Plekhanov Russian University of Economics (in Russian).
Telnov, Y. F., Bryzgalov, A. А., Kozyrev, P. A., & Koroleva, D. S. (2022). Choosing the type of business model for implementing the digital transformation strategy of a network enterprise. Business Informatics, 16(4), 50–67. https://doi.org/10.17323/2587-814x.2022.4.50.67
Telnov, Yu. F., Denisov, А. А., Kazakov, V. A., & Danilov, A. V. (2022). Requirements for the software implementation of the Industry 4.0 system for creating network enterprises. International Journal “Programmnye Produkty i Sistemy,” 25, 557–571 (in Russian). https://doi.org/10.15827/0236-235x.140.557-571
Makarov, V. L., Bakhtizin, A. R., Beklaryan, G. L., & Akopov, A. S. (2021). Digital plant: methods of discrete-event modeling and optimization of production characteristics. Business Informatics. 15(2), 7–20. https://doi.org/10.17323/2587-814X.2021.2.7.20
Keshvarinia, M., MacKenzie, C. A., & Zhao, Z. (2025). A simulation-based digital twin model for data-driven decision optimization. Decision Analytics Journal, 17, 100646. https://doi.org/10.1016/j.dajour.2025.100646
Makarov, V. L., Bakhtizin, A. R., Beklaryan, G. L., & Akopov, A. S. (2019). Development of software framework for large-scale agent-based modeling of complex social systems. Software Engineering, 10(4), 167–177 (in Russian). https://doi.org/10.17587/prin.10.167-177
Beklaryan, G. L. (2024). Agent-based modeling and optimization of the characteristics for research-and-production clusters. Business Informatics, 18(1), 36–51. https://doi.org/10.17323/2587-814x.2024.1.36.51
Rozanova, N. M. (2023). Industry 5.0: Golden Age or leap into the dark? Bulletin of the Institute of Economics of the Russian Academy of Sciences, 6, 61–77 (in Russian). https://doi.org/10.52180/2073-6487_2023_6_61_77
Soldatos, J., Ipektsidis, B., Kefalakis, N., & Despotopoulou, A.-M. (2024). Reference architecture for AI-based Industry 5.0 applications. Artificial Intelligence in Manufacturing, 3–26. https://doi.org/10.1007/978-3-031-46452-2_1
Nahavandi, S. (2019). Industry 5.0—A Human-Centric Solution. Sustainability, 11(16), 4371. https://doi.org/10.3390/su11164371
Kusiak, A. (2017). Smart manufacturing must embrace big data. Nature, 544(7648), 23–25. https://doi.org/10.1038/544023a
Moghaddam, M., Cadavid, M. N., Kenley, C. R., & Deshmukh, A. V. (2018). Reference architectures for smart manufacturing: A critical review. Journal of Manufacturing Systems, 49, 215–225. https://doi.org/10.1016/j.jmsy.2018.10.006
Lu, Y., Xu, X., & Wang, L. (2020). Smart manufacturing process and system automation – A critical review of the standards and envisioned scenarios. Journal of Manufacturing Systems, 56, 312–325. https://doi.org/10.1016/j.jmsy.2020.06.010
Lee, S., & Ryu, K. (2022). Development of the Architecture and Reconfiguration Methods for the Smart, Self-Reconfigurable Manufacturing System. Applied Sciences, 12(10), 5172. https://doi.org/10.3390/app12105172
Telnov, Y. F., Kazakov, V. A., & Danilov, A. V. (2024). Designing a multi-agent system for a network enterprise. Business Informatics, 18(3), 70–86. https://doi.org/10.17323/2587-814x.2024.3.70.86
Grigoryan E. S. (2015). Classification of types of enterprise sustainability. Concept (Scientific and Methodological Journal), 3, 86–90 (in Russian). http://e-koncept.ru/2015/15072.htm
Kaplan, R.S., & Norton, D.P. (1992). The balanced scorecard—measures that drive performance. Harvard Business Review. https://hbr.org/1992/01/the-balanced-scorecard-measures-that-drive-performance-2
Cardoso, E. C. S. (2013). Towards a methodology for goal-oriented enterprise management. 2013 17th IEEE International Enterprise Distributed Object Computing Conference Workshops, 94–103. https://doi.org/10.1109/edocw.2013.17
Prahalad, C. K., & Hamel, G. (1990). The core competence of the corporation. Harvard Business Review. https://hbr.org/1990/05/the-core-competence-of-the-corporation
Hon, K. K. B. (2005). Performance and evaluation of manufacturing systems. CIRP Annals, 54(2), 139–154. https://doi.org/10.1016/s0007-8506(07)60023-7
Druzhaev, A. А., Isaev, D. V., & Ogurechnikov, E. V. (2019). Principles of managing development of EPM systems. Business Informatics, 13(2), 73–83. https://doi.org/10.17323/1998-0663.2019.2.73.83
Dukhonin, E. Y., Isaev, D. V., Mostovoy, E. L., Boyko, A. G., Goryanskiy, P. S., Dukhonina, O. V., Nakhamkina, E. S., Rodionov, A. S., Slepov, Y. V., & Khomaza, D. V. (2005). Business performance management: The BPM concept. (G. V. Gens, Ed.). Alpina Business Books (in Russian). https://pqm-online.com/assets/files/lib/books/dukhonin.pdf
Balaban, N., Belić, K., & Gudelj, M. (2011). Business process performance management: Theoretical and methodological approach and implementation. Management Information Systems, 6, 3–9.
Prokopenko, N. Y., & Artyukh, G. S. (2025). Application of Process Mining technology for data and process analysis (using the Loginom Community platform): A training manual. Nizhny Novgorod State University of Architecture and Civil Engineering (in Russian).
Ryndina, S. V. (2025). Business process modeling using the SILA Union platform: A training and methodological manual. Penza State University (in Russian).
Vashukov, Y. A., Dmitriev, A. Y., & Mitroshkina, T. A. (2012). QFD: Product and technological process development based on customer requirements and expectations: Methodological guidelines. Samara State Aerospace University (in Russian).
Vashukov, Y. A., Dmitriev, A. Y., & Mitroshkina, T. A. (2008). Analysis of types, consequences, and causes of potential non‑conformities (FMEA): Methodological guidelines. Samara State Aerospace University (in Russian).
Telnov, Yu. F., Trembach, V. M., Danilov, A. V., Yaroshenko, E. V., Kazakov, V. A., & Kozlova, O. A. (2019). Constructing network enterprise structure to create innovative products. Open Education, 23(6), 59–73 (in Russian). https://doi.org/10.21686/1818-4243-2019-6-59-73
Telnov, Y. F., Kazakov, V. A., & Trembach, V. М. (2020). Developing a knowledge-based system for the design of innovative product creation processes for network enterprises. Business Informatics, 14(3), 35–53. https://doi.org/10.17323/2587-814x.2020.3.35.53
Copyright (c) 2026 National Research University Higher School of Economics (HSE University)
This work is licensed under a Creative Commons Attribution 4.0 International License.
