Digital twins as effective tools of internal company planning of industrial enterprises in the data economy
https://doi.org/10.17073/2072-1633-2026-1-1454
Abstract
Acceleration of digital transformation of industrial enterprises and the transition to the data economy cause the increasing demand for effective tools of internal company planning. The authors of the article analyze the role of digital twins in managing complex production processes, forecasting changes and optimizing resource usage. The topic is relevant due to the growing competition, changes of the social and economic environment, where the industrial enterprises operate, and the emergence of the new type of competitive advantages for companies, which is based on the ability to work with data and build digital models of planning and rapid response to changes. The purpose of the article is to study the role of digital twins in maintaining the internal company planning at the industrial enterprises in an emerging data economy. Special attention is paid to their ability to provide continuous feedback making it possible to promptly adjust plans and minimize risks. The methodology is based on the system analysis of the scientific publications, cases and data devoted to this area of studies, as well as the general scientific methods: comparison, synthesis and logical generalization. The authors demonstrate the advantages of the “Digital twins” technology, such as reducing time costs, improving the quality of decision-making and developing a culture of working with data. It is pointed out that the implementation of the “Digital twins” technology helps to reduce costs, increase productivity and optimize resources, providing enterprises with competitive advantages. Special attention is paid to the role of the data that is the main and the most important resource of the digital economy as measuring, collecting, processing, and using large amounts of economic data are the crucial elements for the successful implementation of digital twins. The authors show that digital twins are gaining the status of a core universal tool of internal company planning and management in a turbulent economic environment and emerging data economy, where flexibility, the speed of adaptation and the accuracy of decisions are becoming strategically important.
About the Authors
A. D. StolyarovRussian Federation
Alexander D. Stolyarov – PhD Candidate
31 Kashirskoe Shosse, Moscow 115409
S. G. Vagin
Russian Federation
Sergey G. Vagin – Dr.Sci. (Econ.), Professor
20 Myasnitskaya Str., Moscow 101000
V. I. Abramov
Russian Federation
Viktor I. Abramov – Dr.Sci. (Econ.), Professor, Department of Business Process Management
31 Kashirskoe Shosse, Moscow 115409
References
1. Abramov V.I., Andreev V.D. First year of implementation of digital transformation programs in the regions of Russia: problems and results. Public Administration Issues. 2024;(2):110–128. (In Russ.). https://doi.org/10.17323/1999-5431-2024-0-2-110-128
2. Garcia O.B., Gomez-Conde J. de las Heras E. Debt pressure and interactive use of control systems: Effects on cost of debt. Management Accounting Research. 2018;40(3):27–46. https://doi.org/10.1016/j.mar.2017.10.001
3. World Economic Forum. Global Lighthouse Network: Four Durable Shifts for a Great Reset in Manufacturing. White Paper. Sept. 2020. Available at: https://www3.weforum.org/docs/WEF_GLN_2020_Four_Durable_Shifts_In_Manufacturing.pdf (accessed on 18.07.2024).
4. Gregolinska E., Khanam R., Lefort F., Parthasarathy P. Capturing the true value of Industry 4.0. Available at: https://www.mckinsey.com/capabilities/operations/our-insights/capturing-the-true-value-of-industry-four-point-zero#/ (дата обращения: 23.02.2026).
5. Queiroz M.M., Ivanov D., Dolgui A., Wamba S.F. Impacts of epidemic outbreaks on supply chains: mapping a research agenda amid the COVID-19 pandemic through a structured literature review. Annals of Operations Research. 2020;319(3):1159–1196. https://doi.org/10.1007/s10479-020-03685-7
6. Abramov V.I., Gordeev V.V., Stolyarov A.D. Digital twins: characteristics, typology and development practices. Russian Journal of Innovation Economics. 2024;14(3):691–716. (In Russ.). https://doi.org/10.18334/vinec.14.3.121484
7. Gordeev V.V., Stolyarov A.D., Abramov V.I. The role of Digital Twins in production management and the basic principles of their creation. Ekonomika i upravlenie: teoriya i praktika. 2024;10(1):29–39. (In Russ.)
8. Hasan M. Decoding Digital Twins: Exploring the 6 main applications and their benefits. IoT Analytics. March 7, 2023. Available at: https://iot-analytics.com/6-main-digital-twin-applications-and-their-benefits/ (accessed on 18.07.2024).
9. Digital Twin Market Size, Share $ Growth. 2025–2030. Available at: https://www.marketsandmarkets.com/Market-Reports/digital-twin-market-225269522.html (accessed on 18.07.2024).
10. Groombridge D. Gartner: Top Strategic Technology Trends 2023. Available at: https://emt.gartnerweb.com/ngw/globalassets/en/publications/documents/2023-gartner-top-strategic-technology-trends-ebook.pdf (accessed on 18.07.2024).
11. Abramov V.I., Lomakin V.A., Stolyarov A.D. The digital ecosystem of the region as a promising model of territorial economic development. Informatsionnoe obshchestvo = Information Society Journal. 2024;(6):16–27.
12. Hedberg T., Lubell J., Fischer L., Maggiano L., Feeney A.B. Testing the digital thread in support of model-based manufacturing and inspection. Journal of Computing and Information Science in Engineering. 2016;16(2):1–10. https://doi.org/10.1115/1.4032697
13. Schumann R. Preface: big data and analytics. In: Linnhoff-Popien C., Schneider R., Zaddach M. (eds). Digital marketplaces unleashed. Berlin. Heidelberg: Springer; 2018. Р. 633–636. https://doi.org/10.1007/978-3-662-49275-8_56
14. Bonvino C., Giorgino M. A valorization framework to strategically manage data for creating competitive value. International Journal of Production Economics. 2024;269:109152. https://doi.org/10.1016/j.ijpe.2024.109152
15. Attaran S., Attaran M., Celik B.G. Digital Twins and industrial Internet of Things: Uncovering operational intelligence in industry 4.0. Decision Analytics Journal. 2024;10:100398. https://doi.org/10.1016/j.dajour.2024.100398
16. Abramov V.I., Gordeev V.V., Stolyarov A.D. Digital transformation of industrial enterprises into digital business ecosystems: structural components and practical aspects of implementation. Fundamental Research. 2024;(9):78–85. (In Russ.). https://doi.org/10.17513/fr.43680
17. Gürdür Broo D., Schooling J. Towards data-centric decision making for € smart infrastructure: Data and its challenges. IFAC-PapersOnLine. 2020;53(3):90–94. https://doi.org/10.1016/j.ifacol.2020.11.014
18. Развитие экономических систем: теория, методология, практика. Под ред. Б.Н. Герасимова. Пенза: ПГАУ; 2024. 275 c.
19. Zharasov B.S., Abramov V.I. Digital twins in production management: creation principles, implementation problems and development prospects. Modern Economics: Problems and Solutions. 2024;(6):80–94. (In Russ.). https://doi.org/10.17308/meps/2078-9017/2024/6/80-94
20. Актуальные проблемы бухгалтерского учета, аудита и анализа в современных условиях. Под ред. Н.Н. Бондиной. Пенза: ПГАУ; 2025. 297 c.
21. Bolton R.N., Mccoll-Kennedy R.J., Cheung L., Gallan A.S., Orsingher Ch., Witell L., Zaki M. Customer experience challenges: Bringing together digital, physical and social realms. Journal of Service Management. 2018;29(1):776–808. https://doi.org/10.1108/JOSM-04-2018-0113
22. Negri N., Berardi S., Fumagalli S. MES-integrated digital twin frameworks. Journal of Manufacturing Systems. 2020;56(6):58–71. https://doi.org/10.1016/j.jmsy.2020.05.007
23. Juarez M., Botti V., Giret A. Digital Twins: Review and challenges. Journal of Computing and Information Science in Engineering. 2021;21(3):030802. https://doi.org/10.1115/1.4050244
24. Abramov V.I., Andreev V.D. Comparative analysis of Digital Twin of regions. Informatsionnoe obshchestvo = Information Society Journal. 2023;(4):106–117. (In Russ.)
25. Moshood T.D., Rotimi J.O.B., Shahzad W., Bamgbade J.A. Infrastructure digital twin technology: A new paradigm for future construction industry. Technology in Society. 2024;77:102519. https://doi.org/10.1016/j.techsoc.2024.102519
26. Abramov V.I., Gordeev V.V., Stoliarov A.D. Digital Twins using agrodrones in control crop production: features of creation and prospects. APK: ekonomika, upravlenie. 2024;(4):37–49. (In Russ.). https://doi.org/10.33305/244-37
27. Gvozdyanyy S.E., Myaskov A.V. Russian and foreign experience of using digital twins in the energy sector. Russian Journal of Industrial Economics. 2024;17(4):378–387. (In Russ.). https://doi.org/10.17073/2072-1633-2024-4-1368
28. Wright L., Davidson S. How to tell the difference between a model and a digital twin. Advanced Modeling and Simulation in Engineering Sciences. 2020;7(1):13. https://doi.org/10.1186/s40323-020-00147-4
29. Nielsen S. Reflections on the applicability of business analytics for management accounting – and future perspectives for the accountant. Journal of Accounting and Organizational Change. 2018;14(2):167–187. https://doi.org/10.1108/JAOC-11-2014-0056
30. Stolyarov A.D., Gordeev V.V., Abramov V.I. Methodology for searching multi-criteria solutions based on digital twins. Economics and Management. 2023;29(7):851–858. (In Russ.). https://doi.org/10.35854/1998-1627-2023-7-851-858
31. Feng X., Wan J. Digital Twins for discrete manufacturing lines: A review. Big Data and Cognitive Computing (BDCC). 2024;8(5):45. https://doi.org/10.3390/bdcc8050045
32. Banaś W., Gołda G., Gwiazda A., Jarzyńska M., Kampa A., Kalinowski K., Olender-Skóra M., Stawowiak M. The use of simulation techniques and management tools to optimize the logistics process. LogForum. 2024;20(3):401–414. https://doi.org/10.17270/J.LOG.001080
33. Heluany J. B., Gkioulos V. A review on digital twins for power generation and distribution. International Journal of Information Security. 2023;23:1171–1195. https://doi.org/10.1007/s10207-023-00784-x
34. Argolini R., Bonalumi F., Deichmann J., Pellegrinelli S. Digital twins: The key to smart product development. McKinsey & Company. Industrials & Electronics. Our Insights. July 31, 2023 Available at: https://www.mckinsey.com/industries/industrials-and-electronics/our-insights/digital-twins-the-key-to-smart-product-development (accessed on 18.07.2024).
35. Kalaitzi D., Batista L., Lopez N.L. Digital Twins of supply chains: A systems approach. Preprint. TechRxiv. https://doi.org/10.36227/techrxiv.24073527.v1
36. Piromalis D., Kantaros A. Digital Twins in the automotive industry: The road toward physical-digital convergence. Applied System Innovation. 2022;5(4):65. https://doi.org/10.3390/asi5040065
37. Bhatti G., Mohan H., Singh R.R. Towards the future of smart electric vehicles: DT technology. Renewable and Sustainable Energy Reviews. 2021;141(3):110801. https://doi.org/10.1016/j.rser.2021.110801
38. Цифровые двойники: преимущества, недостатки и перспективы. Режим доступа: https://www.itweek.ru/digitalization/article/detail.php?ID=230670 (дата обращения: 18.02.2024).
39. Lee S. 7 Data-driven insights on Digital Twin in manufacturing. Number Analytics. March 27, 2025. Available at: https://www.numberanalytics.com/blog/digital-twin-manufacturing-insights
40. Schleich B., Anwer N., Mathieu L., Wartzack S. Shaping the digital twin for design and production engineering. CIRP Annals. 2017;66(1):141–144. https://doi.org/10.1016/j.cirp.2017.04.040
41. Tao F., Zhang M., Nee A.Y.C. Digital Twin driven manufacturing. Academic Press; Feb. 12, 2019. 282 p. Available at: https://shop.elsevier.com/books/digital-twin-driven-smart-manufacturing/tao/978-0-12-817630-6 (accessed on 18.07.2024).
42. Siemens. Outperform your competition with a comprehensive Digital Twin. Available at: https://www.siemens.com/global/en/products/automation/topic-areas/digital-enterprise/digital-twin.html (accessed on 18.07.2024).
Review
For citations:
Stolyarov A.D., Vagin S.G., Abramov V.I. Digital twins as effective tools of internal company planning of industrial enterprises in the data economy. Russian Journal of Industrial Economics. 2026;19(1):96-109. (In Russ.) https://doi.org/10.17073/2072-1633-2026-1-1454
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