Strategizing Business Processes of Industrial Enterprise Operations in the Cybernetic Era

The relevance of this study arises from the limited effectiveness of digital transformation projects in large enterprises, which are often implemented without alignment with strategic development objectives. In the context of the sixth long wave of technological conjuncture as defined by N.D. Kondratiev, large enterprises have already accumulated vast volumes of big data but lack platforms offering tools for transforming the results of data processing into managerial decisions aimed at improving labor productivity—interpreted in this study as a measurable expression of the strategic interests of the enterprise as an object of strategizing. The aim of the study is to develop an intelligent and adaptive model for strategic management of process development, based on the informed selection of priority areas of transformation within the enterprise’s digital platform. The proposed platform architecture is designed to support managerial decision-making in the strategizing of digital process development under conditions of high technological volatility and organizational fragmentation.
The research methodology consists of several stages. At the initial stage, a classification of business processes was developed, reflecting their functional roles within the designed digital control contour. This was followed by an expert assessment of digital maturity based on a structured survey with criteria including automation, integration with corporate information systems, and the formalization of operational regulations. In parallel, retrospective performance data were analyzed to quantitatively assess the contribution of each process group to labor productivity growth.
The results of both assessments were used to position processes within a matrix of strategic significance, representing the relationship between digital maturity and productivity impact. This made it possible to identify priority transformation areas, high-performing processes, lowpotential segments, and optimization zones where targeted digital solutions and organizational adjustments are recommended.
The proposed architecture of the digital strategic management platform builds upon the results of business process segmentation and implements key functions such as goal setting, scenario analysis, performance monitoring, and coordination of transformation participants. The classification of business processes provides a structural foundation for aligning strategic objectives with the current operational tasks of the enterprise. The findings support the initial hypothesis regarding the feasibility of interpreting labor productivity as a utility function and suggest that the proposed platform architecture offers a viable basis for transitioning from fragmented approaches to a more structured and manageable model of digital transformation. Future research will focus on piloting the proposed model across various industrial sectors. 

1. Akaev A. A. Epochal Discoveries of Nikolai Kondratiev and Their Place in Modern Economic Science // AlterEconomics. 2022. N 19 (1). P. 11–39. DOI 10.31063/AlterEconomics/2022.19-1.2. EDN BTFZKO. (In Russ.).

2. Glazyev S. Yu., Ivanter V. V., Makarov V. L., et al. On the Strategy of Russia’s Economic Development // Economic Science of Modern Russia [Ekonomicheskaya nauka sovremennoi Rossii]. 2011. N (3). P. 7–31. (In Russ.).

3. Dalio R. Big Debt Crises: A Guide to Navigating and Managing Crises. Moscow: Bombora, 2021. (In Russ.).

4. Dalio R. The Changing World Order: Why Nations Succeed or Fail. Moscow: Bombora, 2022. (In Russ.).

5. Efanov V. A. Strategizing the Digital Transformation of Industrial Enterprise Activity Processes. Ph.D. Thesis in Economics. Moscow State University, 2023. (In Russ.).

6. Efanov V. A., Chaadaev V. K., Shlyakhov A. S. Strategizing the Digital Transformation of an Industrial Enterprise (Case of RTRS) // Economics of Industry [Ekonomika promyshlennosti]. 2023. N 16 (1). P. 95–104. DOI 10.17073/2072-1633-2023-1-95-104. EDN ADVHOJ. (In Russ.).

7. Zhuravlev D. M., Chaadaev V. K., Mikheev E. B. Factors of Labor Productivity Growth in the Industrial Sector under Structural Economic Transformation // Economics of Industry [Ekonomika promyshlennosti]. 2025. N 18 (1). P. 49–62. DOI 10.17073/2072-1633-2025-1-1425. EDN QVAPOE. (In Russ.).

8. Zhuravlev D. M. Strategizing the Digital Transformation of Complex Socio-Economic Systems. Ed. by V. L. Kvint. Saint Petersburg: NWIM of RANEPA, 2024. DOI 10.55959/978-5-89781-862-4 (In Russ.).

9. Kantorovich L. V. Mathematical Methods of Organizing and Planning Production. Leningrad: Leningrad State University Publishing House, 1939. (In Russ.).

10. Kvint V. L. The Concept of Strategizing. Kemerovo: Kemerovo State University, 2020. DOI 10.21603/978-5-8353-2562-7. (In Russ.).

11. Kvint V. L., Bodrunov S. D. Strategizing Societal Transformation: Knowledge, Technology, Noonomics. Saint Petersburg: INIR, 2021. (In Russ.).

12. Kuzmin P. S. Industrial Digitalization: Empirical Assessment of Digital Maturity // Strategic Decisions and Risk Management [Strategicheskie resheniya i risk-menedzhment]. 2021. N 12 (3). P. 220–235. DOI 10.17747/2618-947X-2021-3-220-235. EDN GETOJZ. (In Russ.).

13. Sadovnichiy V. A., Akaev A. A., Ilyin I. V., Korotayev A. V., Malkov S. Yu. Modeling and Forecasting Global Dynamics in the 21st Century // Moscow University Bulletin. Ser. 27: Global Studies and Geopolitics [Vestnik Moskovskogo universiteta. Ser. 27. Globalistika i geopolitika]. 2022. N 1. P. 5–35. DOI 10.56429/2414-4894-2022-39-1-5-35. EDN JMEARJ. (In Russ.).

14. Sadovnichiy V. A., Akaev A. A., Ilyin I. V., Malkov S. Yu., Grinin L. E., Korotayev A. V. Trends in the Development of the World-System from a Macrohistorical Perspective: A Brief Analysis // History and Modernity [Istoriya i sovremennost’]. 2022. N 2 (44). P. 124–138. DOI 10.30884/ iis/2022.02.06. EDN IHDNFC. (In Russ.).

15. Taleb N. N. Fooled by Randomness: The Hidden Role of Chance in Life and the Markets. Moscow: KoLibri, 2017. (In Russ.).

16. Kondratiev N. D. Major Economic Cycles and the Theory of Forecasting. Compiled by Yakovets Yu. V. Moscow: Ekonomika, 2002. (In Russ.).

17. Abbasi M., Nishat R. I., Bond C., Graham-Knight J. B., Lasserre P., Lucet Y., & Najjaran H. A review of AI and machine learning contribution in predictive business process management: Process enhancement and process improvement approaches (arXiv preprint 2024. N 2407.11043).

18. Cavana R. Y., Maani K. E. A Methodological Framework for Integrating Systems Thinking and System Dynamics // System Dynamics Review. 2000. N 16 (1). P. 39–68.

19. Efanov V. A. Evaluation of the Readiness of the Enterprise for Digital Transformation // Webology. 2022. N 19 (1). P. 7554–7562.

20. Lewis E. The Paradigm Shift Cycle as the Cause of the Kondratieff Wave. In: Kondratieff Waves: Processes, Cycles, Triggers, and Technological Paradigms. 2022. DOI 10.30884/978-5-70576191-3_03.

21. Mintzberg H. The Rise and Fall of Strategic Planning. Cambridge, Harvard Business Review, 1994. P. 105–114.

22. Müller C., Schulz T., Becker J. Key Performance Indicators for Business Models: A Systematic Literature Review // Information Systems and e-Business Management. 2023. N 21 (2). P. 377–399. DOI 10.1007/s10257-023-00650-2.

23. Rabhi B., Djemame K., Maamar Z., Thiran P. Editorial: Business transformation through AIenabled technologies // Frontiers in Artificial Intelligence. 2025. N 8. Art. 11932997. DOI 10.3389/ frai.2023.11932997.

24. Recker J., Rosemann M., Indulska M., Green P. Business Process ModelingA Comparative Analysis // Journal of the Association for Information Systems. 2009. N 10 (4). DOI 10.17705/1jais.00193.