BUSINESS PROCESSES OF ENTERPRISE MANAGEMENT AS AN OBJECT OF INTELLIGENT MODELING

Abstract

The rapid expansion of artificial intelligence
methods applicable to management data increasingly outpaces the development of a coherent
theoretical foundation defining how a management business process should be understood as an
object suitable for such modeling. This gap is of particular relevance for domestic enterprises,
where the depth of accumulated observation series and the integration of accounting, production,
and customer information systems remain highly uneven, raising the risk that a methodologically
unfounded choice of the modeling object undermines the validity of any subsequently applied
intelligent model.
The article addresses the question of how to bridge the economic theory of the enterprise with
the requirements of machine learning and deep learning by discussing several interrelated issues.
Among the questions examined are: which methodological tradition among the process-based,
systemic, cybernetic, informational, resource-based, and data-driven traditions offers the most
adequate account of a management business process for modeling purposes, and why none of these
traditions is self-sufficient on its own; what functional role the accounting subsystem plays within
the broader information base of managerial decision-making as it evolves from cost accounting
toward strategic management accounting; how a digital information circuit, composed of ERP,
CRM, SCM, MES, and BPMS systems, generates event-level data that differs fundamentally from
periodic aggregated reporting and therefore calls for a different class of predictive models; and
how the concepts of a management business process, business-process data, an artificial
intelligence model within a management system, and managerial model suitability can be defined in
operational terms that make them measurable rather than purely descriptive.
The discussion is intended to clarify the conceptual and methodological prerequisites that must
be resolved before an intelligent predictive model of a management business process can be
meaningfully specified, evaluated, or compared against alternative modeling approaches.
Keywords: management business process; intelligent modeling; accounting subsystem; digital
information circuit; cybernetic approach; event data; artificial intelligence in enterprise
management.

References
1. Porter, M. E. (1985). Competitive advantage: Creating and sustaining superior performance.
Free Press.
2. Davenport, T. H., & Short, J. E. (1990). The new industrial engineering: Information
technology and business process redesign. Sloan Management Review, 31(4), 11–27.
3. Hammer, M., & Champy, J. (1993). Reengineering the corporation: A manifesto for business
revolution. HarperBusiness.
4. International Organization for Standardization. (2015). Quality management systems –
Requirements (ISO 9001:2015).
5. van der Aalst, W. M. P. (2013). Business process management: A comprehensive survey.
ISRN Software Engineering, 2013, Article 507984. https://doi.org/10.1155/2013/507984
6. Recker, J., & Mendling, J. (2016). The state of the art of business process management
research as published in the BPM conference. Business & Information Systems Engineering, 58(1),
55–72. https://doi.org/10.1007/s12599-015-0411-3
7. Scheer, A.-W. (2000). ARIS – Business process modeling (3rd ed.). Springer.
8. Pererva, I. M. (2021). Perevahy vprovadzhennia protsesnoho pidkhodu do upravlinnia
pidpryiemstvom [Advantages of implementing the process approach to enterprise management].
Ekonomika ta suspilstvo, 29, 28–35. https://doi.org/10.32782/2524-0072/2021-29-3 (in Ukrainian).
9. Wiener, N. (1948). Cybernetics, or control and communication in the animal and the machine.
MIT Press.
10. Beer, S. (1972). Brain of the firm: The managerial cybernetics of organization. Allen Lane.
11. Barney, J. (1991). Firm resources and sustained competitive advantage. Journal of
Management, 17(1), 99–120. https://doi.org/10.1177/014920639101700108
12. Teece, D. J., Pisano, G., & Shuen, A. (1997). Dynamic capabilities and strategic
management. Strategic Management Journal, 18(7), 509–533.
13. Warner, K. S. R., & Wager, M. (2019). Building dynamic capabilities for digital
transformation: An ongoing process of strategic renewal. Long Range Planning, 52(3), 326–349.
https://doi.org/10.1016/j.lrp.2018.12.001
14. Brynjolfsson, E., & McElheran, K. (2016). The rapid adoption of data-driven decisionmaking. American Economic Review, 106(5), 133–139. https://doi.org/10.1257/aer.p20161016
15. Burns, J., & Scapens, R. W. (2000). Conceptualizing management accounting change: An
institutional framework. Management Accounting Research, 11(1), 3–25.
https://doi.org/10.1006/mare.1999.0119
16. Kaplan, R. S., & Norton, D. P. (2001). Transforming the balanced scorecard from
performance measurement to strategic management: Part I. Accounting Horizons, 15(1), 87–104.
https://doi.org/10.2308/acch.2001.15.1.87
17. Murphy, B., Feeney, O., Rosati, P., & Lynn, T. (2024). Exploring accounting and AI using
topic modelling. International Journal of Accounting Information Systems, 55, Article 100709.
https://doi.org/10.1016/j.accinf.2024.100709
18. Mancini, D., Lombardi, R., & Tavana, M. (2021). Four research pathways for understanding the role of smart technologies in accounting. Meditari Accountancy Research, 29(5), 1041–1062.
https://doi.org/10.1108/MEDAR-03-2021-1258
19. Abdullah, A. A. H., & Almaqtari, F. A. (2024). The impact of artificial intelligence and
Industry 4.0 on transforming accounting and auditing practices. Journal of Open Innovation:
Technology, Market, and Complexity, 10(1), Article 100218.
https://doi.org/10.1016/j.joitmc.2024.100218
20. Kononenko, L. V., Karnaushenko, A. S., & Savchenko, V. M. (2026). Oblikovo-analitychne
zabezpechennia upravlinnia biznesom v umovakh vykorystannia suchasnykh tsyfrovykh
tekhnolohii: suchasnyi stan, problemy, perspektyvy [Accounting and analytical support of business
management under modern digital technologies: current state, problems, prospects]. Ekonomika ta
suspilstvo, 86, 796–802. https://doi.org/10.32782/2524-0072/2026-86-97 (in Ukrainian).
21. Kononenko, L. V., & Nikolaieva, S. P. (2022). Transformatsiia systemy oblikovoanalitychnoho zabezpechennia yak skladovoi upravlinnia pidpryiemstvom v umovakh staloho
rozvytku ta didzhytalizatsii suspilstva [Transformation of the accounting and analytical support
system as a component of enterprise management under sustainable development and
digitalization]. Ekonomichnyi prostir, 177, 69–75. https://doi.org/10.32782/2224-6282/177-12 (in
Ukrainian).
22. Skliaruk, I. P., & Vovk, N. O. (2024). Upravlinskyi oblik biznes-protsesiv v umovakh
didzhytalizatsii [Management accounting of business processes under digitalization]. Ekonomika ta
suspilstvo, 59, 204–211. https://doi.org/10.32782/2524-0072/2024-59-69 (in Ukrainian).
23. Shmatkovska, T. O., Korobchuk, T. I., & Borysiuk, O. V. (2023). Suchasni informatsiinokomunikatsiini tekhnolohii v systemi oblikovo-analitychnoho zabezpechennia shchodo
modeliuvannia biznes-protsesiv [Modern ICT in the accounting and analytical support system for
business process modeling]. Ekonomika ta suspilstvo, 53, 129–134. https://doi.org/10.32782/2524-
0072/2023-53-68 (in Ukrainian).
24. Petty, R., & Guthrie, J. (2000). Intellectual capital literature review: Measurement, reporting
and management. Journal of Intellectual Capital, 1(2), 155–176.
https://doi.org/10.1108/14691930010348731
25. Sveiby, K.-E. (1997). The intangible assets monitor. Journal of Human Resource Costing &
Accounting, 2(1), 73–97. https://doi.org/10.1108/eb029036
26. Kozlovskyi, S. V., & Syniehub, P. S. (2022). Kohnityvna otsinka intelektualnoho kapitalu
ekonomichnykh system v ekonomitsi znan [Cognitive assessment of intellectual capital of
economic systems in the knowledge economy]. Ekonomika ta derzhava, 8, 48–55.
https://doi.org/10.32702/2306-6806.2022.8.48 (in Ukrainian).
27. Al-Surmi, A., Bashiri, M., & Koliousis, I. (2022). AI based decision making: Combining
strategies to improve operational performance. International Journal of Production Research,
60(14), 4464–4486. https://doi.org/10.1080/00207543.2021.1966540
28. Bellman, R. E., & Zadeh, L. A. (1970). Decision-making in a fuzzy environment.
Management Science, 17(4), B141–B164. https://doi.org/10.1287/mnsc.17.4.B141
29. Ohirko, I. V., Ohirko, O. I., & Kashchevskyi, V. (2024). Informatsiini tekhnolohii ta
kiberbezpeka v upravlinni pidpryiemstvom [Information technologies and cybersecurity in
enterprise management]. In Aktualni pytannia suchasnoi informatyky: Proceedings of the 8th AllUkrainian Scientific-Practical Conference (pp. 95–98). Zhytomyr Polytechnic State University (in
Ukrainian).
30. Hrabariev, A. V., Mozgovyi, S. A., Makedon, H. P., Holovko, N. R., & Kulida, V. I. (2023).
Modulnyi pidkhid do pobudovy informatsiinykh system yak sposib vidobrazhennia ekonomichnykh
biznes-protsesiv [Modular approach to information systems design as a way of representing
economic business processes]. Nauka i tekhnika sohodni, 9(23), 556–568.
https://doi.org/10.52058/2786-6025-2023-9(23)-556-568 (in Ukrainian).
31. Hrybovska, Yu. M., & Kononenko, Zh. A. (2023). Zastosuvannia informatsiinykh system v
upravlinni pidpryiemstvom [Application of information systems in enterprise management].
Ekonomika ta suspilstvo, 47, 288–292. https://doi.org/10.32782/2524-0072/2023-47-84 (in
Ukrainian).
32. van der Aalst, W. M. P. (2016). Process mining: Data science in action (2nd ed.). Springer.
https://doi.org/10.1007/978-3-662-49851-4
33. Ovod, L., & Sidletskyi, V. (2026). Lohistychni vytraty v upravlinskomu obliku ta analizi
torhovelnoho pidpryiemstva: klasyfikatsiini pidkhody v konteksti staloho rozvytku [Logistics costs
in management accounting and analysis of a trading enterprise: classification approaches in the
context of sustainable development]. Visnyk Khmelnytskoho natsionalnoho universytetu.
Ekonomichni nauky, 356(4), 232–238. https://doi.org/10.31891/2307-5740-2026-356-32 (in
Ukrainian).
34. Gama, J., Zliobaite, I., Bifet, A., Pechenizkiy, M., & Bouchachia, A. (2014). A survey on
concept drift adaptation. ACM Computing Surveys, 46(4), Article 44.
https://doi.org/10.1145/2523813