Forecasting of business processes as a decision-making tool within the proactive approach to management

Aim. The work aimed to justify and develop an integrated approach to the use of business process forecasting as a key tool for decision-making in the “from future” logic, with a special emphasis on the integration of digital twin technology and the use of nonlinear sensitivity analysis methods.

Objectives. The work seeks to identify the synergistic effect of the integration of forecasting and digital twin technology based on the presented mechanism for improving the quality and depth of forecasts; to justify the use of nonlinear sensitivity analysis to assess the sustainability of forecast models and the reliability of management decisions; to propose methodological recommendations for integrating forecasting, digital twins and nonlinear sensitivity analysis into a single management decision-making process; to assess the potential benefits, and to identify the key challenges and risks associated with the implementation of such an integrated approach into management practice.

Methods. The study employed methods of theoretical analysis, system analysis and data analysis to solve the problem set.

Results. The work formulates and discloses the concepts of proactive management and forecasting of business processes, and it substantiates an integrated approach to their application based primarily on the use of digital twins. The concept of decision-making from the future is disclosed as a strategic paradigm, while organizations do not simply predict the most probable future with it, but form actively the desired development scenario and create a strategy to achieve it. The necessity and importance of nonlinear sensitivity analysis within the approach described is substantiated.

Conclusions. The study enabled to substantiate and detail an integrated approach to forecasting business processes as a central tool for decision-making in the “from future” logic. It reveals that in the context of increasing dynamism and uncertainty of the external environment, traditional reactive management methods are becoming insufficient, giving way to a proactive paradigm aimed at shaping the desired future. The key element of such a transition is the synergistic combination of advanced analytical methods. 

1. Grieves M., Vickers J. Digital twin: Mitigating unpredictable, undesirable emergent behavior. In: Kahlen F.-J., Flumerfelt S., Alves A., eds. Transdisciplinary perspectives on complex systems. Cham: Springer; 2016:85-113. https://doi.org/10.1007/978-3-319-38756-7_4

2. Abramov V.I., Arefev D.V. Ecosystem development of enterprises: Opportunities, risks and features of assessing their digital maturity. Novoe v ekonomicheskoi kibernetike = New in Economic Cybernetics. 2025;(1):70-84. (In Russ.). https://doi.org/10.5281/zenodo.15165454

3. Abramov A.V., Stolyarov A.D., Abramov V.I. Innovative approaches to interaction with clients based on generative artificial intelligence. Beneficium. 2025;(2):77-85. (In Russ.). https://doi.org/10.34680/BENEFICIUM.2025.2(55).77-85

4. Senge P.M. The fifth discipline: The art & practice of the learning organization. New York, London: Doubleday Business; 1994. 448 p. (Russ. ed.: Senge P. Pyataya distsiplina. Iskusstvo i praktika obuchayushcheisya organizatsii. Moscow: Olymp-Business; 2003. 408 p.).

5. Abramov V.I., Gordeev V.V., Stolyarov A.D. Digital twins: Characteristics, typology and development practices. Voprosy innovatsionnoi ekonomiki = Russian Journal of Innovation Economics. 2024;14(3):691-716. (In Russ.). https://doi.org/10.18334/vinec.14.3.121484

6. Tao F., Cheng J., Qi Q., Zhang M., Zhang H., Sui F. Digital twin-driven product design, manufacturing and service with big data. The International Journal of Advanced Manufacturing Technology. 2018;94(4):3563-3576. https://doi.org/10.1007/s00170-017-0233-1

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 = Economy and Management: Theory and Practice. 2024;10(1):29-39. (In Russ.).

8. Stolyarov A.D., Gordeev V.V., Abramov V.I. Digital twins as tools for increasing company management efficiency. Modeli, sistemy, seti v ekonomike, tekhnike, prirode i obshchestve = Models, Systems, Networks in Economics, Engineering, Nature and Society. 2024;(4):5- 16. (In Russ.). https://doi.org/10.21685/2227-8486-2024-4-1

9. Hammer M. Reengineering work: Don’t automate, obliterate. Harvard Business Review. 1990;68(4):104-112. URL: https://www.vincenzocalabro.it/pdf/reengineering-work-dontautomate-obliterate.pdf (accessed on 09.03.2025).

10. Armstrong J.S., ed. Principles of forecasting: A handbook for researchers and practitioners. New York, NY: Springer; 2001. 850 p.

11. Fuller A., Fan Z., Day C., Barlow C. Digital twin: Enabling technologies, challenges and open research. IEEE Access. 2020;8:108952-108971. https://doi.org/10.1109/ACCESS.2020.2998358

12. Grieves M. Digital twin: manufacturing excellence through virtual factory replication. Digital Twin White Paper. 2014. 7 p. URL https://www.researchgate.net/publication/275211047_Digital_Twin_Manufacturing_Excellence_through_Virtual_Factory_Replication (accessed on 09.03.2025).

13. Zharasov B.S., Abramov V.I. Digital twins in production management: Creation principles, implementation problems and development prospects. Sovremennaya ekonomika: problemy i resheniya = Modern Economics: Problems and Solutions. 2024;(6):80-94. (In Russ.). https://doi.org/10.17308/meps/2078-9017/2024/6/80-94

14. 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 = Agro-Industrial Complex: Economics, Management. 2024;(4):7-49. (In Russ.). https://doi.org/10.33305/244-37

15. Stolyarov A.D., Faizullina A.M., Abramov V.I. Digital transformation of enterprise logistics using digital twins. Beneficium. 2024;(2):23-31. (In Russ.). https://doi.org/10.34680/BENEFICIUM.2024.2(51).23-31

16. Abramov V.I., Andreev V.D. Comparative analysis of digital twins of regions. Informatsionnoe obshchestvo = Information Society. 2023;(4):106-117. (In Russ.). https://doi.org/10.52605/16059921_2023_04_106

17. Stolyarov A.D., Gordeev V.V., Abramov V.I. Methodology for searching multi-criteria solutions based on digital twins. Ekonomika i upravlenie = Economics and Management. 2023;29(7):851-858. (In Russ.). https://doi.org/10.35854/1998-1627-2023-7-851-858

18. Saltelli A., Tarantola S., Campolongo F., Ratto M. Sensitivity analysis in practice: A guide to assessing scientific models. Chichester: John Wiley & Sons Ltd; 2004. 232 p.

19. Bennett N., Lemoine G.J. What VUCA really means for you. Harvard Business Review. 2014;92(1/2):16.

20. Abramov V.I., Golovin O.L., Stolyarov A.D. Methodology of searching Pareto-optimal solutions for the development of smart cities on the basis of their digital twins. Sovremennaya ekonomika: problemy i resheniya = Modern Economics: Problems and Solutions. 2021;(9):8-15. (In Russ.). https://doi.org/10.17308/meps.2021.9/2666

21. Marr B. Big Data in practice: How 45 successful companies used Big Data analytics to deliver extraordinary results. Chichester: John Wiley & Sons Ltd; 2016. 320 p.