Economic and Mathematical Modeling of Risks in the Service Business Model of a Network Enterprise
https://doi.org/10.21686/2500-3925-2025-4-36-51
Abstract
The aim of the research is to develop an economic and mathematical model of risk assessment in the service business model of a network enterprise, capable of formalizing the impact of various risk factors on the stability of the network structure and developing effective strategies for managing them.
Materials and methods. The paper uses stochastic methods, optimization methods, graph theory, and system dynamics. The modeling algorithm includes the stages of risk identification, parameter formalization, cascade effects analysis, network structure assessment, and management strategy optimization. The example of the MindSphere IoT-platform and the ecosystem of its participants is used as an empirical base.
Results. A comprehensive approach to quantitative risk ssessment in digital ecosystems has been developed based on cascade analysis, assessment of the centrality of ecosystem nodes, and damage modeling. A comprehensive approach to quantitative risk assessment involves the integration of methods that allow not only to measure the probability and potential damage of individual threats, but also consider their interrelationships, development dynamics and impact on the structure of the service business model of a network enterprise. This approach provides not only the calculation of expected losses, but also the identification of critical points of the system, the development of preventive measures and visualization of the results for informed decision-making, which is especially important for a complex ecosystem where risks are increased due to the interdependence of its participants.
Conclusion. The developed model allows quantifying interrelated risks in service business models, taking into account the network interconnection of risks and structural vulnerabilities of ecosystems. This ensures informed decision-making when managing the stability of the network structure. The results are of practical importance for the industry, which is actively implementing IoT and cloud solutions
About the Author
A. A. BryzgalovRussian Federation
Alexey Alekseevich Bryzgalov, Assistant of the Department of Applied Informatics and Information Security
Moscow
References
1. . Fliaster A., Dellermann D. The risks of digital innovation: An ecosystem perspective [Internet]. Organizing for Digital Innovation. 2016: 1–22. Available from: https://pubs.wi-kassel.de/wp-content/up-loads/2017/05/JML_619.pdf.
2. Osterwalder A., Pigneur Y. Business Model Generation: A Handbook for Visionaries, Game Changers, and Challengers. Hoboken, NJ: John Wiley & Sons; 2010. 281 p.
3. Li Y., Ding H., Li T. Path research on the value chain reconfiguration of manufacturing enterprises under digital transformation – a case study of B company [Internet].Frontiers in Psychology. 2022; 13: 1–15. Available from: https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2022.887391/full.
4. Parker Dzh., van Alstayn M., Chaudari S. Revolyutsiya platform: Kak setevyye rynki menyayut ekonomiku — i kak zastavit’ ikh rabotat’ na vas = Platform Revolution: How Network Markets Are Changing the Economy — and How to Make Them Work for You. Moscow: Mann, Ivanov and Ferber; 2016. 352 p. (In Russ.)
5. Porter M.E. How Smart, Connected Products Are Transforming Competition [Internet]. Harvard Business Review. 2014; 92; 11: 64–88. Available from: https://www.hbs.edu/faculty/Pages/item.aspx-?num=48195.
6. Repina M.O. Development of cloud technologies in Russia: solution architecture and prospects. Voprosy innovatsionnoy ekonomiki = Issues of innovation economics. 2024; 14; 4: 1169–1190. DOI: 10.18334/vinec.14.4.121856. (In Russ.)
7. Kurbatov V.I. Internet of Things: Main Concepts and Trends. Gumanitarnyye, sotsial’no-ekono-micheskiye i obshchestvennyye nauki = Humanities, Socio-Economic and Social Sciences. 2023; 1: 48–54. (In Russ.)
8. Ruhl J.B. Governing cascade failures in complex social-ecological-technological systems: framing context, strategies, and challenges [Internet]. Vander- bilt Journal of Entertainment and Technology Law. 2019; 22; 2: 407–440. Available from: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3471945.
9. Gyyedrum D., Piter M. Sravneniye standarta ISO 31000:2009 i COSO ERM = Comparison of the ISO 31000:2009 standard and COSO ERM. Moscow: International Institute of Internal Auditors (IIA Russia); 2010. [Internet]. Available from: https://www.iia-ru.ru/upload/documents/applied_materials/risk_management/sravneniyeStandarta ISO 31000_2009 i COSO ERM.PDF. (In Russ.)
10. Sankova L.V., Mirzabalayeva F.I. Employment on platforms: risk aspect. Ekonomika truda = Labor Economics. 2025; 12: 6. DOI: 10.18334/et.12.6.123405. (In Russ.)
11. Radaykin A.G. Regulation of ecosystems based on intelligent tools for risk analysis and management. Ekonomika vysokotekhnologichnykh proizvodstv = Economy of hightech industries. 2024; 5; 3: 271–290. DOI: 10.18334/evp.5.3.121780. (In Russ.)
12. Koshkin D.S., Fil’o V.V., Sherstov A.V. Cy- ber risks: promising control tools (on the example of cyber insurance) [Internet]. Iskusstvennyye ob- shchestva = Artificial societies. 2023: 18. Available from: https://artsoc.jes.su/s207751800024767-2-1/. DOI: 10.18254/S207751800024767-2. (In Russ.)
13. Radanliev P. Cyber risk management for the internet of things [Internet]. Preprints. 2019: 1–16. Available from: https://www.preprints.org/manu-script/201904.0133/v1?specify=1.
14. Kandasamy K., Srinivas S., Achuthan K. IoT cyber risk: A holistic analysis of cyber risk assessment frameworks, risk vectors, and risk ranking process [Internet]. EURASIP Journal on Information Security. 2020; 8: 1–18. Available from: https://link.springer.com/article/10.1186/s13635-020-00111-0.
15. Kuzovkova T.A. Risks of digital transformation of the economy and society and tools for managing economic security of business in the digital environment [Internet]. Elektronnyy nauchnyy zhurnal «Vek kachestva» = Electronic scientific journal “Century of Quality”. 2024; 1: 63–87. Available from: http://www.agequal.ru/pdf/2024/124005.pdf. (In Russ.)
16. Turovskaya K.S. Quantitative risk assessment using the VaR method in the areas of oil and gas, food production and information technology. Stress price boundaries [Internet]. Vestnik yevraziyskoy nauki = Bulletin of Eurasian Science. 2023; 15; s1. Available from: https://esj.today/PDF/18FAVN123.pdf. (In Russ.)
17. Wan J.P., Liu Y.Q. A System Dynamics Model for Risk Analysis During Project Construction Process. Open Journal of Social Sciences. 2014; 2: 451–454. DOI: 10.4236/jss.2014.26052.
18. Bondarenko YU.V. Mathematical methods for supporting project network analysis and assessing planning risk with fuzzy information on work durations. Vestnik VGU. Seriya: Sistemnyy analiz i informatsionnyye tekhnologii = Bulletin of VSU. Series: Systems Analysis and Information Technology. 2023; 2: 100–111. DOI: 10.17308/sait/1995-5499/2023/2/100-111. (In Russ.)
19. Morrison D., Bedinger M., Beevers L. et al. Ex- ploring the raison d’être behind metric selection in net- work analysis: a systematic review. Applied Network Sci- ence. 2022; 7: 50. DOI: 10.1007/s41109-022-00476-w.
20. Bryzgalov A.A. Microservices for information support of multi-agent systems: methods of collection, monitoring, and decision making. Otkrytoye obrazovaniye = Open Education. 2024; 28; 6: 53–66. DOI: 10.21686/10.21686/1818-4243-2024-6-53-66. (In Russ.)
21. Maemura Yoshiura L.J., Martin C.L., Costa A.P.C.S., Santos-Neto J.B.S. A multicriteria decision model for risk management maturity evaluation. Pesquisa Operacional. 2023; 43; 3: 1–21.
22. Pushkar’ A.V. Strategic choice of optimal forms of integration into the global financial space: a multicriteria approach to assessing benefits and risks [Internet]. Vestnik yevraziyskoy nauki = Bulletin of Eurasian Science. 2025; 17: 2. Available from: https://esj.today/PDF/54ECVN225.pdf. (In Russ.)
23. Pescaroli G., Wicks R.T., Giacomello G., Alexander D.E. Increasing resilience to cascading events: The M.OR.D.OR. scenario [Internet]. Safety Science. 2018; 110: 131–140. Available from: https://www.sciencedirect.com/science/article/pii/S0925753516303150.
24. Mohsin, M., Sardar M.U., Hasan O., Anwar Z. IoTRiskAnalyzer: A probabilistic model checking based framework for formal risk analytics of the In- ternet of Things [Internet]. IEEE Access. 2017; 5: 5494–5505. Available from: https://ieeexplore.ieee.org/abstract/document/7906503.
25. Casola V. Toward the automation of threat modeling and risk assessment in IoT systems [Internet]. Internet of Things. 2019; 7: 7. Available from: https://www.sciencedirect.com/science/article/pii/S2542660519300290.
26. Lemekh V. Siemens MindSphere: tsifrovaya platforma dlya promyshlennosti = Siemens MindSphere: digital platform for industry [Internet]. Vladimir Lemekh’s working sphere. Available from: https://www.blog.8m.by/siemens-mindsphere-cifro-vaja-platforma-dlja-promyshlennosti.
27. Siemens AG. Industrie 4.0: The Hour of Implementation Has Arrived. Press Release [Internet]. Nuremberg, 28 november 2017 . Available from: https://assets.new.siemens.com/siemens/assets/api/uuid:9f513c83-6afd-4709-acb8-276e316408d1/PR-2017110082COEN.pdf.
28. Birkel H.S., Hartmann E. Internet of Things – the future of managing supply chain risks [Internet]. Supply Chain Management: An International Journal. 2020; 25; 5: 535–548. Available from: https://www.emerald.com/insight/content/doi/10.1108/SCM-09-2019-0356/full/html.
29. Sharwood S. AWS Frankfurt experiences major breakdown that staff couldn’t fix for hours due to environmental conditions on data centre floor [Internet]. The Register. 2021. Available from: https://www.theregister.com/2021/06/11/aws_eu_central_1_incident.
30. George A.S. When trust fails: Examining systemic risk in the digital economy from the 2024 crowdstrike outage [Internet]. Partners Universal Multidisciplinary Research Journal. 2024; 1; 2: 134–152. Available from: https://www.pumrj.com/index.php/research/article/view/15.
31. Siemens Mindsphere Security Vulnerabilities in 2025 [Internet]. Stack.Watch. Available from: https://stack.watch/product/siemens/mindsphere.
32. Bastos D. GDPR privacy implications for the Internet of Things [Internet]. ResearchGate. 2018: 1–9. Available from: https://www.researchgate.net/profile/Daniel-Bastos-6/publication/331991225_GDPR_Privacy_Implications_for_the_Internet_of_Things/links/5ca4e0df299bf1b86d6322a6/GDPR-Privacy-Implications-for-the-Internet-of-Things.pdf.
33. Wachter S. Normative Challenges of Identification in the Internet of Things: Privacy, Profiling, Discrimination, and the GDPR [Internet]. Computer Law & Security Review. 2018; 34; 3: 436–449. Available from: https://www.sciencedirect.com/science/article/pii/S0267364917303904.
34. Siemens AG. Отчёт за 2021 год = Siemens AG. Report for 2021 [Internet]. Available from: https://companiesmarketcap.com/annualre-ports/552.ar.en.2021.pdf.
35. Regulation (EU) 2016/679 (General Data Protection Regulation). 2016. [Internet]. Available from: https://gdpr-info.eu.
36. Floetgen R.J., Strauss J., Weking J. ea al. Introducing platform ecosystem resilience: leverag- ing mobility platforms and their ecosystems for the new normal during COVID-19 [Internet]. European Journal of Information Systems. 2021; 30; 4: 304–321. Available from: https://www.tandfonline.com/doi/abs/10.1080/0960085X.2021.1884009.
Review
For citations:
Bryzgalov A.A. Economic and Mathematical Modeling of Risks in the Service Business Model of a Network Enterprise. Statistics and Economics. 2025;22(4):36-51. (In Russ.) https://doi.org/10.21686/2500-3925-2025-4-36-51