Keywords: information flow, digital organizational system, rational management, multi-criteria choice, data quality, information availability, integrated risk, control action, rationality index
UDC 004.942:005.311.6:519.816
DOI: 10.26102/2310-6018/2026.58.7.006
The article proposes a formalized model for the rational management of information flows within a digital organizational system. The aim of the study is to develop a procedure for selecting a control action in which the information flow is treated as an independent object of management, characterized by a measurable state, defined constraints, and a set of admissible alternatives. The methodological basis of the study includes a systemic description of the digital organizational system, a six-parameter model of the flow state, normalization of criteria with different units of measurement, weighted aggregation of indicators, and verification of threshold constraints. The state of the information flow is described through a set of parameters: intensity, information transmission delay, data quality, availability, maintenance cost, and integral risk. As a result, a multicriteria model is developed that makes it possible to compare organizational, software-related, and infrastructural control actions using a unified integral rationality index. The study demonstrates that a rational decision is determined not by maximizing a single indicator, but by achieving a coordinated balance between throughput, timeliness, quality, availability, costs, and risk level. To assess the applicability of the model, the article presents a numerical example involving four management alternatives. The results demonstrate the possibility of selecting a compromise control action under conditions of multiple interrelated criteria. The scientific novelty of the proposed approach lies in integrating the information flow state model and the control action selection procedure within a single computational framework. The proposed approach can be applied in the design of digital management loops, corporate monitoring systems, service platforms, and decision support systems.
1. Durugbo Ch., Tiwari A., Alcock J.R. Modelling information flow for organisations: A review of approaches and future challenges. International Journal of Information Management. 2013;33(3):597–610. https://doi.org/10.1016/j.ijinfomgt.2013.01.009
2. Daft R.L., Lengel R.H. Organizational information requirements, media richness and structural design. Management Science. 1986;32(5):554–571. https://doi.org/10.1287/mnsc.32.5.554
3. Duminova D., Tarasevich A. Information flow management as a method of increasing the efficiency of the enterprise's economic activity. Economics and Society. 2019;(11):973–981. (In Russ.).
4. Korablev A.V., Petrushova M.V., Zolkin A.L., et al. Modern approach to the formation of information support of the enterprise management system. Bulletin of the Altai Academy of Economics and Law. 2021;(10-1):41–46. (In Russ.). https://doi.org/10.17513/vaael.1867
5. Gorodnova N.V. A method for assessing the information flows quality in big data amidst the digital economy. Russian Journal of Innovation Economics. 2022;12(1):607–624. (In Russ.). https://doi.org/10.18334/vinec.12.1.114142
6. Klevnov O.G., Mamedova I.A. Information flow management based on ITIL and 7R principles. International Research Journal. 2024;(7):20. (In Russ.). https://doi.org/10.60797/IRJ.2024.145.5
7. Wang R.Y., Strong D.M. Beyond accuracy: what data quality means to data consumers. Journal of Management Information Systems. 1996;12(4):5–33. https://doi.org/10.1080/07421222.1996.11518099
8. Pipino L.L., Lee Y.W., Wang R.Y. Data quality assessment. Communications of the ACM. 2002;45(4ve):211–218. https://doi.org/10.1145/505248.506010
9. Storey V.C., Dewan R.M., Freimer M. Data quality: setting organizational policies. Decision Support Systems. 2012;54(1):434–442. https://doi.org/10.1016/j.dss.2012.06.004
10. Haug A., Zachariassen F., van Liempd D. The costs of poor data quality. Journal of Industrial Engineering and Management. 2011;4(2):168–193. https://doi.org/10.3926/jiem.2011.v4n2.p168-193
11. Aven T. Risk assessment and risk management: review of recent advances on their foundation. European Journal of Operational Research. 2016;253(1). https://doi.org/10.1016/j.ejor.2015.12.023
12. Bojanc R., Jerman-Blazic B. An economic modelling approach to information security risk management. International Journal of Information Management. 2008;28(5):413–422. https://doi.org/10.1016/j.ijinfomgt.2008.02.002
13. Cinelli M., Kadziński M., Gonzalez M., et al. How to support the application of multiple criteria decision analysis? Let us start with a comprehensive taxonomy. Omega. 2020;96(2):102261. https://doi.org/10.1016/j.omega.2020.102261
14. Wang Zh., Nabavi S.R., Rangaiah G.P. Multi-criteria decision making in chemical and process engineering: methods, progress, and potential. Processes. 2024;12(11):2532. https://doi.org/10.3390/pr12112532
15. Krishnan A.R. Past efforts in determining suitable normalization methods for multi-criteria decision-making: A short survey. Frontiers in Big Data. 2022;5:990699. https://doi.org/10.3389/fdata.2022.990699
16. Więckowski J., Sałabun W. Sensitivity analysis approaches in multi-criteria decision analysis: A systematic review. Applied Soft Computing. 2023;148(1-2):110915. https://doi.org/10.1016/j.asoc.2023.110915
Keywords: information flow, digital organizational system, rational management, multi-criteria choice, data quality, information availability, integrated risk, control action, rationality index
For citation: Mikheev V.S. Multi-criteria model of rational management of information flows in a digital organizational system. Modeling, Optimization and Information Technology. 2026;14(7). URL: https://moitvivt.ru/ru/journal/article?id=2380 DOI: 10.26102/2310-6018/2026.58.7.006 (In Russ).
© Mikheev V.S. Статья опубликована на условиях лицензии Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NS 4.0)Received 26.04.2026
Revised 25.06.2026
Accepted 08.07.2026