Mathematical modeling of information flows in the context of digital transformation

Aim. To develop and test mathematical models of an enterprise’s information flows to improve management efficiency in the context of digital transformation.

Objectives. To develop a classification of information flows; to conduct simulation modeling of the dynamics of information exchange to identify critical factors affecting system performance.

Methods. The methodological basis of the study consisted of comprehensive systems analysis, the use of data flow diagrams (DFDs) to visualize the logic of information movement, and ontological modeling for dynamic access rights management. The technical part of the study is based on conducting a full three­factor experiment and statistical data processing in the Minitab software environment.

Results. The author proposes a classification of information flows according to four criteria: directionality, formalization, periodicity, and security. During the simulation of server node operation (sample of 12,500 transactions), an adequate regression model of response time was obtained. It was established that the greatest influence on system stability is exerted by flow intensity (Х₁ = 2,450), while channel expansion (Х₃ = −1.875) serves as the most effective way to compensate for delays. A regression model was constructed showing the dependence of system response time on the intensity of incoming requests, the complexity of data processing, and the bandwidth of communication channels. The experiment revealed a synergistic effect of factor interaction, indicating a nonlinear increase in time delays with the simultaneous increase in task complexity and network load.

Conclusion. The study confirms the need for enterprises to transition to a microservices architecture and implement intelligent data routing algorithms. The practical implementation of the proposed approaches makes it possible to minimize information redundancy, ensure the scalability of digital flows, and increase overall controllability of the entity by 20–30 % in various economic sectors. The scientific novelty lies in formalizing the relationship between the technical parameters of information exchange and the financial indicators of an enterprise. For the first time, it is proposed to use a margin reduction function to assess the impact of data transmission delays on profitability in industrial automation.

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