Digital twin of the technological process for forecasting and anticipatory control

   This article proposes a methodological approach to using a digital twin of a technological process to forecast and support management decisions.

   The relevance of this research is driven by the growing complexity of economic and technological systems, the increasing interconnectedness of their elements, and the need to consider the dynamics of flow processes and uncertainty in decision-making.

   The aim of this study is to develop an approach that integrates system state modeling and the generation of management actions within the unified concept of a digital twin.

   This approach is based on representing a technological process as a dynamic system of interconnected material, information, and energy flows that shape the current and predicted state of a controlled object. The research methodology incorporates system analysis, economic and mathematical modeling, and elements of data processing and intelligent analysis. The digital twin is formalized as a dynamic model that not only reflects the current state of the system but also predicts its evolution based on the analysis of parameter change trajectories. The results of the study demonstrate that the proposed approach enables scenario forecasting, assessment of the probability of the system transitioning to critical states, and generation of anticipatory management actions. Compared with traditional reactive management methods, the proposed approach focuses on preventing unfavorable situations. The use of a digital twin allows for linking the system state model with management objectives at the level of generating management actions.

   The practical significance of this work lies in the potential application of the developed approach to improving the efficiency of technological process management, identifying bottlenecks, and ensuring the stable operation of systems amid environmental uncertainty and variability.

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