Decision Support Algorithm for Selecting Model Classes

The paper addresses the problem of a justified choice of an approach to modeling production processes in the context of the active development of the smart manufacturing concept and the implementation of Industry 4.0 technologies. The relevance of the study is driven by the significant complexity of modern production systems, which renders the traditional intuitive selection of modeling methods ineffective and creates risks of using inadequate analytical tools. The primary objective of the article is the development of a formalized method based on a systematic analysis of management goals, decision-making levels, and the characteristics of a specific production system. A proposed algorithm ensures the alignment of management tasks with the specific capabilities of discrete-event, agent-based, system dynamics, and hybrid models. The method includes a sequence of five stages: from initial task identification to quantitative justification of the choice using the weighted sum model. The developed system of criteria considers parameters such as the level of detail, computational complexity, data requirements, and the possibility of integration with the Industrial Internet of Things. The obtained results are presented in the form of structured mapping tables for strategic, tactical, and operational management levels. The proposed approach allows for a reduction in computational costs and an increase in modeling accuracy, providing a methodological basis for designing digital twins.