Intelligent information processing technologies for managing small and medium-sized enterprises based on a regularizing Bayesian approach

Purpose of the study. To develop and theoretically substantiate a model of intelligent information processing technology designed to support management decision-making in small and medium-sized enterprises (SMEs) under conditions of uncertainty and data incompleteness, based on the application of a regularizing Bayesian approach (RBP).

Methods of research: systems analysis, decision theory, artificial intelligence and machine learning methods, in particular, Bayesian belief networks and neural networks, as well as probability theory and mathematical statistics. The core of the methodology is the regularizing Bayesian approach, which allows for the formalization and consideration of prior information to improve the robustness of models on small samples.

Results. Based on the conducted analysis, a structural and functional model of an intelligent SME management technology is proposed. The model integrates data collection and preprocessing modules and a Bayesian inference kernel implementing regularization procedures. It is shown that the use of BBP reduces the risk of model overfitting with the limited statistical data typical of SMEs and improves the quality of management forecasts and decisions. Recommendations for the application of the technology for demand forecasting, risk assessment, and personnel management are developed.

Scientific novelty: adaptation and development of the regularizing Bayesian approach methodology for solving semi structured management problems in small and medium-sized enterprises. Unlike standard machine learning methods, the proposed technology formally incorporates prior expert information and industry knowledge for decision regularization, which is critical in the highly volatile and data-poor environments typical of the SME sector.