SMART ANALYTICS TOOLS FOR SUPPORTING MANAGEMENT DECISIONS IN THE FIELD OF PRODUCTION SYSTEM MANAGEMENT AT ENTERPRISES IN THE CONTEXT OF SUSTAINABLE DEVELOPMENT

This article addresses the growing importance of smart analytics as a technical tool and fundamental intellectual asset ensuring sustainable development, resilience, and competitiveness of enterprises under digital transformation. The paper examines the Smart Data Adaptive Cycle (SDAC) model, which offers a continuous closed-loop decision-making process in a digital manufacturing environment. The model integrates informational, financial, and cognitive components using self-learning mechanisms and the human-in-the-loop principle. The purpose of the work is to examine the specifics of ensuring sustainable development in an enterprise through managerial decision-making using smart analytics tools in the management of production systems. The study applies general and specialized methods, including a scenario-based approach to determine the relationship between information entropy and economic value added (EVA), a case study for empirical validation of the SDAC model at a metallurgical enterprise, discounting for economic feasibility, and a systematic approach to constructing the conceptual model. A comparative scenario analysis of ex-post, partial, and full SDAC models was conducted. The main results show that the SDAC model integrates information theory and value-based management (VBM) into a single adaptive cycle. The findings demonstrate that entropy analysis allows for the formalization of uncertainty in the production environment (BANI context), providing a quantitative ex-ante risk assessment that is more effective than traditional retrospective methods. Empirical modeling confirms that integrating smart analytics into production management enables the maximization of EVA. Practical testing at a metallurgical enterprise demonstrated the model’s ability to reduce equipment downtime to 8.2%, defect rates to 3.6%, and carbon emissions to 190 kg CO₂/t, while increasing labor productivity. The conclusions confirm the effectiveness of the SDAC model as a tool for improving managerial decision-making and ensuring sustainable enterprise development. Future research includes scaling the model to other sectors and integrating it into approaches for assessing nonlinear extreme events (black swan events).