DYNAMIC MODELING OF BUSINESS PROCESS QUALITY MANAGEMENT IN AGRI-INDUSTRIAL ENTERPRISES

Abstract

The growing complexity of internal and external factors in the agricultural sector, including climate variability, seasonal fluctuations in productivity, and instability in supply chains, has created a strong demand for flexible and adaptive approaches to quality management. This study demonstrates that conventional static control models cannot respond effectively to changes and do not provide sufficient tools for forecasting and minimizing risks that affect overall business performance. An original dynamic modeling framework is proposed, integrating monitoring, risk assessment, and forecasting deviations in business process operations. The model identifies a set of key variables reflecting technological, human, organizational, and environmental-economic aspects of agri-industrial enterprises. Each variable is described in terms of its economic relevance, unit of measurement, and data source, which makes the model applicable to operational and strategic control tasks. The study identifies several reinforcing and balancing feedback loops within the system dynamics framework. These feedback structures enable the development of behavioral scenarios and allow for a detailed analysis of how investments in employee training, innovation, process standardization, seasonal workload, and weather-related conditions affect product quality and operational efficiency. The model can be integrated into contemporary simulation software, enhancing the accuracy of managerial decision-making and reducing uncertainty in enterprise development planning. The findings of the study offer practical value for agri-industrial enterprise management. The proposed model supports business process resilience, ensures comprehensive quality control, and facilitates effective adaptation to a dynamic market environment.