ORGANIZATION OF INTELLIGENT QUALITY MANAGEMENT OF BUSINESS PROCESSES IN GRAIN PRODUCTION

Abstract

The increasing demand within Ukraine's agricultural sector for improved business process quality management and enhanced efficiency in grain production highlights the growing importance of implementing intelligent management systems. This study aims to develop the scientific and theoretical foundations and propose methodological recommendations for introducing a business process quality management system tailored to improve the performance of grain production. A Bayesian belief network was constructed and implemented using the Netica environment to achieve this objective. The model is based on integrating expert knowledge and transforming empirical data into discrete scales. An adaptive Bayesian model consisting of twelve interrelated nodes was developed to test the following research hypothesis: there exists a stable and formalized causal relationship between key determinants of grain production (such as seed quality, soil condition, weather, staff qualifications, equipment status, and compliance with technology), intermediate processes (sowing, vegetation, harvesting, processing), and final outputs (yield and grain quality). This relationship can be effectively modeled using a multilevel Bayesian belief network. The model identifies critical control points and optimizes technological processes through predictive grain quality analysis. Machine learning and validation techniques were employed to train the model and calibrate the network parameters. The study also outlines pathways for integrating Bayesian networks with quality management systems in Ukraine's small and medium-sized agricultural enterprises. The proposed model is scalable and adaptable for resource-constrained agrarian operations, which is particularly relevant in enhancing the overall efficiency of Ukraine's agricultural sector.