The use of artificial intelligence in lean manufacturing for the brewing industry

The paper examines the application of artificial intelligence within lean manufacturing systems in the brewing industry aimed at loss reduction, quality stabilization, and productivity growth. The objective of the study is to identify technological and managerial tasks that can be effectively solved using artificial intelligence under lean manufacturing principles. Key stages of the brewing process sensitive to parameter deviations are analyzed, including raw material processing, fermentation, filtration, and packaging. Fermentation is identified as the most critical stage, where intelligent systems provide continuous monitoring of temperature, acidity, and sugar concentration. The study demonstrates that the use of machine learning algorithms enables optimization of fermentation regimes and reduction of fermentation time from 21 to 7 days without compromising product quality. A classification of machine learning methods is presented, and their application is justified for predicting fermentation completion, early detection of equipment anomalies, and automated quality control. Integration of artificial intelligence into production information systems covering enterprise resource planning, manufacturing execution, and supervisory control levels is considered. It is shown that combining lean manufacturing tools with artificial intelligence produces a synergistic effect by reducing raw material losses, minimizing downtime, and lowering defect rates. The study concludes that artificial intelligence is a practical tool for continuous improvement and increased competitiveness of brewing enterprises regardless of their production scale.