Аннотация
This paper presents an Industry 4.0-based intelligent monitoring and control system for the production of zhaya and molded meat products using Internet of Things (IoT), Supervisory Control and Data Acquisition (SCADA), and Adaptive Neuro-Fuzzy Inference System (ANFIS) technologies. The study considers massaging, injection, salting, thermal processing, smoking, vacuum packaging, cooling, and storage as the main technological stages affecting product quality and safety. In the proposed system, technological parameters such as chamber temperature, internal product temperature, pressure, vacuum level, salinity, processing time, rotation speed, and dosing parameters are measured using IoT sensors and processed at the Programmable Logic Controller (PLC) and SCADA levels. The data collected by the SCADA system are stored in a database and linked to the digital passport of each production batch. An Adaptive Neuro-Fuzzy Inference System (ANFIS) model is employed to predict product quality and safety risks. The model describes complex relationships between technological parameters and quality indicators and enables the classification of production batches into normal, warning, and high-risk categories. The results demonstrate that the proposed IoT–SCADA–ANFIS system provides real-time monitoring, batch traceability, early defect detection, and data-driven decision-making. The proposed approach represents an effective solution for the digital transformation of traditional meat production and the intelligent management of product quality and safety.