Intelligent Neural Network System for Analysis and Optimization of Production Processes Based on a Digital Twin: Comparative Analysis and Strategic Positioning in the Context of Industry 4.0 and 5.0

Abstract

This article is an in-depth analysis of an innovative Intelligent Neural Network System designed to analyze and optimize production processes at food processing enterprises. The essence of the system lies in its ability to revolutionize operational efficiency by automatically analyzing employee movements, identifying deviations from optimized plans, predicting potential delays and dynamically adjusting schedules and tasks in the real-time, which is critically important in the context of daily changes in orders and technological maps.

The main value of the system is a significant increase in productivity and responsiveness for small and medium-sized enterprises (SMEs) in the food sector, which is often characterized by high volatility and complexity. Technologically, the system demonstrates a high degree of sophistication, using advanced artificial intelligence (AI) models, including spatio-temporal graph convolutional networks (GNNs) for detailed motion analysis, recurrent neural networks for robust predictive analysis, and deep reinforcement learning (DRL) for dynamic real-time optimization and resource reallocation.

Strategically, the system demonstrates a strong alignment with the fundamental principles of Industry 4.0, especially in the use of cyber-physical systems and real-time data. Moreover, its emphasis on optimizing human labor through movement analysis and task redistribution positions it as an innovative solution within the evolving human-centered paradigm of Industry 5.0. The system's proactive, adaptive capabilities and robust architecture for integrating different data streams are key benefits that enable SMEs to successfully navigate volatile market demand and achieve sustainable growth