Automated Control and Planning System for the Production of Electronic Meters with Artificial Intelligence

Abstract

In the context of Industry 4.0, modern manufacturing environments increasingly rely on digital technologies to enhance efficiency and competitiveness. The production of electronic meters represents a complex industrial process that demands precise coordination of resources, materials, and scheduling. Traditional methods of production planning often face challenges such as bottlenecks, resource underutilization, and delayed delivery times. To address these issues, the integration of Artificial Intelligence (AI) into manufacturing processes has emerged as a promising solution. The main objective of this research is to develop an automated system for production planning of electronic meters that leverages AI techniques to optimize operational performance. The study proposes a model capable of analyzing historical production data, predicting demand fluctuations, and dynamically adjusting production schedules. The methodology involves the implementation of machine learning algorithms and optimization techniques within a simulated industrial environment. The system evaluates different production scenarios, prioritizes tasks, and allocates resources efficiently to meet production targets while minimizing delays. Results demonstrate that the proposed AI-based automated system significantly improves operational optimization. Key performance indicators, such as production throughput, resource utilization, and lead time, showed measurable enhancements compared to conventional planning methods. Moreover, the system enables real-time decision-making, which aligns with the principles of Industry 4.0 and supports adaptive, data-driven production strategies. In conclusion, the research highlights the potential of combining Artificial Intelligence and automated systems to advance production planning for electronic meters, contributing to more efficient, flexible, and responsive manufacturing processes in the Industry 4.0 era.