Intelligent Model for Productive Planning of Electronic Meters with Industry 4.0 Technological Resources

Abstract

The advancement of digital technologies has significantly transformed industrial processes, shaping the Industry 4.0 scenario, in which cyber-physical systems, IoT, Big Data, and Artificial Intelligence (AI) enhance productivity efficiency and flexibility. In this context, the manufacturing of electronic meters faces challenges such as operational complexity, demand variability, the need for traceability, and data integration across the production chain. Therefore, the adoption of intelligent solutions that support production planning and control in an agile and precise manner becomes essential.

This study aims to develop an intelligent model for the productive planning of electronic meters, integrating Industry 4.0 resources to improve decision-making and optimize operational flow. The model seeks to minimize bottlenecks, forecast material requirements, improve machine utilization, and increase planning reliability. The methodology was structured in four stages: mapping the production process and identifying critical variables; real-time data integration via IoT and MES systems; development of AI models based on machine learning; and the construction of a system capable of simulating scenarios and generating production plans. Tests using historical and real data were conducted. The results indicated higher accuracy in forecasts, reduced idle times, and better balance between capacity and demand. The model proved adaptable to different scenarios, demonstrating its potential to support companies in adopting more dynamic and efficient processes aligned with Industry 4.0.