Method for performance analysis of production lines based on digital models powered by real-time data

Abstract

The intention to improve the performance of the processes involved is considered one of the main guidelines for companies' competitive advantage. Technological advances help to improve yields, operations, decision making and cost reduction when adopted in their daily processes, and assembly lines are constantly monitored, but the detection of adversities is still considered complex. Because of this difficulty, the proposal for a virtual model with greater fidelity to reality, such as the digital twin model, is promising. A virtual model of the shop floor system composed of physical characteristics present in the industrial environment, being fed with data that occurs in real-time, offers the simulation possible predictions of setbacks with greater fidelity. This monitoring provides the user with an improvement in the process control, assisting in the planning of operations, enabling changes in the virtual model of compliance verification, improving performance and productivity, reducing related times and costs for later application in the physical model. In this way, the objective of this work proposes the elaboration of an application method where it is possible to analyze the performance of productive lines through digital models integrated with the data feed system in real-time.