Digital Twin Models for Simulation and Optimization of Processes

Abstract

Digital twin models represent a novel technology of simulating, monitoring and optimization of complex processes in many industries. Digital twin is a virtual image of an actual world system, which has capabilities to combine real-time data, predictive analytics, as well as streamline the processes. Digital twins enable companies to test and anticipate failure, enhance the effectiveness of operations and connect the real world with the digital world without disrupting the operation in real life. This paper will address how digital twins models are used in the process simulation and optimization processes, and how they can be applied to predictive maintenance, resource distribution and decision support. Through the aid of a mixed-method approach, the paper reviews case studies and empirical evidence to find out the effectiveness of digital twins in the process of improvement. The findings reveal that the digital twin models have the potential to substantially increase the visibility of the processes, reduce the cost of operation and also increase the accuracy of the decisions. The implementation issues, including the data integration, computational requirements, and scalability of the system, are also taken into consideration in the research and provide the insight into the best practices in the implementation of the digital twin technology.