Incorporating a Prediction Engine to a Digital Twin Simulation for Effective Decision Support in Context of Industry 4.0

Abstract

Simulation has been widely used as a tool to enhance the manufacturing processes by effectively detecting the errors and performance gaps at an early stage. However, in context of industry 4.0, which involves increased complexity, decisions need to be made more quickly to maintain higher efficiency. In this paper, we use a prediction engine along with a Digital Twin simulation to enhance the decision-making process. We show how, based upon a simulation of a process, a prediction model can be used to determine process parameters based upon desired process outcomes that enhance the manufacturing process. To evaluate our architecture, an industrial case study based on Inventory, Storage and Distribution will be used.