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

Arshad, R., de Vrieze, P. T. and Xu, L., 2022. Incorporating a Prediction Engine to a Digital Twin Simulation for Effective Decision Support in Context of Industry 4.0. In: 23th IFIP Working Conference on Virtual Enterprises, 19-21 September 2022, Lisbon, Portugal. (In Press)

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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.

Item Type:Conference or Workshop Item (Paper)
Uncontrolled Keywords:Digital twins; Industry 4.0; Simulation; Federated simulation; Machine learning
Group:Faculty of Science & Technology
ID Code:37070
Deposited By: Symplectic RT2
Deposited On:20 Jun 2022 15:28
Last Modified:20 Jun 2022 15:28

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