Incorporating a Prediction Engine to a Digital Twin Simulation for Effective Decision Support in Context of Industry 4.0
Authors: Arshad, R., de Vrieze, P. and Xu, L.
Journal: IFIP Advances in Information and Communication Technology
Volume: 662 IFIP
Pages: 67-76
eISSN: 1868-422X
ISBN: 9783031148439
ISSN: 1868-4238
DOI: 10.1007/978-3-031-14844-6_6
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.
https://eprints.bournemouth.ac.uk/37070/
Source: Scopus
Incorporating a Prediction Engine to a Digital Twin Simulation for Effective Decision Support in Context of Industry 4.0
Authors: Arshad, R., De Vrieze, P. and Xu, L.
Conference: 23th IFIP Working Conference on Virtual Enterprises
Dates: 19-21 September 2022
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.
https://eprints.bournemouth.ac.uk/37070/
Source: Manual
Incorporating a Prediction Engine to a Digital Twin Simulation for Effective Decision Support in Context of Industry 4.0
Authors: Arshad, R., de Vrieze, P.T. and Xu, L.
Conference: 23th IFIP Working Conference on Virtual Enterprises
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.
https://eprints.bournemouth.ac.uk/37070/
Source: BURO EPrints