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A Generic Digital T...
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Lu, XinDepartment of Computing and Informatics, Bournemouth University, Bournemouth, United Kingdom
(författare)
A Generic Digital Twin Framework for Collaborative Supply Chain Development
- Artikel/kapitelEngelska2022
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IEEE,2022
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LIBRIS-ID:oai:DiVA.org:his-22474
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https://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-22474URI
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https://doi.org/10.1109/ICCBD56965.2022.10080555DOI
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Språk:engelska
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Sammanfattning på:engelska
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© 2022 IEEEThis research was performed within the project sustainable and resilient supply chain system based on AI and Big data analytics sponsored by Bournemouth University and Natural Science Foundation of China (grant no. 61803169) and the Fundamental Research Funds for the Central Universities (grant no. 2662018JC029). The authors would acknowledge the support from the experimental factory and engineers.
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Current Supply Chains (SCs) are complex and diverse along with fragile to SC disruptions. This leads urgently needs to develop an intelligent, transparent, collaborative and resilient SC system to cope with unexpected SC disruptions. Digital twin (DT) is one of the most promising solutions to develop smart SCs that has been extensively studied recent years. However, SCDT paradigm is still at an early stage. This paper presents a generic and modularized five layers DT framework to provide a flexible and collaborative solution, which can be compatible with different DT systems in various SCs. The feasibility of the proposed framework is validated through a practical implementation in a distributed eyewear industry.
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Wang, WeiHögskolan i Skövde,Institutionen för ingenjörsvetenskap,Forskningsmiljön Virtuell produkt- och produktionsutveckling,Virtual Manufacturing Processes (VMP)(Swepub:his)wanw
(författare)
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Li, WeidongSchool of Mechanical Engineering, University of Shanghai for Science and Technology, China
(författare)
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Jing, YanguoFaculty of Business, Computing and Digital Industries Leeds Trinity University, United Kingdom
(författare)
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Li, XiaoxiaCollege of Informatics, Huazhong Agricultural University, Wuhan, China
(författare)
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Department of Computing and Informatics, Bournemouth University, Bournemouth, United KingdomInstitutionen för ingenjörsvetenskap
(creator_code:org_t)
Sammanhörande titlar
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Ingår i:2022 5th International Conference on Computing and Big Data (ICCBD 2022): IEEE, s. 177-181978166545716397816654571569781665457170
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