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A Generic Digital Twin Framework for Collaborative Supply Chain Development
Department of Computing and Informatics, Bournemouth University, Bournemouth, United Kingdom.
Högskolan i Skövde, Institutionen för ingenjörsvetenskap. Högskolan i Skövde, Forskningsmiljön Virtuell produkt- och produktionsutveckling. (Virtual Manufacturing Processes (VMP))ORCID-id: 0000-0003-1781-2753
School of Mechanical Engineering, University of Shanghai for Science and Technology, China.
Faculty of Business, Computing and Digital Industries Leeds Trinity University, United Kingdom.
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2022 (Engelska)Ingår i: 2022 5th International Conference on Computing and Big Data (ICCBD 2022), IEEE, 2022, s. 177-181Konferensbidrag, Publicerat paper (Refereegranskat)
Abstract [en]

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. 

Ort, förlag, år, upplaga, sidor
IEEE, 2022. s. 177-181
Nyckelord [en]
Digital twin, Supply Chain, Supply chain risk assessment, Supply chains, Collaborative supply chains, Current supplies, Eyewear, Modularized, Risks assessments, Supply chain systems, Supply-chain disruptions, Supply-chain risks, Risk assessment
Nationell ämneskategori
Transportteknik och logistik Produktionsteknik, arbetsvetenskap och ergonomi Annan elektroteknik och elektronik Datavetenskap (datalogi)
Forskningsämne
Virtual Manufacturing Processes
Identifikatorer
URN: urn:nbn:se:his:diva-22474DOI: 10.1109/ICCBD56965.2022.10080555Scopus ID: 2-s2.0-85152411561ISBN: 978-1-6654-5716-3 (digital)ISBN: 978-1-6654-5715-6 (digital)ISBN: 978-1-6654-5717-0 (tryckt)OAI: oai:DiVA.org:his-22474DiVA, id: diva2:1753592
Konferens
2022 5th International Conference on Computing and Big Data, ICCBD 2022, December 16-18, 2022 Shanghai, China
Anmärkning

© 2022 IEEE

This 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. 

Tillgänglig från: 2023-04-27 Skapad: 2023-04-27 Senast uppdaterad: 2023-07-14Bibliografiskt granskad

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Wang, Wei

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Institutionen för ingenjörsvetenskapForskningsmiljön Virtuell produkt- och produktionsutveckling
Transportteknik och logistikProduktionsteknik, arbetsvetenskap och ergonomiAnnan elektroteknik och elektronikDatavetenskap (datalogi)

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