A Generic Digital Twin Framework for Collaborative Supply Chain DevelopmentShow others and affiliations
2022 (English)In: 2022 5th International Conference on Computing and Big Data (ICCBD 2022), IEEE, 2022, p. 177-181Conference paper, Published paper (Refereed)
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.
Place, publisher, year, edition, pages
IEEE, 2022. p. 177-181
Keywords [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
National Category
Transport Systems and Logistics Production Engineering, Human Work Science and Ergonomics Other Electrical Engineering, Electronic Engineering, Information Engineering Computer Sciences
Research subject
Virtual Manufacturing Processes; Production and Automation Engineering
Identifiers
URN: urn:nbn:se:his:diva-22474DOI: 10.1109/ICCBD56965.2022.10080555Scopus ID: 2-s2.0-85152411561ISBN: 978-1-6654-5716-3 (electronic)ISBN: 978-1-6654-5715-6 (electronic)ISBN: 978-1-6654-5717-0 (print)OAI: oai:DiVA.org:his-22474DiVA, id: diva2:1753592
Conference
2022 5th International Conference on Computing and Big Data, ICCBD 2022, December 16-18, 2022 Shanghai, China
Note
© 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.
2023-04-272023-04-272024-07-08Bibliographically approved