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• W4383648237 abstract "With the EU Green Deal and the UN Sustainable Developments Goals, the vision of a greenhouse gas-neutral and resource-efficient economy is already firmly anchored in world politics. In this context, the automotive industry faces many challenges including the increasing scarcity of natural resources, a rising demand in terms of sustainable vehicle design, production and materials sourcing. Due to all this, end-of-life decisions regarding dismantling have become increasingly important. A high proportion of secondary materials will be required in the vehicles of the future. To response to these challenges, companies have turned their focus towards the circular economy as a central approach to close material loops. In the German research project “Catena-X” a new data ecosystem with digital twins is one enabler that is being developed. The digital twins represent a promising approach to the circular economy by ensuring transparent, product-specific and end-to-end data exchange throughout the entire product lifecycle, from the material sourcing to the eventual dismantling and recycling. As one particular and unique solution, a decision framework that facilitates the best circular strategy at the end of a vehicle's life is developed and presented in this paper. The underlying data-driven decision support framework is based on circular economy KPIs. This includes material, components and specific vehicle KPIs to best identify the most suitable circular strategy. The framework was methodologically developed by an interdisciplinary team of partners, who are stakeholders throughout the value chain and participants in the Catena-X project. An integrated approach of user-centered design, an adapted version of the V-model and the Scaled Agile Framework were used for the methodology in the development of the solution. The paper presents the concept of a digital twin for a decision support system, that includes a central decision logic that also includes the relevant KPIs and a survey for evaluating the decision logic utilised with a chosen dismantling company." @default.
• W4383648237 created "2023-07-09" @default.
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• W4383648237 date "2023-01-01" @default.
• W4383648237 modified "2023-10-12" @default.
• W4383648237 title "End-of-life decision support to enable circular economy in the automotive industry based on digital twin data" @default.
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• W4383648237 doi "https://doi.org/10.1016/j.procir.2023.03.150" @default.
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