Hoffmann, Felix ; Koch, Tobias ; Weigold, Matthias ; Metternich, Joachim (2023)
A data-based business concept to support product creation in reducing greenhouse gas emissions.
In: Procedia CIRP, 120
doi: 10.1016/j.procir.2023.09.089
Article, Bibliographie
Abstract
The anthropogenic climate change and the resulting global warming cause a multitude of problems for humans and the environment. For this reason, politicians have set ambitious climate targets with the adoption of the Paris Climate Agreement and the implementation of the European Green Deal. However, especially in the industrial sector, the savings of climate-damaging greenhouse gas emissions have so far fallen short of the politically set targets. A significant lever to reduce emissions in production is offered when optimal decisions are already made in the product creation process. One approach is shifting the focus from the product itself to the provision of its service, so different options for geometry, material, manufacturing process and machines are enabled. By forecasting a carbon footprint for each variation of the mentioned options, the greenhouse gas optimal configuration can be identified. Automating this process requires a trustful data and service exchange framework with interfaces, where data exchanges (e.g. raw material properties) and service provisions (e.g. prognosis algorithm for energy consumption) are simplified. Lastly, an inherent changeover and investments must be justifiable in terms of measurable benefits and return on investment for participating companies. This paper aims to propose a data-driven business model for the greenhouse gas emission reduction in product creation. It addresses the data and revenue streams for the participating stakeholders by a use case from the field of plastic injection molding within the Gaia-X lighthouse project EuProGigant. In this context, the underlying business model architecture is derived. Finally, possible economic potentials and chances for saving greenhouse gas emissions are discussed. Likewise, possible obstacles that could prevent economic operation in industrial practice are also addressed.
| Item Type: | Article |
|---|---|
| Erschienen: | 2023 |
| Creators: | Hoffmann, Felix ; Koch, Tobias ; Weigold, Matthias ; Metternich, Joachim |
| Type of entry: | Bibliographie |
| Title: | A data-based business concept to support product creation in reducing greenhouse gas emissions |
| Language: | English |
| Date: | 2023 |
| Publisher: | Elsevier B.V. |
| Journal or Publication Title: | Procedia CIRP |
| Volume of the journal: | 120 |
| DOI: | 10.1016/j.procir.2023.09.089 |
| URL / URN: | https://www.sciencedirect.com/science/article/pii/S221282712... |
| Abstract: | The anthropogenic climate change and the resulting global warming cause a multitude of problems for humans and the environment. For this reason, politicians have set ambitious climate targets with the adoption of the Paris Climate Agreement and the implementation of the European Green Deal. However, especially in the industrial sector, the savings of climate-damaging greenhouse gas emissions have so far fallen short of the politically set targets. A significant lever to reduce emissions in production is offered when optimal decisions are already made in the product creation process. One approach is shifting the focus from the product itself to the provision of its service, so different options for geometry, material, manufacturing process and machines are enabled. By forecasting a carbon footprint for each variation of the mentioned options, the greenhouse gas optimal configuration can be identified. Automating this process requires a trustful data and service exchange framework with interfaces, where data exchanges (e.g. raw material properties) and service provisions (e.g. prognosis algorithm for energy consumption) are simplified. Lastly, an inherent changeover and investments must be justifiable in terms of measurable benefits and return on investment for participating companies. This paper aims to propose a data-driven business model for the greenhouse gas emission reduction in product creation. It addresses the data and revenue streams for the participating stakeholders by a use case from the field of plastic injection molding within the Gaia-X lighthouse project EuProGigant. In this context, the underlying business model architecture is derived. Finally, possible economic potentials and chances for saving greenhouse gas emissions are discussed. Likewise, possible obstacles that could prevent economic operation in industrial practice are also addressed. |
| Uncontrolled Keywords: | carbon reduction, data economy, injection molding |
| Divisions: | 16 Department of Mechanical Engineering 16 Department of Mechanical Engineering > Institute of Production Technology and Machine Tools (PTW) 16 Department of Mechanical Engineering > Institute of Production Technology and Machine Tools (PTW) > ETA Energy Technologies and Applications in Production 16 Department of Mechanical Engineering > Institute of Production Technology and Machine Tools (PTW) > Management of Industrial Production |
| Date Deposited: | 03 Jun 2024 05:22 |
| Last Modified: | 03 Jun 2024 06:26 |
| PPN: | 518772578 |
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