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Driven by the European Union’s commitment to achieving climate neutrality by 2050 and reducing EU emissions by 55% relative to 1990 levels by 2030, a critical assessment of manufacturing processes for their environmental impacts is necessary. During the strategic planning stage of product development and manufacturing, limited data is available to predict the environmental impacts across supply chains. However, decisions made at this phase significantly influence these impacts. This paper presents a data-provision method that aims to support decision-making in product development with quantifiable metrics of ecological sustainability. This is achieved through predicting carbon footprints, while leveraging the European data infrastructure Gaia-X, which enables secure service offerings and data sharing across the supply chain. The prediction service is implemented as a web application that facilitates the input and variation of hypothetical production scenarios. In a specific use-case, the method is applied to the supply chain of an injection-moulded cup. This concept offers a vision for carbon-optimized product development, enabling significant carbon avoidance in later production stages and showcasing the influence of different production parameters on the supply-chain carbon footprint.

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A Service-Based Approach for Predicting the Carbon Footprint in the Supply Chain of Plastic Injection-Moulded Parts During Product Development

  • 会议论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年

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  • Lukas Nagel,
  • Rainer Gerstbauer,
  • Levon Harutyunyan,
  • Thomas Trautner,
  • Friedrich Bleicher,
  • Matthias Weigold

摘要

Driven by the European Union’s commitment to achieving climate neutrality by 2050 and reducing EU emissions by 55% relative to 1990 levels by 2030, a critical assessment of manufacturing processes for their environmental impacts is necessary. During the strategic planning stage of product development and manufacturing, limited data is available to predict the environmental impacts across supply chains. However, decisions made at this phase significantly influence these impacts. This paper presents a data-provision method that aims to support decision-making in product development with quantifiable metrics of ecological sustainability. This is achieved through predicting carbon footprints, while leveraging the European data infrastructure Gaia-X, which enables secure service offerings and data sharing across the supply chain. The prediction service is implemented as a web application that facilitates the input and variation of hypothetical production scenarios. In a specific use-case, the method is applied to the supply chain of an injection-moulded cup. This concept offers a vision for carbon-optimized product development, enabling significant carbon avoidance in later production stages and showcasing the influence of different production parameters on the supply-chain carbon footprint.