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The European manufacturing industry faces growing pressure to reduce CO2 emissions while maintaining competitiveness. Digital transformation presents opportunities to improve ecological and economic sustainability via advanced tools for product design and production. However, existing digital solutions often neglect environmental aspects or depend on data and models with high uncertainty and low explainability, limiting user acceptance. This paper proposes a novel framework including an innovative Decision Support Tool to help product designers and production planners predict energy and material consumption, CO2 emissions, and related costs across the product lifecycle. Key elements of the tool include hybrid models combining knowledge-driven and AI-driven methods to enhance prediction accuracy and interpretability along with advanced visualization to improve model transparency and user trust. This work highlights critical research gaps and outlines directions for developing a user-centered Decision Support Tool for sustainable product design and production.

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Toward a Digital Decision Support Tool for Sustainable Product Design and Production

  • 会议论文

  • 发表日期：

    2026年

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  • Markus Brillinger,
  • Werner Rom,
  • Jörg Worschech,
  • Heimo Gursch,
  • Tobias Schreck,
  • Ursula Augsdörfer,
  • Markus Jäger,
  • Ouijdane Guiza,
  • Jan Holzweber,
  • Florian Lackner,
  • Chiara Zwickl,
  • Andreas Benjamin Ofner,
  • Manfred Haiberger,
  • Thomas Steiner,
  • Helmut Ecklmayr,
  • Florian Bauer,
  • Andreas Pfleger,
  • Christoph Woisetschläger,
  • Peter Lonsing,
  • Daniel Linecker,
  • Patrick Ackerl,
  • Georg Wagner

摘要

The European manufacturing industry faces growing pressure to reduce CO2 emissions while maintaining competitiveness. Digital transformation presents opportunities to improve ecological and economic sustainability via advanced tools for product design and production. However, existing digital solutions often neglect environmental aspects or depend on data and models with high uncertainty and low explainability, limiting user acceptance. This paper proposes a novel framework including an innovative Decision Support Tool to help product designers and production planners predict energy and material consumption, CO2 emissions, and related costs across the product lifecycle. Key elements of the tool include hybrid models combining knowledge-driven and AI-driven methods to enhance prediction accuracy and interpretability along with advanced visualization to improve model transparency and user trust. This work highlights critical research gaps and outlines directions for developing a user-centered Decision Support Tool for sustainable product design and production.