Issue |
E3S Web Conf.
Volume 349, 2022
10th International Conference on Life Cycle Management (LCM 2021)
|
|
---|---|---|
Article Number | 11001 | |
Number of page(s) | 7 | |
Section | Green Digitalization | |
DOI | https://doi.org/10.1051/e3sconf/202234911001 | |
Published online | 20 May 2022 |
Detecting environmental hotspots in extensive portfolios through LCA and data science: a use-case perspective
1
Fraunhofer Institute for Building Physics IBP, Department Life Cycle Engineering GaBi, Stuttgart, Germany
2
University of Stuttgart, Institute for Acoustics and Building Physics IABP, Department Life Cycle Engineering GaBi, Stuttgart, Germany
3
Adolf Würth GmbH & Co KG, Künzelsau, Germany
* Corresponding author: tobias.manuel.prenzel@ibp.fraunhofer.de
Today, businesses need to reduce environmental impacts significantly along the entire value chain. Yet, full organisational product stewardship seems tough for extensive portfolios of several thousands of individual products varying in material and functionality, as well as production processes and locations. In addition, identifying relevant levers for improvement is more challenging with an increasing amount of influencing parameters. Moreover, while a quantification of environmental sustainability performance is required to derive sound management decisions, life cycle assessment (LCA) approaches particularly for large portfolios traditionally fail to provide effective, time efficient means of assessing more than a couple of scenarios per study. In this context, Fraunhofer IBP determined the CO2-footprint of around 24,000 individual screws in the portfolio of Würth, market leader for assembly and fastening materials, to demonstrate a data science framework for efficient scale-up of environmental sustainability assessments. Hereby, the identification of key hotspots in the portfolio along the value chain was focussed, as well as transparently displaying results and levers for improvement. This contribution builds upon proven methods and tools from LCA and data science and a modularly built approach to achieve a high degree of workflow automation. It offers practical insights into CO2-footprinting and further environmental sustainability analyses for portfolios with large amounts of individual products.
© The Authors, published by EDP Sciences, 2022
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.