Detecting environmental hotspots in extensive portfolios through LCA and data science: a use-case perspective

Abstract

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.