The Sustainability Data Science Life Cycle for automating multi-purpose LCA workflows for the analysis of large product portfolios

¹ Fraunhofer Institute for Building Physics IBP, Department Life Cycle Engineering, 70563 Stuttgart, Germany
² University of Stuttgart, Institute for Acoustics and Building Physics (IABP), 70563 Stuttgart, Germany

^* Corresponding author: daniel.wehner@ibp.fraunhofer.de

Abstract

Life Cycle Assessment (LCA) is a powerful and sophisticated tool to gain deep understanding of the environmental hotspots and optimization potentials of products. Yet, its cost-intensive manual data engineering and analysis workflows restrain its wider application in eco-design, green procurement, supply chain management, sustainable investment or other relevant business processes. Especially for large product portfolios and increasing reporting requirements, traditional LCA workflows and tools often fail to provide the necessary scalability. The Sustainability Data Science Life Cycle (S-DSLC) is a concept for workflow automation for multi-purpose LCA of large product portfolios. The concept integrates the frameworks of LCA, the cross-industry standard process for data mining (CRISP-DM), and the Data Science Life Cycle (DSLC). Key aspects of the concept are deep business-, stakeholder and user-understanding, deployment of LCA results in interactive browser tools (i.e. LCA-dashboards and Guided Analytics) tailored to the needs of individual roles and business processes, as well as the automation of data preparation, model generation and Life Cycle Impact Assessment based on modern data analytic tools. The demonstration of the concept shows substantial scalability improvements for dealing with large product portfolios and broad application of LCA results in various business processes.