Berger, Katharina, Rusch, Magdalena, Pohlmann, Antonia, Popowicz, Martin, Geiger, Bernhard C., Gursch, Heimo, Schöggl, Josef-Peter, and Baumgartner, Rupert J.
Digital product passports (DPPs) are an emerging technology and are considered as enablers of sustainable and circular value chains as they support sustainable product management (SPM) by gathering and containing product life cycle data. However, some life cycle data are considered sensitive by stakeholders, resulting in a reluctance to share such data. This contribution provides a concept illustrating how data science and machine learning approaches enable electric vehicle battery (EVB) value chain stakeholders to carry out confidentiality-preserving data exchange via a DPP. This, in turn, can support overcoming data sharing reluctances, consequently facilitating sustainability data management on a DPP for an EVB. The concept development comprised a literature review to identify data needs for sustainable EVB management, data management challenges, and potential data science approaches for data management support. Furthermore, three explorative focus group workshops and follow-up consultations with data scientists were conducted to discuss identified data sciences approaches. This work complements the emerging literature on digitalization and SPM by exploring the specific potential of data science, and machine learning approaches enabling sustainability data management and reducing data sharing reluctance. Furthermore, practical relevance is given, as this concept may provide practitioners with new impulses regarding DPP development and implementation. Version of record