Causal AI-powered Digital Product Passports for enabling a circular and sustainable manufacturing ecosystem

Digital product passport (DPP) has been recently introduced by policymakers (e.g., the European Commission) to advance sustainable business practices towards a circular economy (CE). As a newly introduced concept, DPP is still relatively high-level and vague. Therefore, its definition, information flow architecture, what relevant information needs to be stored, and how to use such information in the context of a circular and sustainable manufacturing ecosystem, etc., are still open research questions. This paper addresses these research questions by proposing a novel conceptual framework for DPP, facilitating seamless information exchanges among CE stakeholders, and providing a transparent and trustworthy basis for decision-making. Causal AI utilisation is proposed to extract causal relationships among sustainability/circularity KPIs comprehensively, encompassing raw material supply chain, circularity-compliant product design, manufacturing optimisation on the shop floor, and after-sale product usage optimisation. Seamless information exchange will be achieved through semantic interoperability and a comprehensive model of the whole supply chain by employing an ontology model. The causal AI approach is proposed to identify causalities among KPIs and other factors to predict environmental impacts. This way, a causal model integrating domain expert knowledge and causality discovered from measured data will increase the transparency/explainability of prediction/decision made by machine learning algorithms.