Representing Executable Circular Economy R-Strategies using Behavior Trees Embedded in Digital Product Passports

The increasing emphasis on sustainability and resource efficiency in industrial systems has driven the adoption of Circular Economy (CE) principles, including R-strategies such as repair, reuse, remanufacturing, and recycling. These strategies are critical for extending product lifecycles and reducing material waste. However, existing approaches to representing R-strategies are predominantly static or descriptive, lacking the ability to support traceability in automation systems. This raises the central research question: How can R-strategies be formalized in a standardized and machine-interpretable format that integrates seamlessly with emerging digital infrastructures such as the Digital Product Passport (DPP)? This study proposes a methodology in which R-strategies are modeled as Behavior Trees (BTs) (modular, hierarchical control structures capable of representing dynamic process logic) and encoded in XML format for integration within the Asset Administration Shell (AAS). Embedding executable BTs into the DPP enables adaptive decision-making and consistent knowledge transfer across stakeholders. The approach is validated using a real-world use case in the SmartFactory-KL, where a “repair” strategy is applied to a 3D-printed toy truck semitrailer. When printed with poor tolerances, the semitrailer is repaired using a soldering method instead of being discarded, and the Product Carbon Footprint (PCF) is recalculated to reflect the energy used in the repair step. By representing the repair instruction as a BT and embedding it within the DPP, the method enables standardized knowledge sharing across the value chain, thereby supporting the development of intelligent and sustainable manufacturing systems.