DT standards, industry commons and knowledge graphs for circular traceability. The foundation for the circular traceability is available at
https://www.mdpi.com/2071-1050/16/1/396(si apre in una nuova finestra). Also, a method for producing the Plooto ontology automatically have been developed (see
https://ceur-ws.org/Vol-3647/SemIIM2023_paper_13.pdf(si apre in una nuova finestra)).
Business models, strategy and guidelines for digital circularity. Plooto can generate new revenue streams, through 1) new channels e.g. a marketplace, 2) new assets such as data and smart products, 3) pay per use/recurring subscription services and outcome-based models. Novel technologies (e.g. DPP) enable Plooto assets to provide economic benefits, i.e. 1) cost reduction through more efficient use of materials thanks to traceability and re-use, 2) revenue generation through new business models, new service offerings and recycled material 3) operational efficiency through supply chain optimization.
AI and Analytics for circularity. Different forecasting and anomaly detection algorithms have been tested including ARIMA/Residual Analysis and Isolation Forest. Continuous evaluation of combined techniques, aiming to better incorporate expert knowledge of real-life manufacturing and provide a supply chain view, with a focus on environmental sustainability and circularity is ongoing. This component has been integrated for WEEE within the Plooto platform and can compute energy consumption based on measurable process parameters.
RM-recovery and Waste Data Space. Our approach considers governance across data, business, and AI models for various aspects such as: supply chain collaboration, data sharing, and DPP information sharing. Additionally, AI model passports (explainable AI) are being incorporated as part of the governance services, applied on top of each DT layer.
Adjustable robust optimisation for production operation and waste treatment. A generic MILP model has been introduced and validated to assess the robustness of the proposed solutions under specific fluctuations in the energy cost. A two-stage stochastic model that extends the deterministic MILP is adopted to offer additional resilience in circular planning.
Design Science Theory for CRIS. Cutting-edge technologies and tools to enable real-time decision-making, monitoring, and certification of materials and products, facilitating sustainable and resilient manufacturing practices have been adopted. The CRIS has been deployed as part of digital transformation efforts and represents a strategic move to meet the growing demands for sustainability and resilience.
Governance for circular value chains. The pilot requirements related to data and value network collaboration and DPP models to IDS specifications have been analysed and used to derive the IDS compliant architecture and data models. Finally, basic processes (Discoverability, Negotiation, and Data Exchange) and implemented initial collaboration model between Plooto users have been defined to facilitate the collaboration and data sharing beyond Plooto.