DATA-DRIVEN METHOD BASED ON A PROCESS MINING APPROACH FOR AUTOMATED DIGITAL TWIN GENERATION, OPERATIONS, AND MAINTENANCE IN CIRCULAR VALUE CHAINS

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Unité de recherche

SEFRI

Numéro de projet

23.00144

Titre du projet

DATA-DRIVEN METHOD BASED ON A PROCESS MINING APPROACH FOR AUTOMATED DIGITAL TWIN GENERATION, OPERATIONS, AND MAINTENANCE IN CIRCULAR VALUE CHAINS

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Résumé des résultats (Abstract) (Anglais)	The transition towards a Circular Economy is estimated to represent a $4.5 trillion global growth opportunity by 2030. Digital Twins availability is largely recognized as an accelerator and an enabler of Circular Economy in business and in production, but significant challenges are still standing in relation to its development within the present technological framework, the needed skill sets, and the implementation costs. AUTO-TWIN addresses the technological shortcoming and economic liability of the current system-engineering model by 1) introducing a breakthrough method for automated process-aware discovery towards autonomous Digital Twins generation, to support trustworthy business processes in circular economies; 2) adopting an (International Data Space) IDS-based common data space, to promote and facilitate the secure and seamless exchange of manufacturing/product/business data within value-networks in a circular-economy ecosystem; 3) integrating novel hardware technologies into the digital thread, to create smart Green Gateways, empowering companies to perform data and digital twin enabled green decisions, and to unleash their full potential for actual zero-waste Circular Economy and reduced dependency from raw materials.

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Texte

Résumé des résultats (Abstract)
(Anglais)

The transition towards a Circular Economy is estimated to represent a $4.5 trillion global growth opportunity by 2030. Digital Twins availability is largely recognized as an accelerator and an enabler of Circular Economy in business and in production, but significant challenges are still standing in relation to its development within the present technological framework, the needed skill sets, and the implementation costs. AUTO-TWIN addresses the technological shortcoming and economic liability of the current system-engineering model by 1) introducing a breakthrough method for automated process-aware discovery towards autonomous Digital Twins generation, to support trustworthy business processes in circular economies; 2) adopting an (International Data Space) IDS-based common data space, to promote and facilitate the secure and seamless exchange of manufacturing/product/business data within value-networks in a circular-economy ecosystem; 3) integrating novel hardware technologies into the digital thread, to create smart Green Gateways, empowering companies to perform data and digital twin enabled green decisions, and to unleash their full potential for actual zero-waste Circular Economy and reduced dependency from raw materials.