Exploiting the Digital Twin in the Assessment and Optimization of Sustainability Performances

Digitalization has shown the potential to disrupt industrial value chains by supporting real-time, risk-free and inexpensive inputs to decision making towards enhanced companies’ productivity and value networks flexibility. Developing a reliable and robust digital replica of the physical systems of the value chain is one of the most advanced (and challenging) approaches to digitalization, condensed in the concept of Digital Twin (DT). DT plays a fundamental role in creating a data-rich environment where simulation and optimization procedures can be run. With DT expected to become a commodity in the coming years, simulation and optimization become therefore a more accessible instrument for the improvement of manufacturing and business processes also in small enterprises with limited investment capacity. While scientific literature has analysed the adoption of DT in the optimization of products lifecycle, no contributions have yet focused on the exploitation of DT to improve the sustainability performances of whole value chains. In this paper we propose a reference framework where DTs built upon process and system data gathered from the field, allow to quickly assess the sustainability performances of both existing and planned production mixes and to compare achievable impacts with changing processes and technologies, thus enabling advisory features for sustainability-aware decision making in structured, multi-entity value networks. Internal validation will be deployed referring to real case studies.