Towards Model-based Digital Twins for Sustainable Production

Translating the Sustainable Development Goals (SDGs) into company-level sustainability objectives for production remains a challenge due to varying abstraction levels, complex interdependencies, and the absence of established standards. Production planning needs decision-making support that can balance sustainability objectives across varying levels of granularity and across all production processes. Existing research does not sufficiently address how to balance the environmental impact and economic performance of production processes while accounting for both business goals and their ambition to contribute to the SDG targets. We envision using digital twins to explore, analyze, and improve sustainability factors in production environments. Our proposed approach integrates ontologies, knowledge graphs, and model-driven digital twin engineering methods to realize Sustainable Production Digital Twins (SPDTs). In this paper, we illustrate the application of the SPDT concept, using an example from textile production, focusing on balancing energy consumption with other sustainability and performance objectives. The resulting digital twin provides insights into achieving sustainable production processes that balance environmental and economic concerns.