Digital twins pave the way for a sustainable economy

Abstract

Digital twins will boost sustainability and traceability in the manufacturing and process industry. The information needed for this, such as processed materials, past faults and used recipes, is already stored in a wide variety of systems. However, there is no central access point or overview of this information, which means that researching their interrelationships is only possible at great expense in terms of time and money. With an Enterprise Knowledge Graph, all relevant artifacts are linked and their dependencies and effects are made visible at an early stage. As the most native form of knowledge representation, Knowledge Graph technology makes it easy to bridge data silos, making the digital twin a reality. Increased demands for sustainability and traceability have far-reaching consequences for supply chains in the manufacturing and process industries. One example of this is the European End of Life Vehicles (ELV) Directive for the recycling of end-of-life vehicles within the European Union (EU). A second example is the deactivation of Recommendation 019 in the context of the International Material Data Systems (IMDS), which means that since March 2021, all manufactured and resold components contain an exact breakdown of their constituent parts and their respective quantities.
As a digital representation of tangible and intangible business objects (e.g. products, components, assets, customers, suppliers, services or processes), digital twins are a great help in complying with these new guidelines. They also serve to capture the relationships between business objects, and thus their business context, over entire lifecycles. For example, if it becomes known that some components from a supplier exceed the maximum limit of hazardous substances, a digital twin can prevent production from having to come to a complete standstill. To do this, only the parts that
contain components from the affected suppliers are identified. Depending on the material or product, this opens up new possibilities for reuse and sometimes unexpected opportunities for producers not only to comply with current guidelines, but also to expand their activities with circular business models.

So what is stopping manufacturers from creating a digital twin?

Consistently tracking all component- and process-relevant data, from the design to the scrapping of a vehicle, is difficult. The relevant information is stored in different data silos such as ERP systems, service applications, IoT applications, CRM systems or fleet management systems, whose structure rarely matching. A transparent and data-driven supply chain is the key to the required transparency. Linksphere offers the possibility to link all relevant artifacts of the value chain using a knowledge graph, to visualize dependencies and to provide stakeholders with the information centrally. A digital twin is thus an enterprise knowledge graph-based digital representation of the semantic context reality. This makes the supply chain transparent and enables the forward-looking control of the flow of goods and processes,  compliance with guidelines and minimization of risks.
We show how such a digital twin is built and used using specific customer projects in the automotive industry.