By Maximilian Mayr, Lead Web Developer, Tributech Solutions GmbH
A holistic view of products, processes, or even the whole value chain to enable closed-loop engineering, cross-company digital services, and more is a crucial success factor for any data-driven organization. The next generation of digital twin technologies will play a key role in standardizing and automating business processes across current boundaries of systems and companies. This blog post introduces the general concept behind digital twins and the latest standards in this area.
A digital twin is usually a digital representation or abstraction of a real-world entity like a machine or device. They are already a part of the daily business in many industries and applications. However, most of today's digital twin solutions are siloed applications with a minimal scope regarding the life cycle or value chain. We are just beginning a robust growing technology ecosystem that will transform businesses worldwide by rapidly increasing their process efficiency and automation. A recent market analysis from grand view research predicts an annual growth rate of 42.7% from 2021 to 2028, expanding the market size from $7.14 billion to $86.09 billion.
The next generation of digital twin technologies and standards will enable applications with a holistic view along the value chain and life cycle. These technologies will also simplify development and business operations across boundaries of a system or company by standardizing access, configuration, and representation of physical assets, processes, and more. As a member of the Digital Twin Consortium, Tributech is working at the forefront of a rapidly growing ecosystem around new digital twin technologies.
In this blog post, we are going to look at the general concept of digital twins. In a nutshell:
- Digital twins are a digital clone/representation of an entity, asset, process, or more.
- Organizations can use them to standardize operations by introducing a common and business-specific vocabulary.
- Organizations can use them to run simulations without affecting the actual devices.
- Organizations should reflect changes to an actual device in the digital twin and vice versa.
For example, an organization can model an (IoT) thermometer as a digital twin by describing its properties, interactions, and data. In technical terms, a digital twin should be the Single Source of Truth (SSOT) of a physical asset. The twin offers the possibility to communicate between the digital and the physical world. Due to that, organizations should apply any property change n the twin on the actual device. Additionally, real-time data of the device should also be accessible on-demand via its digital twin.
Going back to our thermometer example, instead of looking at the device from a high-level perspective, it is also possible to represent the thermometer as a combination of different components like sensors, processors, and antennas. If each of these components again has its capabilities and data output defined using a digital twin, new possibilities would arise. By basing all device interactions on these digital twins, we can, for example, take one of the components of the actual device and replace it with its digital twin. In this way, we simulate how a device will react to different states and inputs of a replaced component. Modeling a complete production line on digital twins, an organization can see the production increases or decreases based on various parameters.
Available Digital Twin Standards
We define digital twin vocabulary based on ontology, which allows the description of categories, properties, and relationships between concepts, data, and entities. Ontologies are expressed in a machine-readable format like RDF, Turtle, JSON-LD, and others. The most commonly known ontology is the schema.org ontology created by Google, Microsoft, Yahoo, and Yandex that is used throughout the world wide web to add metadata to websites that can get picked up by crawlers. Google, for instance, uses this information to populate its knowledge graph. You can use the graph to display an infobox in your search, where the essential information is immediately displayed. Furthermore, it is used to improve the overall results by understanding how entities are connected.
The goal is to have entities that can describe themselves using a so-called self-description. This self-description, shortly called SD, should tell you everything you need to know about the digital twin.
Each of those standards has some heavy players behind them. We at Tributes evaluated all of the standards and decided to use the DTDL standard initiated by Microsoft. The main reason for choosing DTDL is that it allows us and our customers to easily describe their use-case as a custom vocabulary based on linked data. Combined with the limited set of building blocks (Interface, Telemetry, Property, Command, Component, and Relationship), organizations can use DTDL to describe physical or digital things, processes, products, or entities that offer enough flexibility keeping the complexity as low as possible. Additionally, the representation of a DTDL instance is always the same, eliminating the need for complex parsing logic on the edge side.
We learned that we could use digital twins to handle assets, entities, or processes standardized, but why stop there? Why shouldn't we take a step further and introduce the benefits like time and money savings to other areas?
One example we use internally is configuration twins for IoT devices and microservices. Instead of adapting service configurations, we only modify the corresponding twin manually, and the service automatically adjusts itself.
I hope this article provided some new insight into the next generation of digital twins and their possibilities.