Why Digital Twin Matters

Why Digital Twin Matters

1.     Digital twin is at the heart of Industry 4.0

2.     Digital twin is the best design pattern for information modelling of our physical world.

3.     Digital twinning unifies all digital capabilities around our physical subject of interest to provide actionable insights for decision making and/or hyper automation.

Industry 4.0, also known as the fourth industrial revolution, refers to the integration of advanced technologies, for example:

·       Horizontal and vertical integration: increasing communication and collaboration, improving efficiency and responsiveness, and optimizing value chains

·       Industrial Internet of Things: collecting real-time data, monitoring and optimizing operations continuously and remotely, and enabling the integration of sensors, edge devices, and other IT, OT, and ET systems

·        Big data and AI analytics: Mining value from structure & unstructured data, identifying patterns & trends for optimization, and simulating the best course of actions & predicting the future conditions and events with actionable insights/intelligence

·       3D models and data visualization: expediting product design and prototyping; creating rich user experiences for training and operations, and enabling the better understanding and interpretation of data and intelligence

Industry 4.0 technologies also include Cybersecurity, Robotics & Drones, Cloud & Edge Computing, Cyber Physical Systems, enabling smart manufacturing and interoperability.

According to Digital Twin Consortium (Capabilities Periodic Table), digital twin capabilities fall into 6 areas: Data Services, Integration, Intelligence, User experience, Management, and Trustworthiness, which cover almost all industry 4.0 technologies. It means digital twins, if implemented for some complicated use cases, could integrate all these technologies and then produce the desired outcomes that industry 4.0 promised.

A Digital Twin represents the characteristics and behaviors of the physical counterpart. The first digital twin capability or function that might pop up into our mind is model, be it visual or 3D model, information model, analytical or ML model. The combination of these models with Data provides a 360-degree view (Single Version of Truth) of the physical twin with the characteristics and behaviors that the expected digital twin solution will comprise.

In order to provide valuable and sustainable Digital twin functions and services, we’d better start with the basic features (nature) of the physical object, which are built as information models. IDTA, Industrial Digital Twin Association has been instrumental in creating standards and templates for asset information models or sub models as Asset Administration Shell (AAS). The properties, functions, behaviors, knowledge and formulas should be modelled and then provide the proper context for the second key digital twin ingredient: Data, for better analysis and value creation. The modelling languages could be OWL, Azure DTDL, or other data structure/schema used for Ontology Management and Knowledge Graph creation.

At this point, it should be clear that digital twining is more than Connectivity and Synchronization.

Digital Twinning unifies all digital capabilities around the physical subject of interest to provide actionable insights for decision making and/or hyper automation with the capabilities of simulation, optimization, and prediction to reflect the past, understand the present, and see into the future.

I would like to conclude the post with a statement from Digital Twin Consortium (with minor modifications):

Digital twins are motivated by outcomes, tailored to use cases, powered by integration, built on data and models, guided by domain knowledge, and implemented among IT/OT/ET systems.