What is a digital twin?

This blog is the first in a three-part Aras Primer on the digital twin. Part 1 will review what a digital twin is, Part 2 will present the digital twin in various industrial contexts, and Part 3 will bring the information back around and focus on use cases.

A digital twin is the virtual representation of a physical product or asset that reflects the real-time configuration and can trace maintenance activities and operational performance that have occurred to the product over time, including decisions made during the engineering and manufacturing processes.

It's important to note that a digital twin differs from a digital model. A digital model reflects a view of a product to support design and engineering scenarios. It does not reflect the final product's reality or how it evolves once it is in service. For example, during the engineering and manufacturing of the product, exceptions, part replacements, supplier changes, and engineering design changes occur, creating a significant gap between the digital model used in the design phase and the real-world product.

Two distinct elements are essential to understanding the digital twin. The performance side, which includes time series data streaming back from sensors where predictive analytics capabilities are used, is called the Digital Twin Performance. Today, much focus and investment have been placed on the performance side of the digital twin solution through pilots and deployments for a combination of capabilities, like cloud storage, data lakes, gateways, artificial intelligence and machine learning, analytics, and dashboards. But often, these pilots don’t scale, or the value degrades over time when deployed.

The current as-running asset configuration—called the Digital Twin Configuration—is equally important and provides the context for interpreting, analyzing, and simulating time series data. Without this, you lose the individual story each product has to tell. The configuration of every product in operation can differ, from vehicle to vehicle, machine to machine, plane to plane, and ship to ship. The longer something has been out in the field, the more it changes. To effectively analyze the sensor data, you need accurate context. Digital Twin Performance cannot happen in isolation; instead, it requires the individual Digital Twin Configuration of the managed asset.

Your Digital Twin Configuration data will be quite different depending on what business value you are trying to deliver to customers. It is different because the use case requirements are very diverse—predictive maintenance, performance optimization, and over-the-air software updates require vastly different Digital Twin Configurations. Therefore, the tools built in the past will not effectively support your digital twin's future.

In a perfect world, the Digital Twin is created when the physical asset is completed with its manufacturing processes and serialized information recorded. If it needs capturing during another phase, there are plenty of opportunities to catch and manage changes during the commissioning and operation of an asset. The key is to capture and manage it; this is the context you need for practical predictive analytics implementations.

What data is needed for a digital twin?

Everything must be connected through to the final manufactured product’s digital twin. This digital twin configuration has the ability to link all product data history, decisions made, who made them and why, including CAD models, simulations, and requirements. The initial version of the digital twin can be updated as changes to the asset are made so organizations have a complete historical record of each individual asset. 

Once the product is in service, additional data can be collected and leveraged for analysis. This process includes operations data based on sensors placed on intelligent connected products or the Internet of Things (IoT). Other information can be collected from the assets' surroundings, including temperature, humidity, weather, etc. 

Finally, maintenance records, service manuals, CAD and simulation models, and job plans can be connected to the digital twin configuration to continue to build each product's unique history to support failure and predictive maintenance requirements and operational decisions.

Stay tuned for Part 2 in this series. In the meantime, if you’re interested in learning more about the Digital Twin, check out our solutions page.