This blog is the third in a three-part primer on the Digital Twin. Part 1 reviewed what a Digital Twin is, and Part 2 presented the digital twin in various industrial contexts. Part 3 focuses on use cases.

What Industries use Digital Twins?

Most industries can pursue opportunities to leverage Digital Twins in their specific industries. Here are a few industry examples for inspiration:


NuScale Power’s mission is to improve the quality of life for people worldwide by providing scalable advanced nuclear technology for producing electricity, heat, and clean water. This case study follows NuScale's journey as they implement the Aras platform. NuScale will be the first nuclear power plant to be designed and managed with PLM as the backbone for its single source of data.  This will enable them to provide an as-built Digital Twin configuration of the reactors at the time of delivery with traceability to all product data created during the engineering and manufacturing of the product.

Oil and Gas

For Caltex Oil Tools, a change in ownership and corporate leadership led to a strategic shift in focus to highly specialized engineering capabilities and solutions. The team recognized the importance of PLM in developing improved engineering processes—and Aras was the solution to drive their digital transformation. In the future, they will deliver an as-built Digital Twin configuration with the physical product, enabling them to develop field service support.

Industrial Manufacturing

Many industrial companies are still managing their product-related data in spreadsheets, which leads to a loss of visibility, traceability, and the risk of incorrect information. This is because the volume of product data is constantly increasing. As a consequence, these companies find themselves struggling with data silos. This was the case for the fast-growing Finnish compressor manufacturer Tamturbo. The company decided to switch to Aras Innovator® to create "a central station for all of our product-related data,” enabling them to create a Product-as-a-Service (PaaS) business model.

Is Digital Twin IoT?

Getting the latest as-running condition of the asset operating in the field using Internet of Things (IoT) sensors is of great importance to aircraft designers and OEMs in identifying potential design flaws and facilitating design changes. Comparing real-time data against the as-running Digital Twin configuration allows for the ability to apply various simulation models to predict the behavior of the physical asset and create different operating scenarios to decide when to plan or defer maintenance in order to keep the asset from failing. This is a significant step forward in the industry’s use of predictive maintenance.

Maintenance is usually performed based on the OEM’s recommendation on a fixed schedule, resulting in increased maintenance costs. Predictive maintenance provides a flexible schedule-based approach to predict the probability of failure. Maintenance can be scheduled to prevent the asset from failing before it happens. This decreases the cost of maintenance cost while also increasing the effective utilization of the asset. This is called prescriptive maintenance.

Promising technologies such as IoT, predictive analytics, and simulations all have value, but only when used in the context of the individual asset. This means building and maintaining a digital record of the configuration of products as they are manufactured, maintained, and upgraded. This is the key to keeping the asset in the field, and it's Digital Twin synchronized.

The Digital Twin is updated whenever a significant change happens to the asset. For example, if electric motor serial number #001 is replaced with electric motor serial number #002, the corresponding change is made to the Digital Twin configuration. The electric motor, serial number #001, becomes part of the Digital Twin’s history and is forever linked with the Digital Thread.

With a constantly up-to-date Digital Twin configuration, simulation models can be built to the specific characteristics of a particular asset and coupled with Time Series and IoT data generated from the physical asset to predict potential failures.

What is the difference between a model and a Digital Twin?

One emerging trend is to use simulation models, or CAD models created during the engineering phase of the product lifecycle, as the Digital Twin of an asset. The concept is that comparing these digital models with operational data while running different simulation models may result in the identification of failures. See our Digital Twin Primer Part 2 for more information. 

The Digital Twin in aircraft design

Building a Digital Twin configuration creates an opportunity to bring modeling and simulation techniques to the operations phase of an asset. These capabilities can be used to compare operational data, simulate the behavior of the asset now, as well as the future state of the asset if the behaviors continue. This allows organizations to improve accuracy of operations and maintenance decisions to optimize their aircraft fleets and can be used by OEMs to support future product improvements in next-generation aircraft. 

Modeling and simulation capabilities are separate from a Digital Twin

They are often used in the design phase and referred to as digital models, not twins. To improve accuracy, a Digital Twin configuration is required so that you are modeling and simulating the exact representation of the asset. Connecting the Digital Twin configuration to the Digital Thread of product data allows operations and maintenance to access original models and simulations created during the design phase for use as a comparison.

What technology can create a Digital Twin?

You cannot rip and replace legacy software systems to accelerate the building of Digital Twins. There can be hundreds of systems and related data globally supporting the various aspects of the product lifecycle, encompassing different disciplines. The answer is a platform approach.

Platforms can enable organizations to move quickly and build flexible individual Digital Twin configurations. They can adapt and change as your business, or the environment they operate in, changes, or the profile of the product itself changes―as in the case of upgrades.

If you don’t use an overlay approach, the other issue you may encounter is building Digital Twin configurations with static metadata models. Organizations are quickly finding that when they build an individual Digital Twin working with a static data model, they have trouble changing it to meet new needs. The data model needs to be flexible to keep the Digital Twin up to speed.

Existing PLM systems can trap valuable information, or worse, and users stop capturing some of the data and decisions within them. Important information can be trapped in existing systems because they are closed. This makes it difficult to create meaningful relationship connections between all of a product’s digital assets and their revisions across the lifecycle― –bill of material(s), parts, software, electronics, CAD models, documents, requirements, process plans, service manuals, and maintenance history.

Do Digital Twins predict the future? 

With context and traceability in place, it’s time to predict the future. There is an emerging opportunity to do this with the many predictive analytics tools out there today, but those become more relevant when you use them to predict behaviors based on individual contact with a product or system of products.

As simulation moves out to the field, it becomes operational simulation, which is about trying to understand what a product’s been through in its lifetime that might affect its operation once we apply an update. Based on an individual configuration of an individual asset and use simulations, you then can predict what will happen if we replace a part, or update software.

The other opportunity here is simulating what will happen to a currently operating product. Let’s say that we start to see failures in the product once we reach a certain temperature. We can then use that data to predict what will happen if the temperature goes up even further. It's simulating not only the current version of the Digital Twin but also the future based on real-time product feedback.

Are Digital Twins the future?

Many articles and analyst reports describe the proliferation and growth opportunities related to Digital Twin technology. The bottom line…it has taken off. This means organizations in a variety of industries have expressed or are testing specific technologies related to the Digital Twin. A multitude of solution providers have flooded the market with messages about their technology, which creates confusion as to where to start, and answer the ultimate question, where is the business value? The current consensus is that although many projects have been implemented or are in the process of being implemented, the business value has been limited. 

Why? Most pilot projects lack the ability to scale. They see success initially because they pick a specific asset or location (manufacturing site) to create a Digital Twin view, then start visualizing and gathering data to these physical spaces digitally. When they try to scale across the enterprise, it becomes very difficult because they are thinking about one side of the equation – its performance. Many Digital Twin pilots started here before understanding the total picture. Implementing Digital Twin solutions focused on understanding the generated data, how to add more sensors to gain additional data, then analyzing this information using advanced analytics, artificial intelligence, and machine learning capabilities. 

Scaling your Digital Twin success

This is all for naught if you want to scale your Digital Twin success. There is an equally important side of the digital twin market – the Digital Twin configuration. This is the as-built and as-maintained asset model and creates the context required for the Digital Twin performance. It is necessary to accurately represent analytics, visualizations, and simulations to support specific use cases across any industry.  When existing pilots fail to scale, they haven’t thought about how to sustain the Digital Twin configuration of the physical asset as it changes over time. This makes analysis increasingly inaccurate, creating a loss in the initial business value they were attaining during the pilot/proof of concept phase.

Only when organizations get the total picture and begin to implement the Digital Twin configuration technologies in conjunction with digital twin performance will accurate and sustainable business value emerge.

What to learn more?

Here is a list of recommended reading:

The Digital Twin Time Machine

Putting Failure into Context with the Digital Twin

Digital Twins: The Landing Pad, the Toolkit, and the Palette

The Digital Twin: Everything, Everywhere, All at Once (Part 1)

The Digital Twin: Everything, Everywhere, All at Once – Part 2

How to improve maintenance planning and diagnosis with digital twins

Predict and simulate the future with digital twins

Ships, trains, and airplanes; using the digital twin to support product as a service