Digital Twins

A digital twin is a virtual representation of an object or system designed to reflect a physical object accurately. It spans the object's lifecycle, is updated from real-time data and uses simulation, machine learning and reasoning to help make decisions.

It is composed of the following three elements:

• A physical entity in real space.
• The digital twin in software form.
• Data that links the first two elements together.

A digital twin functions as a proxy for the current state of the thing it represents. It also is unique to the thing represented, not simply generic to the category.

While many digital twins have a 2D or 3D computer-aided design (CAD) image associated with them, visual representation is not a prerequisite. For example, the digital representation, or digital model, could consist of a database, a set of equations or a spreadsheet.

The data link, often but not necessarily two-way, is what differentiates digital twins from similar concepts. This link makes it possible for users to investigate the state of the object or process by querying the data, and for actions communicated through the digital twin to take effect in its physical counterpart.

The Digital Twin Consortium, an industry association working to build the market and recommend standards, adds an important phrase to the basic definition: "synchronized at a specified frequency and fidelity."

These qualifiers refer to three key aspects of the technology:

• Synchronization ensures the digital twin and the represented entity mirror each other as closely as possible.
• The frequency, or speed, at which data gets updated in a digital twin can vary enormously, from seconds to weeks to on demand, depending on the purpose.
• Fidelity is the degree of precision and accuracy of the virtual representation and the synchronization mechanism.

Types of digital twins

Several ways of categorizing digital twins exist, but the following four categories, organized in a hierarchy, are the most common:

• Component twins (also referred to as part twins): The most basic level; it's not for simple parts like screws but for things like mechanical subassemblies.
• Asset twins (product twins): Two or more components whose interaction is represented in the digital twin.
• System twins (unit twins): Assets assembled into a complete, functioning unit.
• Process twins: Systems working together to serve a larger goal.

Some observers employ less abstract categories for digital twins and name them by the type of physical object they represent. Examples are infrastructure twins for highways and buildings, and network twins for processes such as supply chains that involve entities operating in a network.

Benefits of digital twins

Because they're virtual, digital twins can reduce the cost and risk of having to work on the physical things they represent. Further benefits include the following:

• Improved operational efficiency from having more timely data and faster, more effective production.
• More effective and less expensive R&D and reduced time to market because physical prototypes, which can be expensive and hard to modify, are replaced with virtual prototypes, which are more flexible and produce more data.
• Better product quality as digital twins help identify defects earlier in the production process.
• Longer equipment uptime from predictive maintenance enabled by analyzing individual digital twins instead of having to shut down all the equipment to isolate a problem.
• More accurate and efficient remote monitoring of facilities and equipment through integration with their digital twins.
• Improved product end-of-life processes, such as refurbishment and recycling, thanks to more accurate information about the age and contents of a product.

Challenges of digital twins

Organizations looking to develop digital twins face other daunting hurdles. Here are six of the biggest digital twin challenges:

• Data management. Data cleansing is often needed to make data from a CAD model or IoT sensor usable in a digital twin. A data lake might need to be established to manage the digital twin data and perform analytics on it. Deciding who owns the data is another problem.

• Data security. Digital twin data is timely and mission critical, but it also travels through several networks and software applications, which makes securing it at every stage challenging.

• IoT development. As the preferred data source for most of the real-time and historical data about an entity or process, IoT sensors are usually a basic requirement of digital twins. Implementing IoT presents its own challenges in network infrastructure and storage capacity, device and data security, and device management.

• System integration. Digital twins often begin life in CAD software but get more use in PLM, where they're used in post-sale services, such as performance monitoring and equipment maintenance. Numerous CAD and PLM software vendors have one-to-one integrations, but it isn't always adequate and smaller vendors might have no built-in integration.

• Supplier collaboration. The numerous participants in a supply chain must be willing to share information from their own production processes to ensure that the information in a digital twin is complete.

• Complexity. The data collected in the different software applications used by a manufacturer and its suppliers is not only voluminous, it changes often. Last-minute design changes, for example, must make it into the final version of the twin so the customer and manufacturer have the most current information.