Understanding Digital Twins: An In-Depth Guide From ABBYY Perspectives

What Is a Digital Twin?

Tamas Mahr

July 19, 2024

In this digital-first era, you’ll find a wealth of data lying dormant in every component, platform, and system involved in an organization’s business operations. The challenge—and undoubted opportunity—lies in converting this rich data into comprehensive insights to support business transformation and operational excellence.

By leveraging performance data and combining it with insights gathered through process intelligence tools, digital twins provide an unparalleled, remote view of any process flow at a low cost.

In this article, we’ll unpack what a digital twin is, how it works, the different types of digital twins, and its most crucial benefits.

What is a digital twin?

A digital twin is a virtual model of a physical object or system. It spans the object’s full lifecycle and is updated using real-time data to simulate its behavior.

The primary purpose of a digital twin is to remotely monitor an object’s performance, enabling you to identify potential problems and make informed decisions to enhance the original physical asset.

How does a digital twin work?

A digital twin works by creating a virtual representation of a physical object or system, mirroring its real-time behavior, performance and features. To create a digital twin, the physical object – for example an aircraft engine – is fitted with smart sensors to collect data on its functionality. This data could include its temperature, energy output, pressure, and more. The system will process and actively apply this information to the virtual model.

Once it has all the relevant data, the virtual model can be used across the object’s lifecycle to:

analyze its performance
run process simulations
test and monitor potential improvements.

The knowledge gained from the digital twin is then fed into improving the original, physical object.

Digital twins use a number of technologies to create the virtual representation. These include:

Internet of things (IoT)

IoT refers to the network of physical objects (such as devices, vehicles, and appliances) that are embedded with sensors and network connectivity, allowing them to gather and share data. IoT facilitates communication between connected devices and the cloud, enabling real-time updates and monitoring through a software platform.

Cloud computing

Cloud computing is the delivery of computing services (such as servers, storage, software, and databases) over the internet or cloud. Cloud computing enables storage, access, and aggregation of the vast amounts of data that IoT sensors collect, which is essential for creating and managing a digital twin.

Artificial intelligence (AI)

AI is technology that enables computers and digital devices to solve cognitive problems and perform tasks that typically require human intelligence. The AI technique that’s integral to creating a digital twin is machine learning (ML).

ML creates statistical models and algorithms, enabling computer systems to perform tasks and make predictions based on patterns, without explicit instructions. ML technology analyzes the vast amounts of data gathered by IoT sensors to uncover patterns. Both AI and ML are employed to create digital twins, enabling them to deliver insights such as performance optimization, efficiencies, and maintenance.

Simulation

Simulation of business processes to test potential improvements can help businesses avoid wasting human resources to correct poor performance not anticipated by process designers. Process simulation enables the automatic creation of a true process digital twin that allows businesses to understand the impact of changes before they are made.

Types of digital twins

There are various types of digital twin —some of which only replicate one part of an object’s performance, while others offer insights on how overall systems work together. Multiple types of digital twins can run together within a system or process because they each provide crucial virtual representations.

The four main types of digital twin:

Component twins

Component twins, also known as parts twins, are digital representations of individual components within a system, such as a motor in a wind turbine. They’re the smallest units of a digital twin, but essential to understand and manage each part of an operation.

Asset twins

According to digital twin technology, an asset is formed when two or more components work together within a system. Asset twins provide a virtual model of these interconnected components, so you can analyze their interactions and build actionable performance insights to inform decision-making.

System twins

System twins are a higher-level digital twin, which show how different assets come together to form a larger functioning system. A system twin provides visibility of how assets interact, enabling you to identify performance enhancement opportunities.

Process twins

Process twins provide a big picture view of how multiple components, units, and assets collaborate to create an entire system, while also enabling the user to inspect different, individual parts of the process. Process twins can blend together multiple process twins to represent a part of the process or the overall process in one. For example, you can virtually reproduce a manufacturing facility to gain insight into how units work together. At this level of magnification, you’ll identify asynchronism, inefficiencies, and delays that may be affecting your system.

Benefits of digital twin technology

Digital twins provide several short-and-long-term benefits to enterprises across all industries. The most notable benefits of digital twins are:

Interactive process visualization

Digital twins differ from traditional, retrospective process views by offering real-time visualization of any process flow or system. You’ll see how your operational processes work in detail, from end to end, so you can respond to process deviations and unexpected behaviors.

Predictive insights

Digital twins provide full digital visibility of an object or system, whether it’s an engine in a machine or a whole manufacturing facility. With insight into how thousands of components work together, you can predict timings of activities and process outcomes with greater accuracy and confidence.

Increased speed of innovation

You can accelerate your physical prototyping and production phase by creating a digital twin to run scenarios on.

Combining this with process simulation , which involves simulating potential process changes to evaluate their impact on an entire business process, you’ll see how your overall system responds to proposed changes before you start production. This will reduce time-consuming trial-and-error cycles and enable implementation teams to focus on optimal process designs.

Improved efficiency and performance

The smart sensors fitted to components will flag problems as they occur, building a bank of performance data for you to monitor and review. Access to these real-time performance insights will enable you to respond to problems before equipment collapses, to mitigate the risk of failure and ensure consistent performance productivity.

Digital twin applications and case studies

Several industries can benefit from using digital twin technology to visualize their physical objects or systems in the virtual world. These industries include:

Manufacturing

Digital twin technology is a crucial tool throughout the manufacturing life cycle to enhance operations and improve product quality.

A manufacturer could create a digital twin to test and finalize product designs, reducing the need and cost of physical prototypes. Alternatively, you could simulate changes like introducing robotic machinery or reordering assembly stations to see how they affect assembly line efficiency.

Banking

Banks and financial services organizations need to continuously increase operational efficiency in order to stay competitive.

For instance, a bank may want to speed up its account opening process to reduce application abandonment rates. It can use a digital twin to visualize its current process and simulate potential improvements, to support its digital transformation and monitor the overall customer experience over time.

Healthcare

Hospitals and healthcare facilities have countless use cases for digital twins.

A single patient journey involves several components, which can be mapped out through process discovery. Before making adjustments, such as modifying treatment schedules or staff allocation at peak times, the hospital can use a digital model of its patient experience to run simulations. The model will illustrate the impact of proposed solutions on quality standards and the patient experience.

Insurance

The process-driven nature of insurance functions makes it particularly valuable to find opportunities for optimizing efficiency. Insurance organizations can use a digital twin to model their claims cycle. You’ll be able to identify and address bottlenecks or slow manual processes that will speed up workflows and improve customer wait times.

Get started with a digital twin

Unlock the full transformative potential of your business processes with ABBYY Timeline . Our process intelligence solution utilizes the digital twin concept to provide an interactive visualization of your business processes, with real-time data updates and 100 percent visibility.

A distinguishing feature of our platform is process simulation. You can simulate potential process optimizations and measure their impact on your entire business. Our platform also allows you to embed execution rules, KPIs, SLAs, and compliance standards for continuous monitoring and automatic responses to deviations. This end-to-end, proactive approach to process discovery will equip you to accurately predict activity timings and anticipate potential issues before they arise .

ABBYY Timeline will enhance your business productivity and customer satisfaction by automatically unearthing the causes of process behaviors and providing advanced tools to remedy them. Our cloud-based platform offers detailed process insights without the need for coding or manual intervention, reducing the time and cost associated with traditional process assessments. Packaged in a point-and-click interface, Timeline empowers you to quickly identify inefficiencies and make informed, data-driven decisions to sustain optimal business output.

Tamas Mahr

Head of ABBYY Timeline Development, Administrative Director of ABBYY Hungary

Connect with Tamas on LinkedIn .

Editorial note: This blog post was originally published on July 21, 2021. On March 30, 2023, we updated this blog to reflect the new capabilities of ABBYY Timeline 6.0 including new UI, deeper analytics, and an advancement of ABBYY’s process intelligence capabilities, process simulation. On June 25, 2024, we updated this blog with insights on four key steps of process mining and benefits why to use process mining, we provided clarified explanations of how process mining works with process intelligence and how to optimize business processes, and answered three frequently asked questions all around process mining, RPA, and AI.

Frequently asked questions

What’s an example of a digital twin?

Digital twins have become indispensable for increasing production efficiency in the manufacturing industry. One example of a digital twin in manufacturing is creating a virtual replica of a production line in an automotive factory.

Each component and machine in the production line will be fitted with sensors to collect data on performance metrics, such as temperature and operational speed. This data is then fed into a digital twin, to build a digital representation of the whole production line.

Engineers can use the digital twin to monitor the real production line’s performance, simulate potential scenarios, and predict mechanical or maintenance issues. For example, you can use the digital twin to simulate changing the order of the production line, to see how it would affect operational efficiency and staff downtime, before implementing the changes in the real world.

What’s the history of the digital twin?

Digital twins were first discussed in David Gelernter’s 1991 book, Mirror Worlds. But it was in 2002 that Dr. Michael Grieves, who was on faculty at the University of Michigan at the time, formally introduced and announced the digital twin software concept at a Society of Manufacturing Engineers conference in Troy, Michigan.

Several years later, in 2010, NASA’s John Vickers gave the concept its name, “digital twin,” in a 2010 Roadmap Report. Beyond coining its name, NASA is widely recognized for pioneering use of the technology during its space missions in the 1960s. Digital twins were used to create, test, and study virtual simulations of spacecraft.

Since then, the utility of digital twins has continued to grow its profile. In 2018, Gartner spotlighted digital twins as one of the top 10 strategic technology trends for 2019 . Over time, the technology has proven to be one of the most dynamic process mining tools (process mining is a data-driven technique used to understand, track, and improve processes by analyzing data from information systems to see how a process works).

What’s the difference between a digital twin and a virtual twin?

Although the terms “digital twin” and “virtual twin” are often used interchangeably, there are notable differences between the two.

A digital twin is a real-time, digital replica of a physical object or system. Sensors are placed on the physical objects, and by gathering their performance data, you can monitor and simulate their behavior. On the other hand, a virtual twin is a static model of a real-world product, asset, or process. The model is often based on theoretical scenarios, used from design through to testing.

There are two key differences between the technologies:

Application: Digital twins provide real-time data updates to enable continuous monitoring and real-world analysis. In contrast, virtual twins work best for hypothetical scenarios, to simulate potential outcomes.
Interaction: Digital twins perform well with minimal manual interference, enabling you to passively receive and monitor its data. Whereas, virtual twins require more active involvement to adjust parameters or even directly engage with the virtual environment.

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Understanding Digital Twins: An In-Depth Guide From ABBYY Perspectives