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What are Digital Twins and How Can You Benefit From Them?

Digital twins

Digital Twins is another booming concept that is set to become a USD 137.67 billion market by 2030!

But what exactly are digital twins?

And do they make any difference to your business?

Chances are that you know the answer is yes. But let’s understand the very basics of the concept to understand exactly how much your business stands to benefit from it.

What to expect: In this guide, we will explore some of the common concepts associated with digital twins and how this tech interacts with IoT. We will also look at some examples that show how the technology is being used across industries.

What is a digital twin?

Imagine you have a toy car, but it's not just any toy car, it's a perfect copy of your actual car. Every little detail, from the engine to the paint color, is exactly the same. Now, imagine you could test out different scenarios with your toy car without actually affecting your real car. You could see how it performs in different weather conditions, see what happens when you change the tires, or find out what happens if you tweak the engine a bit.

That's the basic concept of a digital twin. A digital twin is a virtual replica of a physical object, system, or process. It's created using real-time data from the physical counterpart. This digital model allows you to test different scenarios, anticipate problems before they happen, and plan for the future.

For example, NASA uses digital twins to monitor and maintain their spacecraft and other systems. They can run simulations on the digital twin to predict potential problems and find solutions before they happen in real life. This helps them reduce risks and costs, as well as improve the overall performance and lifespan of their spacecraft.

Digital twins are being used in many industries today, from manufacturing to healthcare. They can help businesses optimize their operations, improve their products and services, and make better decisions. They can also provide valuable insights and predictions, which can be crucial in a world where data is becoming increasingly important.

Remember, digital twins are not just 3D models. They are dynamic and connected to their physical counterparts, receiving real-time data and providing real-time feedback. This makes them incredibly valuable for testing, analysis, and decision-making.

How Do Digital Twins Operate?

The creation and operation of digital twins is a multi-layered approach. It involves several steps such as:

  1. Data Collection: This is the first step in the process of creating a digital twin. Data about the real-world object or system is gathered from multiple sources. These sources could be sensors installed on or in the object, which can monitor everything from temperature to pressure, and from movement to light exposure. For example, if we're creating a digital twin of an airplane engine, sensors might measure RPM, fuel consumption, temperature, and vibration among other things.
  2. Digital Model Creation: The collected data is then used to build a digital model of the object. This could be a 3D visual representation or even a mathematical model of how the system behaves. This model isn't just a simple copy; it's designed to behave in the same way the real object does. So in our airplane engine example, the digital twin would model the internal processes of the engine, such as fuel combustion and mechanical motion.
  3. Data Integration: This is where the "twin" part of the "digital twin" comes into play. The digital model is linked with the real-world object, using IoT (Internet of Things) technology, and is continually updated with real-time data from the sensors. So if our airplane engine starts running hotter than usual, the digital twin will reflect that change instantly.
  4. Simulation and Analysis: The digital twin can now be used to simulate different scenarios and analyze their outcomes. For instance, engineers can use the digital twin of the airplane engine to see what would happen if they changed the fuel mix or modified a component. This kind of virtual testing can save time, money, and resources.
  5. Feedback and Optimization: The results from the digital twin can be used to optimize the performance of the real object or system. In the airplane engine example, if a simulated modification improves fuel efficiency in the digital twin, engineers might decide to make that change in the real engine.
  6. Continuous Learning and Updating: As more data is collected and analyzed, the digital twin can "learn" and improve over time. This makes it an increasingly valuable tool for predicting future performance and optimizing the object or system.

In essence, a digital twin is a living, learning digital replica of a real-world object or system. It functions as a dynamic tool for simulation, analysis, and optimization, continually improving as it receives more data from its real-world counterpart.

Digital twins vs simulation

Digital twins and simulations are both valuable tools used in engineering and data analysis, but they are used for slightly different purposes and function in different ways.

A simulation is a mathematical model that imitates a situation or process. It's a way to predict behavior and outcomes under different conditions. However, a simulation is typically a standalone model. It does not continue to receive real-time data updates once it has been created, and it's not necessarily linked to a specific physical object or process. Instead, it's often used to test theories, explore possibilities, or predict outcomes in a general sense.

For example, an aerospace engineer might use a simulation to model how an aircraft would behave under different wind conditions. They'd input the aircraft's specifications and the wind conditions, then run the simulation to see the results. But once the simulation is created, it doesn't change or update - it's a static model.

A digital twin, on the other hand, is a dynamic, digital replica of a specific, real-world object, system, or process. Unlike a simulation, a digital twin is continuously updated with real-time data from its physical counterpart. This allows the digital twin to mirror the state of the physical object or system as it changes over time.

In the aerospace engineer example, they might create a digital twin of a specific airplane. Sensors on the airplane would continuously send data to the digital twin, allowing it to reflect the airplane's condition in real-time. The engineer could then use the digital twin to run simulations, predict future outcomes, or optimize the airplane's performance.

So while simulations and digital twins both involve modeling and predicting behavior, the key difference is that a simulation is a static, standalone model used for general testing and prediction, while a digital twin is a dynamic, connected model that mirrors a specific, real-world object or system in real-time.

Digital twin vs. predictive twin

Another concept in the industry that is confused with digital twins is predictive twins. While they are similar, there are various differences.

A predictive twin is a specific type of digital twin, but with an added layer of predictive analytics. While a standard digital twin provides a real-time digital replica of a physical object or system, a predictive twin goes a step further by using data from the digital twin, along with machine learning and artificial intelligence, to predict future states of the physical object or system.

Let's break it down a bit further:

Digital Twin: A digital twin is a digital model of a physical asset, process, system, or device that is continuously updated with real-time data. It serves as a mirror of the physical object, providing a means to simulate, analyze, and optimize the object or system.

Predictive Twin: A predictive twin enhances the capabilities of a digital twin by using predictive analytics. It leverages historical and real-time data, statistical algorithms, and machine-learning techniques to anticipate future conditions or events.

For example, consider a digital twin of a wind turbine. The digital twin would replicate the turbine's real-time operations, reflecting changes in wind conditions, turbine speed, power output, and more.

On the other hand, a predictive twin of the wind turbine would use the data from the digital twin, along with historical data and machine learning algorithms, to predict future performance under varying conditions. It could forecast potential maintenance needs, predict energy production based on anticipated weather patterns, and more.

So, while all predictive twins are a type of digital twin, not all digital twins are predictive. Predictive twins offer an advanced level of insight and future-casting that can help organizations anticipate issues and optimize performance more proactively.

What are the different types of Digital Twins?

Digital twins come in various types and they are typically used in different contexts, based on their purpose and complexity. Let's go through the three main types of digital twins:

Digital Twin Prototype (DTP): These digital twins are primarily used in the design and development phase of a product. They are used to understand how a product behaves under different conditions even before the actual product is manufactured. For example, automakers might use a DTP to analyze the aerodynamics of a new car design or to simulate crash tests.

Digital Twin Instance (DTI): These are tied to a specific instance of the product in the real world and are used to monitor and optimize the operation of the product during its lifecycle. For example, a wind turbine in a wind farm might have a DTI that collects data about its operation, such as wind speed, turbine RPM, and energy output. These digital twins can also help in predictive maintenance. According to a report from Deloitte, predictive maintenance could reduce maintenance costs by 12%, reduce unplanned downtime by almost 9%, and extend the lives of machines by years.

Digital Twin Aggregate (DTA): DTAs take multiple DTIs and aggregate them to understand and optimize the operation of a complex system. For example, a DTA of a wind farm would take the data from the DTIs of each individual wind turbine and use it to optimize the operation of the entire wind farm. In smart cities, for instance, DTAs can help manage and optimize city-wide systems like traffic, power grid, and water supply. According to MarketsandMarkets, the smart cities market is projected to grow from USD 410.8 billion in 2020 to USD 820.7 billion by 2025, at a CAGR of 14.8% during the forecast period, and digital twins will play a key role in this growth.

These are just broad categories, and the specifics can change based on the complexity of the system or object being modeled, and the use cases for the digital twin. Although depending on industries and function the types of digital twins can be further classified.

Asset Twins

Asset Twins can be thought of as a type of DTI, where each asset in the real world has its own digital twin. These are digital replicas of individual assets such as a machine in a factory or a vehicle in a fleet.

System Twins

System Twins can be thought of as a type of DTA. They represent an entire system or process that's made up of multiple individual assets. They provide a way to understand the interactions between different assets and optimize the whole system.

Process Twins

Process Twins are digital representations of specific processes, whether they're manufacturing processes, business processes, or other sequences of events. They allow companies to simulate and optimize these processes.

Component Twins

Component Twins are digital representations of individual components within an asset or system. For example, a Component Twin might represent a specific part of a machine, like a pump or motor. They allow for very granular monitoring and optimization.

Remember, the terms used to describe different types of digital twins can vary across industries and can even overlap to some extent. The key point is that digital twins, regardless of type, are digital replicas of physical entities that provide real-time data, which can be used for simulation, analysis, and optimization.

Applications of Digital Twins

The applications of this technology are endless and can be applied to several industries.


Digital twins can simulate manufacturing processes, helping to increase efficiency, reduce waste, and improve product quality. For instance, Siemens has been using digital twins to optimize their manufacturing processes. They use a digital twin to simulate, test, and optimize their product designs and production processes in a virtual environment before implementing changes in the real world, saving time and resources.


Digital twins can be used to create personalized models of a patient's specific health condition. Philips, for instance, is working on creating digital twins for patients that can simulate how different treatments might affect their specific health conditions. This could allow doctors to optimize treatment plans and predict how a patient might respond to a treatment based on their digital twin.


Digital twins are being used in the design, testing, and maintenance of aircraft. For example, Rolls-Royce uses digital twins of its jet engines to predict when maintenance will be needed. By monitoring the digital twin, they can provide maintenance services proactively before a problem occurs, which reduces unexpected downtime and increases efficiency.

Smart Cities

Digital twins can model and simulate the complex systems within a city, such as energy consumption, traffic flow, or waste management. The city of Singapore, for instance, has created a digital twin of the entire city to optimize urban planning, infrastructure, and environmental impact. This digital twin allows city planners to test different scenarios and optimize city systems for efficiency and sustainability.


In the energy sector, digital twins can optimize performance and predict maintenance needs for equipment such as wind turbines. For example, GE Renewable Energy uses digital twins to predict the performance of its wind turbines under different weather conditions. This allows them to optimize energy production and reduce unexpected downtime.

Each of these examples demonstrates how digital twins can optimize performance, reduce costs, and predict future outcomes, making them a valuable tool in virtually any industry.

Digital Twins and IoT

Digital Twins play a significant role in the Internet of Things (IoT) ecosystem. IoT devices collect and share data, which when analyzed, can be used to optimize processes, make informed decisions, and create better products or services. This is where digital twins come in.

In an IoT framework, a digital twin serves as the bridge between the physical and digital world. It is a dynamic software model of a physical thing or system. These digital twins use data from connected IoT devices to represent the state of a device or system in real time. For example, a digital twin of an IoT-connected HVAC system in a building could provide real-time data on temperature, humidity, power usage, etc. This data can be analyzed to optimize energy usage and comfort.

In the context of IoT, digital twins are often known as device shadows. This term is primarily used in the Amazon Web Services (AWS) IoT suite and refers to a JSON document used to store and retrieve the current state information for an IoT device.

Like a digital twin, a device shadow allows applications to interact with a device, by providing a RESTful API to read and set the device state. This means the applications can communicate with the device using the device shadow even when the device is offline. When the device gets back online, it can sync with its shadow to get the latest state that was requested by the application.

So, whether we call it a digital twin or a device shadow, the concept is the same: it's a digital representation of a physical device or system that reflects its current and historical state, and allows interaction with the device or system in a more abstracted way.

Digital Twins and IIoT

Digital twins, also known as device shadows, are especially valuable in the Industrial Internet of Things (IIoT).

They offer a way to collect, visualize, and analyze data from industrial equipment in real time, as well as to store historical state information for these devices.

Here's a practical example:

Imagine an industrial setting with hundreds of IoT-connected machines. Each machine has a device shadow, which is a digital representation of the machine that mirrors its current state, records historical data, and even predicts future performance.

When a machine operates, it continuously sends data about its state - such as temperature, pressure, speed, and other operational parameters - to its device shadow. This can be done through an internet connection, using various IoT communication protocols.

Now, let's say that a certain machine in the factory begins to overheat. The machine's device shadow reflects this condition in real-time. Alerts can be set up to notify operators when certain parameters (like temperature) exceed safe levels, allowing them to intervene before the situation becomes critical. This can prevent damage to the machine, avoid costly downtime, and ensure the safety of workers.

But device shadows in IIoT go beyond just mirroring the current state of a machine. They can also be used for predictive maintenance and system optimization. By analyzing historical data stored in the device shadow, along with machine learning algorithms, you can predict when a machine is likely to fail or require maintenance. This allows for issues to be addressed before they cause downtime.

Furthermore, the use of device shadows in IIoT enables the remote operation of industrial machines. Operators can send commands to the device shadow, which then communicates the changes to the physical machine. This makes it possible to operate and manage industrial machinery from anywhere in the world, greatly increasing operational flexibility.

To sum up, in the context of IIoT, device shadows serve as the key enabling technology that allows for real-time monitoring, predictive maintenance, remote operation, and system optimization of industrial machinery.

Benefits and risks of digital twins

Digital twins in IoT offer several significant benefits for businesses, such as:

  • Predictive Maintenance: By monitoring the real-time data from a digital twin, businesses can predict when a piece of equipment is likely to fail or require maintenance. This allows them to proactively address the issue and prevent costly downtime.
  • Improved Efficiency: Digital twins enable businesses to optimize their operations by simulating different scenarios and understanding the potential impact of changes before they are implemented in the real world.
  • Innovation and Product Development: Digital twins can speed up the product development process by allowing businesses to test new ideas and designs in a virtual environment before creating a physical prototype.
  • Reduced Costs: By identifying potential issues early, optimizing operations, and improving the product development process, digital twins can help businesses significantly reduce costs.
  • Improved Customer Service: With digital twins, businesses can provide better service to their customers. For example, they can use real-time data to troubleshoot issues remotely or use predictive maintenance to address problems before they impact the customer.

However, along with these benefits, there are also some potential risks and challenges:

  • Data Security: As with any technology that involves the collection and transmission of data, there is a risk of data breaches. Businesses must ensure they have robust security measures in place to protect the data collected by their digital twins.
  • Data Accuracy: The effectiveness of a digital twin is dependent on the accuracy of the data it collects. If the sensors collecting the data are faulty or the data is not processed correctly, it could lead to inaccurate conclusions.
  • Integration Challenges: Integrating digital twin technology into existing systems can be complex and time-consuming. It may also require significant changes to a business's current processes and systems.
  • High Initial Investment: Developing and implementing digital twin technology can require a substantial initial investment. However, this cost can often be offset by long-term benefits.

In conclusion, while digital twins in IoT offer significant benefits, businesses must also consider and mitigate the potential risks. By doing so, they can leverage the technology effectively to improve their operations and drive innovation.

Digital Twins Case Studies

Here are two examples of how companies have successfully implemented digital twin technology:

General Electric (GE)

General Electric (GE) is a company that's made extensive use of digital twins, particularly in their Predix platform. Predix is designed to manage and analyze data from industrial machines, which it does by creating digital twins of each asset. These twins are continuously updated with data from their physical counterparts, allowing GE to monitor equipment health in real time and predict future issues.

One particular application of this technology has been in their wind farms. By creating digital twins of individual wind turbines, GE was able to account for factors such as weather conditions, historical performance data, and design differences. Using these digital twins, GE could optimize the performance of each wind turbine, ultimately improving the efficiency of the entire wind farm by up to 20%.

Royal Dutch Shell

In the oil and gas industry, Royal Dutch Shell has been a pioneer in the use of digital twin technology. One of their notable implementations is the creation of a digital twin for their Prelude Floating Liquefied Natural Gas (FLNG) facility. The Prelude is the world's largest floating offshore facility, and the digital twin includes an exact replica of the facility, including all its equipment and systems.

The digital twin allows Shell's engineers to simulate different scenarios, monitor the performance of equipment, and perform predictive maintenance. For example, if they wanted to see how a change in the production process would impact the overall system, they could test it on the digital twin first. Additionally, because offshore facilities are hard to reach, having a digital twin allows engineers to troubleshoot issues remotely, saving time and reducing risk.

Wrapping Up

As we've seen, digital twins - or device shadows - represent a powerful tool for businesses looking to leverage their IoT data for predictive maintenance, system optimization, product development, and improved customer service. However, implementing and managing this technology can be a complex task.

A tailored service in device shadow and management, such as Bytebeam, can play a crucial role in this context. By providing a platform that facilitates the creation, monitoring, and management of device shadows, Bytebeam can help businesses get actionable insights that significantly improve operational efficiency.

Moreover, Bytebeam's services also help mitigate some of the risks associated with digital twins, such as data security and integration challenges. They allow businesses to focus on leveraging their IoT data to drive innovation and improve processes, while Bytebeam takes care of the complexities of managing the underlying technology.

If you would like to know more about Bytebeam, feel free to reach out to us and let us know if you think digital twins could enhance your business.