Discovering the potential of digital twins


Discovering the potential of digital twins

A virtual representation created to accurately represent a physical thing is called a digital twin. The item under study is equipped with a variety of sensors that are connected to key functioning regions. These sensors generate information on various performance facets of the actual object, which is subsequently transmitted to a processing system and applied to the digital replica. The virtual model may then be used to run simulations, analyze performance problems, and suggest potential modifications in order to get useful insights that can later be applied to the real physical device.

How Digital twins are different from simulations

While both digital twins and simulations use digital models to reproduce a system’s many operations, a digital twin is truly a virtual world, making it far richer for research.

The main distinction between a digital twin and a simulation is scale: a digital twin may perform as many meaningful simulations as necessary to explore numerous processes, whereas a simulation normally only analyses one specific process.

There are yet more variances. For instance, real-time data is typically not advantageous for simulations. However, digital twins are built around a two-way information flow that begins when object sensors provide the system processor with pertinent data, and continues when the processor shares insights with the original source object.

Digital twins are able to study more problems from far more vantage points than standard simulations can because they have better and constantly updated data related to a wide range of fields, combined with the added computing power that comes with a virtual environment, which has a greater potential to improve products and processes in the long run.

Types of digital twins

There are various types of digital twins depending on the area of application. It is common to have different types of digital twins co-exist within a system or process. Some of the types of digital twins are discussed below:

Component twins

The fundamental building block of a digital twin and the simplest illustration of a working component are component twins. Parts twins are essentially the same thing, although they relate to significantly less significant parts.

Asset twins

An asset is created when two or more components function together. With asset twins, one can examine how these elements interact, producing a wealth of performance data that can be analysed and transformed into useful insights.

System twins

System or unit twins, which lets to see how various assets combine to create a whole, functional system, are the next degree of magnification. System twins offer visibility into how assets interact and may make performance suggestions.

Process twins

The macro level of magnification, called process twins, reveals how systems interact to build a whole manufacturing plant. Are those systems coordinated to run at maximum efficiency, or will delays in one system have an impact on others? Process twins can be used to pinpoint the timing plans that eventually affect overall efficacy.

Benefits of Digital Twin:

Digital twins can result in improved R&D as they can produce a wealth of data regarding expected performance results, facilitating more efficient product research and creation. Before beginning production, businesses may use this data to get insights that will help them make the necessary product improvements.

Digital twins can aid in monitoring and mirroring production systems even after a new product has entered production, with the goal of reaching and maintaining peak efficiency throughout the whole manufacturing process, hence leading to higher efficiency.

Digital twins can also assist producers in determining how to handle items that have reached the end of their useful lives and require final processing, such as recycling or other actions. They can be used to improve the product life cycle.

Applications of Digital Twins

Manufacturing: Digital twin has the potential to transform the industry’s present structure. The way things are created, manufactured, and maintained is significantly impacted by digital twins. While lowering throughput times, it optimises and improves manufacturing efficiency.

Industrial IoT: Businesses that have used digital twins in their operations may now track, monitor, and manage industrial systems digitally. In addition to operational data, the environmental data that the digital twins collect, such as location, configuration, financial models, etc. , aid in forecasting future operations and abnormalities.

Healthcare: Digital twins and IoT data may be used to monitor patients, perform preventive maintenance, and deliver individualized treatment while also reducing costs.

Smart cities: The planning and implementation of smart cities using digital twins and IoT data help to improve economic development, effective resource management, ecological footprint reduction, and overall quality of life for citizens. City planners and policymakers may use the digital twin concept to plan smart cities by learning from diverse sensor networks and intelligent systems. They also use the data from the digital twins to make future judgments that are well-informed.

Automobile: The creation of a virtual representation of a linked vehicle may be done in the automotive industry using digital twins. It records the behaviour and operational data of the car and aids in the analysis of both the linked features and the overall performance of the vehicle. Additionally, it aids in providing clients with a service that is genuinely individualised or tailored.

Retail: In the retail industry, a positive customer experience is crucial. By building virtual twins of consumers and dressing them in clothes, the use of digital twins may significantly improve the retail customer experience. Additionally, digital twins aid in more effective energy management, security deployment, and in-store planning.


The global digital twin industry was valued at $6.5 billion in 2021, and is projected to reach $125.7 billion by 2030, growing at a CAGR of 39.48% from 2022 to 2030, according to allied market research. In terms of industry, the automotive and transportation sectors held the biggest market share for digital twins in 2021 and are projected to increase by 2030. The broad application of digital twin technologies in the automotive and transportation industries for the development of digital models of linked cars is primarily to blame for this market domination.

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