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Digital Twin — Industry Use Cases & Compatible Technologies


Mar 13, 2023 - 9 minute read

Digital Twin Blog
Kamil Buczek Development Guild Master

Development Guild Master and a member of the CTO Office at Objectivity. He has a lot of experience with .NET, Azure cloud and multiple Frontend technologies. He’s an enthusiast of innovation and new technology trends. 

See all Kamil's posts

2988 HC Digital Transformation 476X381


Table of contents

  1. What is a Digital Twin?
  2. Data Synchronisation — What Constitutes a Digital Twin
  3. Digital Twin Use Cases in Different Industries
  4. Identifying Viable Sources of Truth
  5. How Digital Twins Can Benefit From Other Tech Trends
  6. How to Start Digital Twin Implementation

What is a Digital Twin?

Digital twin is a powerful concept that’s revolutionising industries across the globe. As more and more companies begin to recognise its potential, barriers to its development are being broken at an increasing rate. When combined with other upcoming tech trends, it has an even greater potential. But what exactly is a digital twin (DT)?
As is often the case, there are many definitions of digital twin and most of them are correct and can be helpful. However, I’d like to use and analyse the definition proposed by the Digital Twin Consortium — an organisation dedicated to promoting the widespread adoption of digital twin technology and the value it delivers.

The definition reads as follows:

“A Digital Twin is a virtual representation of real-world entities and processes, synchronised at a specified frequency and fidelity.”

This concise definition emphasises two important aspects. Firstly, it signals that a digital twin can be a digital representation of not only objects but also of real-world processes and systems. Secondly, it highlights the crucial aspect of connecting these two worlds by drawing attention to the fact that the frequency, quality, and quantity of data is not predetermined but, rather, depends on its specific use case. Sometimes this can be complex data from jet engines transmitted thousands of times per second with incredible precision, other times it can be a simple metric showing the amount of waste in a production process.

Data Synchronisation — What Constitutes a Digital Twin

It’s important to distinguish a digital twin from a simulation or a digital model. The key to understanding the difference lies in synchronisation. It should occur automatically and mutually between the physical process or object and its digital representation. Simulations and digital models are often static, based on many assumptions detached from real-life, answering the question of "could be", without direct influence on its real "twin". On the other hand, a digital twin assumes active, automatic data exchange, answering the question of "what is", often retaining a much broader context than individual simulations (simulations and digital models are often components of a DT). This way, it remains constantly connected to the system it represents and is able to influence or change it.

Fig. 1 Digital Twin - Data Exchange Flow Classification. Source: The 3 Levels of the Digital Twin Technology (

The diagram above shows a classification based on a data exchange flow. It also includes a transitional stage called ‘digital shadow’. Implementing a digital shadow is often a good starting point. Information flowing from the real world to the virtual representation (without a feedback loop or the ability to automatically influence physical assets or processes) can also provide significant business value. This is often a good iterative approach towards digitalisation and achieving the "full" digital twin.

Digital Twin Use Cases in Different Industries

First, I’d like to dispel the myth that the digital twin concept is mainly applied in the manufacturing industry. It is true that Industry 4.0 brings an entirely new industrial revolution that’s rooted in data, automation and interconnectivity. This idea is very close to the subject of this article, and can benefit from a digital twin implementation to deliver on its promise. The digital twin technology can help achieve the necessary automation, intelligent decision-making, and create responsive environments allowing for providing more efficient, flexible, and customisable production processes and supply chains. Nevertheless, with rapid technological development and increasing digitalisation taking place across industries, there’s a place for a digital twin in almost every field.

For example, in healthcare, DT can support you in patient monitoring, telemedicine, clinical research, clinical management, and the optimisation of medical device usage. It can also have a positive impact on the entire drug manufacturing process. In the public space, digital twin is used to implement Smart City and Smart Building concepts, influencing the comfort and ergonomics of our daily lives (see case study).

The retail industry is experiencing a real data revolution. Apart from the obvious use of digital twin for optimising production and supply chains, whenever retailers build customer profiles, they’re in fact creating a virtual copy of reality. The input data for this model includes purchase history, shopping preferences, and other identifiable habits. But how to maintain the feedback loop, which is one of digital twin’s mandatory prerequisites? Well, it’s most often implemented using one of the most powerful IoT devices — a mobile phone. With its help, it’s possible to acquire and transmit all kinds of information to the customer in real-time, influence their choices, and ensure a constant feedback loop.

To access the benefits of digital twin implementation, you’ll have to find the right use case for your business. This may significantly differ depending on your company’s exact needs and the industry you’re operating in. The digital twin concept is very broad. Here’s a simple list of the most common use cases.

  • Predictive maintenance — maintenance schedules can be predicted by providing real-time performance and condition monitoring of equipment, resulting in reduced downtime and increased efficiency.
  • Risk management — digital twins enable the simulation and analysis of potential risks, such as natural disasters or equipment failures, allowing for the creation of risk mitigation plans.
  • Operations optimisation — optimisation of production processes, supply chain operations, and logistics, which can lead to increased efficiency and cost reduction.
  • Quality control — monitoring and controlling product and service quality by ensuring they meet customer expectations and industry standards.
  • Asset performance management — digital twins enable the management and monitoring of the performance of physical assets, such as buildings and infrastructure elements, identifying areas for improvement and ensuring peak operations.
  • Remote monitoring and control — remote monitoring and control of physical assets, such as industrial equipment or infrastructure, reduces the need for on-site controls and increases efficiency.
  • Training and education — virtual training environments can be enriched using digital twins, providing employees with a safe and controlled environment to practice and learn.
  • Research and development — digital twins allow for the simulation and testing of new products, processes, and designs, reducing the need for physical prototypes, and enabling faster development cycles.
  • Smart cities & smart buildings — digital twins can optimise the performance of smart cities and smart buildings, including transportation systems, energy distribution, public services, HVAC systems, lights, and security systems.
  • Virtual & augmented reality — digital twin technology enables immersive integration with virtual and augmented reality experiences, allowing users to interact with real-world objects in a virtual environment.

The above list is only a rough outline of the possibilities and potential applications of this technology. Nevertheless, it’s up to technical and business leaders to determine the desired scope of the digital twin implementation and in which areas it will generate the greatest value.

Identifying Viable Sources of Truth

Implementing business functionalities based on the digital twin concept requires a large amount of data. Unsurprisingly, IoT devices and other types of hardware are often first selected as data sources for DT. After all, IoT devices seem to be the perfect candidate for providing the feedback loop needed to integrate the digital twin with the real world. However, it's essential to recognise that IoT isn't the only relevant data source. In fact, other equally important data sources are often necessary and can enhance the designed digital world. Let’s list them here:

  • Engineering models — such as CAD and 3D models, can provide detailed information about the physical characteristics of the asset (size, shape, building materials, etc.).
  • Historical data — maintenance records, and operating logs, can be used to provide information about the asset's history (usage, repairs, maintenance, etc.).
  • External data & external systems — information about external conditions that can impact the asset or the process it’s supporting (temperature, sales data, logistic data, etc.).
  • Simulation models — can be used to simulate assets’ behaviour under various conditions
    (thermal, mechanical, and electrical behaviour, etc.).

As you can see, the list of viable sources extends far beyond IoT devices. It’s important to remember about the various other options during digital twin implementation because data, which we often already have, is very valuable in the digitalisation process. It has to be utilised and integrated with the delivered solution.

How Digital Twins Can Benefit From Other Tech Trends

Now I’d like to focus on why I believe that the digital twin concept, in combination with other current trends, has even greater potential. Let's analyse the impact that other trends on the market can have on the development of digital twin:

  • AI-powered digital twins — by incorporating artificial intelligence (AI) into the digital twin technology, you’ll get access to more advanced models capable of predicting, maintenance planning, optimising, and simulating complex systems. However, there’s another, maybe even more promising opportunity — digital twins capable of autonomous decision-making, which are able to react to changing conditions and make real-time adjustments. This way, you can automatically close the feedback loop between real and virtual objects and processes, unlocking the full potential of the digital twin technology.
  • Quantum computing could significantly impact digital twin’s potential by increasing data processing capabilities. One of the main challenges related to digital twins is the need for processing and analysing large amounts of data in real-time. Traditional computers can be a bottleneck in this area. However, by implementing quantum computing, you’ll have access to incredible computational power for data processing and analysis. This, coupled with an innovative approach to building algorithms and solving complex problems, helps quantum computing create more advanced digital twin models. All this can result in more accurate simulation results, including maintenance and system performance predictions.
  • The development of 5G and IoT devices allows for creating more accurate and up-to-date digital replicas of physical assets and processes by leveraging the increasing availability of high-speed, low-latency wireless connectivity. With 5G networks, greater performance, availability, and a popularisation of IoT devices, digital twins can support a broader range of use cases and new business models.
  • Edge computing already has a significant impact on the development of the digital twin technology. Since DT’s success relies heavily on collecting and processing data from sensors and other IoT devices, transferring all this data to a centralised place for processing can be hindered with significant latency and bandwidth limitations. Edge computing addresses this issue by enabling data processing and analysis to occur at the edge of the network (or even on the sensor itself), closer to where the data is being generated. Further development of edge computing should help identify more use cases for the digital twin technology.
  • Blockchain can help to create a decentralised digital twin ecosystem. Combining these two technologies creates a secure and transparent digital ledger (data source) that can store and share data between multiple parties involved in the digital twin ecosystem, such as manufacturers, operators, and maintenance providers. Blockchain can also enable new business models, as well as the development of new services and revenue streams, by facilitating secure and efficient transactions.
  • Virtual & augmented reality — the question is whether a digital twin must provide visualisation in the form of 3D objects? Technically speaking, the answer is ‘no’, but for us humans, this is the most natural method of receiving and visualising objects from the real world. Therefore, 3D visualisations are often used in the context of digital twin implementations. The development of virtual and augmented reality (VR/AR), allows for an even more immersive and interactive experiences of digital twins that are more natural and intuitive for the users. With VR/AR, the users can explore and manipulate the digital twin in real-time, making it easier to identify potential issues, optimise processes, and make informed decisions. This can be especially valuable in industries such as manufacturing, where complex equipment and processes can be challenging to understand without hands-on experience.

How to Start Digital Twin Implementation

There is no one-size-fits-all guide to digital twin implementation. The number of solutions and platforms that support DT's development is already quite large. Leading cloud providers and PaaS platforms offer support for this concept, making it easier to start and integrate, especially when it comes to hardware and data from IoT sensors. Since the digital twin concept is so broad and has numerous applications, there is currently no universal solution. The entire process requires an individual approach and often custom development. The first step should be a thorough analysis of your organisation’s needs, leading to the identification of an MVP that will reveal the business value of the planned solution.

To carry out a full digital twin implementation, you will need in-depth expertise with data and software development. Seeking a trustworthy technology partner is one of the options to ensure access to such capabilities. If you’re interested in identifying the most beneficial digital twin use cases for your business, feel free to visit our Data & AI page. Our data experts will happily answer any questions you may have and are able to help with planning and implementation.

2988 HC Digital Transformation 476X381
Kamil Buczek Development Guild Master

Development Guild Master and a member of the CTO Office at Objectivity. He has a lot of experience with .NET, Azure cloud and multiple Frontend technologies. He’s an enthusiast of innovation and new technology trends. 

See all Kamil's posts

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