Hype or necessity: Digital Twin

The concept of a “digital twin” is a significant topic of discussion lately.

A digital twin, attracting attention in important sectors such as manufacturing, urban and factory/business management, energy and even healthcare, can be defined as a real-time virtual copy of a person, machine, building, factory, or even a city.

The digital twin, created in the virtual world is continuously updated with data collected and classified from the real world and used to simulate the future behavior of the object.

What is a digital twin used for?

Digital twins are frequently used to simulate events, predict potential future scenarios, create models related to them, monitor complex structures and systems in real-time, and manage operations.

The second part of the question is a bit more complex.

The meaning of the digital twin concept can be interpreted differently for each company and institution. While a digital twin provides significant gains in terms of organization for large manufacturing facilities and smart city projects, it may not provide the same benefit for a smaller-scale workshop.

Therefore, it is necessary to look at how digital twins work and where they are useful. This detailed look will also help everyone understand whether a digital twin is necessary for them.

The emergence of the digital twin concept

The foundations of the digital twin concept were laid in the early 2000s with the idea of ​​better managing processes in all phases of a product, from design to the end-user stage. This approach evolved over time and its scope of use expanded.

One of the most effective areas where this concept came to life was the space industry. Institutions working on space-related projects, in particular, began using digitally produced copies of real systems to test them without putting them at real risk. Therefore, complex and expensive systems began to be tested and tried in a safe and controlled environment without any damage.

As technology made digital twins more widespread, logical, and accessible to everyone, we began to talk about them more. 

The decrease in the cost of sensors used to collect data, the widespread use of cloud systems for data storage, the ease of storing and accessing data in the cloud, and the support of artificial intelligence made it easier to quickly make this collected data meaningful and understandable.

When you add the possibility/need to develop more efficient, sustainable, and more controlled systems to all this convenience, digital twins spread even faster.

What are the benefits of a digital twin?

The biggest and most important benefit of a digital twin is that it makes it possible to take preventative measures before a problem arises. Regardless of what is being twinned, because it is constantly monitored, potential malfunctions or problems can be predicted in advance, and these can be resolved before they occur, preventing disruptions.

Unlike real-world tests with real products, simulations in a digital environment allow for more comprehensive predictions about developed products or a better understanding of the consequences of decisions made.

  • Usage areas of digital twins
  • Do you really need a digital twin?

What are the applications of digital twins?

You can think of a digital twin of a manufacturing plant as a copy of the plant’s production line. This technology monitors all machines on the line in real time, identifies where slowdowns occur, and before a new product is added to the line, runs certain scenarios to determine potential risks in the production process. It then offers suggestions and optimizations to mitigate these risks and take necessary precautions. This increases production efficiency, prevents production errors, and brings products to market faster.

Sensors placed on machines can transmit data from the machines to the digital twin, providing advance warning of impending failures. This allows for maintenance planning before a failure occurs, rather than repair afterward. Maintenance is less expensive than repair, reduces downtime, and minimizes the likelihood of delays in scheduled projects.

For buildings, digital twins make it easier to track the building from the design and construction phase to delivery. It becomes possible to foresee the building’s energy consumption, when and which parts need maintenance, and potential risks associated with the building.

Nowadays, buildings are being constructed larger, requiring more efficient and secure management methods. Therefore, developing predictions based on a digital twin can help optimize construction costs.

A digital twin of an electricity grid or power plant can be used to predict when and how much energy will be produced, when that energy will be needed, and other consumption-related predictions. This helps optimize energy consumption, reduce waste, minimize outages, and ensure a balanced energy supply.

A digital twin of a city provides important insights into where traffic congestion will occur, where infrastructure improvements are needed, and how to develop emergency scenarios for the city, helping to understand which areas are more vulnerable to risks. Having these predictions makes it possible to build more planned and livable cities.

In healthcare, the digital twin approach involves creating a digital persona using patient data. This twin, fueled by patient data, allows for more comprehensive treatment options and enables the early detection and prevention of future illnesses.

Doctors can simulate different treatment scenarios on a digital twin to choose the most appropriate approach or understand why other treatments are unsuitable without conducting real experiments on the patient.

Personalized treatment is one of the jobs of the future, and the concept of a digital twin holds a very special and important place in the development of this treatment.

Why don’t all digital twins succeed?

The most fundamental reason why not all digital twin projects succeed is the inability to provide the large amount of data required for the project. If the data collected from the field is incomplete, outdated or cannot be correctly read by the system to which the twin is connected, it cannot be said that the digitized model accurately represents its twin in the field. In such a case, the digital twin becomes a more confusing problem instead of a facilitator.

It should not be overlooked that the initial setup cost of a digital twin, which operates in real-time and is updated with data from the field with a small delay, will be high. Furthermore, managing and maintaining these systems requires as much knowledge and experience as creating them. Therefore, it is not wrong to say that projects undertaken without a specific goal or those started with large strides will cause more harm than good.

Security is a crucial factor often overlooked when developing digital twin projects. Unless it is a very private and closed project, digital twins are generally internet-connected systems, and being connected to the general network brings with it data privacy issues. The risks in digital twin projects, especially in critical areas such as sensitive technology and healthcare, are more comprehensive and should be evaluated, with greater emphasis on safety procedures.

Do you really need a digital twin?

The answer to this question varies for each organization.

The first question you need to clarify to find the right answer is: What is the problem you want to solve?

For example, you own a factory and your machines frequently stop, increasing your operating costs; or you operate an energy grid and energy consumption in a part of the city is out of control.

If there isn’t a clear and measurable problem, you probably don’t need a digital twin, and investing in one would be unnecessary.

After finding the answer to the first question, you need to understand whether this problem can be solved through data collection, processing, and analysis.

In other words, can you collect meaningful data from the sensors and systems you install in the field, and can you roughly calculate the benefits you will gain from this data?

A practical and low-budget way to understand this is to start a pilot project. If you see tangible benefits when you test it with a single machine, a single production line, a single grid, or a single building, you can expand the project and grow the digital twin.

These days, infrastructure is a really significant expense, and it requires considerable thought to consider its cost.

Do you have the time, budget, and awareness of the risks involved in creating a digital twin? Also, if a simpler and more economical solution works for you, you may not need a digital twin.

In short, creating a digital twin for everything is illogical. However, those planning to undertake this project can start with critical units and lines where productivity issues are prevalent.

How to start your digital twin project?

The first thing to clarify at the beginning of the project is what you want to achieve.

Reducing downtime for machines in the factory, optimizing energy usage, reducing waiting times at traffic lights?

If your goal isn’t clear, you don’^t see the benefit of creating a digital twin; the system you create will become complex and useless. This time, you can start by focusing on the concrete problem that needs to be solved, not the whole picture.

Instead of digitizing and creating a twin of the entire system or line at once, start from a small point and create a twin of only a part of the system. Pilot programs allow you to understand what data needs to be collected, check the status of the sensors, and verify whether the data obtained is accurate. Then, a display screen is created, and simulation and prediction capabilities are added through visualization.

After starting the pilot application, results are measured for a period, necessary calibrations are performed, and improvements are made. If the data obtained is promising and truly beneficial, new steps are taken in the digital twin project.

The development process should include planning for system security, authorization, and how the collected data will be stored, and necessary precautions should be taken. In short, creating a digital twin should be considered not as a huge leap, but as a controlled, measurable, and gradual journey.

…

When used correctly and in the right place, a digital twin is a very powerful tool. If it solves a clear problem, has a robust data flow and database, and is well-planned, it is quite useful.However, does every organization need a digital twin? The answer is clearly no. Small business’ and firms that lack the appropriate conditions and budget to provide data to the system should prioritize focusing on simple data collection and analysis solutions…

References and further reading:

Photo by Tony Dube on Unsplash

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