Digital Twinning: How to Test Billion-Worthy Ideas with Minimal Risks

Digital twinning is nothing new. The concept was first invented in 1991 (yes, so long ago) and then introduced to the public in 2002. Since then, many things have changed but the idea remained the same: what if you could create a digital prototype of a physical object to safely test its performance and your ideas?

Today, with IoT and AI at our disposal, digital twinning has become as powerful as ever and offers a plethora of opportunities to business owners. Curious to know how to approach digital twinning and in which industries can this trend shine the most? Scroll down and let’s get started.

Digital Twinning: How to Test Billion-Worthy Ideas with Minimal Risks

Digital twinning explained: the definition and a bit of history

As already mentioned, digital twinning is the creation of a digital “copy” of a physical object. This copy replicates all processes that happen with the physical copy with an aim to monitor and test it. Such replication is possible due to the ongoing collection of real-time data about the physical object and the transfer of this data to the computer model aka the digital twin.

The first person to come up with the concept of digital twinning was David Gelernter who first mentioned it in the book ‘Mirror Worlds’ in 1991. Later, the concept was actually applied in manufacturing by Michael Grieves from the Florida Institute of Technology and Grieves was the one who introduced digital twinning to the public in 2002.

As you see, digital twinning has been around for quite some time but it’s been the last couple of years when the concept started gaining traction due to technological advancement. With the help of IoT, AI, and robotics, digital twinning is now a powerful solution for those companies that wish to accurately assess risks and test their businesses in a safe environment.

Three types of digital twinning

Depending on the purpose of use, we can single out three types of digital twins:

  • DTP, aka the digital twin prototype, is usually built before the physical product is created. Its main goal is to test how the product might behave and assess the risks.
  • DTI, aka the digital twin instance, is built when the physical product is already created. This solution helps run different tests to monitor various use cases.
  • DTA, aka the digital twin aggregate, is used to aggregate the information about a physical product with an aim to collect and monitor its parameters and capabilities and run prognostics. 

How exactly do you build a digital twin?

The process of developing a digital twin varies according to different experts but all of them seem to agree that there are three main stages of the development process. Though these stages are known by various names, we will refer to them as toolkit development, digital twin generation, and digital twin operation.

Toolkit development

A DT toolkit is basically a collection of technologies and tools that will allow you to build digital twins in the future. In addition to toolkit development, this stage also involves gathering information about a physical object to use it later for DT modeling. This information includes both information from sensors and information collected from other sources such as CAD ( computer-aided design) systems or point clouds.

The development of a toolkit is the core stage of the DT development process and it requires experienced data scientists to work on. As a result of their work (information gathering and creation of machine learning models and algorithms), you will have a mathematical model that can be used to simulate the physical object.

Note: the skills needed for toolkit development will include applied mathematics, machine learning, statistics, data science. 

Digital twin generation

At this stage, you will create an actual digital twin with the help of the toolkit that we have described above. The main goal here is to match the digital copy with the physical object by using the collected data and ensuring it corresponds to requirements. For that, you will need data engineers that will be able to use the designed ML model to build the needed prototype. The engineers will also be responsible for troubleshooting so the DT does not have critical errors during the release.

Digital twin operation

The last stage is the release of your DT into production. Once released, the digital twin will constantly receive real-time information about its physical copy and will provide valuable insights about the behavior of a physical object, its status, and possible issues to pay attention to. 

The benefits of deploying digital twinning

Before looking at real-life use cases of digital twins across industries, let’s quickly have a look at the biggest benefits:

  • Accurate risk assessment due to constant object monitoring and smart IoT sensors;
  • Immediate notification about any issues and hence, opportunity to proact instead of reacting to an issue or risk;
  • Remote monitoring of physical objects in real-time;
  • Risk-free testing of physical objects and risk prevention;
  • More accurate decision-making due to testing and real-time information update.

Digital twinning across the industries

As described above, the main idea behind digital twinning is safely testing your hypotheses and estimating whether you are doing the right thing. Now, to be more specific, let’s see how DT impacts different industries.


Logistics is among the industries where digital twinning shines at most. While there are many use case scenarios, we’ll focus on the biggest ones. By using digital twinning, you can:

  • Test different shipping methods and see how packaging will behave;
  • Check whether the shipment is safeguarded and whether there are any problem areas;
  • Monitor your storage and detect any problem areas;
  • Visualize your storage and optimize it;
  • Ensure the safety of goods by optimizing shipping and storage conditions.

A real-life example would be the Supply Chain Twin introduced by Google. The solution aims at making the data more visible and offering companies a more holistic view on their processes. As well, Google launched the Supply Chain Pulse module that provides real-time advanced analytics, interactive dashboards, and alert notifications. Needless to say, the module can be efficiently used together with the Supply Chain Twin and the solution is already deployed by such huge brands as Renault.


The use of DT in warehousing is similar to its use in logistics when it comes to storing goods: digital twinning helps optimize the storage space, receive real-time data about storing conditions, and plan the most efficient product transportation and placement.

Considering the popularity of implementing tech advancements into warehousing (RPA or driverless transportation as examples), digital twinning can help you estimate whether the introduction of such technologies would be a good idea or not. 


While not being so popular specifically in healthcare, digital twinning can still bring certain benefits to the industry. Digital twinning can be used in healthcare for the following reasons:

  • Genomic medicine for testing new drugs;
  • Drug dosage optimization;
  • Surgery simulations;
  • Optimizing daily hospital workflow to narrow the critical treatment window;
  • Optimization of supply chains;
  • Faster and safer drug research and development.


Another big area of DT is manufacturing. Normally, there are quite many risks involved in the manufacturing process and digital twinning can help reduce these risks greatly.

You can use digital twinning in manufacturing for predictive maintenance and quality control. Considering the constant inflow of IoT data, the digital solution can immediately display whether anything is wrong and can provide you with an accurate forecast for future estimation. And obviously, a digital twin of a physical plant can help you manage your resources more wisely and streamline your processes so you save up time and money while boosting productivity.

Summing up

Digital twinning is great but is also expensive. Would we recommend it to anyone? Probably not. Taking into consideration the amount of time and resources needed to create a digital prototype, we believe it’s the perfect solution for big enterprises. In this case, the cost of risk is pretty high and it will be more efficient to create a digital twin rather than pay for mistakes. For smaller companies, though it will probably be a better idea to either create a machine learning solution for drawing future forecasts or start with thorough business analysis and move to more sophisticated solutions afterward. 

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