The business world will always appreciate being able to anticipate risks. The Digital Twin model has gained great relevance by offering the possibility of digitally representing an object, process, or physical service.
Michael Grieves coined the Digital Twin concept in 2002 during a talk on the possibility of creating digital representations of physical systems and constructing those representations as entities on their own.
This technology has been so well received that Gartner included the Digital Twin solution among its top 10 strategic trends in 2018. Gartner assured that “well-designed digital twins based on business priorities have the potential to improve significantly business decision making. In addition, the consultancy encouraged business architecture and technology innovation leaders to consider the Digital Twins in their Internet of Things strategy.
What is a Digital Twin?
A Digital Twin is a digital representation of a physical product, process, or service used to perform simulations and accurately understand the scope of the entity being represented.
The motivation behind Digital Twin technology is being able to anticipate any eventuality so as to make better decisions, identify opportunities for improvement, and collect data and valuable information. Digital Twin technology resembles Process Mining, which allows you to analyze opportunities for improvement within processes.
In other words, a Digital Twin, as its name suggests, is an exact virtual copy of a process, product, or service that allows organizations to perform different analysis and prediction operations.
Examples of Digital Twins
There are currently hundreds of companies and industries using Digital Twin technology to improve their operations.
NASA was among the first to implement this type of solution; creating systems and mechanisms that could be manipulated or repaired remotely required testing those systems before sending them into space.
The US government agency began creating virtual prototypes of space hardware in order to iterate as many times as was necessary. Thus, NASA only sent hardware into or implemented procedures in space once it had verified that they met all the requirements.
NASA’s use of Digital Twin technology was put to the test during the Apollo 13 mission, which was saved thanks to NASA’s models and the tests it performed on the Digital Twins.
In addition to planning and rescuing space missions, there are hundreds of other applications for this technology. For example, the BMW Group works hand in hand with NVIDIA on running factory simulations and optimizing their operations.
Since BMW builds more than 40 car models with 2,100 possible configurations each and offers more than 100 options for each car it sells, the company faces major challenges in keeping materials stocked on its assembly lines.
For this reason, BMW uses a fleet of human assistance robots with digital personas in simulation scenarios in the pre-production stage. These robots allow the company to test robot applications on Digital Twins on the factory floor before putting them into production.
Digital Twin technology is also applied in testing fatigue and corrosion resistance in marine turbines. Digital Twins are also useful in the field of computer network architecture, where they are used to simulate cyberattacks and keep organizations safe.
How is a Digital Twin created?
A Digital Twin is created with the help of software that collects real data about the process or product to be cloned. These data may include information about physical characteristics or conditions.
In general, those who create Digital Twins are usually experts in applied mathematics or data science as they are responsible for developing a mathematical model identical to the original.
Digital Twins can be complex or simple, depending on the amount of data used to create them. The more information used to build a Digital Twin, the more accurate it and the analyses it offers will be.
What is a Digital Twin for?
Digital Twins are used to carry out advanced analyses, avoid failures, and make predictions without intervening with the real product, object, or process.
Avoiding physical prototypes reduces development time while improving the quality of the finished product or process.
In short, a Digital Twin connects the online and offline worlds, helping users to predict the effects and behavior of a product, service, or object in different scenarios.
In which sectors are Digital Twins used?
Digital Twins are useful in multiple sectors. Figures collected from around the world demonstrate this. For example, a study by Mordor Intelligence concluded that about 35% of industrial and manufacturing companies in the United States use this technology to improve their processes.
Among the sectors that use Digital Twins are:
- Health sciences: to improve patient diagnoses or medication. In one case, thanks to the application of Process Mining we reduced wait times by 23%;
- Emergency management and medicine: to perform test scenarios to anticipate the appropriate response to an emergency. Surgeons can use Digital Twin technology for surgical and other types of training;
- Transportation of fragile or dangerous goods: to test environmental conditions and avoid accidents;
- Smart cities: to test technologies before officially implementing them; and
- Marketing: to test campaigns and predict user behavior.
The good and the bad of Digital Twins
Digital Twins, like Process Mining, were created to transform processes and help organizations in different sectors detect problems in advance and solve them promptly and easily. This technology can work autonomously, analyze proposed scenarios, and propose optimized solutions.
Digital Twin technology is so beneficial that, in 2018, the IDC consulting firm detected 30% improvements in the critical processes of companies that implemented the technology in their operations.
However, there are costs that come with Digital Twin technology. Digital Twins are difficult to manufacture, and implementing the technology requires contracting specific services from specialized companies.
Digital Twin and Process Mining
Process Mining is a practice in which information is collected and analyzed with the aim of understanding a company’s processes. Digital Twins are used to create replicas and thus run simulations and identify opportunities for improvement.
With these definitions, we can see how Process Mining and Digital Twins complement each other in evaluating and improving processes. Process Mining focuses on what has already happened, on how a given process worked; Digital Twin technology uses historical data to offer predictions that account for certain conditions and desensitization parameters.
Currently, the two technologies are applied simultaneously in different sectors, including the retail, manufacturing, and consumer goods industries. In the manufacturing industry, companies can optimize in-process interactions between human workers and robots by having better process visibility, design, and efficiency. For example, here you can see how we use Process Mining to detect improvements in an automotive factory.
If you want to know more about our solution and how we apply Process Mining to process improvement, you can click on this link to register and request a demo.