Digital Twins Concept: The Complete Guide
Catchy and a bit of cosmic, this is how the term “digital twins” often sounds to the audience. A bit cosmic, since NASA was among the first organizations to include it in their technology roadmaps almost twenty years ago. Wait. What about today? What are the benefits of the mysterious digital twins technology in the business world? Let’s unravel this mystery in our article.
What is the Digital Twin Concept About?
There’s nothing all that mystical about the digital twins concept. It’s only high technology that’s come about with the rise of the Internet of Things. Speaking of IoT, Gartner claims that 75% of organizations implementing Internet of Things already use digital twins or plan to within a year. The digital twin (DT) technology is used to describe and reveal the insights of a physical product, system, or service through their digital copy. Digital twins supported by the data gathered by the sensor and other IoT devices located on a physical object. Its main purpose is to combine real, with virtual worlds, revealing the way an object’s potential goes against various scenarios. Moving incrementally, the technology totals information and renders a virtual copy of a physical system.
Probably the main reason why the digital twin concept sparks such interest as an element of the new industrial revolution is its ability to substitute bulky physical testing with digital models. The digital models of the subjected system duplicate all the elements and dynamics of its operation throughout the entire system’s lifecycle. Data gathered by IoT sensors allows a digital model to act exactly precisely as the “twin” from physical reality. For engineers and researchers, it means monitoring, testing, and manipulating can now be precise without assumptions or expectations.
How Do Industries Use the Digital Twin Approach in Practice?
Let’s start with the connected components. Sensors in terms of a physical “twin” collect data related to the working conditions or status of the object (thickness, color, hardness, speeds, and so on). The process is continuous, and data can be varied and differ in size, depending on the capacity of the IoT device and goals laid out before the model. The digital twin’s components are then connected to the cloud-based platform to analyze the gathered data. After that, the platform provides manufacturers with lessons-learned output rendered virtually. Now they can analyze, manipulated, and optimize the objects in a digital testing environment before transferring the changes to the real world.
Industrial Possibilities Digital Twins Bring to the Table
This might sound overly confident, but we happen to believe that digital twins can affect all industries. It’s just a limitation of technological capacity and lack of respective engineers holding it back. The figures support our statement: by 2025, the global digital twin market size can reach USD 26.07 billion. But what are the industries that have already applied the digital twin technology for their needs? Mainly those are manufacturing, automotive, healthcare, utilities, and construction.
Digital Twins in Manufacturing
We intentionally start from manufacturing as a major field which doesn’t spare resources to inject digital twins technology into their Industrial Internet of Things (IIoT) initiatives. Gartner predicts that at least 50 percent of well-established manufacturing companies will have at least one digital twin initiative launched by 2020. The value manufacturers see in digital twins is in the new opportunities this technology brings for employees training, streamlining existing operations as well as testing new products and procedures.
A digital twin can simulate certain procedures, components, or the entire manufacturing system. It depends on the goals and the development of the IoT network. For example, in integrated iron-and-steel works, the plants’ ecosystem is complex and interconnected with many continuous processes. It means that real-world changes are often dangerous, with errors resulting in safety incidents and extended downtime. A cloud-based digital twin of complex machinery or the indented process change can help manufacturers examine operation; test the suggested improvements and predict the outcome of such a change. Other popular use cases for digital twins in manufacturing include:
- Equipment troubleshooting, especially in remote locations
- Quality management based on real-time data gathered by IoT devices
- Evaluation of production decisions based on analytics rather than assumptions
- Improved logistics planning and monitoring
Digital Twins in Automotive
Digital twins are significant for such challenges in the automotive industry as vehicle product design and manufacturing, sales, and automobile maintenance.
Initial steps in the automotive product life cycle are the concept and design phases. In this stage, automakers think of a DT as a realistic replica to optimize the vehicle before it goes to production. The car’s behavior, interior, and exterior, software, electronics, and mechanics – all that become objects for the digital twin tech. At this point, the DT validates product design and handles the development of approaches to avoid failures and make the product more cost-effective.
During the next stages of a lifecycle, which are manufacturing planning and executing, the entire production line can be streamlined in virtual environments via the digital twin. The benefits come down to the reduced time and effort, at the next stage when cars are being actually produced.
A digital twin is somehow a link between a car and the way it’s being produced. All the data are integrated into the cloud to enable predictive maintenance and easier manufacturing in the future.
Car usage and renewal stages use digital twins to predict the source of an error or issue, streamline the user experience and increasing safety.
A digital twin paradigm in healthcare consists of various levels. The first thing that comes to mind is a digital copy of a particular human body part. However, medical equipment or the entire patient-service provider system can also get a digital sibling. Researchers usually create models of organs or their systems to receive a digital candidate for research, treatment, clinical trials, and other scientific and commercial uses. As to the medical equipment, the use of digital twins is similar to those with manufacturing equipment.
The technology helps to:
- Improve the design of medical devices and hospital equipment
- Optimize the work of the existing equipment
- Reduce time-to-market for new medical products
- Identify maintenance needs before they arise
The digital twin concept can also create entirely new models of care delivery. Clinical records, patient’s environment, previous treatment results, social exposures – all those are data sources the algorithms. DT, in this case, is used to reveal a patient’s peculiarities based on the facts obtained from those data sources. We have a set of machine-created recommendations and rule-based predictions that approach personalized care, called precision medicine.
Digital Twins in Utilities
Digital twin technology enables utility companies to develop new sophisticated models. They are featuring digital copies of their physical assets, modeling in the cloud their behavior from design and development to the end-user. According to Accenture’s recent Industry X.0 study, more than 46% of utility executives are using digital twins to improve operational efficiency. How can utility companies gain this efficiency in practice? Through the following benefits, DT brings about:
- Assessing long-term strategic scenarios and investment decision
- Manpower optimization
- Effective working of 3D simulation platforms for design optimization
- Providing data-based predictions for maintenance procedures
- Improving the operator’s visibility of system performance
The contemporary face of the construction industry is all about modeling, resource planning, and safety compliance. Digital twins can help to flesh out strategies in all these directions. Just several examples of how construction companies can do the following in real-time, and in virtual space enabled by DT technology:
- Monitor construction progress. A digital replica of the construction site, real-time changes in a twin model, can help to compare the course of execution against what was laid out in a plan.
- Plan logistics and resources more rational. Predictive modeling incorporated into a digital twin model help avoid over-allocation of resources and predict where logistic plans need adjustment.
- Increase in safety. Companies can build twin models to find out the most hazardous locations, or use of which materials can be safety-critical as well as prevent disruptive behavior on a construction site. It can make one of the most dangerous industries in the world a little bit safer.
- Assess quality remotely. Digital twin modeling helps to verify the quality of construction materials as in real-time as after a while.
Are There Any Cases Against the Use of Digital Twins?
The biggest challenge of using digital twin concept is typical for all the sizzling hot technologies. And, it’s complexity.
Complex implementation, complex architecture and operational complexity.
Businesses should avoid overcomplicating modeling digital twins. It’s necessary to spare computational and hardware resources. Considering an experienced technology partner who knows all the ins and outs of digital twin architecture can be rock-solid advice in this case.
Moreover, a digital twin must solve specific business problems, so it’s better to think them through thoroughly. If a particular business case can be settled only using a smaller number of IoT sensors, growing a digital twin is economically unviable. Even before using a technology partner, weighing the risks against the economic value, to avoid issues while implementing your tech strategy.
Digital twin technology is so much more than yet another attempt at collecting and structuring data. It’s a progressive approach to manufacturing and an industrial driver of global digital transformation. The value it brings is exceptional. First of all, in terms of quality, operation costs, the introduction of new products, and revenue growth channels. DT helps to detect quality and safety issues, improve product engineering, reduce time-to-market for new products, as well as their overall cost. Not to mention the substantial business benefits, the digital twin concept is a real game-changer in the technocratic industrial revolution. But for the twins to be effective, they must be perceived along with other components in a big family of AI and IoT technologies.