The Digital Twin Consortium defines a digital twin as “a virtual representation of real-world entities and processes, synchronized at a specified frequency and fidelity”.
The digital twin technology uses design, engineering, geospatial and operational data to represent a physical object/facility and associated systems. The digital twin uses IoT-enabled sensors to collect and process asset data used in machine learning (ML) models.
In addition to physical assets, digital twin technology can be used to replicate processes to collect data to predict their performance, remote control physical objects, and more.
What sets the digital twin apart from the standard 3D model is that it is linked to a real-time data stream, which enables it to evolve along with its real-life counterpart.
This means that it can offer an accurate analysis of what is happening in real time and check future performance and assess potential risks. This creates new opportunities for efficiency gains, cost reduction, processes streamlining, and more.
To better understand digital twin technology adoption level, let’s look at its current market stats and future forecasts.
According to Grand View Research, the global digital twin market was valued at $11.12 billion in 2022 , and it is expected to grow at a CAGR of 37.5% from 2023 to 2030.
The COVID-19 pandemic caused supply chain disruptions and production halts, which resulted in the suspension of various activities along the value chain in industries like manufacturing, aerospace, and automotive. This had a negative impact on the digital twin market during the initial stages of the pandemic in 2020. Nevertheless, as the number of COVID-19 cases decreased and restrictions were lifted, the digital twin market started recovering strongly as industries increasingly adopted automation and virtualization of products and processes.
By combining digital twin technology with advanced technologies like artificial intelligence, cloud computing, and IoT, the market growth is projected to increase. AI and IoT technologies are being implemented by businesses to gather and analyze behavioral data from connected products and existing IoT devices, which can then be utilized to replicate the usage and performance of the current device in the digital twin model.
This enables product engineers and designers to monitor product performance, identify any issues, and anticipate future iterations of common problems. Additionally, the adoption of these technologies helps organizations enhance operations and system productivity, leading to improved overall product performance.
According to Research & Markets, the key factors driving the adoption of digital twins in organizations today are:
Speaking of the constraints holding back the massive adoption of digital twin technology, data security due to the widespread use of IoT and Cloud platforms is one of the main ones.
With the proliferation of cyberattacks on critical Cloud infrastructures over the past decade, cybersecurity has become a major concern for industrial automation users and vendors. As such, the growing threat to the security of data connected to the Cloud is expected to be a major impediment to the growth of the digital twin market.
Another major challenge on the path to efficiently designing and deploying digital twins is the lack of awareness of the cost-benefit of implementing digital twins.
Despite the barriers, 31% of companies surveyed by Gartner in 2021 said they were already using digital twins to improve the safety of their employees or customers by implementing remote asset monitoring, control, and tracking.
There are three main types of digital twins:
The combination and integration of these three types of digital twins is known as the digital thread. This can be incorporated into other products by collecting data from all product life cycle stages and production.
For a long time, creating digital twins was costly. Companies needed to hire full-time developers and engineers and invest in sophisticated software. Testing and data visualization were limited. Then, mixed reality technology and wearable gadgets came to the fore and changed the game by simplifying digital twin creation.
The benefits of a digital twin differ depending on when and where it is used. For example, using digital twins to monitor existing products, such as a wind turbine or oil pipeline, can reduce maintenance costs and save many millions in associated costs.
Digital twins can also be used to create prototypes before production, reducing product defects and shortening time to market.
Common benefits of digital twins include:
Digital twins can use augmented reality (AR) and gaming technologies (e.g. Unity) – a.k.a. Mixed Reality – to train driver assistance systems with synthetic sensor data. A detailed walkthrough of each scenario helps validate safety requirements and build vehicles that can respond correctly without the driver being present.
Now let’s have a closer look at digital twin use cases across various industries and domains.
Manufacturers use digital twins to optimize everything from end-to-end supply chain to operations, quality management, and custom manufacturing. Testing multiple solutions before actually building products helps determine the best cost, service, and capacity options. Organizations can minimize the impact of disruptions by identifying the best secondary source of supply.
Boeing used digital twins to predict the performance of various components. This resulted in 40% better quality the first time around, saving development time and money. The company used IoT sensors to achieve the perfect load balance, ensuring that every flight delivers the perfect amount of cargo.
With digital twins, manufacturing companies can improve reliability and efficiency. They can constantly learn and help avoid future problems.
The medical sector has benefited from the digital twin technology in organ donation, surgical training and procedure risk reduction. Digital twin systems also mimic the flow of people through hospitals and track where infections may exist and who may be at risk of contact.
The Dassault Systèmes team has created a virtual human heart to develop new medical devices and analyze drug safety. Researchers at Linköping University Hospital have created digital twins of diseases, including breast cancer and influenza, to improve diagnosis and treatment.
According to EY, the adoption of digital twins can reduce real estate operating costs by up to 35%, drive down carbon emissions, deliver a healthier workplace, and enhance user experiences.
According to TechWire Asia, of all real estate companies that already use custom digital twins:
50% report increase of sustainability and resiliency,
35% report increase of building maintenance and operational efficiency;
30% admit increase in productivity, and
15% admit better space utilization.
Digital twins are complex solutions, so specialized companies usually build and manage them as part of their R&D and custom product development value proposition. Digital twin development companies take care of the entire lifecycle of the digital twin. They also provide custom onboard data collection platforms and applications, dashboards, and tools that harness the power of the digital twin tech.
Your organization has probably already realized the value of data and digitization, but may not have the internal competence and capacity to fully exploit it.
Understanding the digital twin life cycle will help you understand what capabilities you need from a custom digital twin software development company.
Digital twin architecture by EY
The development phase includes building a toolkit that can be used to create unique digital twins for various assets.
Toolkit development is the hardest part of the digital twin’s lifecycle. It is typically done in R&D by highly specialized data scientists, machine learning specialists and more.
Development work involves gathering information about the physical characteristics of the asset, the operating environment, and any conditions under which the asset is operated. In addition, the automatically-collected data from the asset is required to develop the required machine learning methodologies. The result is a mathematical model that can be used to model the behavior and performance of an asset.
There are two main elements in developing a digital twin. First, you need to select the supporting technology required to integrate a physical asset into its digital twin to enable real-time data flow from IoT devices and integration with operating rooms.
You must have a clear understanding of what device and modeling software are needed to create a 3D representation of the asset, and who will have access to the information in the digital twin or gain control of the physical asset through it.
Managing IoT devices securely is critical to overcoming the risks associated with identifying devices on your network.
It provides capabilities to authenticate, provision, configure, monitor, and manage each device. An identity-based IoT platform enables you to do this quickly and securely at scale.
This leads to the second design element. You must understand what type of information is required throughout the life of an asset, where this information is stored, and how it can be accessed and used. The information must be structured and reusable so that it can be quickly and efficiently exchanged between systems.
An identity-based IoT platform can manage the identity of each item in the digital twin and provide messaging services to automate secure communication between those people, systems and objects.
Standalone digital twins can be useful for monitoring key metrics or states of one specific asset, such as HVAC or environmental systems. They can also be used to match key building site elements with their original design.
A stand-alone digital twin usually contains a small number of data sources. At this point, you can create a twin enhancement toolkit using accurate “as is” data from other sources. For example, data from CAD systems, 3D models, or point clouds generated by laser scanning can be used to correlate with the sensor output.
Digital modeling of a duplicated digital twin can help prevent real problems. Smart components connected to the cloud can collect sensor data, allowing real-time status analysis and comparison with previous metrics.
The duplicate digital twin contains all the important and measurable data sources needed to represent the entire asset.
Standalobe digital twin elements can be combined to form a duplicate model. Here, the granularity of the model may be too detailed for your needs. Make sure your goals are clear so you don’t collect too much data.
Database-driven BIM models provide an excellent foundation for duplicating a model. BIM was developed to enhance collaboration in the construction industry. It also provides a single source of truth platform that allows flexible viewing of data. These capabilities will also help digital twin models deliver the right information to the right people at the right time. Software analytics and AI will update the digital twin as its physical counterpart changes.
Harnessing the potential of duplicated digital twins involves moving away from sensors and dashboards to advanced virtual representations of physical assets, with the risk of added complexity.
Digital twins can go far beyond individual pieces of equipment or individual buildings. The future of the digital twin is determined by scale.
The enhanced digital twin expands to include other data sources from the connected site. This will include correlated environmental data from external sources and information from external analytics and algorithms.
The more complex the system, the more difficult it is to manage and control all of its components. This is why digital twins are opening up tremendous opportunities for the interconnected cities of the future. They will provide the data and analysis you need to optimize people, planning and resources.
Digital twins have many benefits as they improve the behavior of processes and products, which usually means more efficient operations.
Digital twins allow us to anticipate potential problems in the future. This reduces product defects and, among other things, shortens production times.
Digital twins help improve and optimize production processes through real-time information. They help reduce unplanned downtime due to potential errors and reduce the number of accidents, allowing us to simulate all kinds of possible situations.
Digital twin technology helps reduce maintenance costs by performing preventive maintenance tasks. It provides opportunities for continual improvement through simulations and the detection of failures and inefficiencies.
The digital twin is a great Industry 4.0 tool you need to leverage to become a future-ready organization.
At Rinf.tech, we have a full-fledged R&D Embedded Business Unit that specialises in custom software product engineering and proof-of-concept (PoC) project development. From computer vision powered intruder detection software to robotic arm and its digital twin to sophisticated deep learning models, we use experimental approaches and lessons learned to build top-notch solutions and prototypes to future-proof business ideas and emerging tech.