What is a digital twin?
Imagine a bridge, an aircraft, or even a human heart having a dynamic, digital counterpart — a complete mirror image that not only reflects the physical structure but also imitates real-time behavior, predicting problems before they appear and offering solutions before they’re needed. Or envision a bustling smart city, where traffic flows seamlessly, energy consumption is optimized, and urban planning is a breeze — all thanks to virtual replicas of infrastructure working tirelessly behind the scenes. Or picture a hospital where a patient’s response to treatment is emulated on their digital twin before actual treatment is administered, ensuring personalized and effective care. This is the promise of digital twins, and these are not far-off aspirations but rather burgeoning realities made possible by the fusion of real-time data, advanced simulations, and cloud computing encapsulated in digital twin technology.
While the basic principle of creating replicas for analysis isn’t entirely new — engineers and designers have long used sketches, models, and mockups for this purpose — the digital twin definition bears this principle into the era of technology. It capitalizes on advancements in data analytics, cloud computing, and the Internet of Things, weaving them together into a cohesive tapestry of innovation.
The concept of digital twins — more than just a digital replica — has begun to reshape industries, from manufacturing floors to the vast expanses of cities and even complex healthcare diagnostics. Gartner estimates that by 2027, more than 40% of enterprises will be using digital twins. As industries grapple with increasing complexities and demand for greater efficiency, this technology serves as a signal of modern-day problem-solving, offering unprecedented insights, forecasting capabilities, and an unparalleled platform for experimentation. A recent ResearchAndMarkets study has estimated that the global digital twin market will grow from $10.1 billion in 2023 to an impressive $110.1 billion by 2028, increasing tenfold. This has the potential to revolutionize the way we design, manufacture, operate, and maintain physical objects and systems. In this article, we dive deep into digital twin technology, its origins, its applications, and its boundless potential for shaping the future of innovation.
Key benefits of digital twins for business
By harnessing the power of data, simulation, and analytics through a digital twin, companies gain a deeper understanding of their operational processes and optimize their physical assets or systems. These virtual replicas empower organizations to get a profound look at the features and bugs of their real-world counterparts, opening up a range of opportunities and insights.
- Predictive analysis. Before an issue appears, companies with digital twins are already on it. Data engineers monitor the real-time state and condition of the physical system and compare it with the digital twin representation to identify where the physical system can be optimized. The insights obtained from such proactive analytics help identify problems quickly, prevent unplanned downtime, and implement targeted interventions beforehand.
- Reduce costs. Digital twins provide a real-time view of how resources (whether raw materials in a manufacturing setup or energy in a building management system) are used. By analyzing these patterns, companies can identify waste or inefficiencies, ensuring resources are allocated optimally. For example, in 2023 BMW partnered with Nvidia to digitally replicate their new EV factory, integrating design databases to enhance efficiency, leading to cost savings through improved production cycle.
- Innovate by providing a virtual platform for experimentation and testing. Companies use digital twins to try out new designs, simulate prototypes, and assess the impact of potential modifications. This innovation allow us to evaluate prototypes quickly and iteratively design new systems that speed up the development cycle by up to 50%, as indicated in a McKinsey study.
- Understand and provide insights into the behavior, performance, and functionality of a physical counterpart. Digital twins enable organizations to get a more comprehensive view of their physical systems, analyze data, identify patterns, and uncover insights that lead to better-informed decisions.
- Simulate and predict by leveraging real-time and historical data. Using advanced analytics and modeling techniques, digital twins can simulate different possible scenarios, predict future behavior, and evaluate the impact of potential changes. This capability of virtual replicas helps companies optimize their processes, improve efficiency, and minimize risks by enabling proactive decision-making.
- Optimize and improve the performance of real-world counterparts. Each system or service holds the potential for excellence. By constantly monitoring and comparing virtual and real-world parameters, data engineers outline recommendations to boost the performance of a physical service, ensuring it operates at its peak. For example, Intellias has developed a fleet telematics solution that tracks vehicle positions in real time, gives better control over fleet movement, and enables performance optimization.
- Remotely monitor and control. With remote monitoring and control prowess, digital twins dissolve geographical barriers. Data engineers can keep a vigilant eye on their far-away assets and fix problems without being physically present. This not only reduces the need for on-site interventions but also enhances predictive maintenance strategies.
- Detect fraud. In today’s complex world, anomalies often go unnoticed. A digital twin creates a precise model of normal system behavior against which actual behavior can be compared. All abnormalities can be flagged for further investigation. For example, in manufacturing, a digital twin system might monitor the amount of raw material required for a batch of products. If the actual amount used is greater, it might indicate fraud.
In essence, digital twins aren’t just a reflection of the physical system — they are the architects of a better, more informed future, building paths of efficiency, innovation, and accurate foresight.
How digital twins are created
Crafting a digital twin involves integrating a physical system into a virtual model that mirrors the behavior of the real-world system. The components of every solution vary significantly depending on its purpose; they can represent anything from a single mechanism to a whole factory, a building, a city, or even more complex systems. The technology is most frequently used in the manufacturing industry. McKinsey estimates that 70% of manufacturers will use digital twins regularly. Let’s consider a production line in a factory as an example of a digital twin to see a breakdown of the solution creation process.
- Data collection. Creating a digital twin of a production line starts with installing sensors on all machinery and equipment in the line. The sensors collect two types of data, if available.
- Sensor data that includes temperature, pressure, speed, vibration, energy use, etc.
- Real-time and historical data that includes information about the production line’s performance. This data might be used to train ML algorithms to predict future production line performance.
- Creating the digital twin. Using the collected data, software engineers create a virtual model of the production line that precisely mirrors its structure and operation. The digital twin includes all collected sensor data and exactly replicates not only the physical structure of the line but also all its behavior and performance. In advanced digital twin setups, the mode can also replicate external data, such as environmental conditions or market demand.
- Digital twin synchronization. The digital model is a dynamic model, connected to the physical production line via IoT devices. The virtual replica is constantly updated with new data from the physical production line. This way, the virtual model fully represents the current condition of the production line and not just its design or historical state.
- Digital twin simulation. Data engineers simulate the behavior of the production line by running different scenarios and observing and analyzing how the digital twin responds. For example, they can increase the operating speed of a machine and see how it impacts performance. Or they might detect that a particular machine is using more energy than expected and identify potential issues.
- Physical system optimization. The insights obtained from digital twin simulation will drive tangible changes. These can range from minor tweaks, like fine-tuning production line settings, to major overhauls, such as redesigning machinery components.
- Performance prediction. A standout capability of digital twins is their foresight. By analyzing trends and patterns, a digital twin can preventively recommend maintenance or operational changes, thereby averting failures and optimizing cost efficiency.
- Continuous learning and refinement. The digital twin isn’t a static entity. As it continually obtains data, its model is refined and its predictions sharpened. Data engineers enhance model so that it improves its predictions and uncovers new insights. Continuous learning enables perpetual performance optimization of the physical system and ongoing efficiency improvement.
Challenges of digital twin implementation and ways to overcome them
Though the advantages of digital twin technology are abundant, its implementation doesn’t come without challenges. Here’s a closer look at some of the hurdles that businesses might come across and factors to consider when adopting it.
Acceptance
Digital twins are relatively new technology, and this novelty brings a certain level of apprehension and resistance. Some professionals opt for traditional approaches without delving into the possibilities of the unknown. This lack of understanding can create misconceptions about the technology’s effectiveness, accuracy, and helpfulness.
How can you drive the acceptance of digital twins? Seeing successful case studies and demos of digital twin technology is a compelling way to understand how the technology works. A small-scale pilot project is also a good start, providing hands-on experience. Workshops and talks with experts from the field will facilitate a deeper understanding of how enterprises can benefit from such innovations.
High initial investment
Digital twins require a considerable amount of data to be collected and managed. This data can come from diverse sources, including sensors and equipment. In some cases, they may necessitate investment in modeling, simulation, cloud storage, and data analytics tools. Though the returns on this investment are substantial, the starting outlay can be a barrier for some companies.
How can you cut down digital twin expenses? Moving data to the cloud will make digital replicas more accessible and scalable, easing up data retrieval. Integration of AI will automate data collection and modeling by eliminating manual errors and saving time that was spent on these tasks. Collaborating with a knowledgeable team like Intellias ensures high-quality delivery for digital twins, reducing costs in the long run, as there’s minimal need for code refactoring when code is developed by professionals.
Integration
Digital twins have to be integrated with complex physical systems, and seamless integration might be a difficult task. Especially if legacy systems are involved, implementing this technology may require full modernization of current systems. Moreover, as physical systems evolve or are replaced, virtual replicas have to remain compatible.
How to simplify digital twin integration? Using open standards paves the way for quicker interoperability between the digital twin and physical system. In particular, open standards decrease the need for frequent adjustments or overhauls and bring up uniformity, making it simpler to create and maintain digital twins without being stuck with system-specific quirks.
Data collection and synchronization
The accuracy and reliability of digital twin models largely depend on the collected data, which may come from different sources, machines, and sensors. Not only is the volume and velocity of data overwhelming; each set of data has its own format, fidelity, and frequency, and unifying this huge amount of information from disparate sources might pose a challenge.
How to overcome digital twin data synchronization challenges? A company implementing such a solution might consider using advanced data validation and cleansing procedures to ensure the data is accurate and comprehensive. Apart from that, applying standardized protocols and APIs will facilitate smooth integration of data from various sources. And real-time data processing algorithms will make certain that the information is up-to-date. This holistic approach guarantees that a digital twin is a strong and reliable representation of a physical system.
Modeling and simulation
Creating a precise representation of the physical system is vital and underpins the digital twin’s effectiveness. However, achieving this becomes particularly challenging when dealing with complex systems with numerous interacting components. Over-complicating the model can make it cumbersome, reducing its usefulness and making it challenging to create, maintain, and validate the replicas.
How to create a manageable digital twin model? Breaking down a digital twin into smaller modules that represent a specific part of the system will help to avoid overloading the whole system. Iterative development in sprints ensures that only necessary details are added, preventing unnecessary complexity. Advanced simulation tools help decrease model complexity, automate processes, and make sure the model is scalable.
Data security and privacy
A digital twin requires a continuous exchange of data, which is crucial for the virtual system to be functional. It is also a gold mine for malicious entities if accessed. Security breaches can lead to privacy infringement, especially if they contain sensitive patient data.
How to ensure digital twin security? Implementing robust access security measures such as end-to-end encryption, conducting regular security audits, ensuring strict access control with multi-factor authentication, and implementing strong password policies will ensure digital twins are secure and can stand strong against potential attacks. Even more, it is crucial to segment the virtual replication environment from other networks to guarantee that, in the event of a security breach, the replicated counterpart remains isolated from the surrounding infrastructure.
Skills and expertise
A successful digital twin implementation requires a team with IoT skills to effectively deploy and connect all sensors and hardware. Moreover, the professionals creating such a sophisticated solution have to be able to discern patterns, predict trends, and translate the results into actionable insights. Beyond technical skills, a deep understanding of the industry domain will help to ensure the digital twin model is relevant and truly depicts the real-world system.
How can we find digital twin talent? Navigating the complex world of digital twins necessitates a knowledgeable and seasoned partner, and Intellias is primed to be just that for your company. With a deep understanding of every aspect of their creation, from initial planning and design to implementation and solution deployment, we can guide you through the digital twin journey. Whether you’re in mobility, supply chain, oil & energy, healthcare, or any sector looking to take advantage of the plentiful benefits of digital twins, partnering with Intellias means entrusting your vision to a team committed to bringing it to life with precision, innovation, and efficiency.
Real-world applications of digital twins
Digital twins can be widely applied across industries, helping companies gain a holistic view of their physical systems, make the right decisions, forecast future challenges, and innovate. Here are some examples of how they can be leveraged for different industries.
Aerospace and defense
A Capgemini report found that investment in digital twins in the aerospace and defense industry is growing by 40% year-over-year. They are used to model and simulate rockets, shuttles, and airplanes. Most importantly, these solutions help aerospace engineers improve the design of spacecraft and reduce the risk of failures. They are especially useful for training pilots how to fly dangerous and expensive aircraft, allowing them to be better trained and prepared for real-life conditions.
Mobility
Digital twins are extensively used in the automotive industry for design optimization, process improvement, predictive maintenance, and performance monitoring. They can be tested on production lines, where engineers can try out different settings and choose those that maximize performance. Intellias has launched a Portable Automotive Kit, which is a hardware and software solution that can be leveraged to create digital twins of a variety of automotive components including engines, transmissions, and brakes. One more example is Ford’s leveraging of digital twins to detect energy losses and find areas where energy can be saved, enhancing production line performance.
Oil and gas
Digital twins are used to test new oil and gas tubes (Shell has been doing this since 2017), optimize wells, forecast oil and gas production levels, and assess risks by simulating different scenarios. For example, Equinor, a Norwegian energy company, recreated 50 physical installations in a huge visualization tool.
Energy and utilities
Digital twins help with monitoring the performance of power grids and identifying the most efficient ways to operate them. Edison, the largest electric utility company in Southern California, is leveraging the technology to simulate a real-time power grid environment and forecast when grids are likely to fail. Harbin Electric, the second largest manufacturer of steam turbines, uses virtual replicas to improve the accuracy of turbine design and reduce product cycles.
Healthcare
Digital twins reproduce high-resolution models of patients. These models are then computationally treated with hundreds of medications to find the one that’s optimal for a given patient. This will lead to greatly improved healthcare, providing knowledge on how patients respond to various drugs. Based on this information, drug companies can develop new medications and therapies and spot potential side effects before treatments are tested on humans.
Smart cities
Digital twins are at the forefront of the evolution of smart cities. They offer an all-encompassing tool for incorporating complicated systems into contemporary city environments. One of the most prominent examples is the Singapore Land Authority, which has created a digital twin of the whole country. This technology also enablse more efficient traffic management with strategies that aid in reducing congestion. For example, in Los Angeles, authorities are working on creating a digital twin model that can manage traffic.
Supply chain and logistics
Digital twins optimize the efficiency of supply chain management and operations by providing better visibility and identifying bottlenecks and inefficiencies. For example, they can predict when some parts of the supply chain will likely fail. They can improve fleet management by tracking the location and status of vehicles in a fleet.
Construction
Digital twins are used more and more often in the construction industry, suggesting inventive solutions to optimize construction processes. Digitally enabled replicas improve the planning, execution, and maintenance of buildings. For example, Topcon applies this technology to resurface roads, and Earthcam overlays and synchronizes live construction images with their digital twins.
Digital twins are transforming how physical assets are designed, monitored, and maintained, unlocking unparalleled value and inventiveness. Their influence is immense, with ripple effects anticipated across industries, opening up new paradigms of operational efficiency and decision-making. As digital twin technology continues to evolve, we anticipate even more innovative and revolutionary applications of this technology in the years to come.
Conclusion
Adopting a digital twin is similar to having a meticulous, ever-watchful auditor and strategist. The virtual system offers a clear understanding of resource allocation, identifies potential cost sinks, and paves the way for leaner, more efficient operations. Industries across the board, but particularly manufacturing, engineering, automotive, and energy, are already harvesting the rewards. Through regular simulations and predictions, companies are witnessing transformations in design, maintenance, and overall performance, testifying to the transformative prowess of digital twins. It’s not just pure simulations: it’s a new era of data-driven decision-making and dynamic problem-solving. The future is being mirrored in the virtual world, offering greater accuracy, foresight, and effectiveness.
Yet, as with any emerging technology, digital twins come with a set of challenges. These range from technical aspects like modeling and simulation to human-centric ones like acceptance and require proactive strategies to address. Through modular design, iterative development, and robust cybersecurity measures, organizations can increase the effectiveness of digital twins while mitigating their risks. What is crucial is finding the right balance between embracing this novel technology and making certain it’s grounded in practical, actionable solutions. As businesses keep growing, those who skillfully navigate the challenges and harness the power of digital twins stand to obtain a significant competitive advantage, driving innovation and competence in an increasingly interconnected world.
If you are interested in learning more about how Intellias can help you, please contact us today.
