Challenges of creating digital twins are increasing exponentially, especially with the advancement of technologies like simulation, modeling, and data analysis, digital twins of objects and environments are increasingly becoming more accessible and adaptable across various industries. Furthermore, with the integration of Artificial Intelligence with Machine Learning & Deep Learning, digital twins will transform industries across different spectrums, including the manufacturing industry.
The Fourth Industrial Revolution, or FIR or Industry 4.0 in short, is the automation of traditional manufacturing, production & other related industries with the digital transformation of traditional practices through modern technologies. Thus, industry 4.0 will be the age of digital technologies. Machine to Machine communication (M2M) and the Internet of Things (IoT) will work together to enable automation, self-monitoring, real-time optimization, and the production industry’s revolution.
Digital twins will be at the forefront of Industry 4.0. With its power of rapid designing & development, iteration & optimization in almost every engineering process & practice, digital twins will enable new opportunities and possibilities. In addition, digital twins will transform various manufacturing & production processes, drastically reduce time & costs, optimize maintenance and reduce downtime.
While digital twin technology is not entirely new, its growth and adoption are skyrocketing across various industries in recent years, while the challenges of creating digital twins are also rising. As a result, the valuation of the global digital twin market was sitting at 5.4 billion US Dollars in 2020. Furthermore, although its market was experiencing a slump in 2020 due to the COVID-19 pandemic, it will undoubtedly recover and experience exponential growth again. Consequently, researchers expect that the global digital twin market will reach 63 billion US Dollars by 2027 while rising at the growth rate of 42.7% annually.
Over the last decade, the evolution of the manufacturing and production industry has been mainly focusing on reducing costs, increasing quality, becoming flexible, and reaching customer needs across the supply chain. The manufacturing industry is adopting different modern technologies to achieve these goals. Millennium digital technologies have also been part of this technology stack due to the innovation and opportunities it brings to the table.
Different companies and organizations are using twin tech accordingly in different scales and nature. Due to this, the technology in use varies across the industry, such that some industries use the latest bleeding-edge systems while others use legacy and proven techniques. Companies generally use the latest tech when it becomes available to use the latest features and functionalities. On the other hand, proven legacy systems are in use due to their stability and ease of use.
Likewise, different uses of twinning sims in various industries possess other challenges. Apart from this, integration technologies like the Internet of Things (IoT), cloud, big data, and different approaches to digital twin integration will only increase the challenges for digital twins in terms of the sheer complexity of implementation. However, this also presents an enormous opportunity for industries to adopt and align these technologies to suit different needs to solve these complexities and challenges. Subsequently, companies like FS Studio solve the challenges of creating digital twins, providing a platform for the manufacturers or companies to work on without dealing with complexities.
Generally, the goal of any twin manufacturing is to create a twin or model of a real-world object in digital form. Furthermore, the aim is to make indistinguishable virtual digital twins from the actual physical object. Therefore, from the perspective of a manufacturer or a product development company, a digital twin technology will create an actual physical product experience in digital form. Hence, a digital twin for a product, object, or environment will consistently provide information and expertise throughout the whole product cycle.
A virtual twin can also serve companies for feedback collection alignment, useful for the product or the design team. Results from various tests may provide results that can be useful too. The design/engineering/manufacturing team can compile this information, feedback, and results for multiple purposes from the digital twin model. Furthermore, this compilation can also provide additional insights into the product, which can be very useful to tweak, change or even redesign the product entirely. This digital approach will consume much fewer resources, effort, and costs than the traditional physical approach. Moreover, these changes will also be reflected on the twin's systems instantly as the teams make these changes. This will ultimately allow crews to perform true real-time optimization of a product or a manufacturing process.
It will drastically improve the efficiency of designing and developing a product or a process. In addition, digital twins also enable higher flexibility across the overall design and development process. Furthermore, this flexibility comes at a lower cost and additional agility in manufacturing or product development. Hence, digital twin technology becomes very appealing for manufacturers and product developers due to these advantages and benefits.
One of the main challenges of creating digital twins remains to be the convergence of existing data, processes, and products in the digital form to be easily accessible and usable for the current or future teams in involvement. Moreover, such convergence may also change a company’s complete organizational structure from their R&D technology and product innovation to sales and promotion. Furthermore, incorporating technologies like IoT, the actual development of 2D or 3D models & simulations, and data analysis for consistent process, quality & authentic experience of the product remains a very complex process.
Apart from this, the actual use of digital twins created is also another challenge. The infrastructure and platform needed to use such digital twins are also essential, albeit complex, things to build. For example, suppose a team can create a car’s digital twin for a car manufacturer company. But problems with digital twins are that there is no actual use of the digital twin except for visualizing the vehicle. Even for proper visualization of the car across teams, different platforms and tools are necessary to often serve niche use cases of the company.
For instance, a car company needs a motor, brake, acceleration, air dynamics, and other niche simulations for the digital twin of their car. The technology stack should be able to perform various maneuvers a vehicle performs on the road. Aerodynamics and gravity simulation is a massive deal for car manufacturers. Integrating these simulations is also a monumental task.
Along with this, for the actual process of testing and developing products, the platform has to simulate various objects, environments, and conditions necessary for such functions. Alongside this, the platform should also be able to report errors & statistical data on simulations running while constantly monitoring and diagnosing the product during its testing or development. Collaboration between team members on the platform is also necessary for a large-scale company. Integration of Artificial Intelligence and technologies like Machine Learning and Deep Learning is also a very challenging task to accomplish.
Digital twin technology is also often associating itself with complementary technologies like Virtual Reality (VR) and Augmented Reality (AR). The use of VR and AR in a digital twin platform will upgrade the realism and accuracy of the product experience. With realistic simulations and modeling in VR and AR’s capability to enhance a product experience, the 4.0 industry will incorporate these technologies at the forefront with digital twin technology, increasing the challenges of creating digital twins. Alongside this, integrating the digital twin with the actual physical manufacturing process is also a huge challenge.
Although companies will have to adopt this new industrial revolution 4.0 with digital twin-driven smart manufacturing, the overall process will not be that complex. The hard part is the convergence of different technologies to enable a platform for generating this digital twin and integrating it with the actual physical process in product development or manufacturing. However, since the digital twin simulation accurately represents the actual physical product, the product/manufacturing team will have almost no difficulty incorporating this digital twin tech in their physical process.
Therefore, companies like FS Studio help product developers and manufacturers to focus only on product development and design rather than the process of adoption of the digital twin. While different industries are transitioning towards Industry 4.0 technologies, various platforms and solutions establish themselves as leaders in cutting-edge technologies like the digital twin model with AR VR to eliminate the complexities present while the transition happens. It will help the companies and organizations focus on their primary and core goals instead of shifting their resources and concentrate on their growth to the next industrial revolution.
Realization of challenges for the convergence of technologies like IoT, design, and generation of 2D or 3D models & simulation and analysis of existing data remains. With this, the incorporation of Artificial Intelligence, Machine Learning, and data analysis also pose challenges regarding automation, self-monitoring, and real-time optimization. Subsequently, corporations and manufacturers moving towards Industry 4.0 must place digital twin technology at its core.
It will help companies and organizations transition smoothly towards the industry 4.0 revolution, which incorporates product development and digital transformation. With the power of rapid design and development, new production and R&D innovation will take over the industry, reducing the challenges of creating digital twins in the transition to industry 4.0. Subsequently, with digital twin technology, industries across the spectrum will be growing exponentially in their move towards the next industrial revolution.
IoT and Digital Twins can reduce the costs in the manufacturing industry, minimizing unexpected downtime. These emerging technologies also help to perform complex simulations, offer deep insights and suggest equipment improvement. IoT technology also safeguards the interest of the manufacturer adding speed and flexibility in every situation.
What is a Digital Twin in simple words? Or, for that matter, IoT?
Digital Twin is the virtual twin or copy of the actual product. The digital twin connects the physical and digital worlds.
On the other hand, IoT, or the Internet of Things, is a physical "things" network. IoT platform is the medium for connecting and exchanging data between material objects. Generally speaking, we embed the sensors and software into physical objects over the internet.
As we live during the industrial revolution 4.0 (Industrie 4.0), manufacturers and industries embrace emerging tech to automate traditional systems. They are using emerging technology like AI, Robotics simulation, Biometrics to speed up the industry systems.
Emerging tech like IoT and Digital Twins are also reducing costs in astonishing ways.
IoT & Digital Twins Case Studies:
The evolution of IoT has made data transfer hassle-free by connecting sensors to the cloud and other "things." Apart from this, IoT is also serving as an effective tool for predictive maintenance.
Conversely, Digital twins leverage IoT to aid organizations in monitoring assets or processes virtually. Unfortunately, these are assets that are hard to check due to their distant location or a hazardous environment.
IoT and Digital Twins have unimaginable use in reality. For instance, power grids breakdowns create hindrance in every life, causing delays in businesses and services.
We can now tackle unwanted interruptions in power distribution thanks to IoT predictive maintenance.
Finland's electrical substation is the exemplary model of the predictive maintenance case study. In 2018, the electrical sub-station used Haltian's Thingsee wireless sensors for the first time. But, unfortunately, these sensors require manual checks from the human side.
The electrical sub-station used the sensors to collect temperature components, including humidity, air pressure, and distance.
This IoT-based predictive maintenance helped to increase efficiency in the electrical sub-stations while eliminating equipment failures. In addition, predictive maintenance helped to detect flopping assets and understand the factors leading to abnormal operations and disrupting schedule maintenance activities.
Finland's electrical sub-station isn't the only example of successful IoT in industrial applications.
Ericsson Panda manufacturing plant in China is another IoT case study we need to discuss.
The Ericsson Panda plant in Nanjing used Cellular IoT and connected 1000 devices to form a gigantic branch. In addition, the system had embedded IoT modules to send and receive data in real-time.
The IoT modules are said to transmit about 100 bytes of data per 8 hours from recent usage. Later, Ericsson Panda used the data in a cloud solution for analysis. The IoT solution costs just $20 per unit, will cut 50% maintenance work, saving USD 10,000 annually, and achieving breakeven for Ericsson Panda in 2 years.
The Ericsson Panda manufacturing plant is the first cellular IoT –based smart factory, and its immense success has contributed to the expansion of IoT worldwide.
Today, IoT technology has become a key element in the global supply chain already.
Since the beginnings of the industrial revolution, companies were eager to measure the temperature of the transported goods using the low-cost solution. IoT-based predictive maintenance and analysis applications in refrigeration systems help to understand when the system may fail. Therefore, it helped to avoid wastage of valuable agricultural goods and medicines and save money and time.
Similarly, companies have managed to keep the maintenance costs of factory equipment under control by 40%. IoT -based predictive maintenance has also helped to reduce equipment downtime by 50%. It reduced equipment capital investment by 3% to 5%. It saved the overall capital investment by 3% to 5% by extending the life of machinery.
Digital Twins can save money by predicting future failures. So, companies can repair defects at their earliest at a much lesser cost. It also recommends best strategies to improve the product development cycle, maximizing profitability. In this way, companies using Digital Twin can maintain a good relationship with their consumers.
As we can see, emerging technologies can help industries in the most remarkable ways. However, the expansion of Digital Twin and IoT isn't just limited to electrical sub-stations, supply chains, or manufacturing plants.
IoT and Digital Twin have expanded to other utility industries like healthcare, rail transportation, and oil and gas.
Oil and gas industries are adopting Digital Twins faster to minimize the costs of assets and productions. These industries have costly investments and handle them very carefully. Thus, it's no surprise that they aggressively adopt digital twins for modeling operations such as oil rigs, pipelines, and processing facilities.
Oil and gas companies have digitalized their systems to cut off weeks of unplanned downtime while reducing production costs. In addition, these industries have adopted predictive maintenance and IoT analytics to review historical data to detect failures in major components located at their offshore oil platform.
Digital Twins have transformed the transport industry as well. Today, the transport industry applies high-value rolling stock, such as locomotives, to maximize fuel efficiency and optimize maintenance.
The transport industry is willing to achieve the highest fuel efficiency possible to save hundreds of dollars to buy fuel. The rail transportation industry had reported saving about 10% on maintenance costs when they switched to condition-based preventive maintenance of rolling stock.
The digital twin is making remarkable contributions in the healthcare industry as well.
Q-Bios can be a great example to discuss. Q-Bios is the first clinical digital twin platform that harnessed the ability of digital twins to replicate anything indifferently.
Q Bios Gemini Digital Twin platform has built Mark-I, a computational biophysics model to scan the whole body. The company reported that Mark- I will examine the human body in 15 minutes and doesn't require radiation or breathe of the actual person.
Q Bios Gemini has claimed that Mark- I can work 10X better than the traditional MRI scanners for many critical diagnoses. In addition, Mark-I, the computational model, can eliminate bias or hallucination risk from AI and machine learning.
Another most significant advantage of the Mark-I is that it shields the patients from exposure to radiation, protecting them from running into the risks of developing cancer cells in the future.
Q Bios Gemini has received over $80 million from Andreessen Horowitz and Kaiser Foundation Hospitals to develop and expand its breakthrough whole-body scanning technology. In the future, the full-body scanning tech from Q Bios Gemini will provide data-driven and affordable care for all.
Medical and software companies are collaborating on digital twinning projects to create exact replicates of human body organs like the heart and the brain. The aim is to minimize risks in critical surgeries and aid organ donations.
Sim&Cure, a medical technology company, has built a digital twin called Sim&Size. This digital twin simulation will make brain surgery safer for Aneurysms patients as they will need less invasive surgery using catheters to install implants.
In another instance, Dassault Systèmes SE, a French software company, developed a Digital Twin heart using MRI images and ECG measurements. This digital twin model of the heart replicates the structure and some functions of the human heart. Now, heart surgeons can feed the patient data into the Digital Twin heart to determine whether the surgery will be successful.
Dassault Systèmes SE has launched the Living Heart Project in collaboration with academic and industrial members like Medtronic, Philips, and Boston. All the Living Heart Project members are working together to build safer and effective cardiac devices for patients.
All the major industries are gaining massive value from IoT and Digital Twins. In other words, they are saving and making money simultaneously.
According to the predictions of McKinsey & Co, IoT technology would reach $11.1 trillion in economic impact by 2025. In addition, Cisco reported that data derived from IoT devices would surpass 800 Zettabytes by the end of 2021. There's no doubt that industries using IoT devices are experiencing explosive growth.
These industries are witnessing such massive growth because they managed to cut off shocking downtimes with Industry 4.0 technologies and build the ability to predict future failures and make necessary repairs using a digital twin.
Sadly, many companies have no idea about unplanned downtime's costs, root causes, and consequences. According to Service Max, 82% of companies reported that they had experienced unplanned downtime for three consecutive years. In addition, these companies experienced an average outage duration of 4hours every day with a median cost of $2 million.
Service Max also concluded that 70% of companies have no idea when their production machines will need maintenance or upgrades.
So, we can say that companies adopting IoT and digital twins are increasingly performing better than those avoiding emerging technologies. It happens because IoT and digital twins improved situational awareness and aided industry leaders in making faster business decisions.