With the evolution of simulations and 3D tech, innovative technologies are starting to emerge. Digital Twin is an emergent technology gaining massive momentum in the industry. As the Fourth Industrial Revolution comes closer, digital twins’ technologies are maturing and evolving rapidly, increasing the utilization of practical applications of digital twins.
Moreover, with the incorporation of technologies like Artificial Intelligence (AI), Machine Learning (ML), or Big Data, companies are converging digital twin technology with emerging technologies like Augmented Reality (AR) and Virtual Reality (VR). As a result, it enables rapid design and development and allows smart solutions in production, sales, logistics, and the global supply chain.
Digital twins are a massive boon for rapid prototyping during the design and development of a product. Furthermore, due to the ability to enhance current manufacturing & product development, industries worldwide are incorporating digital twin technology in their business, product development, and even consumer experience. The current global digital twin market sits at 5.4 Billion US Dollars, but this slump is due to the COVID-19 pandemic shutting down many industries and production along with it. As a result, the world was simply not ready to adopt it rapidly.
However, with adaptation, digital twin technology is rapidly rising in applicability and usability and increasing accessibility even at the end-user side. With this in hindsight, researchers predict that the global digital twin market will cross 63 Billion US dollars by 2027. This estimation shows a high annual growth rate of 42.7%. Furthermore, it shows that the market, industries, and even consumers are moving towards the much-awaited digital transformation of Industry 4.0.
Read more: Reduce Costs with IoT and Digital Twins
To understand the practical applications of digital twins, we first have to understand the technology itself.
Know Your Technology: Digital Twins
Digital twins technology is evolving in both its technological reach/sophistication and its meaning. While the idea of digital twins is not new, it is undoubtedly going through a massive revelation in the industry currently. Furthermore, with technologies like 3D models, simulations are rising. As a result, digital twins are also gaining momentum in the industry.
The digital twin accurately represents a real-world physical object or an environment in a digital form. Do not confuse digital twins with 3D models or simulations. It is much more than that. Digital twins represent a subject (any object in the real world) not just in a static manner but in a dynamic way. It means that the digital twin will always represent the product/object throughout its lifecycle. The twin always reflects any change or modification on the real-world object or vice versa, in which the real-world object demonstrates a shift in the digital twin.
While 3D models just simulate some properties and structure of an object, the digital twin represents and accurately reflects all properties and characteristics of the real world. From design, materials, behaviors, and properties, the digital twin represents them all. So it becomes easier to reflect changes of both the digital twin and the real object. Furthermore, it remains accurate throughout the whole design phase, developmental phase, prototyping, or even after production for maintenance or repair, effectively reflecting all stages of a product.
Furthermore, unlike a 3D model, which is just an informational model, digital twins react and behave in a certain way similar to the real object in different environments and conditions. Due to this, the digital model is more dynamic and adaptive. Moreover, with AI at its core, digital twin technology enables communication, updating, and even learnability similarly to its real-world counterpart through the exchange of data among each other.
With technologies like AI with ML or data analysis, digital twins are becoming more accurate and smart. It also enables more flexible product phases for the design and development of a product. They help product developers explore different solutions freely without concerns relating to physical material costs or loss. Companies worldwide are rapidly adopting digital twin technology, enabling various applications and use cases to arm themselves with this type of revolutionary technology.
Read more: How Digital Twins Can Help In Saving The Environment
Here, we list some of these potential uses and practical applications of digital twins technology as shared by 13 different tech experts of the Forbes Technology Council.
1. To calculate product performance statistics and measures
Michael Campbell from PTC shares that with innovations enabling digital twins to be a comprehensive digital equivalent of a product or process in the real world, product developers or manufacturers can understand how the product is in use or performing. They can even track if the product or supply line may break down or is low in supplies. Campbell remarks that all this can lead to a better experience for the end consumer.
2. Simulating complex manufacturing scenarios
Eugene Khazin from Prime TSR remarks that digital twins have great use in the form of a precise virtual representation of a production supply chain. It will use advanced analytics and machine learning systems to predict and simulate different complex “what-if” scenarios without running these in actual production. As a result, manufacturers and production sites will utilize resources more efficiently and accurately to increase product quality.
3. Removing risks from different experimentations and analysis
Kathleen Brunner from Acumen Analytics Inc states that digital twin technology is a game-changer saying that it can eliminate the need to perform various experiments and studies with actual equipment or processes. Digital twins offline can enable multiple investigations of various complex and what-if analyses of different scenarios. Practical applications of digital twins allow optimization of other parameters and outputs with a digital representation or replica interface that responds to human and environmental inputs. These digital experiments significantly de-risks these physical experimentations by deeming them unnecessary.
4. Improving software products
Vince Padua from Axway explains that one way for the practical application of digital twins is to leverage actual customer usage data. This data can improve enterprise software products through its analysis. The data collection can include whether users are using a particular feature and how they receive notifications or collaborate with other users. Developers can create a digital twin of the customer experience using this data, while Artificial Intelligence can determine and predict the fastest and most efficient ways to solve various issues.
5. Real-Time information sharing and analysis
Gerald Rousselle from One Concern shares that digital twins can produce new functionalities since they represent the physical world in a form that computers can understand. He says that a GPS in mobile can be a digital twin of the natural world to provide accurate and real-time direction and navigations to your destinations.
6. Creating valuable digital assets
Ghufran Shah from Metsi Technologies Ltd explains that there is a lot of hype around cryptocurrency and non-fungible assets/tokens or NFTs. He clarifies that NFTs are a way to represent a physical asset such as a picture, video, or even a music clip in a digital format. Once a physical object is mapped into an NFT, a unique identity of this asset can now live forever within the blockchain. These assets can even gain monetary value and become valuable collectible.
7. Facilitating hybrid teaching methods
Zeng Fan from the University of Miami Herbert Business School says that the schools and universities are equipping classrooms to accommodate virtual conferencing tech for virtual teaching due to the pandemic. This technology is similar to one of the practical applications of digital twins, face-to-face and digital/virtual class deliveries. This technology can also be in use for recording asynchronous digital course content.
8. Improving vehicle safety
Stefan Kalb from Self Engine explains that it's costly to use real cars and crash test dummies to get actual life data about car crashes, potentially saving lives. If digital twins technology is used, it can collect sensor data from inside a car as in the real world. This data, over time, can go through analysis and study and perform numerous cost-effective and efficient car crash simulations. These simulations can provide data that can improve the safety of real-live cars.
9. Supporting sustainable clothing practices
Julia Dietmar from Vue.ai explains that an excellent example of digital twin technology can be a “digital passport” for different pieces of clothes that are manufactured. Such “passports” can contain various information such as product attributes, raw materials, factory information, and even previous owner information. It can prove to be very useful for sustainable clothing practices.
10. Collecting and providing input for databases
Vitaly Kleban from Everynet says that the lack of ML and data analytics data is a genuine concern, even putting multimillion-dollar investments at risk. But digital twins can serve as an interface between real-world hardware and sensors to collect data from the physical world. The practical applications of digital twins can even prove to be a key to providing enough data for ML systems.
11. Preventing sports injuries and enhancing athletic performance
Laurie McGraw from AMA explains that the NFL has a digital twin for every player through field cameras and sensors. It can recreate every move or body posture of the players. This level of sophistication has huge potential regarding injury prevention and even improving player and game performances. These types of data and information can prove to be very useful for more than just elite athletes.
12. Providing personal assistance
Kerrie Hoffman from getting Digital Velocity and Focal Point Business Coaching state that smartphones are already digital twins of every person. Smartphones are already acting as our digital twins since they provide various functionalities like “Swipe to Pay '' when entering a coffee joint or providing alternate routes when there is a traffic jam ahead.
13. Optimizing traffic flows
Joaquin Lippincott from Metal Toad explains that practical applications of digital twins in the transportation sector are enormous. With smart vehicles and smart cities, planning and real-time adjustments to traffic are possible, optimizing traffic flows and saving time. Such technology may be dangerous, but we can test, optimize, and later implement such technology much more safely with digital twins.
From everyday market consumers to innovative technologies like robotic simulation services, offline robot programming, AI, AR, and VR, one thing is for sure, the robotic technology in the future will reach places and fields that are unforeseen even today. So, researchers and market enthusiasts have already started to predict what the industry will be like in the future. Hovering over thousands of ideas and scenarios, they have come down to these top three predictions for the robotic industry.
The Robotics industry is continuously evolving and growing. Researchers estimate that the market for the robotic industry globally in 2020 was more than 27 Billion US Dollars. This figure, however, has high expectations to grow astronomically to more than 74 Billion US Dollars by 2026. Researchers also pair this expectation with an annual growth rate of 17.45%, which again believes it will grow more.
The mainstream market also reflects this growing influence of robotics. The demand for robots and robotic technology is increasing in industries and factories, and regular consumer space. It shows that the robotic industry will become more and more mainstream with its uses to be making places even in fields that we cannot foresee today.
Read more: Are You Still Manually Teaching Robots?
With the COVID-19 pandemic, industry and consumer trends are shifting. During the pandemic, automation and remote operations experienced a boom that saw changing needs among manufacturers and consumers. In addition, people working from home, communication technology was on top of its game, with industries relating to remote communications increasing in value and influence.
It also brings together the sensing technology along it. With automation of tasks, even daily tasks being in demand, the robotic industry and the consumer industry focus on automation and sensing technology that enables it. Moreover, with automation comes data. Hence the data-driven industries like cloud technology are also increasing. Today’s data industry is so big that the tech giants of the current world are determinants of the amount of data they control and can process.
Another significant technology in communication, the 5G technology, is also a rave among consumers and industry alike. With this, the robotic industry is also taking advantage of 5G technology, with robots being more capable of high-speed communication and being more data-driven than ever.
We can compile all this information and trends of the current world into three things: Mainstream consumer space, Automation, and the data-driven industry and communication and sensing technology.
The demand for robotic and other state-of-the-art technology is increasing in the mainstream market. As a result, consumers are getting warier with these technologies and are willing to invest in them. It shows that the mainstream consumer market is undoubtedly aware that robotics technology is the future.
Furthermore, with or without the pandemic, communication and sensing technology is increasing in adoption and innovation, giving the green light to the predictions for the robotics industry. But due to the pandemic, it experienced a rapid increase in its adoption and development. Moreover, with people working from home and companies emphasizing remote working, communication technology is experiencing a high rise in demand. It is no different in robotic technology. Since robots integrate other technologies that are very advanced and highly complex, communication and networking will experience colossal development.
Consumers will expect their devices to be able to communicate with them more seamlessly. Furthermore, every use case of any robotic technology will want to fully utilize this advancement in communication technology to enable different possibilities. With high-speed communication possible, fleets of robots will communicate more efficiently and rapidly, creating even more use cases. Furthermore, Fleets of communicating robots capable of working together as a unit to complete specific tasks together will also be a high possibility with newer communication standards like 5G.
Along with communication comes sensor technology. With sensors getting smaller with more efficiency but less power, it will be possible to use them even in unforeseen places and use cases. Furthermore, with home security systems improving daily and technologies like computer vision and natural language progressing, sensors adept at these technologies will also enhance more. So naturally, the robotics industry will also take advantage of this.
Since the robotic industry is mainly based around sensors and their capabilities, with the increasing efficiency of sensors, it will be possible to include more significant, more capable sensors in any robot.
Read more: What Does Nvidia and Open Robotics Partnership Mean For The Future Of Robotics
Predictions for the robotic industry are getting wilder; however, the accomplishments don’t fail to amaze us. Like the battery technology is improving further, and these sensors are getting more and more power-efficient, it is almost certain that we will use various kinds of sensors in different fields that are even seen as not possible today. For instance, take our phones, for example. Mobile technology is improving at such a fast pace that with each increasing year or two, people feel obliged to upgrade their phones to a newer model since they have started to feel old even if they are only a year or two old.
Since phones are getting smarter, so are the sensors inside them. A smartphone has numerous sensors, from cameras to accelerators to some phones even having LiDAR sensors in them. Compare this advancement to only a decade back, when phones with even a camera were tough to find. It acts as a testament to how far sensing technology has come and is improving at a fast pace. Of course, this also applies to robotic technology.
With sensors getting more efficient, smaller, more powerful while being more power-efficient, it will be possible for robot developers to pack more robust and accurate sensors in their robots. It will enable more probabilities. Furthermore, with sensors comes to their data. Sensors are devices that extract enormous amounts of data. However, to process and handle this, data-driven technologies are promptly evolving, if not even more.
The data-driven industry is evolving at a pace that exceeded the predictions for robotic industries made before the pandemic. With almost all kinds of technology now capable of dealing with data, manufacturers are constantly packing their products with more data-driven features, thanks to the efficiency of processing units getting better. The data industry is so important today that the top tech leaders of the current world are determinants of the efficient utilization of data technology; with devices capable of collecting large amounts of data, whether, through sensors or user interactions, data-driven applications are certainly thriving.
With data comes technologies like Machine Learning, Deep Learning, and Artificial Intelligence (AI) applications. With AI comes the automation of the industry. The Robotics industry is undoubtedly at the forefront of automation technology, with humans having a vision of automated robots way back. However, what’s even more exciting about this data-driven technology is that it helps a robot have practical and smart applications and even helps to develop and build robots.
Innovative technologies like Simulations, AR, and VR will thrive under the data-driven industry after all these technologies rely heavily upon data. But with data-driven technology developing at a rapid rate, these technologies are also improving very fast. Moreover, simulations are now capable of imitating real-world environments and phenomena with very accurate physics engines. Robotic development is also possible with these technologies, especially since the robotic industry is a costly industry due to its high risk for humans and economic benefits and resource consumption.
Robotic research and development usually require many resources and skills willing to take a risk with high-value components, and research is for waste. Furthermore, since simulations and digital technologies like Robotic Simulation Services or Offline Robot Programming Software Platforms are mainstream, the future robotic industry will depend on these technologies.
With various advantages like rapid prototyping, faster and efficient designing process, fewer resources, and fewer requirements of highly skilled personnel, simulation technology will thrive in the future for the robotic industry. The robotic industry will design, test, develop, and research robotics inside simulations with technologies like digital twins.
The predictions for the robotic industry also indicate that the industries and production sites will be using technologies like Offline Robot Programming Platforms for teaching and programming robots, resulting in fewer downtimes and progressing more smoothly. It is because the robotic industry will have its core lying in digital technologies like these.
Robots of the future will also focus more on the human-robot collaboration where robots will be more capable of working together with humans. For this, integrating technologies like AR and VR in robotics and AI will be crucial. AR and VR will allow the robotic industry to venture towards complete digital premises along with remote technology.
Compiling all this information and trends in the world today, we can be sure that the future of the robotic industry looks to be very promising. From everyday market consumers to innovative technologies like robotic simulation services, offline robot programming, AI, AR, VR, one thing is for sure, the robotic technology in the future will reach places and fields that are unforeseen even today. With this, the top 3 most significant predictions for the robotic industry are:
The chip giant NVIDIA and Open Robotics partnership may mark a significant stride in the robotics and Artificial Intelligence industry.
NVIDIA is one of the most potent entities for chips manufacturing and computer systems, along with Open Robotics being a giant in the robotics space. This partnership brings these two giants together to develop and enhance Robot Operating System 2 (ROS 2).
As put forth by Chief Executive of Open Robotics, Brian Gerkey, users of the ROS platform were using NVIDIA hardware for years for both building and simulating robots. So the partnership aims to ensure that ROS2 and Ignition will work perfectly with these devices and platforms.
ROS is not a new technology. From its inception in 2010, ROS has been a vital source of the developmental platform for the robotics industry. Also supported by various big names like DARPA and NASA, ROS is an open-source technology that combines a set of software libraries, tools, and utilities for building and testing robot applications. ROS2 is the new version with many improvements upon the old ROS and was announced back in 2014.
However, Open Robots’ Ignition simulation environment primarily focused and targeted the traditional CPU computing modes over these years. Conversely, on the other hand, NVIDIA was pioneering and developing AI computing and IoT technology with edge applications in their Jetson Platform and SDKs (Software Development Kits) like Isaac for robotics, NVIDIA toolkits like Train, Adapt, and Optimize (TAO). All this simplifies AI development and deployment of AI models drastically.
Read more: Are You Still Manually Teaching Robots?
NVIDIA was also working on Omniverse Isaac Sim for synthetic generation of virtual data and simulation of robots. Jetson platforms are open source and are available to developers. But now, with its combination with the Omniverse Issac Sim, developers will be able to develop physical robots and train them using the synthetic data simultaneously.
The NVIDIA and Open Robotics partnership majorly focus on the ROS2 platform, and it’s boosting its performance on the NVIDIA Jetson edge AI and its GPU-based platforms. The partnership primarily aims to reduce development time and performance on various platforms for developers looking to integrate technologies like computer vision and Artificial Intelligence (AI) and Machine Learning (ML), and deep learning into their various ROS applications.
Open Robotics will improve data flow, management, efficiency, and shared memory usage across GPUs and other processing units through this partnership. This improvement will primarily happen on the Jetson edge AI platform from NVIDIA.
This Jetson Edge platform is an AI computing platform and is mainly a supercomputer-based platform. Furthermore, Isaac Sim, a scalable simulation application for robotics, will also be interoperable with ROS1 and ROS2 from Open Robotics.
The NVIDIA and Open Robotics partnership will work on ROS to improve data flow in various NVIDIA processing units like CPU, GPU, Tensor Cores, and NVDLA present in the Jetson AI hardware from NVIDIA. It will also focus on improving the developer experience for the robotics community by extending the already available open-source software.
This partnership will also aim that the developers on the ROS platform will be able to shift their robotic simulation technology between Isaac Sim from NVIDIA and Ignition Gazebo from Open Robotics. It will enable these developers to run even more large-scale simulations with the enablement of even more possibilities. As put by the CEO of Open Robotics, Operian Gerkey, “As more ROS developers leverage hardware platforms that contain additional compute capabilities designed to offload the host CPU, ROS is evolving to make it easier to take advantage of these advanced hardware resources efficiently.”
It implies that developers will openly leverage processing power from different hardware platforms with more powerful, low-power, and efficient hardware resources. So, for example, ROS can now directly interface with NVIDIA hardware and take its maximum advantage, which was hard to do before.
The NVIDIA and Open Robotics partnership also put forward possibilities of results to come out around 2022. With a heavy investment of NVIDIA towards computer hardware, modern robotics can now utilize this hardware for enhanced capabilities and more heavy AI workloads. Furthermore, with NVIDIA's expertise in inefficient data flow in hardware like GPU, the robotics industry can now utilize this efficiency to flow large amounts of data from its sensors and process them more effectively.
Read more: Robot Programming Platform Conquers Complex Parts and Outperforms the Competition
Gerkey further explained that the reason for working with NVIDIA and their Jetson Platform specifically was due to NVIDIA’s rich experience with modern hardware relevant to modern robotic applications and efficient AI workloads. The head of Product Management, Murali Gopal Krishna, also explained that NVIDIA’s GPU accelerated platform is at the core of AI development and robot applications. However, most of these applications and development are happening due to ROS. Hence it’s very logical to work directly with Open Robotics to improve this.
This NVIDIA and Open Robotics partnership also brought some new hardware-accelerated packages for ROS 2, aiming to replace code that would otherwise run on the CPU, with Isaac GEM from NVIDIA. These latest Issac GEM packages will handle stereo imaging and color space conversion, correction for lens distortion, and processing of AprilTags and their detection. These new Issac GEMs are already available on the GitHub repository of Nvidia. But it will not include interoperability between Isaac Sim from NVIDIA and Ignition Gazebo from Open Robotics as per expectations of it arriving in 2022.
Meanwhile, though, the developers can explore and experiment with what's already available. The simulator on GitHub already has a bridge for ROS version 1 and ROS version 2. It also has examples of using popular ROS packages for navigation and manipulation through boxes nav2 and MoveIT. While many of these developers are already using Isaac Sim to generate synthetic data for training perception stacks in their robots.
This latest version of the Isaac Sim brings significant support for the ROS developers. Along with Nav2 and MoveIT support, the new Isaac Sim includes support for ROS in ROS April Tag, Stereo camera, TurtleBot3 Sample, ROS services, Native Python ROS support and usage, and even the ROS manipulation and camera sample.
This wide range of support will enable developers from different domains and fields to work efficiently in robotics. For example, developers will quickly work on domain-specific data from hospitals, agriculture, or stores. The resultant tools and support released from the Nvidia and Open Robotics partnership will enable developers to use these data and augment them in the real world for training robots. As Gopala Krishna put it, ”they can use that data, our tools and supplement that with real-world data to build robust, scalable models in photo-realistic environments that obey the laws of physics.” He claimed with the remark that Nvidia would also release pre-trained models.
On the remark about performance uplift in these perception stacks, Gopala Krishna said, “The amount of performance gain will vary depending on how much inherent parallelism exists in a given workload. But we can say that we see an order of magnitude increase in performance for perception and AI-related workloads.” Nvidia’s Gopala Krishna also remarked that the program would increase performance and much better power efficiency with appropriate processor use for an acceleration of different tasks.
Gopala Krishna also noted that Nvidia is working closely with Open Robotics to streamline the ROS framework for hard accelerations. The framework will also see multiple new releases of its hardware-accelerated software package, Isaac GEM. Some of these releases will focus on robotics perception, while further support for more sensors and hardware will arrive on the simulation technology side. The release will also contain samples that are relevant to the ROS community.
This development will aid the growing market of robotics. Especially after the COVID, the growth of the robotic market seems to skyrocket, with more and more industries and companies lining up to use and adopt robotics, from manufacturing and production lines to health care and agriculture usage.
Nvidia and Open Robotics partnership will see the advancement of AI and technologies like Machine Learning and Deep Learning at a rapid pace now with the support of NVIDIA hardware in robotics. Researchers estimate that the global robotics market will cross 210 Billion US Dollars. This estimate is likely to increase with the rapid development of AI and technologies like semiconductor technology, sensors, networking technology with 5G.
This collaboration between Nvidia and Open Robotics will only add valuation to this market with innovative platforms like Nvidia Isaac and ROC, helping developers develop more efficient, robust, and innovative robots and robotic applications.
It will also help the open-source community of robot development since this partnership brings together two of the most significant robotics development communities with ROC and Nvidia Isaac. Furthermore, FS Studio collaborates with this growing community to release its robotic simulation solution, ZeroSim, alongside the Nvidia and Open Robotics partnership. Thus, it will help the development bring together with collaboration and push the robotic development further. Now with the dawn of Industry 4.0, companies are moving towards digital technology. This movement can be seen with industries adopting digital solutions with robotics in different fields from production and manufacturing to the board paradigm of human-robot collaboration possibilities.
Teaching robots is a time-consuming and laborious task, especially when you’re manually teaching robots. Particularly with robots of niche applications, use cases, and robots with complex movements or robots within specific environments like industries and production. Robotic technology is continuously evolving, and so is its complexity. However, robotic tech is also becoming easier to use, more accessible, and more adaptable with increasing complexity. Conversely, teaching robots through traditional approaches like Teach Pendants is getting more and more challenging and complex.
The Robotics industry is complex because of the sheer complexity of the technology and the cost of developing, building, and deploying a robot. Robot research and development and deploying robots are challenging tasks because of the sensitive nature of testing in robotics. Testing a robot is an expensive task. Consuming massive resources and time, testing robots along with training them is a very resource-intensive task.
Read more: Robot Programming Platform Conquers Complex Parts and Outperforms the Competition
However, due to the advancement of technology and the Fourth Industrial Revolution (FIR) inching closer and closer, industries are rushing towards digital technology and automation, which, in some scenarios like industries and production only possible with robots. Consequently, the importance of robotics in the production industry is increasing day by day. As a result, manufacturers and production sites are getting more eager to adopt their production line with robots with digital technology at its core. And manually teaching robots would only slow the production down and eventually leave you behind in the competition.
The Complexity in Robotics
With robotics comes its complexity. A robot is not a single entity but an integration of several different parts, components, and systems working together. These parts, components, and systems are usually various mechanical parts, motors, actuators, hydraulics, sensors, processing systems, networking interfaces, and many more. These components are very hard to build and even complex to perfect. Furthermore, integrating these parts to work together simultaneously with efficient cohesion to achieve a system that can perform specific tasks is complex on another level.
The integration may well be complete and the robot ready. But another major hurdle comes in the form of programming/coding the robot. Programming a simple robot with a particular function may be easy, but the robots that have to perform complex tasks while performing complex movements with precision are strenuous. This difficulty only scales up for industrial robots that have to accomplish tasks with accuracy and repeatability and perform various activities and functions within the production environment.
Why Manually Teaching Robots Will Hold You Back?
Programming a complex robot also requires a complex teaching process. The traditional approach to programming and coding robots is to use teaching pendants. Teaching pendants are a device that helps robot operators to control and program an industrial robot remotely. For example, these devices can code or teach a robot to follow a specific path or perform certain actions in a particular manner. With teaching pendants, robot operators or developers have to teach these robots manually.
Manual robot teaching may be easier on robots with low movement paths, simple actions, or singular axes. But industrial robots are a whole another story. They need to be constantly working in a usually adaptive and harsh environment of production. Such robots are complex and also very sensitive. Hence training the robots with teaching pendants is a difficult task. It is a very time-consuming task with the requirement of the teaching personnel to be present at all times. Furthermore, the robots have to be in teaching mode during all this time which means they cannot perform other tasks. Add this to the fact operators have to take them out of production during this long teaching process. All this makes manual teaching very cumbersome.
Read more: Why Use Offline Robot Programming Software And How to Get Started
The downtime while teaching the robots is a massive issue to production. Moreover, this downtime is not only a one-time thing. Since industries have to be at the top of their game to thrive, they need to evolve and adapt over time. New changes and upgrades are necessary. Maintenance and repair works are inevitable. And even the failure of robots is not a common thing. All this requires teaching pendants, which is again very slow and a tedious approach to programming robots. It will add more delays, difficulties, costs and consume more resources. And this is a massive bottleneck for production.
Instead of wasting time in this slow and cumbersome manual approach, using new and better solutions with automation at its core is the way to go.
Learn About Offline Robot Programming
Offline Robot Programming is an “offline” approach to robot programming. Offline Programming (OLP) is a software solution to manually robot teaching by replacing the teaching pendants with simulation software. This “offline” solution teaches the robots virtually through software remotely. Thus, OLP takes leading away from the manual approach and takes out the requirement to remove the robots from production.
Although Offline Robot Programming is not a new technology, its evolution in recent years puts it in the spotlight in robot programming and the whole paradigm of robotics. It’s because of the advantages and benefits of using offline robot programming. Offline robot programming replaces the teach pendants with a more elegant solution. Furthermore, OLP allows for industries to train robots and their programming/coding through software updates. Robotic Programming Platforms also offer different software solutions to generate these instructions.
It means there is no need for the actual physical robot to be present in any generation phase or testing the training program/code. Instead, all this happens within the simulation technology inside the robotic programming platform itself. The evolution of simulation technology is so far ahead that it can now accurately simulate almost any object or environment with all the characteristics and behaviors of the original real-world object or environment.
Simulation technologies today can simulate every robot’s functionalities, features, and operations. Various behavior, states, and phenomena of robots and their components can simulate without manually teaching robots. Simulations can accurately simulate the mechanical elements of different parts with different materials and their operation in different environments and conditions. Along with this, fluid dynamics for air and water is also possible to simulate. Collisions, movements, etc., are also potential. It is due to the ability of simulations to accurately simulate and imitate the real-life physics of materials and the environment.
In addition to this, simulations can also imitate electronic components and processes. For example, it can accurately simulate the processing of CPUs and progressing units or even network interfaces and data exchange. Along with this, simulations can even test technologies like Artificial Intelligence (AI) with Machine Learning (ML) and deep learning. All these possibilities allow simulations to simulate all behavior, state, and properties of a robot along with its features and functionalities effectively.
Robotic simulation software solutions are already available, and different industries and companies are already leveraging their benefits. These simulations make innovative technologies like OLP possible to exist and thrive, creating manually teaching robots irrelevant. With offline robot programming, companies need not go back to the old approach of using teaching pendants. Such an old approach is very time-consuming while also requiring enormous resources, workforce, and investment. In contrast, OLP provides companies with elegant future-proof solutions that are effective and efficient.
OLP successfully reduces downtimes from production due to its ability to upload programming instructions in robots that they are working on without taking them out of the output. They can also enable new roads to generation and testing robot programs far from the manual testing method and age of robot codes or instructions. Simulations make it very easy to try these codes, while AI automation enables self-diagnosis and real-time optimization of production lines.
OLP is often seen as a technology that is very complex and requires high skills to utilize. There is a huge misconception that only the sides with deep pockets can afford to use OLP solutions, and there won’t be any demand for manually teaching robots anymore. But that is not the case. OLP solutions are pleasing on paper and easy to integrate and adapt even in existing production. Companies like FS Studio are working hard to bring out innovative solutions and state-of-the-art R&D technologies, including robotic OLPs, to make this transition of using OLP solutions smoother. With decades of experience and collective knowledge of various skillful people, FS Studio brings out solutions like Robotic Simulation Services for multiple companies and industries.
With the increasing pace of the industry’s move towards Industry 4.0, every industry is eagerly shifting towards digital technology while replacing old technologies like Teach Pendants with newer, more elegant, and efficient solutions like Offline Robot Programming platforms. Offline robot programming opens the road to newer possibilities and opportunities, enabling rapid prototyping, testing, training, and superior research and development, saving you from manually teaching your robots. In addition, it will help companies bring out efficient production and help them maximize their efficiency with a proven feat of achieving higher Return of Investment (ROI) in production lines and product innovation. Furthermore, this will help industries and companies innovate and remain at the top of their game to surpass and outperform their competitors.
Building intelligent infrastructure with digital twins has helped several companies to collect, extract, and analyze data. Digital twin technology or virtual twin is overgrowing with increasing accessibility and adaptability. As Industry 4.0 comes closer, technologies surrounding digital twins are also maturing and continue to develop. With the incorporation of technologies like the Internet of Things (IoT), data analysis, and Artificial Intelligence (AI), digital twins enhance R&D innovation with intelligent services like automation, self-monitoring, and real-time optimization. It enables rapid design & development and smart solutions in production, sales, logistics, and overall supply chain.
With the ability to enhance current manufacturing & product development, industries worldwide are incorporating digital twin technology. We can already see this accelerating adoption of digital twins across the industry. Although the global twin market was currently at 5.4 billion US Dollars in 2020, much of its slump is due to the worldwide pandemic. In addition, several industries shut down due to lockdowns and social distancing being the new norm during 2020 because of COVID-19. Nevertheless, the digital twin market is slowly rising again, with a tremendous rise expected after 2021. As a result, the global digital twin market will likely reach 63 billion US Dollars by 2027 due to a high growth rate of 42.7% annually.
What is Digital Twin?
While the idea of building intelligent infrastructure with digital twins is not entirely a new concept, due to its current exponential rise and growth, digital twins are undoubtedly growing more and more prominent. Along with the advancement in IT and digital technology infrastructure, digital twins are also evolving rapidly. In general, the concept of digital twinning represents a physical object or environment in a digital form that possesses its accurate characteristics and behavior. While 3D models and simulations also can describe an object or environment, twins systems do more than that.
A digital twin generally represents a physical object or environment not just in a static manner but in a dynamic form. A digital twin represents every phase of the lifecycle of a physical object or environment. A digital twin represents a physical object or environment from its design phase to manufacturing and maintenance and changes due to re-resign, iteration, and refining the object.
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Hence, a digital twin is less of a 3D model rather more like an information model. Unlike traditional 3D models, building intelligent infrastructure with digital twins needs a more dynamic and adaptive approach. They can evolve and change over time concerning changes and enhancement in information and data. Digital twins can communicate, update and even learn similarly to their physical counterparts through data exchange with Artificial Intelligence at its core.
Artificial Intelligence with technologies like Machine Learning and Deep Learning enables a digital twin to behave as accurately as possible in contrast with its physical counterpart. Due to this dynamic nature of digital twins, they are in use to explore solutions, detect and prevent problems even before they happen and essentially plan for the future. Armed with these intelligent and smart solutions, companies and organizations worldwide rapidly adopt these technologies in their operations and global supply chain.
Building Intelligent Infrastructure with Digital Twins
Digital twins have five levels of sophistication. Ranging from a level 1 twin that can describe and visualize the product to a level 5 twin model that can operate autonomously, different levels of digital twin require different levels of infrastructure. For instance, a level 1 twin does not require advanced Artificial Intelligence or Machine Learning systems, but a level 5 twin does need them. Level 2 digital twin is an informative twin that needs to incorporate additional operational and sensory data. Furthermore, level 3 is a predictive twin that can use these different data to infer and make predictions. On the other hand, the Level 4 digital twin is a comprehensive twin that can consider and simulate future scenarios to predict and learn from them.
Building digital twin technology includes converging technologies like IoT, data analysis, design & development of the twin either in 2D or 3D, and incorporating AI and technologies like machine learning and deep learning. The digital twin infrastructure is not only in a digital form but also in physical form. This is because a digital twin simulation model resides in a digital format and connects the physical world alongside it. This connection is the representation of both digital models and physical models such that they represent and replicate each other. Every change in the digital or physical model must be synchronized, and both should also respond to each other’s differences.
The actual connection is made through digital models. We can link the physical world with the virtual world by twins modeling and simulating the physical world to map and represent it in digital form. On the other hand, we can connect the virtual world with the physical one by replicating any changes and updates made in the virtual world in the physical world itself. It will ensure that neither the digital form nor the physical form is not synchronized.
In digital twin technology, synchronization must be in real-time when building intelligent infrastructure with digital twins. Real-time synchronization and simulation of the product is the following infrastructure for digital twins. Whenever a product is in the developmental phase of production, the status of the digital twin must also reflect that. The changes occurring in the digital twin must also be replicated in the physical product. Therefore, the changes in materials, processes, environmental, and every other change must be synchronized across physical and digital forms.
Read more: How Digital Twins Can Help In Saving The Environment
Apart from this, the digital twin infrastructure also requires data analysis for deep learning and intelligent systems. Artificial Intelligence generally powers these intelligent systems along with Machine Learning and Deep Learning capabilities. This is necessary for smart analytics and prediction. ML and deep learning systems must be capable of analyzing substantial amounts of data. This data must be representing the actual physical product in real-world environments. Such data are generated and collected by sensors placed in the physical world and physical development.
The data collection is a crucial metric for a system to detect anomalies or errors through analysis in the digital twins platform. Usually, ML systems process these data types and perform pattern recognition to make predictions or suggestions. Thus, these systems enable self-monitoring, predictive maintenance and diagnosis, alert systems for possible future errors, and detection of abnormalities or inconsistencies in the product.
Due to this, the data must be accurate and representative of the actual physical product and environment with great precision. These types of data also are helpful for the corporations or organizations for their product analysis and study.
These infrastructures together enable all the digital twin advantages. The convergence of these technologies is a complex task. Nevertheless, the resultant solution offers an intelligent system that can track past system analytics to predict future solutions and real-time product optimization. Companies are rapidly advancing towards implementing digital twin technologies in their platforms and systems to leverage such benefits.
Building the Infrastructures
Building digital twin infrastructures is a very complicated and complex process. Since digital twins incorporate various technologies together, it is tough to integrate these technologies to work together flawlessly. Only with such integration can one enable proper digital twin technology and can leverage its benefits.
Since the technologies part of the model twins infrastructures are different, companies must be willing to take on R&D for every technology when building intelligent infrastructure with digital twins. Moreover, if not for flawless integration, the technologies must at least be working together, which is a challenging task. However, technology is rapidly growing, and so is its accessibility and ease of use. Hence, integrating these technologies is increasingly easier to enable the tech stack for digital twins.
With the power of the cloud, technology today is dependent mainly upon real-time computing. With the help of the cloud, companies can leverage virtually endless amounts of computing to enable various services, including digital twins. Furthermore, cloud computing allows companies to build intelligent systems that are ideal for integrating multiple infrastructures of the digital twin technology.
One of the most prevalent uses of cloud computing is Artificial Intelligence. Due to the nature of Machine Learning and deep learning, immense computing power is necessary to develop these systems. Cloud computing shines brightly in this field due to its vast pre-built infrastructure and network of computer systems. In cloud computing, these computer systems are connected through an extensive network of servers and processing systems. Cloud computing service providers serve this network of different systems as a single system with enormous computing power.
Alongside this, a system for efficient and accurate modeling of the physical world with high-performance systems for real-time optimization and synchronization is mainly necessary. Moreover, deep learning and data analytics with intelligent AI systems to enable smart solutions with automation at its core is also imperative. Furthermore, a unified system integrating all these technologies is crucial while building an infrastructure for digital twins.
Companies like FS Studio pioneer product innovation and transformative R&D technology through already established and proven digital infrastructure. Since deploying and building intelligent infrastructure with digital twins is very complex and challenging for companies and organizations, FS Studio provides innovative and smart solutions for these problems. Consequently, companies can focus on their primary product innovation rather than shifting their resources towards building a digital infrastructure.
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.
Read more: How Are Industries Creating New Opportunities By Combining Simulations and AI
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.
Read more: Simulation in Digital Twin for Aerospace, Manufacturing, and Robotics
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.
Digital Twin decarbonizes the energy systems by bringing all technologies together. The virtual twin can model energy flows and changes of variables in real-time.
Initially, designers and engineers used it to build prototypes of new products, but it proved to have more practical uses. Moreover, integrating the Internet of Things and AI has strengthened it to carry out multiple tasks.
Digital Twin can model a raft of internally connected systems through big data analytics. So, Digital twin can aid the reduction of CO2 and other greenhouses gases released into the earth's atmosphere.
Digital Twin can map out all the aspects of the energy systems to help with decarbonization. Starting from electrical production to distribution losses and localized demand, it would lay out the whole system in the virtual environment.
The use of Digital Twin is expanding as we are developing the technology to be more effective with time. For example, it has proven effective in energy management, electrically powered public transport infrastructure management, and sector coupling.
It is not possible to derive the best outcomes from the Digital Twin without knowing the factors. Therefore, one needs to know these factors below to decarbonize the energy systems without failure with Digital Twin.
Get Digital Twin Experts on Board:
When you are up for decarbonization of energy systems, you'll need technical know-how. So we are expecting that your in-house talents already possess the understanding of electrical generation, supply, and distribution.
Next, you will need a more profound working knowledge of Digital Twin to use this for the digital decarbonization of energy systems.
You must have a clear understanding of building digital twins aligned with each phase of the decarbonization process. Additionally, you need to know how to deal with data modeling and intelligent data technology to make digital twin work for you.
It's better to outsource the Digital Twin project to experts if you lack the in-house skills needed to make this project successful. Companies have to deal with the unique sets of challenges of building and deploying a digital twin.
Digital Twin is a framework for building a bridge between the physical and digital worlds. Therefore, you must satisfy a range of requirements to develop and deploy virtual twins successfully, including:
The biggest challenge of deploying a digital twin is that it shares the same characteristics as the physical entity. The Digital Twin (DT) decarbonizes the energy systems better if the team can deal with the challenges below:
Make use of your best Datasets:
Preparing the data set should be the next step in your list before building the digital twin.
We're living in the big data age where high-quality data sets are crucial to success. Feed high-quality data into the Digital Twin and you can expect fantastic outcomes. Feed trash into the twin system and the result will be the other way round.
To sum it up, it means that you need to clean up, gather and structure data sets to set up the twin system. Also, cross-check the Data's relevancy in real-world settings to ensure you will be feeding high-quality sensible data into the twin system.
When you are swamped with data and need to analyze different sets for different purposes, it adds complexity.
For instance, analyzing meteorological data like temperature, dew point, wind direction, and speed next to electrical consumption in buildings from photovoltaic, solar thermal, and hydropower plants at locations worldwide can become messy.
In brief, you'll need to combine patience and years of professional experience to locate, collect and prepare the best data sets.
Digital Twin Decarbonizes the Energy Systems Better if You Avoid Preconceptions About Model or Configuration:
Start building and deploying Digital Twin with an open mind. Don't keep any preconceived notions about which specific model or design will work for your program.
Keeping an open mind will give you the flexibility to draw the best model and configuration from a variety of ideas and solutions.
Your digital twin will end up with average quality outcomes if you have a preconceived technological pathway and follow it.
Examine all the possible solutions to decarbonization before going with one specific approach. In this way, you can make the best out of the digital modeling and AI computing calculations and stats.
Adopt Digital Twin for the whole system:
Before starting your digital twin program, you must figure out the pathway to optimize the entire energy system.
You'll need to conduct an end-to-end analysis of the initial situation and the entire technological framework conditions. Digital Twin decarbonizes the energy systems better if you consider the parameters below of each site individually for decarbonization of the energy system:
Prepare the decarbonization strategy before jumping into the program:
You must set clear goals before starting the decarbonization program with the digital twin. Digital Twin decarbonizes the energy systems better if these factors are implemented:
Don't look for Instant success:
It's not possible to achieve success within a short period with digital decarbonization as it's a high multi-dimensional program.
The digital twinning of the energy system will need care because it's a complex model. So, building the model may take a few weeks to months, depending on the size of the venture. Also, onboarding the right people for the fieldwork and synchronizing all the steps together cannot happen overnight.
How Digital Decarbonization can Change the World?
Digital Twin decarbonizes the energy systems in a different class when you apply a tactical approach. Reducing carbon emissions in the atmosphere will help climate change and keep our earth greener. Both the private and public sectors will feel the impact of using digital twins in decarbonization.
Adopting decarbonization applies mainly to the utility sector. However, other high energy demanding sectors such as the chemical industries should think about it as well.
We cannot deny that buildings are responsible for about 40 percent of all current carbon emissions. So, digital decarbonization of energy systems can make a massive difference to the climate of tomorrow.
However, the question may arise: Is decarbonization possible?
Digital decarbonization would be hard to implement but not impossible to achieve. We can reduce indirect carbon emissions through electrification and clean energy only. It is hard to control direct carbon emissions. So, we cannot deny that heavy industries would still be responsible for at least 20% of GHG emissions.
Digitalization of industries has already begun. So, companies that won't embrace modern trends like digital decarbonization processes will be left out. Decarbonization and digitalization are getting popular. As a result, industries have to switch from their traditional business models to stay relevant.
Every company must adopt the digital decarbonization process. At present, businesses are in a situation where they need to decide on a sustainability strategy that will work from the economic perspective. To do that, they must transform into a lower-carbon business model company.
Suddenly, this change of direction cannot take place overnight. Therefore, cooperation between industries is required to implement a lower-carbon business model. The enterprises will need to review climate strategy, set measurable goals, and clearly define their action plans. They will also need to assess their performance from time to time and optimize as necessary.
Adopting digital decarbonization can benefit industries in many ways. For example, businesses can run cost-efficient systems designed for specific localities when Digital Twin decarbonizes the energy systems.
Digital decarbonization can aid district heating systems and energy storage management systems of the community. In addition, it can help to increase the capacity of electric vehicle charging stations while ensuring saving potentials in environments. Moreover, digital decarbonization can reduce electricity waste and daily operational costs.
Businesses that adopt digital decarbonization will gain the rewards of CSR too. First, they will achieve better brand recognition as environmentally friendly companies. It, in turn, will strengthen their brand image among the public.
Therefore, implementing Digital Decarbonization is a win-win situation for businesses, our environment, and society.
Simulation experts in industry 4.0 must have a passion for digital twin technology today for industry research. Digital Twin simulation is entering mainstream use as more industries are adopting this technology. According to Gartner's IoT implementation 2019 survey, 75% of organizations already use Digital Twin or plan to in a year. Notably, all the companies willing to adopt Digital Twin are implementing Internet-of-Things.
Digital Twinning is not a new technology. In 2002, Michael Grieves of the Florida Institute of Technology introduced the concept of Digital Twin publicly. In 2010, NASA showcased the first practical implementation of Digital Twinning to improve the physical model simulation of spacecraft.
We can see Digital Twin has been around for two decades now. Yet, many businesses are confused about the value of Digital Twin Technology. In addition, many companies don't know the use of Digital Twin in the modern energy, chemical, and process manufacturing industries.
Many companies still don't know that Digital Twin can enjoin the disconnected processes cutting out manual efforts that can be time-consuming.
For instance, interns or low-level employees will still follow the outdated method to gather engineering information. They would walk around one department to another to collect data required for engineering research.
Many engineers use CAD and PLM software and other sources to collect data to make informed design decisions. A few engineers are lucky enough to use enterprise search engines to pull information from various departments from hundreds of documents, folders, presentations, etc.
Read more: Simulation in Digital Twin for Aerospace, Manufacturing, and Robotics
Now that we are entering the age of automation, companies must adapt to cultural change and access the right technology. They need to integrate technologies like Digital Twin to help teams gain information without any hassle. They also need to save employees from the pain of surfing through numerous record systems.
There are other reasons to consider Digital Twin to accelerate business innovation. Here we've discussed 3 of them below:
1) The Rapid build-up and expansion of data:
The business environment of the energy and chemical industries is already volatile. On top of that, the decision-making cycle is in an array across these industries due to piled-up data sources.
System Digital Twins made for entire plants, or factory systems can rescue energy and chemical industries. A massive amount of operational data can be collected, organized, and analyzed from various devices and products.
Human decisions are not rational, even if we make sound judgments after weighing evidence and assessing probabilities. It happens because the human brain tends to simplify information processing. So, cognitive biases, including memory and attention biases, influence human decision-making.
System digital twins can eliminate human bias for critical decision makings. System digital twins can also provide a single logical view of the actual situation based on evidence, probabilities, and analytics.
It is also essential that you know your needs before adopting Digital Twin Technology. Therefore, you must ask these three questions to ensure your success with Digital Twin:
2) What type of analytics should Industry players seek?
The factory systems and manufacturing plants involve complex processes today. So, measuring KPIs isn't easy now.
The digital twin can resolve this problem by providing deeper analytics from factory systems and plants, taking multi-dimensional factors and non-linear trade-offs into account.
The digital twin can build an accurate understanding of the future based on historical and present performances data. The digital twin can recommend the best strategies that can maximize profitability for the industries. Next, the experts will need to assess each recommendation and its impact to make the best decision for the businesses.
Therefore, industries can use the digital twin technology as a supporting tool to aid decisions enabling improved safety, reliability, and profitability.
3) Digital Twin Model Utility across the entire lifecycle of the asset:
Manufacturers use digital twins differently at each stage of the product development cycle.
Initially, manufacturers start working with Digital Twin Prototype or DTP. Then, manufacturers use DTP to design, analyze, and plan out the process to predict the future shape of the actual product.
In the next phase, manufacturers use Digital Twin Instance or DTI. DTI is the virtual twin of a physical asset. Developers will use DTI to run multiple tests and determine how the product will behave in different scenarios.
The DTI stays connected with the physical asset throughout its lifecycle. As a result, developers will add more operational data to improve it over time.
In the final phase, manufacturers will use Digital Twin Aggregate or DTA. Manufacturers use DTA to cross-examine the physical product, predictions, and learning based on the collected data from the previous phase.
People from engineering, operations, supply chain, shop floor even board room can look inside the assets and processes of the Digital Twin technology at every stage.
Companies integrate AI, machine learning, predictive analytics, etc., into the system with high hopes. They do it because they believe that digital transformation will cut out all the manual workload. However, when they realize that a lot of the work still depends on the human end, they get shocked.
Industries may have entered the automated age and have innovative IoT solutions at their disposal. However, automated systems cannot replace the human touch in many critical areas of business. For example, humans still need to implement and monitor automated systems in manufacturing plants.
Read more: How Are Industries Creating New Opportunities By Combining Simulations and AI
Automation cannot replace other tasks like enhancing product design, building strategies, and growth roadmap, decision making, communicating with stakeholders, applying creativity to solve problems, etc. These areas will continue to need human intervention.
Companies need to set clear expectations when moving forward with the digital transformation of the assets.
The purpose of digital transformation and digital twin is to make the technical aspects of the job easier. In addition, Digital Twin technology will provide you the intelligence to help you focus your hard work on beneficial outputs.
Companies building Digital Twin Technology today are the pioneers of shaping the agile and intelligent industries of tomorrow. So, they need to develop the right digital twin platforms to leverage the full potential of digital transformation to create an exemplary model that others can follow.
The first steps will always be challenging. You can expect objections and hurdles to come your way. However, all these troubles are manageable if you know the proper ways to manage them.
Here is a brief guideline to follow for successful digital twin adoption in business:
To sum it up, digital twin adoption has the scope to attract more stakeholders' buy-in. Companies can show them the data-driven rewards based on concrete analysis instead of flawed predictions. So, the stakeholders will always have the know-how of the direction they are heeding with you.
"The true benefit of a digital twin: it gives you business intelligence to make better decisions in the future. It doesn't eliminate or minimize the work you're doing now, but it fundamentally changes what you're going to do next." - Former chief executive of Cambridge City Council, Andrew Grant
As we deduce the statement of Andrew Grant, it's a life lesson that industries have learned the most brutal way around the world after the Covid-19 shock. Thus, many enterprises are seriously considering the concept of Digital Twin and thinking big to expand it.
Companies are now interested in optimizing business operations based on the real-time insights gained from manufacturing plants and product use. As a result, they are more focused on satisfying orders, resolving root causes that are hindering growth, and maximizing factories' performance based on solid predictions.
We have already entered the age of automation as the 4.0 industrial revolution has begun. Today, companies maybe just interested in predictive maintenance. However, the use of Digital Twin Technology will expand where it will be integrated not for products but into manufacturing processes and entire factory systems.
