The advent of next-generation technologies like Simulations, AR, VR, and AI continues to grow rapidly. With continuous evolution in their advancement and increasing accessibility, they can exponentially add value to manufacturers. Hence, influencing industries across the globe to adopt these technologies at an increasing rate. For example, artificial intelligence with immersive technologies like AR and VR swiftly transforms manufacturing processes and product development. But, on the other hand, robotic technology redefines the possibilities and opportunities in various fields and industries.
The increasing sophistication of robotic technology is visible due to giant leaps in the capabilities of current robotic systems. With technology evolving swiftly, the industry is also adopting newer technologies in its manufacturing and product development processes. One of these newer technologies the industry is moving towards is simulation technology.
With the dawn of Industry 4.0 upon us, industries undoubtedly need to advance towards digital transformation. In this advancement, simulation technology is a boon for manufacturers. Although simulation technology is not new due to its rapid evolution in recent years, it is expanding its horizon of possibilities and opportunities. Robotic technology is one of the unknown frontiers of simulation tech.
Simulation software has the power to enable rapid prototyping, testing, and development of product development processes and R&D technology. Computer simulation is one of the vital tools for industries like robotic development and manufacturing. With the crucial role of robots in the manufacturing industry, the development and advancement of robotic technology are significant for the whole manufacturing industry.
Why Simulation Softwares in Robot Development?
Robot research and development, along with its design and production, is very complex. It is not just because of the sophistication of the technologies in a robot. But also because of economic reasons and risks in robotic development. They also have to add value to the manufacturers as well.
Robots are usually expensive pieces of machinery. Industrial and manufacturing robots are costly due to the niche application following the niche research and development requirement. Moreover, even general robot design and development require massive resources, cost, time, and multidisciplinary skills. Furthermore, prototyping robots for testing, evaluation, and assessment need equally, if not more, resources, time, cost, and abilities. Add this with risks present in the real world, and robotic development truly becomes a huge undertaking.
Computer simulations for robotic development can solve all these problems. Computer simulations offer efficient and elegant solutions that are more cost-effective and less time-consuming. Any computer simulation software usually provides a 3D digital space to test and develop a product. Similarly, robot simulation software offers different environments and tools in a digital 3D area to test, run, research, evaluate and develop a robot.
Real robots in the real world consist of parts like motors, batteries, joints, arms, sensors, actuators, controllers, and other mechanical parts. Furthermore, robots also consist of networking, processing, and data handling components to analyze data and communicate. Apart from this, some robots also need to be smart and capable of making various decisions in real-time to add value to manufacturers. Consequently, due to these causes, robots in the real world are very complex and expensive.
However, robotic simulation software provides all these tools, components, and parts in its digital space. Due to the high advancement of simulation software, today, simulation software can simulate all these parts and subsequently a fully functional robot that can run/operate in different conditions and environments. One just has to bring these parts and models together digitally. The simulation software also supports the design and development of these parts and models digitally. Hence, developing or putting together a robot in a simulation environment is very quickly relative to the real world.
Moreover, just like in the real world, robotic simulation software also allows for the testing and evaluation robots in different environments and conditions. Simulation software can simulate fluid and air dynamics, collisions, and many more physical, real-world phenomena with very accurate and modern physics that reflect real-world physics. All this happens similarly to the real world, except the simulations are fast and easy to develop and do not have to suffer huge risks and significant economic setbacks.
With computer simulations on hand, the risks and costs in association with robot development become redundant. It also ensures that the developers do not exhaust their time worrying about resources and cost but instead focus on the actual robot development. It also provides the developers with flexibility and space to develop the best robot for their requirements without compromising developmental risks and costs.
How they Add Value to Manufacturers
With the vast advantages of using simulation software in robotic research and development, manufacturers are beginning to realize the potential it carries. Furthermore, minimizing risk in robot development in manufacturing and factories also means developing robots with better design that suits the requirements to a far greater degree. As a result, companies or factories using robots in various product manufacturing processes can undoubtedly reap the benefits of better and cost-effective robotic solutions, which is possible due to robotic simulation software.
Proper simulation software can ensure the best systems for different applications and use cases. With rapid design and development in the card, even if a system is not up to the mark, companies can simply re-design it in the digital form with much lower costs and resources. In addition, with computing systems becoming cheaper and efficient, simulations can now help manufacturers build their robotic solutions to stay competitive in the market with new and better solutions.
There are numerous ways the robotic simulation software can add value to manufacturers, for example, cohesion with better designs, processes, and efficient investment.
With manufacturers expanding their product spectra to a wide range, robotic systems in use are not always general robots but tailored with specific needs and requirements in play. For instance, a car manufacturing company cannot automate the assembly line process without the same robots. Development of robots enters completion with niche use cases in mind. One robot installs engines while another robot paints the car; another robot detects flaws in the painting. Another installs wheels, another lifts machines before installation, and so on. Each different use case requires another robot.
Hence in this scenario, designing different robots for different use cases in the real world is very expensive as well as being time and resources consuming. However, creating robots for other use cases is much simpler, more accessible, faster, and cost-effective through simulation software. Consequently, robotic simulation software can also help manufacturers to customize and fine-tune robots according to their needs. Moreover, such systems can undergo design and development to seamlessly fit into their existing facilities and systems quickly relative to traditional methods.
Automation also becomes much simpler with the capability of simulation software to test automation and smart technologies in a full-blown manner even before the final design is ready. Furthermore, simulation consisting of accurate and minute details add value to manufacturers, helping them configure their automation system so that the resultant robotic systems can meet their goals. However, manufacturers usually have to take significant risks for proof of design and automation process verification without simulation systems.
Due to all these advantages, simulation systems can return great results on the manufacturer’s investment. Furthermore, simulation software capable of self-diagnosis and automatic error reporting ensures that the finished designs and products are free of errors and potential flaws. It also ensures that the robotic simulation systems function with precision with known efficiencies in different environments and conditions. Thus, it helps manufacturers get maximum returns on their investment.
Moreover, the investment also becomes largely more safe and secure relative to the investment in traditional approaches. Furthermore, with the successful design and development of robots or systems meeting all requirements and needs beforehand, manufacturers can ensure further lucrative benefits and returns. Eventually, the end goal of manufacturers is to get returns from the end product. It largely depends upon the manufacturing process, which depends on the systems and procedures, including robotic systems used for manufacturing.
Hence, ultimately a successful result is a massive win for manufacturers. Robotic simulation software ensures that this result is successful and that the manufacturers get there with much lower costs, resources, time, and skills.
Industry 4.0 or the Fourth Industrial Revolution (FIR) is all about the digital transformation of enterprises. With Industry 4.0 approaching more closely than ever before, industries and manufacturers must keep up with advancements in technologies like simulation and artificial intelligence, AR, and VR. While it may seem that the transition to digital technology and simulations for product innovation, R&D, and robotic development is complex, the result in-store has enormous benefits with lucrative returns.
Hence, companies like FS Studio are working hard in these innovative technologies to ensure that manufacturers can experience a smooth transition to Industry 4.0. For example, ZeroSim, a technology in development and service by FS Studio, is a robotic simulation software technology built on Unity3D, a game engine, and ROS (Robotics Operating System). It provides a multitude of tools for building robots and simulation environments in Unity to interface with ROS.
Technologies like these add value to manufacturers, making robotic simulations faster, easier, and hassle-free to use for manufacturers. It also ensures that manufacturers can easily leverage the lofty benefits of robotic simulation software to transition themselves towards the next industrial revolution.
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.
Oil and gas industry operators benefit from digital twin through a quick response when something goes wrong. With digital twin technology, operators can use data they collect from sensors on their equipment to create accurate models that replicate how these machines operate in real-time.
It means that if an anomaly occurs, there's no need for expensive trial-and-error or lengthy troubleshooting procedures - open up your model, and you'll know what went wrong. In addition to saving money by avoiding costly repairs, this will save time which means more production time!
This article describes how digital twins are helping organizations make sense of large volumes of diverse data sources—whether internally generated or provided by third parties—and use them effectively for making.
Emerging Technologies and Digital Transformation:
The new face of the oil and gas industry is quickly becoming digitized. Emerging technologies allow for production to become a cycle, automated, efficient, and streamlined - but this also means that you get to deal with operational intelligence.
Digital transformation will affect every stage of a company's lifecycle- from upstream operations to midstream labor-management down into downstream sales efforts. Even services in oil fields can be managed more efficiently digitally through Emerging Technologies. It will challenge operators to transform substantial data sets acquired in various processes into actionable intelligence.
Oil and gas industry operators benefit from digital twin through advanced analytics in their plant operations to improve the performance of assets, reduce unplanned downtime, and extend equipment life. In addition to these things, it also allows for a greater return on investment by identifying complex problems.
Digital transformation provides opportunities for improved return on investment by identifying quick fixes upstream, midstream, and downstream processes.
In addition, with the digital twin, a machine's maintenance and operational intelligence are never compromised.
With predictive analytics for maintenance and prescriptive analytics for operations intelligence, your business will always have the edge over any of its competitors by being able to fix problems before they even happen! In addition, the augmented reality provides tools that improve both productivity time and the effectiveness of the repair.
Digital Twin Mirrors Manufacturing Big Data:
The oil & gas industry is a massive business that generates an incredible amount of data. The oil & industry data will typically have quality reports, process control history, operational deviations and variations, product blends and formulas, etc., related to the production process.
The Bureau of Labor Statistics found that this sector had more stored data than any other business or industrial sector in a recent survey among US manufacturers.
The data generated by today's connected world comes in a wide variety of formats and needs to be aggregated, analyzed, and converted into actionable information.
The digital twin is a virtual representation of your production plant that can provide personnel with operational intelligence. This process starts by combining Big Data, statistical sciences, rules-based logic, and artificial intelligence into one easy-to-use package called predictive analytics.
Advanced machine learning allows the company to discover complex problems shaping up in their manufacturing processes and then determine ways to resolve them before they become costly.
The move from predictive analytic models will eventually lead manufacturers out on top because it utilizes big data effectively without adding too much cost or complexity along the way.
Digital Twin and Machine OEMs:
The relative benefits of the digital twin will depend on many factors, not limited to complexity and quality. As assets increase in sophistication, demand for a digital representation is bound to overgrow, too - with one difference: ubiquity across its lifecycle. The genuine virtual version will contain information about design as well as manufacturing and service life.
There has been some debate over who should be overseeing them: those with knowledge or experts in data science? Without answers, we won't know how best to utilize their potential capabilities
The oil and gas equipment OEMs (Original equipment manufacturers) are traditionally the best informed about information, such as engineering analysis data. However, end-users of these assets require this operational performance data to be successful in their jobs.
For a digital twin to work effectively, the manufacturer should share the information or offer an online service-based business to monitor and optimize digital and physical asset performances.
It includes servicing, optimizing operations with real-time data analytics, improving safety in complex environments like offshore drilling rigs, or carrying out hazardous tasks like handling chemicals at a refinery.
Implementing this type of initiative could be done through partnerships between IIoT software vendors that develop solutions to support these new approaches. In addition, there are emerging opportunities within large organizations that have been adopting advanced techniques across their business units.
Manufacturers of long-lifecycle products such as gas turbines and pumps are coming to understand that after-sale service is a significant differentiator for them. Implementing digital twin services will improve efficiency in the field, which can be very helpful when considering how many people it takes on average to change out oil filters at most factories worldwide.
By connecting remote sensors with real-time data analytics, companies have new opportunities not only have they have never seen before but also ones that were previously unaffordable due to cost considerations or complex engineering problems involved.
Manufacturers who implement this intelligent technology into their manufacturing process stand poised to provide better customer satisfaction rates and reduced downtime through continuous monitoring, thereby increasing profitability by improving quality control metrics.
Oil and Gas Industry Operators Benefit from Digital Twin & Asset Performance Management:
With digital transformation, oil & gas companies are redefining their business models and operations, but these changes would not be possible without effective asset performance management (APM).
APM can help oil & gas firms to increase maintenance efficiency and effectiveness.
It helps to avoid costly unplanned downtime while minimizing the need for scheduled downtime. It also improves safety by cutting down on risks of accidents.
With this strategic approach to managing assets in place, the company's regulatory compliance costs will also decrease as well as minimizing the risk of non-compliance which is always a top concern when it comes to environmental protection regulations
Data is a valuable resource, but it cannot be easy to manage due to the sheer abundance and variety of sources. Modern APM can alleviate this by collecting all information into one system for ease-of-use and quicker analysis periods so that valuable insights are never lost again!
Imagine life without oil & gas. It would be much less convenient, not to mention plain dangerous. That's why you should invest in the industry today!
Operators collect data and analyze it. The approach enables companies to develop new techniques with better efficiency, safety, yield rates, etc., leading us towards a brighter future for all involved parties in your investments.
The technology around collecting and analyzing data has enabled many improvements for those invested in this sector. This work can lead industries into their "brightest" futures through increased production flexibility or more efficient operations...and it only gets easier when people are willing to dedicate themselves fully toward these goals.
APM is a new way to monitor and manage oil production from unconventional sources. APM integrates into the larger automation environment, enabling companies to take advantage of shale oil and gas opportunities, ultra-deepwater, or subsea applications.
Accurate and timely data is the lifeblood of a company's success. In today's business world, oil companies have to constantly adapt their operations to improve efficiency and safety standards for employees operating on site.
It becomes difficult to comply with regulations across different sectors without an efficient way of collecting accurate information about all aspects, from production levels and equipment status up through downstream applications like environmental impact reports or health & safety assessments.
Midstream operators can now benefit from improved visibility into what goes wrong when things go wrong to act quickly. It is possible because integrated APM solutions aggregate real-time operational event intelligence at every level - including plants, refineries, pipelines, and transportation networks.
Fossil fuels have powered the world ever since the Industrial revolution. However, Digital technologies like artificial intelligence (AI) and Blockchain are making the process of extracting energy more accessible, cheaper, more efficient, less risky - and cleaner!
Digital twin technology is a new, innovative innovation that has the power to change the way we work. For example, we can use this new technology to create digital replicas of our environments and assets – also known as virtual simulations – and have them interact in real-time with their physical counterparts.
It means you could simulate making any significant changes or decisions which would otherwise be costly!
Digital twins are changing today's way we operate by providing information about our environment and previously unavailable assets.
Oil and gas industry operators benefit from the digital twin significantly. The benefits include increased safety, improved production rates, lower maintenance costs, and reduced downtime. With these advantages in mind, it's no wonder why more companies are jumping on board the digital twin train!
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.
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.
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.