Why digital twin is important?

Why digital twin is important? : Companies can test and validate a product before it even exists in the real world with the aid of a digital twin . A digital twin enables engineers to find any process flaws before the product is put into production by simulating the intended production process.
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You can figure out why some batches of products have more variation , flaws, or inefficiencies by comparing the products and the manufacturing processes used to make them. Finally, you may be able to replicate your best runs or the golden batch with the aid of these insights.

The issue is that manufacturers generate production data in a variety of systems and formats. Some businesses amass an excessive amount of raw data but lack the resources to use it productively. Employees who are unsure of what to do with data may be an example of this. It is challenging to put together a complete picture of production at other companies because of incomplete data connectivity. There are also companies that merely struggle to obtain accurate information because their processes are overly complex or their data is useless.

Regardless of which of these or a few other factors is to blame, the main problem is that even when there is raw data available, many businesses are left with inaccurate information. A lot of manufacturers work to improve their machines, processes, or materials. They make every effort, but some of the less obvious sources of variation remain elusive.

This is where Digital Twins can make a big difference in helping your manufacturing company break through data fatigue and turn data into your competitive advantage. But what exactly aremanufacturing Digital Twins and how are they used?

What is a Digital Twin in manufacturing?

A Digital Twin is a digital replica of any process, system, or physical asset that will enhance applications serving business objectives. In manufacturing, Digital Twins can be built for assets, specific production lines, by end product, or for any other “real world” scenario within a productionprocess. 

The physical and digital worlds can be combined by using a digital twin. The operational and simulation phases of the lifecycle of a product or process are handled by digital twins. Having a digital representation that you can use to learn more and have a greater understanding of your production is the end result of creating a digital twin, regardless of how you go about doing it.

Every product produced and every process executed is unique. There are hundreds, if not thousands, of key input variables to describe products, assets, entire lines, and processes. In the caseof manufacturing operations, a Digital Twin is a dynamic replica that is designed to capture, map, and structure process variables into a continuously updated database. This database can be accessed and used across the organization. By making this data more readily available in a digital environment, teams can use data in other applications, models, or third-party programs to make meaningful discoveries.

Whyuse a Digital Twin in Manufacturing?

Once you have a digital replica of your process with all the data from relevant sources linked to the right end product (called a process map model), you can start solving your biggest challenges. Let’s look at how a Braincube Digital Twin—built by process, asset, or thread—makes this possible.

For instance, process digital twins can act as your organization’s central source of truth. Each item, batch, or serial code you produce has its own unique set of manufacturing conditions encoded in them. Teams can use this to their advantage by using our Smart IIoT Platform, which has over 25 business apps, to solve everyday problems and improve visibility across departments and facilities.

Engineers and data scientists save valuable time using process Digital Twins. Gone are the days of manual data pulls and complex manual analyses. A Digital Twin automates data pulling, cleaning, structuring, and transforming. This puts the most important information directly into the hands of your skilled engineers. Your teams can focus on solving problems that move your company forward instead of manual data manipulation. 

With Digital Twins, teams can focus on solving problems that move your company forward instead of manual data manipulation.

Operational teams can use a Digital Twin to determine which input variables make the biggest impact on specific outputs. Identifying these key input parameters is a crucial step in making the necessary process changes to drive your goals forward. 

Data accessibility and Digital Twins

Since almost anything can be transformed into a digital twin (a machine, a process, a system, etc.), ), attempting to determine the type of Digital Twin your company needs can be overwhelming. While selecting the appropriate Digital Twin type is crucial, it is only one part of your digitalization strategy.

The way a Digital Twin is built—in other words, how the data is collected, modified, mapped, and made usable—can have huge implications on your overall ROI and digital effectiveness. In many cases, the way aDigital Twin is built is directly correlated with your objectives.

All digital twins are intended to capture and contextualize data in some way, whether it be data from a single machine or data from a specific step in your manufacturing process. In contrast to the majority of manufacturing Digital Twins on the market, Braincube’s advanced Digital Twin is special. To begin, our process DigitalTwin not only continuously gathers data but contextualizes it to include real-time production conditions based on continuously calculated lag times (i. e. observing the movement of the materials). In the absence of sensors, it also includes calculated variables for lag times or other flows.

Basing Braincube-powered Digital Twins on your process map allows our middleware to cleanse data (removing outliers, merging formats, ETL) as well as produce up-to-date product data. This transformsyour production data into interconnected information, making it available for further visualization, analysis, decision making, and innovation.

To guarantee that teams always have access to the most recent, accurate production information, data is continuously ingested through Braincube middleware once it has been built. Because of this distinctive differentiation, teams do not need to manually prepare or pull data to understand what occurred during production. As each product leaves the production line, the Digital Twin data is continuously updated.

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Data collection is extremely beneficial because it establishes a single source of data truth within your company. But information is only worthwhile when it can be put to use. As an illustration, even if a Digital Twin prepares your data, data scientists are most likely the only staff members capable of spotting patterns and developing predictive models.

While data scientists greatly benefit from having prepared information to use in their predictions, manufacturingtools today also need to empower citizen data scientists. By equipping process experts (aka those with day-to-day operational knowledge), companies take one step closer to organization-wide transformation. 

While data scientists greatly benefit from having prepared information, manufacturing tools today also need to empower citizen data scientists.

For instance, a data scientist might notice a difference in asset performance and inform the operations teams of it so they can consider it as a potential area to improve throughput. It makes sense that production was affected because, as the process engineer points out, a specific machine was not in operation at the time. Working independently on the same overarching objectives, both teams start over.

Even though this is exaggerated, the lesson is that by restricting data use to a small number of teams, unexpected causes of variation may go unnoticed. For everyone in your organization, the data isn’t really democratized. A large portion of your employees are still unable to make discoveries and advance progress if advanced technical skills are required to extract value from the data.

This is where other Industry 4.0 technologies, such as IIoT Platforms and advanced Applications, come into play. They enable your Digital Twin data to be utilized by both technical andnon-technical teams, making it possible for any at your company to uncover optimizations. 

How do IIoT Platforms and Digital Twins interact with each other?

Your data for your digital twin is accessed through IIoT Platforms. An Industrial IoT platform (IIoT Platform) collects real-time data from sensors, hardware, software systems, and other data sources into a centralized environment that is typically accessible to a large number of users. By pulling that data into a centralized system, typically in the cloud but occasionally also on-prem or on edge, it bridges the gap between systems, people, and machines.

However, improved data access does not necessarily equate to improved data usability, as we have discussed. Most of the time, an employee still needs technical expertise to make significant discoveries.

Here at Braincube, we believe the future of manufacturing lies inthe Smart IIoT Platform: an IIoT Platform enhanced with advanced business applications. These apps make it easy for anyone to get real-time visibility into production conditions or dive deeper into Digital Twin data using proprietary AI to optimize your processes and quality.

Data democratization and the empowerment of citizen data scientists are goals of the Braincubes Smart IIoT Platform. Everyone at your organization can benefit from your Digital Twins thanks to our comprehensive product suite’s use of self-service AI, connectors, and business intelligence applications.

The Smart IIoT platform stores your Digital Twins in a centralized location that is open to a large user base. The only source of truth for your organization is a Digital Twin created by Braincube. Teams can use digital twins to address quality issues, find process improvements, or even create predictive models in the Braincube suite of apps or on other platforms. Manufacturing companies can find, test, and implement process improvement ideas more quickly by using a digital twin.

When building out the way our Digital Twins worked in conjunction with other 4.0 technologies, our goal was to build a multi-factor technological solution that starts with a customer’s challenge or objectivein mind and with the right App suite to leverage it. In this sense, our Digital Twin works in conjunction with what you want to achieve. 

Actionable insights from using a Digital Twin

With a process Digital Twin in place, you can leverage the replicas in a Smart IIot Platform to: 

  • Test operating conditions and receive the expected resultsdigitally
  • Optimize production
  • Find new revenue streams
  • Reduce cost, waste, and energy consumption
  • Personalize products
  • Perform predictive maintenance 
  • Improve quality and customer satisfaction
  • Easily trace each product from start to finish
  • And more


The market for digital twins is expected to grow to $48 as manufacturing uses them more frequently. 2 billion USD by 2026. It can be tempting to adopt the first solution that seems appropriate given all the talk about digital twins and their benefits.

Think about your short- and long-term goals before choosing your Digital Twin vendor. Is the data model built to include future initiatives, even if you are unsure of what they will be, will the data be easily accessible and usable to more teams, or will it still be siloed to just your most technical teams, and will the lack of specific data be made up for by the appropriate calculated variable?

All different types of manufacturers can find opportunities within their own processes with the aid of Braincube’s advanced process Digital Twins, Smart IIoT Platform, and Business Intelligence apps, which are a leader in helping manufacturers transform their operations. Get in touch with us to find out how our special manufacturing-specific expertise and tools can assist you in achieving your objectives both now and in the future.


Why digital twin is important?

What are the types of digital twin? : Generally speaking, there are three types of digital twin – Product, Production, and Performance, which are explained below. The combination and integration of the three digital twins as they evolve together is known as the digital thread.
Is digital twin part of AI? : Digital twins’ artificial intelligence is a theoretical and technical system that is broadly applicable and has a wide range of uses, including in the design of products, the manufacture of equipment, the analysis of medical data, the aerospace industry, and other areas.
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When we design machines for a connected world, the traditional operation manager or engineer’s toolbox may look rather empty. As our assets and systems become more complicated, the way in which we develop for, manage and maintain them needs to evolve, too. We need tools to meet the new realities of software-driven products fueled by digital disruption. Enter the digital twin. It’s a technological leap ‘through the looking glass’ into the very heart of physicalassets. Digital twins give us a glimpse into what is happening, or what can happen, with physical assets now and far into the future.

Let’s start with the basics: what is a digital twin?

A digital twin is a virtual representation of an item or system that spans its lifecycle, is updated from real-time data, and uses simulation, machine learning, and reasoning to aid in decision-making.

In plainEnglish, this just means creating a highly complex virtual model that is the exact counterpart (or twin) of a physical thing. The ‘thing’ could be a car, a building, a bridge, or a jet engine. Connected sensors on the physical asset collect data that can be mapped onto the virtual model. Anyone looking at the digital twin can now see crucial information about how the physical thing is doing out there in the real world.

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Digital twins let us understand the present andpredict the future

This means that a digital twin is an essential tool for engineers and operators to understand not only how products are performing, but also how they will perform in the future. We can make these predictions by analyzing the data from the connected sensors and combining it with data from other sources.

Organizations can learn more quickly with the help of this information. They can also dismantle preexisting barriers relating to complex lifecycles, value creation, and product innovation.

Digital twins help manufacturers and engineers accomplish a great deal, like:

  • Visualizing products inuse, by real users, in real-time
  • Building a digital thread, connecting disparate systems and promoting traceability
  • Refining assumptions with predictive analytics
  • Troubleshooting far away equipment
  • Managing complexities and linkage within systems-of-systems

Let’s look at some of these in more detail.

Use cases for digital twin: an engineer’s point of view

Let’s examine a real-world application of digital twins. Let’s consider it from the perspective of engineers, who are digital twins’ main users.

An engineer’s job is to design and test products – whether cars, jet engines, tunnels or household items – with their complete lifecycle in view. In other words, they need to ensure that the product they are designing is suitable for the purpose, can cope with wear and tear, and will respond well to the environment in which it will be used.

Creating real-world scenarios, virtually

For example, a computer simulation would be used by an engineer testing a car’s braking system to determine how the system would react in various real-world situations. Compared to building numerous physical cars to test, this method has the advantage of being much faster and less expensive. There are still some issues, though.

First of all, computer simulations like the one previously described are restricted to the circumstances and settings of the present. They are unable to foresee how the car will respond to varying conditions and potential future scenarios. Second, modern brake systems are more than just mechanical and electrical components. Additionally, they contain millions of lines of code.

This is where digital twin and the IoT come in. A digital twin uses data from connected sensors to tell the story of an asset all the way through its life-cycle. From testing to use in the real world. With IoT data, we can measure specific indicators of asset health and performance, like temperature and humidity, for example. By incorporating this data into the virtual model,or the digital twin, engineers have a full view into how the car is performing, through real-time feedback from the vehicle itself. For more on the engineering POV, read the customer case study: Ushering in Industry 4.0 with an agile approach to systems engineering.

Using a Digital Twin means more effective R&D, greater efficiencies and a more comprehensive approach to product end-of-life.

The value of a digital twin: understanding product performance

Businesses can now have an unmatched understanding of how their products function thanks to digital twins. A digital twin can be used to locate potential issues, conduct remote troubleshooting, and ultimately raise client satisfaction. Additionally, it aids in improving the quality, add-on services, and product differentiation.

If you can see how customers are using your product after they’ve bought it, you can gain a wealth of insights. That means you can use the data to (if warranted), safely eliminate unwanted products, functionality,or components, saving time and money.

Unprecedented control over visualization, from afar

A digital twin also has additional benefits. Digital twins give engineers and operators a thorough, intricate view of a physical asset that may be located far away, which is one of the most significant benefits. The engineer and the asset don’t even need to be in the same country or even the same room with the twin.

Imagine, for example, a mechanical engineer in Seattle using digital twin todiagnose a jet engine sitting in the hanger of O’Hare airport. Or engineers visualizing the entire length of the Channel Tunnel from Calais. Thousands of sensors in a dozen modalities, like sight, sound, vibration, altitude and so forth, mean an engineer can ‘twin’ a physical thing from almost anywhere in the world. That means an unprecedented clarity and control over visualization.

How IBM works with digital twin

A lot of work has been done by IBM with digital twin technologies. Applications continue to expand across various industries. the application of Augmented Reality (AR) to asset management. For your workforce, the IBM Maximo lab services activate numerous voice (Natural Language Processing) and visual features. With immediate access to important data, you can now see your assets in a new light. Then, using an AR helmet with voice/video in the visor, you can share those revelations with others. As a result, conversing is the next step in the evolution of working.

Digitaltwin also plays a key role in the systems engineering lifecycle, and is a key function of IBM Engineering Systems Design, where teams can employ MBSE to help streamline product design and development.

The future of the cognitive digital twin

Digital twins are already helpingorganizations stay ahead of digital disruption by understanding changing customer preferences, customizations and experiences. This knowledge means businesses can deliver products more rapidly, with higher quality, from the components, to the code. Yet the promise of digital twin can still go further.

The capabilities and scientific fields of the digital twin are increased by the use of cognitive computing. Natural Language Processing (NLP), machine learning, object/visual recognition, acoustic analytics, and signal processing are just a few examples of technologies and techniques that complement traditional engineering skills. A digital twin, for instance, can help determine which product tests should be performed more frequently. It may also aid in determining which individuals need to retire. To design and improve future machines, cognitive digital twins can help us go beyond human intuition. The one-size-fits-all model must end. As an alternative, each machine is unique. This is because the cognitive digital twin is not just about what we are building, but also about who we are building it for.

Watch: IBM client Schaeffler embraces digital transformation

[/lightweight-accordion]What is the difference between digital twin and simulation? : Real: Unlike a digital twin, which simulates what is actually happening to a specific product in the real world, a simulation only represents what might happen to a product. A designer who needs to input any changes has only their creative license when it comes to altering a simulation.
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Digital Twins is a concept rather than a specific item or piece of technology. To bring the concept to life, a variety of technologies—including 3D simulation, IOT, 4G/5G, big data, blockchain, edge computing, cloud computing, and artificial intelligence—must be combined. The fundamental idea is that there is a virtual world equivalent to every physical thing or asset.

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One question frequently asked is, “Are Digital Twins and simulations the same?” Given the emerging nature of the Digital Twin concept, it’s quite natural to have this question. While simulation is an important and an integral part of the digital twins, the purpose of the digital twins stretches beyond simulations. Let’slook at the three key differences:

Simulations are frequently employed in design and, in some circumstances, offline optimization. On the other hand, real-time design, execution, change, and decommission lifecycles are handled by digital twins.

Consider a car pulling into a garage for routine maintenance. Before deciding on a service plan, the assigned service engineer will customarily conduct the owner interview, visual inspections, and download the logs. Imagine a virtual replica of the car that is fully informed about every detail up to the point of use, including regular data on the vehicle’s performance, the parts it has had replaced in the past, the history of its maintenance, any potential problems that the sensors may have detected, and more. Digital twins contribute to time, cost, and effort savings in this operational scenario. The more accurate information that is readily available at the right time will be beneficial to all stakeholders.

The Present

Simulationsat best can help understand what may happen in the real world Digital Twins not only help understand what may happen, but crucially what is happening (how the design is behaving in the real-world)

Anyone who has looked for a project status report will recognize the trickle of information, the inconsistent updates, and the difficulty in understanding the data. The issue is complex, but some of the common causes include the lack of timely updates, the need for manual data collection, the logistics involved in gathering the data over long distances, and the problem itself. Imagine a scenario in which a virtual model is created using the real-world data that was collected via sensors. The real-time view is provided by the digital twin in an intuitive 3D visual format. The timely, simple-to-consume information will be helpful to human operators monitoring and interpreting the current status of large-scale projects, complex processes and machinery, operating assembly lines, and even patients receiving care. Real-time insights will help avoid many poor decisions, carry out preventive maintenance, and lessen undesirable incidents in a fast-moving world where time is of the essence.

The Future

Traditional simulations don’t provide insights into the interactions of the physical Digital Twins will be a source for understanding the clashes and conflicts, when physical things interact

Typically, simulations of crucial assets are where organizations begin their journeys with digital twins. The what-if scenario for the asset can be played out using these simulations. However, in the real world, there are numerous undesirable and unexpected behaviors that appear when assets interact with other products, people, and processes. According to Gartner, the evolution of digital twins will go beyond assets to encompass entire organizations that cover people, process, and behaviors. Operators can better understand interactions and incorporate them into their simulations with the help of digital twins and their perspective on the real world. To better understand how a city, port, or human body functions, standalone simulations will merge into digital twins. Additionally, whereas a standalone simulation cannot, Digital Twin Aggregates (DTA) are expected to provide previously undiscovered insights on the collective.

The Digital Twins’ journey has just begun. An organization’s current standing on the simulation spectrum would vary across the blueprints, 2D and 3D models. Irrespective of where they stand, digital twins are the next step in their digitaltransformation journey. Tesla has a digital twin for every car manufactured. Every day, thousands of miles of data from the cars, are fed into the simulation models back in the factory. That’s a thousand and more ways to learn and optimize from the real-world, which wouldn’t be possible with simulation models only. In the autonomous future, the vehicle may drive itself into a garage for a check-up, when it feels under the weather, without having to “trouble” the owner.


Additional Question — Why digital twin is important?

What is digital twin in simple words?

A digital twin is a virtual representation of an object or system that spans its lifecycle, is updated from real-time data, and uses simulation, machine learning and reasoning to help decision-making.

What is a digital twin example?

Examples An example of digital twins is the use of 3D modeling to create digital companions for the physical objects It can be used to view the status of the actual physical object, which provides a way to project physical objects into the digital world

What is digital simulation?

A hybrid of the discrete and continuous domains is digital simulation. Although it mimics a continuous system, it is implemented on a computer, so the basic calculations are digital. It is utilized to describe the behavior of a continuous system, much less laboriously than the Laplace transfer function.

What are simulations?

A simulation is a model that replicates the operation of an existing or proposed system, offering evidence for decision-making by allowing the testing of various scenarios or process changes. For a more immersive experience, this can be combined with virtual reality technology.

Is a simulation digital instrument?

Simulations in computing are digital models that replicate a system’s actions or processes. These simulations are employed for idea testing and new system performance analysis.

Can a digital twin be 2D?

Digital twins of an asset can be displayed as 2D or 3D models. The plumbing and electrical systems are all included in a high-quality digital twin’s architectural blueprints, and these various systems can be turned on or off to layer the precise data you need.

What is digital twin in BIM?

At its core, a digital twin can be an output of a BIM process and is essentially a ‘living’ version of the project or asset view that BIM processes exist to create able to evolve and transform using real-time data once the asset is in use

What is digital twin in IoT?

A digital twin on an IoT platform is a virtual representation of a real asset, machine, vehicle, or device. The asset’s data, processes, operational states, and lifecycle are all represented digitally.

What are the challenges of digital twin technology?

Accurate Capturing of Physical Properties is the main challenge that digital twins must overcome to achieve these capabilities. Cooperation on a project. Real-Time Automatic Update. Finding and resolving conflicts Digital and physical object interaction

How does digital twin work?

A digital twin uses data from connected sensors to tell the story of an asset all the way through its life-cycle From testing to use in the real world With IoT data, we can measure specific indicators of asset health and performance, like temperature and humidity, for example

What are the key elements to form a digital twin?

(WSN) integration and data analytics are two of the elements needed to create a digital twin [24]. Building’s 3D CAD model taken from BIM or a custom 3D model of the structure can be used for digital twin visualization.

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