Digital Twins: Digital evolution to the consumer-driven and flexible production cycle

digital twin

Unlike other aspects of business, the production cycle had not witnessed its fair share of digitization. It has become rigid, slow, and vulnerable. Rigid because once started, it cannot be altered or stopped else the manufacturer will suffer loss. 

Slow, as it may take years for a new product to journey from product designing to manufacturing to a consumer or even to improve an existing product for that matter.

Finally, it became vulnerable because it is highly susceptible to external and internal factors like government regulation, internal and external economy, supply chain issues, etc. 

This makes the successful execution of a product cycle a challenge. However, things are about to change now, production cycles are going through an evolutionary digital transformation on the back of Digital Twins.

The new technology brings multiple business advantages such as speed and efficiency to the production process, better product design, and additional revenue stream, through“Living” products i.e. products that keep evolving throughout their lifecycle, and much more. 

What are digital twins? Why are they important? And most importantly, how you can use them for your business? All these questions will be answered in this blog covering the groundbreaking technology of Digital Twins and its different aspects.

What is Digital Twin? More than a 3D model

In the simplest terms, a digital twin is a three-dimensional digital equivalent of a physical entity, but be certain that they are not like any 3D model we see common in product design. 

Digital twins are much more than that, especially when joined by AI. They have the potential to influence the whole manufacturing process or even beyond depending upon their execution. So, while they may appear as a 3D digital model, they have a very vast area of influence and it totally depends upon their suitability and execution by the business in question. 

Hence, in terms of their execution, they can be defined in two ways. 

Digital Twin = 3D models + Dynamic data

One of the simplest execution of digital twins we see is when the virtual model is integrated with the real product and is manually updated throughout the lifecycle of the product to reflect its wear and tear. As a result, it helps spot manufacturing defects of any sort over the period. 

Digital Twin = Sensors-enabled 3D model + AI

Another prominent definition of digital twins is a 3D model created and manipulated in a live setting using sensors. This model is then fed to AI to identify the shortcomings of the product and find solutions for the same.

For example, this is what Tesla has done with its fleet of electric vehicles. It creates a digital simulation of each car and uploads it to the cloud using the data coming from sensors on each vehicle. Then, using AI analyses the faults and breakdowns along with their future possibility and minimizes the need for vehicle owners to take their cars to service stations.

The term Digital Twin mostly represents the second type of execution and leads to several innovative developments in the manufacturing process that were impossible earlier such as:

  • Speeding up and enhancing the design and manufacturing cycles from years to months
  • Customer-led product manufacturing wherein their feedback gets constantly incorporated throughout the different phases of the production cycle.
  • Intelligent, resilient, and more integrated production process with all stakeholders basing their decision on a single source of information, the digital twin. Additionally, they are also made aware of the impact their decision would have on the other stakeholders and the whole process.
  • Flexible product development that responds quickly to changing preferences, government regulations, or supply chain challenges
  • Development of a complete ecosystem for innovation where regularly updated data keep informing the manufacturer about the changing consumer needs.
  • Developing “Live” Products that keep on evolving with time as per the feedback from users and the utility data captured and shared to the manufactured by the product itself.

We will discuss these aspects in more detail in the coming sections. Before that let’s explore digital twins more and learn about the components that come together to make them work.

Components & Working of a Digital Twin

Digital Twin technology as a system comprises six components that work together in a physical-digital-physical loop to improve the efficiency of the product or process in question.

  1. Sensors — Sensors are strategically placed to capture operational and environmental data. If it is the product then the product itself can send the required data on which AI can operate.
  1. Data — There can be two sources of data. One can be the product or process itself wherein the data is directly being fetched to improve that particular aspect. Next, it can the data from sources that are connected to the subject on a secondary level such as the enterprise, consumer, retailer, etc. 
  1. Integration technology— This component connects the physical world with the digital one and facilitates data communication between them. Some examples of these technologies are edge computing, communication interfaces, and data security.
  1. Analytics — Once the data is received by the digital twin, it uses analytics techniques through algorithmic simulations and visualization routines to produce relevant insights.
  1. Digital twin application— This application combines all the above four components to replicate the subject in the digital setting and identify problem areas.
  1. Actuators — The actuators are the component that initiates the physical process on the basis of the finding of the digital twin. This is subject to human intervention.

All these above components work together in tandem to materialize the digital twin. Here, are the steps that go into the functioning of a Digital Twin.

Data Capturing – The first step is the installation of different sensors to collect the required data. The data can be of two types:

  1. Operational Data
  2. Environmental Data

Operational Data pertains to the performance of the product or assets such as frequency, torque, tensile strength, etc.

Environmental data, on the other hand, encompasses ambient data in which the subject is functioning such as temperature, atmospheric pressure, and humidity. 

The data so collected is then encoded and transmitted to the digital twin. This is where the second step comes in.

Integration – This step establishes connections between the physical and digital systems and integrates them into a single setup. There are three components for the integration – 

  1. Edge Processing: The data and signals from the sensors are processed near the collection point itself.
  2. Communication Interface: These interfaces transfer the processed data to the digital setup.
  3. Edge Security: This component ensures the security of the edge and communicated interfaces through firewalls, application keys, encryption, etc.

Data Storage and Processing – Once the data is ready and relayed, it is stored and processed for analytical purposes. The processing can take place within the premises or it can be a cloud-based setup, depending on the business requirements.

Data Analysis –  Then, the data is analyzed using the latest platforms and technologies to develop to generate insights and recommendations that guide

decision-making by the businesses.

Insight Generation: The insights so generated post-data analysis is presented through the dashboards of the system. Initially, these insights are used to match the performance of the physical and digital models. 

But, in the later stage, the insights are screened to find the actionable ones and the actions are taken on the organizational level. Also, the steps are fed into the digital system again to keep them in sync with the physical one and make both of them actual twins of each other.

How Digital Twin gives birth to more flexible, consumer-oriented, and innovative production models?

Consumer-driven manufacturing model

Till now, businesses operate in a pretty much linear fashion when it comes to production. It starts with Research and development then goes to manufacturing and then to marketing and so on. The consumer has a very limited say in the process, despite the fact they are the ones who are ultimately using the product.

Digital Twin will tip the balance in the favor of consumers by completely remodeling the process. First, on the individual level, manufacturers would be able to update a particular product to suit the user’s behavior, while the user is still using it. This will bring the highest level of product personalization to the consumer.

Second, with more data from different users, manufacturers would be able to alter the process for the upcoming batch right from the start and make it much more suitable for consumer needs than ever. Thereby, making the product highly contextualized.

The data will feed the digital twin of the product available to all the different teams involved in manufacturing. They will draw insight relevant to their responsibilities and improve the product accordingly. But this is not the only way that digital twin will help the team, there is much more to it. Read the next section.

Flexible, and integrated product development process

The digital twin while acting as a single source of information for all the different organizational levels will help businesses in transforming their product development process. It will help in making it more flexible and highly integrated.

As we saw in the above section, the data from the consumer will feed the digital twin from where teams involved in the manufacturing process such as designers, engineers, marketers, etc. will draw insights.

But this consumer-driven data will not be the only source of data for the digital twins. A digital twin will also include data and information about the ongoing manufacturing process as well. 

Thereby, It will assist the team to collaborate much more deeply by getting complete know-how on the direction of the process. Additionally, they will be helped with AI-based smart suggestions that will also cover all the possible scenarios as per the decision they choose to make. 

For example, while they will be able to get real-time data such as lead time, cost of the part, raw material availability, etc,  they will also be informed about the implication of their decision on the working of other teams.

Why Digital Twins?: Advantages for Business in every aspect of the manufacturing cycle and even beyond

Digital twins bring advantages to various different aspects of production cycles making it imperative for the business to incorporate them into their manufacturing model and after-sale service mode. 

Especially, when the emergence of high-capacity storage, low-powered computing, and expansive network connectivity is increasing the number of use cases and possibilities to enable a digital twin.

Quality Improvement and maintenance

A digital twin will help improve the overall quality of the product or the process. With the help of transparent monitoring of the whole process, they will detect and predict the causes leading to defects in a pin-pointed manner. Moreover, they will also identify and improve the quality control mechanism in the process.

Warranty Management  

Digital twins will readily detect the issues in a particular product and understand its actual configuration while the consumer is still using it and deliver warranty services much more efficiently. Even better, manufacturers can proactively and accurately determine warranty and claims issues to reduce overall warranty costs and improve customer experiences. 

Operations cost 

The high volume of data and deeper collaboration assisted by Artificial intelligence will optimize the overall operational cost. It will improve the performance of equipment and reduce operational variability. 

Digital Record Maintenance

Digital Twin will help manufacturers create and maintain digital records of the product and its parts to better manage recalls, and warranty claims and meet tracking mandates by the government. The same will also apply to the raw materials and only other components of the product and process.

New product development

Digital Twin reduces the cycle time as well as the cost of introducing a new product in the market. Thereby, helping the businesses meet the demand before competitors and in a much better way. 

Further, businesses can better recognize long-lead-time components and their impact to the supply chain.

Additional Revenue streams

Digital twin brings a lot of growth opportunities to businesses by identifying sold products that are ready for the upgrade. Thereby, improving the efficiency and cost of service products.

Queppelin is creating a digital twin of an International Airport

Queppelin is working on a digital twin project for one of its clients. We have completed the first phase of the project involving the creation of an elaborate Airport setup. Our client wanted to replicate their services and function in a digital setting so that they could identify the shortcoming and improve their operations. 

As evident in the video, we identically replicated the real-world airport so that our client can successfully monitor the operation. For example, just like the real airport, there are different sections, like the waiting area, check-in and checkout points, and help desk as well as screens showing real-time information about the flight. 

However, the best part is how the digital twin is integrated with the physical entity to solve a real-world problem.  

One of the issues spoiling the user experience was the uncertainty around the time they have to wait for their checked luggage to arrive after landing at their destination airport. The process is unclear and varies on the basis of various factors such as the number of passengers traveling in the airplane, the number of luggage they are carrying, multiple flights arriving at the same time, and the airport capacity is less, among others.

All this may result in a waiting time anywhere between 15 minutes to over one hour, which becomes unbearable when you have arrived completely exhausted at the airport and want to reach your place and rest but instead end up waiting endlessly with idea whatsoever when your bag will arrive.

Toward this end, we installed sensors at the check-in points to count the number of passengers and luggage entering the particular flight. This is combined with the data related to the destination airport, such as the airport capacity and the number of flights arriving at that particular time, we could successfully calculate the stipulated time for passengers to get their luggage post-flight arrival. As we keep on feeding data and feedback to the system, the digital twin will enhance its performance resulting in a much more accurate calculation.

Now, we are entering phase two of the project, where we are expanding the scope of our digital airport with more and more data pouring in. In this phase, we will try to predict the number of passengers that will enter the airport in a particular time period. Thereby helping the management in better managing the airport resources and improving the user experience.

For this, we have already installed sensors that will track the number of passengers throughout the year along with the data that impact this number. This combined data will train the AI over the period so that from the next year, our digital twin will start predicting the number.

The best part of AI is that it keeps on improving with time as more and more data it finds to process and learn from.

Where is Digital Twin used?

A digital twin spans the lifecycle of its real-world counterpart. Every change in the physical object gets reflected on the virtual model through the connection developed on a real-time data basis.

Operators can add new data and information into the virtual system and fine-tune them to achieve desired results. Then, replicate these results in the real world by putting in the same input that helped achieve desired results in the virtual settings in the first place.

The technology is finding extensive use cases across industries. Here are some examples of how.

Transportation Industry

The transportation industry can use digital twins for product designing, testing, and simulation. Engine manufacturers can develop and test large engines such as jet engines and locomotives using their digital twins. 

Further, repair and maintenance of such huge engines will be made much easier through faster issue-tracking through their digital twin version.

Construction Industry

Digital twins possess huge potential for construction companies first, they can help in designing highly disaster-resistant construction through real-world scenario testing like applying the laws of physics and checking how well or worse the construction holds its ground.

Then again, they can help in repairing and maintaining such huge structures and improve the life-safety system installed therein. 

Manufacturing Industry 

As we saw in the above example of car manufacturing, digital twins can help improve the 3Ps – process, product, and plant. Not only the technology improves these three components of manufacturing individually but also enhances the system as a whole.

Hence, once the product is improved to serve its purpose better, then the manufacturing process and the ergonomics of the plant can be improved for efficient manufacturing of the improved version of the product.

Healthcare industry

The sensor-generated data will help profile the patient and track the various health indicators. It will help in tracking the existing health condition as well as the potential health threat for the future such as chances of organ failure, recovery status, etc.

These are just a few examples of how digital twin is transforming industries.

Types of digital twins

Businesses can examine different types of digital twins and choose the one that perfectly suits their business needs. Also, these digital twins can co-exist together and interact with each other as we saw in the above example of car manufacturing. In that example, there was a digital twin of the car model and there was a digital twin of the manufacturing plant. Both of these systems could exchange information and result in a much better car manufacturing solution. 

Below we have discussed some types of digital twins existing right now.

Component twins 

Component twins are the smallest functioning component of a digital twin ecosystem. Several component twins may function together to create a whole virtual model of an object. As we also saw in the above example the small component twins of brakes, axle, steering wheel, and engine were joined together to create an asset twin of a car.

Asset Twins

Asset twins are formed by combining at least two components of twins capable of co-functioning. Asset twins are used for studying the interaction between the component twins and gathering the interaction-related data for optimum synchronization among components for the efficient performance of the overall asset twin. These insights can then be applied to the real-world counterpart of the asset twin.

The digital twin of a car is an example of an asset twin.

System Twins

Going on the further level, as the name suggests, system twins represent a whole system where different asset twins made up of their related component twins interact with each other. System twins help explain the relationship between different assets twins that form an entire functioning system. 

The interaction between testing environments built on the laws of physics can be an example of a system twin.

Process twins

Process twins are where various systems interact together to improve an end-to-end process. Here the synchronization, co-efficiency, and impact of one on another are put under the scanner. 

A car production facility is an example of a process twin where different system twins like the product designing department, assembly line, and testing ground interact with each other and help decision makers make efficiency improvements in the facility.

Conclusion – Digital Twin Services by Queppelin

Digital Twin is a fairly amazing technology that is transforming across industries. It applies to all components of a business whether tangible products and machinery or intangible services and processes.

They can imitate simple to complex entities in their entirety and help researchers and decision-makers with accurate data that usher precision.

Queppelin is one of the leaders in developing highly functioning digital twins. We have a name in the market for bringing immersive 3D technologies that bring a digital object to life including your very own business facility. Contact us today to discuss your project:

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