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Digital Twin: What is? What it isn’t?

October 29, 2020 | Divya Khare

Blog / Digital Twin: What is? What it isn’t?

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“ The collaboration with Pluto7 and Google Cloud was smooth and efficient. Pluto7’s expertise in data science and Google Cloud’s platform were instrumental in the success of this project. We did not face any major challenges with existing tooling or data movement. We frequently migrate large amounts of data between different cloud platforms and have not experienced any usability issues,” 

Charles Jeong, AI & Reservoir IE Discipline Manager, Schlumberger

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What is Digital Twin?

A digital twin is a virtual version of a product, service, or process that can be used by the data scientists and IT pros to run simulations before the actual machines are built and deployed. They are also modifying the optimization of technologies like IoT, AI, and data analytics. The technology behind these digital twins has to expand largely and is more likely to expand further with people and processes. The idea of having digital twins first arose at NASA, to diagnose the problems in the orbit, eventually paving way for fully digital simulators. 

The term came highly in trend once Gartner named it as one of the top 10 strategic technologies for 2017. The report mentioned that within the next three to five years, “billions of things will be represented by digital twins, a dynamic software model of a physical thing or system”.  A year later, Gartner once again named digital twins as a top trend, saying that “with an estimated 21 billion connected sensors and endpoints by 2020, digital twins will exist for billions of things in the near future.”

Designed by specialists in data science or applied mathematics it is constructed to receive input from the sensors gathering data from the real-world counterpart. Digital Twins are in action ever since 2002, thanks to IoT (Internet of Things) for making it cost-effective to be implemented as it is a vital part of businesses today. 


How does digital twin work?

Talking about how this technology works, it is a bridge between the physical and digital world. Initially, the physical items are merged with the smart components that use sensors to gather data about the real-time status and working conditions. These components are linked to the cloud-based system to receive and process all the data. Broadly, the digital twin model is likely to be modified with the incorporation of Artificial Intelligence. The network technology is connected with the digital equivalents to enhance their capabilities of getting rid of problems and provide higher operational recital. 

In the current scenario, sensors accompanied by physical objects gather data and transmit data in their digitally created similar versions and this communication can be used to further enhance the performance of the physical object. The perk of a digital assistant is that they can be created before the machine is constructed physically in the manufacturing units. 

If you want to hear Forrester’s view of what’s digital twin, what it isn’t, and how to implement it, you can watch this on-demand webinar. 

A Digital Twin must demonstrate six key characteristics

As per a report published by Forrester, 16% of respondents currently implement digital twins in manufacturing whereas 24% plan to do so in the next twelve months. Explaining further Forrester defined the six key characteristics that a digital twin must demonstrate: 

    1. Physical and virtual
    2. Bidirectional data
    3. Timely updates
    4. Maintain state
    5. Modeling and analytics
    6. Reporting

Pluto7 has been actively associated with over 100+ Google Cloud customers binding the learning and insights together to empower your selling perspective using artificial intelligence as a digital twin.

Potential challenges you’ll face when implementing a digital twin

There are always two twins – one physical, one digital. A ‘digital twin’ without its physical sibling is really a simulation or model: useful, but different. When it comes to implementation, these are some of the potential challenges you will face: 

  • Support bidirectional data flows: IoT sensors are used to pass field data from physical to digital. There is a need for IoT actuators to implement digital instructions in the real world
  • Send new data as quickly as required: The ground crew is not concerned about the consumption of the fuel, considering the amount consumed is fairly used, but autonomous vehicles need a real-time feedback loops.
  • Maintain state: There must be an intermittent connectivity between the digital twins to maintain the connection even while the twins are drifting apart.
  • Support modeling and analytics: Be ready with the modeling structures as the digital twins do not just reflect past or present but also predict what may happen in future by which you can gain insights to optimize the processes.

Recommendations from our AI experts

 The importance of digital presence is now recognized widely for huge organizations which makes digital twins essential knowing the power it contains to drive innovations.

Establish a Documented Practice: A well-documented design practice is a helpful way to communicate ideas across the departments, in all organizations. For multiple users of Digital Twin a well-structured process makes it easy to alter in the model without destroying existing components. Best in class documentations increase transparency and helps streamline collaborative work.

Build a complete product value chain: Every department in an organization faces challenges in their operational front and digital twin provides ready solutions top such problems with the ability to coordinate across end-to-end supply chain processes. Inputs from the decision makers with inclusions of digital twin will ensure a more efficient design and value to the business transformations.

Ensure a long-term strategy: If digital twin is implemented using a proprietary design software runs a risk of locking the owners into a single vendor, which ties a long-term viability of the digital twin to longevity of supplier’s product. So, it is necessary to ensure a long access lifecycle for the assets to overcome IT risks and have less sustainable over time.

Data from multiple sources should be unified: Both internal or external data should be taken up from multiple sources to create a realistic and helpful simulations. predicting the errors all manually is a tedious task for humans which means that the digital twin should have a robust analysis with a vast amount of data. 

Pluto7, along with Google is equipped with expert and most talented product technicians with capabilities of monitoring, analytics, and core knowledge about the digital twin technology. We have been assisting companies to improve the customer experience by understanding their needs and developing enhancements in the existing products. 


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