Sandeep Patel

Digital twins taking on Industrial  Manufacturing - GSI at heart of it

Blog Post created by Sandeep Patel on Jul 11, 2017

We  have been working with  few GSI where they  have been working on  Digital Twin concepts on few of our competitors platforms.

 

As per the  Manufacturing business owners, customers and  few GSI  leaders (  some outside in view ) :

 

  • Digital twins offer a unique, highly accurate digital representation of your assets and systems across their design, build, run, operate and service lifecycle.  Came across the following presentation in IIoT seminar this week .
  • Cognitive digital twins will create a new massive economy around semi- or fully automated digital smart services defined and offered by the CDTs themselves in collaboration with the physical twin(s) and possibly humans partners.

 

 

GSI  are looking  for a  roadmap to digital twin-enabled industrial apps. Our  competitors-  GE & Siemens  have taken a path which is described as below at high level. We can’t predict  how successful will they be in long run but excited a lot of interest.

 

  • § Build asset/system: Data scientists package asset data and intelligence, applying analytics, models, and machine learning.
  • § Provides standard toolkits to help accelerate the build process.
  • § Run: The platform runs and persists digital twins for each asset/system.
  • § Consume: Apps and developers access context data, APIs, and insights from the digital twin.

 

In a nutshell, With the digital-twin approach and current maturity we can  build stochastic simulations, or prescriptive models. We do this by creating rules that map from design to performance and add randomness to simulate risk. The prescriptive data from the simulations shows how new products will work. By analyzing it, we can detect design flaws early. We can predict and minimize cost. And we can use this mountain of intelligence to build improved products in the future. Because randomness is inherent in the model, we can simulate the kind of uncertainty encountered in the real world. And since computing power is cheap, we can afford to run millions of scenarios, anticipating an entire spectrum of possible outcomes, rather than just a single expected result. In fact, we can learn as much from the digital twin as we can from the real-world original.

 

In meanwhile  ,we have vendors like GE talking about Digital Twin at Scale and few car vendors talking  about digital twin to production.

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