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Producing the Digital Twin for the Industrial Internet of Things

Producing the Digital Twin for the Industrial Internet of Things

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Episode 84

The digital twin, or software-defined product as I prefer to call it, is the most important tech in IoT, yet you hardly hear anything about it, when compared to other IoT tech such as sensing, networking and analytics. Maybe it’s because it’s such a new and abstract concept for most people. Well, that’s about to change. In this episode of the IoT Business Show I speak with Dimitri Volkmann about producing the digital twin for the Industrial Internet of Things.

In this episode of the IoT Business Show I speak with Dimitri Volkmann about producing the digital twin for the Industrial Internet of Things.

Dimitri is a Software Executive with history in IT Platforms and an expertise in bringing together Product Management and Product marketing in order to deliver products to the market successfully. At GE Digital, Dimitri is leading Digital Twin Thought Leadership and Product Marketing on the Predix Platform.

The importance of the software-defined product (SDP), or digital twin, cannot be over stated. Independent of the type of IoT, the SDP is the cornerstone of not only IoT tech, but IoT business too. That’s because it’s in the middle of value generation, being shared by both analytics and the application to create value.

In the IIoT the software-defined product, or digital twin as it’s referred to here, is used in progressively more sophisticated ways. First for asset optimization, by using it for equipment health checking, and then for failure and maintenance modeling. Then it steps up a level and is used for operational optimization – so that’s maximizing your classic, asset utilization and operational efficiency, both of which have an affect on the bottom line.

Here’s What We’ll Cover in this Episode

  • Using the digital twin to optimize assets, systems and operations.
  • The difference between IoT models and traditional models.
  • The origin and definition of the digital twin.
  • The important interplay between the IoT platform and digital twin.
  • The importance of separating the digital twin from it’s operating environment (the IoT platform) in order to expose and share it.
  • Using outcomes to design the digital twin.
  • Where we are in digital twin standards and interoperability.
  • The computer programming languages used to develop the digital twin.
  • Machine learning and the digital twin.
  • Gold data.
  • Data scientist versus data analyst – do you need both?

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