Digital Twin is the ability to take a virtual representation of the elements and dynamics of how an Internet of things (IoT) device operates and works. It’s more than a blueprint or schematic. It’s not just a picture. It’s lot more than a pair of glasses. It’s the understanding of how a device operates and lives throughout its life-cycle. It’s the understanding of all of the device’s dynamics, no matter whether it’s the enclosed parts are being moved or if it’s the device that moving itself. It’s about the elements that compose it and the dynamics of how that device is put together.

Done correctly, a digital twin will influence how design, build and operations of a device are constructed in a single life-cycle.

The design phase is where the engineering tooling comes together, bringing together physical elements, physical bill of materials and pulling together the virtual elements. For example, the softwares on the cars and all the different elements, being able to coordinate and collaborate into a single facility of operational oriented design that is designed to bring out the highest quality product.

In the build phase, it is about understanding how the devices that make the product influence the product’s tolerances, stresses and designs. It’s about better manufacturing to drive the correct tolerances and correct outcomes that you want to see for the product that you are actually making.

The digital twin facilitates the actual operation of the product as well. Product’s age, different environments the products go through – they deal with things like whether and they will have different tolerances and they will drift along with the product as they age and that feedback. When done correctly, not only facilitates the operations of the product, but helps facilitate better design and better manufacturing by the lessons that are learned and the re-calibrations takes place along the way.

Now, there are essential capabilities that must be present in a digital twin.

First, you have to apply analytics at every single step. The amount of information that we are dealing with to ply digital twin to a small device or to a complex device such as an automobile or an aircraft is staggering. Analytics has to be real-time, but has to be operational, has to have quality, it has to be predictive oriented in its nature.

The data that comes from the digital twin need to be open, you have to be able to access it for a variety of different sources, you have to be able to pull it together into a federated model and you have to be able to bring it together so you can get that interaction, that dynamics to play. It’s not just a schematic or a picture that you are making, you are actually making a dynamic model that you are going to shift as you go through the design, the build and the operation phase of what you do with the life-cycle of that product.

You may actually use the same product differently in two different industries and have two different digital twins for that one product based on how the industry uses that product. For example, a pump used in the oil and gas or the pump that used in the municipal or wellness and water, the outcome is based on the industry context of how that device is going to be used. So, the digital twin not only captures the engineering aspects, but it also captures the industry context, the dynamics of how that product is used at the same time.

The digital representation provides both the elements and the dynamics of how an Internet of things (IoT) device operates and lives throughout its life cycle

Disclaimer : The views and opinions expressed in the article belong solely to the author, and not necessarily to the author's employer, organisation, committee or other group or individual.