Digital Twins Can Be The Intelligent Edge For IoT

The second critical aspect of a digital twin is the ability to share this digital view of machines irrespective of viewer’s physical distance. This, therefore, allows a large number of individuals to see, track and benchmark manufacturing installations globally. This ability also removes the delay in reporting alerts to management, removes single points of failure due to human error and makes seeking expert help easier.

A digital twin expands the horizon of access of the shop floor to product managers, designers and data scientists. Armed with this new understanding of how processes and machines are working or not working, they can design better products and more efficient processes, as well as foresee problems/issues much earlier than before, saving time and reducing materials wasted on building physical models. They can also see the gaps between desired and actual, and do root cause analysis.

Digital twins are different from traditional 2D or 3D CAD images in scope and use. While CAD images and simulations consist mainly of the data of dimensions of a single piece of equipment or subparts, digital twins focus on capturing more holistic data of the equipment in terms of how it interacts with other equipment and the environment. This entails measuring the data and configuration of the installation (including space and other dimensions between different equipment) and data of the ambient environment (temperature, pressure, vibration, etc.). This data is fed on a continual basis from the physical to the digital twin through the digital thread. In terms of use, while CAD drawings are primarily used early in the product lifecycle to influence design decisions, digital twins are used primarily for manufacturing and service operations.

So, what should a business considering digital twins examine? First ask, “What do I need to know about my manufacturing operations that will allow me to drive decisions?” This forms the basis of what kind of data to capture and what kind of visualizations to implement. The follow-up question is, “What are the top three to five roles in my business for which I primarily want the digital twin?” The answer to this question can effectively clarify what views to create from the captured data. Digital twins, by definition, are customized to roles to ensure only relevant data is shown, thereby reducing visual clutter. The final step is to create an incremental roadmap to make the digital twin richer over time. This can be done by either adding more relevant data sets to the existing imagery or by providing access to a wider set of roles within the business. A great example of how to build an incremental digital twin is Google Maps. Google Maps today emulates location and traffic data in much more detail and more accurately than it did a decade back. It has constantly evolved over time in terms of richness of data and hence utility.

The benefits will be worth the preplanning that a digital twin requires. Industrial companies that have digital twins will be able to create sustainable competitive advantage due to better products, higher efficiency and faster release cycles (from product ideation to market). The key, therefore, is to start even with smaller projects and keep reinvesting benefits/ ROI to create better or more complete systems in the near future.

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Digital twins can be the intelligent edge for IoT
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