Digital twins can be the intelligent edge for IoT
If you are involved with IoT, you have witnessed a surge of activity around the idea of digital twin. The digital twin concept is not a new one — the term has been around since 2003, and you can see an example use in NASA’s experiments with pairing technologies for its Apollo missions. However, until recently, technology barriers made it difficult to create a true digital twin. Today, asset-heavy enterprises and others are using the breakthroughs in technology to plan or implement digital twin products or manufacturing processes. We can expect this interest and growth to continue: Gartner predicts that by 2021 half of all industrial companies will use digital twins and see an average efficiency gain of 10% from them.
The most simple definition of a digital twin is an image connected to a physical object by a steady stream of data flowing from sensors, thereby reflecting the real-time status of the object. The data flow stream connecting the physical and digital is called a digital thread. In some implementations, the digital twin not only reflects the current state, but also stores the historical digital profile of the object.
We cannot overstate the importance digital twins will have for a number of industries, especially for manufacturing installations and processes that need close interactions between machines and human. There are two key reasons for this: visualization and collaboration.
Visualization
If you were to measure human senses in bandwidth, sight is the highest. As a result, human decision-making is reliant on being able to see the situation in full and take necessary action. This is why factory floor managers usually had a floor overlooking the factory floor. Today, with manufacturing installations and machines becoming more complex, that advantage of being able to see the processes has largely disappeared. Instead, computerized systems feed data to shop-floor managers to enable decision-making through data sheets or basic charts.
A digital twin can combine the best of both worlds by presenting to decision-makers in real time the data in an exact visual replica, including information previously not available as easily, such as temperature or internal wear and tear. In fact, a digital twin far enhances the efficiency of the visual bandwidth by removing non-critical information, processing basic information into a format much more easily absorbable and providing a more flexible (e.g., 360-degree or micro/macro) view of the system.
Finally, the visual aspect also helps immediately benchmark and compare across historical data or best-in-class data in real time. The potential of this aspect is tremendous as it identifies areas of improvement, shows areas of immediate concern and enables fast decision-making — all in real time.
Collaboration
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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