IoT and Digital Twin: Explaining the Connection

Many different kinds of digital twins exist, yet they all share common functionality. The key feature that distinguishes digital twins from other virtual representations, such as simulations, is the continuous, real-time data measurement, which is always anchored in the physical aspect. A simulation of a motor, for instance, might have all the same base data as the digital twin, in the beginning. Over time, however, it is likely inconsistences would emerge as the simulation is operating purely in the hypothetical, virtual realm.

The exact functioning of a digital twin varies depending on the type, but the principles remain consistent. IoT sensors, smart products, and other computer devices capture the information requested and then feed it into a digital interface. Depending on the application, this data can be analyzed with AI to quickly deduce insights and prioritize the most important information at that moment.

Again, the key feature is consistency. For any digital twin to function properly, the data stream must remain constant. Organizations without proper IoT infrastructure will find trying to create effective digital twins difficult, if not impossible.

Component digital twins, as their name suggest, reflect individual pieces of a product. Again think back to a motor, which has numerous gears, screws, and other system parts all working together. Not every piece needs a component twin, however, and teams usually prioritize targeting just those pieces that see tremendous stress, temperature variation, or vigorous usage.

Component twins are used to measure and better understand these particular pieces so that they may be improved in subsequent iterations of product design and refinement. That said, there is nothing to stop an organization from making a component twin for every single piece of the product they are building, provided they have the resources available to do so.

An asset twin is simply another name for a product digital twin. As such, this twin type focuses on reflecting the whole product, and not just individual components. Product digital twins often specialize on analyzing the interactions that occur between all these components, so that the end user can better understand just exactly how the asset is functioning over time.

If many components work together in a product, so too do many products work together in a system. System digital twins (sometimes called unit twins) represent these interactions. One manufacturing line, for instance, typically has numerous hardware types all working together to create one product. A systems twin would measure and record these interactions, helping end users better understand where and how to invest in optimizations that will improve efficiency.

Pulling back one level higher, a process twin mirrors the data created within all these systems interacting together. Process twins tend to be some of the most complex digital twins in existence and require impressive digital infrastructure to achieve peak efficiency. A robust process digital twin might reflect the work of an entire manufacturing site, both in terms of various hardware and even employee behavior.

Given their variability, it’s no surprise that digital twins have a plethora of use cases. Again, no matter the circumstances around the twin’s usage, we want to remind readers that the principles remain constant — and consistently depend upon IoT infrastructure. Digital twins are not an easy entry-level digital transformation initiative and are only effective once the correct foundation has been laid. Attempting to bypass IoT is akin to driving blindfolded: You won’t ever really know what’s going on and you can crash at any moment.

Digital twins can be widely applied in the manufacturing space, from large-scale process twins capable of tracking and representing an entire factory, down to component twins analyzing individual pieces in one machine. Regardless of which type of digital twin is deployed, the desired result is usually the same: improve productivity.

Digital twins in manufacturing help reduce cost and downtime by allowing manufacturers predictive intelligence into which machines will break and when. This allows issues to be corrected before they escalate into problems that can shut down production lines.

Digital twins can be incredibly helpful for products post-release as well. In the automotive industry, digital twins can provide feedback to vehicles once they are on the road, showing exactly how the various systems are handling driving conditions.

Digital twins are present in numerous aspects of healthcare. In fact, certain organizations (Apple, Google, etc.) are using IoT scanners in wearables to create rudimentary people twins to track certain health aspects, such as heart rate and sleep patterns. The implications, however, go beyond this to medical devices themselves. Machine downtime can literally be a life-or-death situation in healthcare, so the predictive analytics made possible through digital twin usage greatly help medical providers to not only identify but correct problems within medical devices before the situation escalates to life-threatening.

The implications for digital twins regarding sustainability are strong and diverse. Optimizing manufacturing processes reduces waste creation and improves machine durability, leading to fewer new parts being needed. Yet this is only the surface.

Outside of manufacturing, digital twins can better predict environmental reaction, as well as reduce energy consumption. The smart city concept, for instance, is in fact a large-scale complex location digital twin. By creating a digital twin of an entire city, users can better direct energy flow, understand commute patterns and re-optimize infrastructure accordingly, and conserve material usage wherever possible.

Digital transformation has many aspects, and IoT can be seen as a foundational digital transformation initiative. Deploying sensors and creating smart connected products allows organizations access to information that is otherwise impossible to gather. This data can be used in many ways, one of which being to lay the foundation for many types of digital twins. It’s very much a cart-and-horse scenario, where the digital twin (the cart) is only made useful through the horse (IoT) as the two together bring tremendous value to the end user.

Digital twin can be seen as giving structure to IoT. IoT by itself is useful, but the sheer amount of data can be overwhelming. Digital twins break down the information into understandable categories — people, products, places, processes — that are more immediately clear and understandable. Grouping the data is a solid first step to unlocking actionable insights, but often more help is still needed — hence why many digital twin programs use AI in the form of advanced analytics to better provide the user with prioritized information and readouts. That said, categorization is not the only benefit brought by digital twins and IoT. Like many aspects of digital transformation, these investments provide multiple competitive advantages.

Predictive maintenance is a crucial advantage for manufacturers, as it allows organizations to more quickly identify and correct problems, sometimes before any downtime occurs. Digital twins are important for any company actively pursuing predictive maintenance, as they provide comprehensive data analysis into exactly how the selected component, asset, unit, or process is performing. Seeing the problems before they occur means reducing downtime, keeping factories operational longer and improving profitability.

Predictive maintenance benefits extend further. Not only is downtime reduced, but spotting a problem earlier often means less drastic action needs to be taken. A bolt can be reinforced rather than being replaced, or an entire machine can be salvaged when it would have before had catastrophic failure.

Predictive and preventative maintenance are all about proactively stopping problems before they truly develop, rather than simply reacting to the situation. In any industry, it’s better to be thinking ahead. In manufacturing, this translates to reduced downtime and generally lower maintenance costs.

Digital twins don’t stop helping at the maintenance level. They provide an incredible look into whatever they’ve been asked to replicate. As such, the user now has more information and knowledge than ever, and can use this to optimize overall quality. In process twins, this means greatly improving production time through eliminating wasteful behaviors or redirecting energy usage. In product twins, it could mean using after sale data to better track how a product performs, correct any troubled occurrences, then redesign accordingly for superior iterations.

Whether it’s a positive or negative prediction, digital twins provide a window into the future—one that is almost always more accurate than a simulation, given the continuous data feedback. Clearer visions of the future allow organizations to better adjust, positioning themselves for the improvements and success they hope to see. Likewise, general performance standards should also raise, though this also requires action on the part of the user. Digital twins will merely show the data they are supposed to show. The decision making on what to do with this information is still firmly within the hands of its end users, as even digital twins with AI analytical capabilities will not make any drastic decisions without human intervention.

In order to accelerate, it helps to fully know and understand the baseline. If current production rates, for instance, are in fact only using 85% productivity when before it was assumed every machine was at 100% capacity, suddenly there is immediate room to grow. Machines that can be operated more efficiently, while also providing operators with insight into if and when downtime might occur, will produce faster — reducing the time to market and time to value for the manufacturer.

Industrial internet of things (IIoT) is simply a term to express focus on IoT in professional, industrial spaces. The two terms are often used interchangeably, and really it is user preference how they would prefer to describe their IoT operations. That said, regardless of how you express IIoT, there is no denying it brings numerous enhancements to digital twins:

• Digital twin of products: IoT offers visibility into the full product lifecycle

Smart connected products have replaced assumptions with facts; real-world IIoT data closes the feedback loop with product usage data, which then informs future iterations — and even business model changes, including product-as-service. Product telemetry also gives engineers and product designers behavioral characteristics of deployed products or fleets of products. Providing a frame of reference to compare the ‘as-is’ versus ‘as-used’ product usage is an extremely powerful IIoT-enabled insight that can inform the development of future product iterations. Its applicability can range from replacing or modifying certain features to drilled-down insights into the specific performance of part(s). Expanding visibility into the product lens through cross-functional collaboration can also drive downstream efficiencies. This includes change management in manufacturing and service processes, which lowers scrap, rework, and lead times.

Real-world example: Whirlpool is achieving data-driven design by connecting deployed appliances through IIoT and analyzing operating performance metrics (torque, drum speed, motor temperature, etc.) across fleets of products to improve future iterations.

• Digital twin of processes: IoT unlocks deeper operational intelligence

Many operational processes are plagued by two factors: disparate and black-boxed information sources. IIoT unlocks these unknown insights and threads them with different sources both in real-time and historical systems of record. Twins of these connected assets and workers and how they interact are critical to a constructing a process lens — essentially a system-wide view of an industrial environment. IIoT through a process lens can drive critical manufacturing KPIs. For example, improving the uptime of a single asset on a factory floor through IIoT-driven predictive insights can drastically improve throughput while a twin of a production line can reduce bottlenecks through enhanced operational visibility. This connected operational intelligence from diverse assets creates the real-time 360-degree visibility manufacturers need to be flexible and agile — a necessity in today’s changing markets and shifting customer demands.

Real-world example: Woodward is gaining operational visibility by integrating its technologies and workers through the IIoT across its factories. An IIoT platform contextualizes myriad information sources in its production facilities, including connected devices (torque wrenches, pressers, etc.), manufacturing execution systems (MES), and product centric-software (CAD, PLM), to give an end-to-end operational view.

• Digital twin of service: IoT optimizes maintenance

Much of a product’s operational condition and performance in the end user’s environment hasn’t been accessible to the manufacturer or customer. With maintenance and service being critical functions to reduce asset downtime and differentiate offerings, digital twins with IoT can drastically improve these metrics and enable new revenue streams.

Digital twins can bolster remote service IIoT use cases where software updates, patches or reboots for deployed assets can negate the need to send a technician on-site. IIoT’s flexibility can enable mission-critical systems to sample data every second to inform services, or less frequently to optimize resources, all depending on the digital twin use case.

Telemetry data can also feed into the deployed asset’s digital twin to gain a baseline of its health and apply next-generation predictive maintenance modules blending machine learning and physics-based simulation techniques. Simulating historical patterns of machine performance with design expectations against real-time sensor data will reduce unplanned downtime and add another layer of intelligence, which further maximizes asset utilization.

Real-world example: Howden is helping customers succeed through its ‘Data Driven Advantage’ program. This initiative has embedded IIoT-driven insights into customers service and maintenance workflows to save millions in unplanned downtime and reduce business risk.

The full fidelity of a digital twin will become available as IIoT is added to organization’s products, processes, and people. This ‘live’ data will also serve as an entryway for next-generation use cases, as it’s a lucrative inputs for physics-based simulation, artificial intelligence, and computer vision applications.