Addressing the data challenges in the Digital Twin

The digital twin – a digital representation of a physical object – is one of the best examples of truly harnessing the power of the Internet…

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Bob Slevin

November 28, 20186 minutes read

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The digital twin – a digital representation of a physical object – is one of the best examples of truly harnessing the power of the Internet of Things (IoT). It is often said that Industry 4.0 needs digital twins to really push it forward. The demand for digital twins is exploding as the practical applications for business grow. This is raising data challenges that need to be urgently addressed if organizations are to fully benefit from their use of digital twins.

Creating a digital representation of a physical object has some clear benefits. You can see how it’s currently performing, stress test it to find out how it would perform under other circumstances and monitor it to predict when something will go wrong before it happens. It’s easy to see how the more digital twins an industrial or product-driven company has, the lower the risk of failure and the greater productivity of its assets.

The digital twin provides a complete software model of the object’s characteristics and state – often based around the 3D CAD model used in initial design and then layered with relevant sensor or operational data as the product passes through its lifecycle.

Of course, modeling assets to monitor operational and maintenance performance is hardly new. The difference with the digital twin comes from the range of data sources that can be applied to the model and the advanced capabilities that it can accommodate. These include the twin’s ability to communicate and control the physical object, its ability to run complex simulations and its capability to run advanced AI-assisted analytics to drive data-driven decision-making.

These advanced capabilities enable the digital twin to move beyond operational and maintenance capabilities by delivering insight and analysis for new product design, enterprise-wide process improvement and the creation of completely new business models.

Digital twins and the big data challenge

The digital twin has definitely moved from the hype phase into widespread adoption. Research suggests that the digital twin market will be worth $15.66 billion by 2023, at an incredible CAGR of almost 40%. Estimates suggest that 48% of companies implementing IoT have already or are planning to use digital twins. Gartner predicts that by 2021, half of large industrial companies will use digital twins, resulting in those organizations gaining a 10% improvement in effectiveness.

Effectiveness grows with the amount of digital twins you have and the number of IoT sensors and enterprise systems you have feeding them with data. That’s where the issues begin. Forbes identities four distinct (but inter-related) types of a digital twin:

This is a digital twin of an individual component within an asset, like a bulb in a scanner or a blade in a turbine. Data at the individual component level allows for data driven decisions for operations and maintenance of that component, the sub-system is sits within and the overall asset.

This digital twin is a model of an entire asset – a piece of manufacturing equipment, a motor or a pump – that gives a view of its workings to optimize performance and enable condition-based maintenance and as more and more data is gathered, predictive maintenance.

System or Unit
This is a model of a collection of assets that perform a function in a manufacturing setting – such as a production line in a factory. The digital twin delivers data that demonstrates exactly how the ecosystem of assets function together.

A process digital twin is the highest level model. It provides a business-level view to measure operational characteristics that underpin business operations across the enterprise. It gives end-to-end visibility to optimize throughput, quality and performance of the process. It enables organizations to visualize and simulate alternative approaches to re-engineer entire processes.

Large organizations can quickly amass hundreds or thousands of digital twins, receiving data from thousands or hundreds of thousands of IoT devices. In addition, the manufacturers of IoT-connected assets are increasingly delivering products that include a digital twin. More than this, Gartner predicts that through 2023, 75% of digital twins of these products will involve at least five different kinds of integration endpoints. Companies now have to think about deploying composite digital twins that allow them to integrate all the different digital twins involved in a particular operation or process together.

The cascade of data is enormous. For example, a jet engine will have digital twins at the component, asset and system level, each receiving real-time or near real time information from IoT sensors. The digital twins from the components will be feeding the digital twins for the asset that in turn are supplying data to the system digital twin.

Bad data cannot be allowed to enter into an integrated system where each twin relies on the previous one to supply trusted information – especially for something as important as a jet engine. At its simplest, data quality begins with device validation. You must be able to ensure that each and every IoT thing is exactly what it says it is, it has the rights to be on the network and that its producing reliable data. Without this assurance, the digital twins are highly at risk of delivering flawed results.

The role of the IoT platform

In its report, ‘The digital twin accelerates IoT development’, Forrester suggests that digital twins are driving IoT platform adoption because of the digital twin capabilities the leading platforms offer. In fact, it’s possible to think of an IoT platform as an advanced digital twin. With the OpenText™ Covisint Internet of Things (Covisint IoT) platform, we digital twin everything by creating a secure digital ecosystem of people, systems and things.

A key capability of the OpenText Covisint IoT platform is the integrated identity management that allows for the rapid and secure provisioning, management and removal of things from your IoT network – easily scalable to accommodate an almost limitless number of IoT devices.

Only this level of device identification and attestation will enable you to have confidence in the data that is driving your digital twin environments.

In the next blog in the series, I’ll look further at the role of the IoT platform in delivering the benefits of digital twins. If you’d like to know more about how OpenText can help you get the most from deploying digital twins, please complete the short contact form found here.

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Bob Slevin

Bob Slevin is the Director of Product Marketing for IoT at OpenText. Bob is an Internet of Things (IoT) architect and evangelist with more than 25 years’ experience in telecommunications spanning Military and Private sectors. He has collaborated with partners to deploy millions of connected devices across business and consumer markets. An IoT thought leader with an MBA in Technology Management, Bob is focused on identifying business challenges and building innovative solutions to improve operational efficiencies, drive growth and mitigate risks.

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