Digital twin

In the second part of his three-part series, David Socha discusses what Digital Twin actually means and what it might be able to do for you, via a short history and a few current examples.

Last month, I began with a review of the Gartner Hype Cycle concept, later mentioning that Digital Twin was racing up the peak of inflated expectations in the (then most recent) 2017 Hype Cycle for Emerging Technologies. The 2018 version was published in August. As expected, the Digital Twin’s climb has continued. It is now, in Gartner’s own words “at the peak”.1 It’s the Digital Twin’s turn to be the saviour of humanity. Sigh.

A short history lesson

Last month I described a Digital Twin as simply a virtual representation of a physical asset, system or process. I also mentioned that they’re not a new concept at all, irrespective of the current excitement and interest levels. So let’s provide a little background.

There’s no clear first use of the term Digital Twin. No Professor, Management Guru or Captain of Industry can (or as far as I can tell has seriously tried to) lay claim to coining it. The most regular historical reference you’ll come across is NASA, with a date of something like “the early days of space exploration”. NASA is used to really difficult challenges.

As a colleague of mine2 is often wont to say, that stuff actually is rocket science. One of NASA’s recurring challenges is that they send the incredibly complex things they build millions of miles into space. Unfortunately, this makes them really hard to monitor, control, analyse. To help them with that, NASA developed stay-at-home digital models of their physical, but extremely remote machines that could be used to assist in the operation, simulation and analysis of their systems.

Around the same sort of time, the first SCADA3 systems were being developed to monitor and manage – often also remote, but at least earthly – industrial assets and systems. Those too required user interfaces and, with or without the addition of separate front-end applications, evolved to include digital representations in oil & gas exploration, electrical networks and generating plants, manufacturing facilities etc. These space-age and down-to-earth examples are the beginnings of the Digital Twin.

The time is right

Back to the present, and our world of Digital Transformation, the Internet of Things, Artificial Intelligence and all that. All of these exciting things revolve around a single critical enabler: doing more with data.

That means taking advantage of new data from digitisation of previously paper-based or otherwise analogue processes; integrating new (or newly accessible) data from sensors on assets; and doing more with it to deliver value. This combination of rapid digitalisation, plus the even more rapid development of new analytical capabilities is the perfect environment for the accelerated growth of Digital Twins.

The Digital Twin grows up

Digital Twins will evolve in two key stages. Let’s call the first stage the ‘what-it-does-today-but-better’ stage. Though I might have to work on that name. Anyway, the more astute of you may have noticed in the History section above that the first Digital Twins were very much about Operational data. The OT world. That continues to be the case for most modern Digital Twins.

If you buy high-value assets from Siemens, GE, Schneider, Hitachi…whoever, there’s a fair chance those assets will come with their very own Digital Twin. Potentially, you’ll be able to use this to better monitor the asset; run simulations to understand the implications of various modes of operation or interventions; and potentially even use it to predict when you should intervene or how you should operate it to meet your most important criteria.

The Digital Twin you’re interacting with will be accurate, because it will have lots of data from all those sensors on its physical counterpart. It’ll be intuitive. Maybe even pretty. And it will have lots of functionality to do the kinds of analysis I mention above. These are good things.

But this is only the beginning. At this stage, the Digital Twin remains a mostly standalone application - no matter how smart, complex or valuable. It’s presenting, modelling, analysing data from its twin, just as you’d expect. And doing it better than ever. But what if the model shows that we need to do a major repair in a few weeks? Who ensures the parts and labour are available to do that? Or that the outage is scheduled for the least disruption? Not the Digital Twin. Or what if the model shows that reducing burden would enhance the life of the asset by 10%? Who decides if that 10% extra life is more or less valuable than the potential reduced output or efficiency of the whole process? Or if it has an effect at all? Not the Digital Twin. Not today, at least.

The second key stage - and the future for Digital Twins is an integrated one. Like other enterprise applications and systems, their greatest value will be delivered when their data, their models, their insights and predictions are integrated with those in the Supply Chain; in the Maintenance systems; in the HR systems; and of course, in the wider Operational systems specific to each industry.

Then, a Digital Twin will be part of an end-to-end process ensuring parts and labour are available for maintenance. It will be part of a wider analysis that sees the overall cost, risks and benefits of different modes of operation across the entire value chain. It will be integrated into the business4.

The Digital Twin Maturity Model

Next month, in the final part of this series I’ll introduce a simple maturity model that pulls together what we’ve talked about so far. It will demonstrate the path from simple, standalone visual reporting of operational parameters all the way to the point where our Digital Twin - just like Big Data - pretty much ceases to be thought of as a thing in and of itself and instead is just another critical part of 21st Century Business as Usual. I hope you’ll find it valuable. See you then.

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1 It also features on the current Hype Cycle for IoT. It’s pretty close to the peak there, too.

2 Jane McConnell. More from her next month.

3 Supervisory, Control And Data Acquisition. But you probably knew that.

4 It might even be that in future, we call a fully modelled end-to-end business value chain a…. Digital Twin too

About the author

David Socha is Teradata’s Practice Partner for the Industrial Internet of Things (IoT). He began his career as a hands-on electrical distribution engineer, keeping the lights on in Central Scotland, before becoming a part of ScottishPower’s electricity retail deregulation programme in the late 1990s. After a period in IT management and consulting roles, David joined Teradata to found their International Utilities practice, later also taking on responsibilities in Smart Cities and the wider Industrial IoT sector.