How Siemens and TfL are transforming transport with IIoT
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How Siemens and TfL are transforming transport with IIoT

Maxine-Laurie Marshall — August 2018

Industrial IoT is presenting businesses with seemingly boundless possibilities. Siemens CDO Devina Pasta and Transport for London’s CTO Shashi Verma outline the routes they have taken to successful implementations — and the challenges the industry must still address.

From power generation and industrial automation to transportation and imaging systems, engineering giant Siemens is historically associated with the world of large physical machines. But its product lines in recent years have become increasingly defined by intelligence and connectivity. Today, a million connected Siemens assets generate an estimated 8 zettabytes of data daily. And the challenge that presents, says Devina Pasta, chief digitalization officer at Siemens’ Power & Gas division, is how to maximize the value of such a wealth of constantly flowing data — for Siemens’ customers, suppliers and for the company itself — without always knowing where the exploration will take them.

She is not alone in sensing the fathomless possibilities such data opens up. Shashi Verma, CTO and director of customer experience at Transport for London (TfL), the government agency responsible for the city’s underground, road and bus network, is equally aware that he is now sitting on a potential data goldmine, increasingly fed by countless numbers of sensors.

“Anything that has been brought into TfL in the last 10 years comes with a huge amount of built-in sensors. That means we have 350,000 devices pumping out absolutely phenomenal quantities of data. It’s very easy to get inundated, so it’s really important to calibrate what’s coming in, what matters and what doesn’t, and to find the mechanisms to separate the signal from the noise.”

“The skill-set deficit is a big roadblock for IIoT. This is where many digital transformation projects are failing.” — Devina Pasta, CDO, Siemens

Verma says machine learning and AI are already presenting “fantastic possibilities” as part of that mechanism, and he cites an example. “A lot of track monitoring on the underground is carried out by cameras fitted to the bottom of trains, which generate huge amounts of visual data. Today the practice is for a specialist to examine that video data to identify problems. But this is exactly the sort of area where AI is able to improve the efficiency and accuracy of fault detection.” 

In a similar vein, Siemens has used AI and sensors to provide predictive maintenance on the failure of key components in the trains it supplies to Spanish rail operator Renfe. The result, says Pasta, is that there have been virtually no unplanned failures of parts, giving the trains 99% availability.

And that has translated into competitive advantage, enabling Renfe to compete directly with airlines on high-speed routes. Its reputation for reliability has encouraged 60% of fliers on some routes to switch their allegiance to train travel, says Pasta.
Business — not technology — focus

But the pace of technological change and seemingly endless possibilities can have their downsides.

Pasta explains: “In the world of heavy industry, we have always been used to having a ‘diamond’ — a product we typically planned for over five to 10 years and was in manufacturing for three to four years, without things changing too much. But now we’re moving in to a world that’s a lot more fluid.”

She says that for some customers this new environment has led to a fear of the unknown, even as, paradoxically, they expect the inclusion of the latest technologies in products — often without really knowing what they would do with these new capabilities.

“It is vital to think about the ‘why,’” she emphasizes. “What’s the business goal, why do you want to actually implement something like AI?”

“It’s very easy to get inundated, it’s really important to find the mechanisms to separate the signal from the noise.” — Shashi Verma, CTO, TfL

Verma agrees: “Everything has to have a use case and a business problem that needs solving. We can then work backwards to see what technology we should best apply to that.” A good example at TfL relates to the status of London Underground ticket gates. Staff have moved from being able to monitor static data to see if a gate has failed to dynamic data, where the machine can relate its current state. The next stage is a predictive capability where the machine can tell staff when and how it is likely to fail in the future.
Challenges to overcome

Predictive maintenance may be the leading use case for industrial IoT (IIoT) today but such implementations have to be accompanied by a change to a more agile mind-set, says Pasta. Many in the industry who are still focused on five-year project horizons are finding they are forced to reintegrate IIoT data every three to four months as new connected devices are added in.

Another universal challenge is an acute shortage of skills. “The skill-set deficit is a big roadblock for IIoT especially when it comes to scaling projects. This is where many digital transformation projects are failing,” says Pasta.

That barrier can be partially overcome through collaboration, she says. “No one knows all the answers. But by participating in an ecosystem of partners we can jointly create value. It might not always be easy in the industrial space, but we must collaborate, co-create and discover.”

Devina Pasta and Shashi Verma were speaking at TechXLR8 2018

• Read more expert opinion on digitalization strategies in transportation

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