The Industrial IoT Must Transform

Here's how companies can better take advantage of the IIoT.

Nigel Upton | HPE

The Industrial IoT (IIoT) can be seen as a way to optimize existing processes and business models, for instance by achieving higher degrees of automation or by avoiding outages with the help of predictive maintenance.

However, IIoT can and must be more.

We have seen how new digital business models have disrupted industries like media, retail and travel – and the same will happen over time to industries like manufacturing, chemicals and energy.

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The IIoT is about which companies will become an extended workbench of a predominantly digital value creation.

Thus, at its core, the IIoT is not about achieving some percentage points of efficiency. It’s about which companies will capture which portion of this trillion-dollar opportunity – and which companies will become an extended workbench of a predominantly digital value creation.

This means that adopting the IIoT must go beyond optimization – it has to be a transformation.

It means introducing new data-enabled processes in research and development, production, marketing and sales, new forms of cooperation in the supply chain, new ways of creating and commercializing products and services – all backed by a technology architecture that enables interoperability between “things” and provides data insights with the required speed.

Together with Industry of Things World, one of the leading IIoT conference series globally, Hewlett Packard Enterprise (HPE) conducted a survey to find out whether company leaders approach IIoT as optimization or transformation, how successful they have been and their biggest obstacles.

We also wanted to know their technology architectures, the importance of the public cloud for IIoT and the role of edge computing.

Mixed success rate of IIoT projects

Asked about the business goals they want to achieve with their IIoT initiatives, by far most respondents (64 percent) said, “increase efficiency.” Similarly, other IIoT goals like greater flexibility (48 percent) and reduced time to market (35 percent) aim to optimize the existing business rather than create something new.

In comparison, transformative goals like establishing new business models (34 percent), improving marketing (27 percent) and product development (26 percent) ranked relatively low.

To be clear, increasing efficiency or time to market are important business goals – however, the dominance of optimization goals in the context of IIoT can be seen as an indicator that many companies have not yet fully embraced the transformational nature of this concept.

Can we conclude that our respondents’ IIoT projects have not been entirely successful?

Yes, we can. Fifty-three percent said their IIoT projects in the past 12 months either met or exceeded their goals, while 47 percent did not reach their goals – a small portion of which even say their projects were a complete failure.

So, why did companies struggle with their IIoT projects?

Respondents named the lack of skills and the culture within their own company as the biggest obstacles (both 38 percent). This clearly underlines the fact that being successful with IIoT requires a company transformation – you need new skills and mindsets.

Other major challenges include missing organizational structures (27 percent) and wrong governance and management (21 percent).

The survey makes clear that transformation also applies to the technology underpinning IIoT initiatives. You can’t just buy IIoT technology – IIoT requires a fundamental redesign of the information technology (IT) and operational technology (OT) architecture.

Transforming your technology architecture

Closely after skills and culture, missing standards (36 percent) is named as one of the biggest IIoT roadblocks. This refers to the difficulty of making the “things” in the IIoT speak with each other – be it a Kuka robot speaking with an ABB robot, a production machine speaking with an IT systems or a manufacturing plant speaking with an energy supplier.

This requires common standards as well as new technology architectures that create convergence of information technology (IT) and operational technology (OT).

Accordingly, the No. 1 skill companies are looking for is the ability to design new common architectures for IT and OT (45 percent), and nearly as high is the ability to create a unified approach to the operations and support of IT and OT (32 percent).

The fact that software development is also one of the most desired skills (42 percent) shows that we need new types of software often not available on the market as a packaged application.

Edge and cloud computing will grow strong

We also asked which role edge and cloud computing will play for IT and OT architectures in the coming years. Many market observers have emphasized the crucial role of the cloud for the IIoT. However, as our survey shows, edge computing is as important and will grow equally strong.

Edge computing means that compute and analytics resources are not running in a central datacenter or a cloud but near to the things or in the things themselves – think of the IT embedded in a car, IT systems embedded in a machine, running on the factory floor or on an oil rig.

First, if we look at how much of the sensor data today is analyzed on IT systems close to the data source (in other words, the edge) and in the cloud, the percentages are quite low on both ends, with more than half of companies analyzing less than 30 percent of sensor data at the edge and in the cloud.

The rest of the sensor data is processed in a traditional company datacenter.

But if we look five years ahead, these numbers will change significantly. The ratios will be turned on their heads, with the majority of companies planning to analyze 30 percent to 70 percent – or even more than 70 percent of the sensor data at the edge and in the cloud.

This might seem to be a contradictory result, but it’s not. To realize the promise of the IIoT, we have to deeply transform our traditional IT architectures, and we need both the edge and the cloud capabilities to make that happen.

Security, latency and bandwidth

Why is this the case?

First, the three most important reasons for using edge computing in the IIoT are security (52 percent), latency (41 percent) and bandwidth (35 percent).

Let’s look at latency and bandwidth first. Imagine a self-driving car driving 100 kilometers per hour toward an obstacle in the road – the IT systems in the car have to analyze megabytes and gigabytes of sensor data within milliseconds to avoid a crash.

There is simply no time to send that sensor data to a remote cloud and wait for an answer. This equally applies to production machines and other things.

And security: Sending all that data via the network opens a big attack vector for hackers, so it’s better to analyze the data on site and only send selected and encrypted data to the cloud.

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The top three reasons to use the cloud include correlation analysis, deep learning and horizontal integration.

Similarly, there are important reasons to use the cloud – the top three are correlation analysis (66 percent), deep learning (51 percent) and horizontal integration (36 percent).

It’s not enough to have intelligence in one machine, car or plant – you can create more value if you bring the data of these machines, cars or plants to one central place to compare and correlate their behavior.

Then you are able to derive deep insights from the data – deep learning – which you can play back to the things and enable them to perform better, adapt to new and unknown situations and avoid outages. This also allows us to better coordinate cars, machines and plants, enabling things like swarm intelligence in traffic or highly automated supply chains.

This means the IIoT will be a hybrid world. And one of the key tasks will be to create integrated architectures that bridge from the edge to the core datacenters to the cloud and all the way back.

Much work remains

Again, the journey toward the IIoT goes beyond optimization – it is a transformation that requires change on all company levels: technology, architecture, processes, people, and business models.

Overall, our survey results show that the industry still has a learning curve in that regard. However, we have to consider that IIoT is an emerging concept, and it’s encouraging to see that transformational approaches already play a significant role in the way companies plan and execute IIoT.

Similarly, I’d suggest we talk about a 53 percent success rate, not a 47 percent failure rate. The glass is half-full, not half-empty. This also means there’s still a lot of work to do.

This is my appeal to the industry: Embrace and master transformation, and accelerate your journey – there’s not much time left.

This article first appeared on HPE Newsroom and was republished with permission.

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Nigel Upton is General Manager of the IoT and Global Connectivity Platform at Hewlett Packard Enterprise.

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