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 help of predictive maintenance.
However, IIoT can and must be more than that. We have seen how new digital business models have disrupted industries like media, retail or travel – and the same will happen over time to industries like manufacturing, chemicals or energy. 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 R&D, 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.
“Adopting the IIoT must go beyond optimization – it has to be a transformation.”
Top-three reasons for edge computing: security, latency, bandwidth
Why is this the case? We also asked that question. First, the three most important reasons for using edge computing in the IIoT are security (52%), latency (41%) and bandwidth (35%). Let’s look at latency and bandwidth first. Imagine a self-driving car driving with 100 kilometers per hour towards an obstacle on the road – the IT systems in the car have to analyze mega- 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.
Similarly, there are important reasons to use the cloud, the top three being correlation analysis (66%), deep learning (51%) and horizontal integration (36%). 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 be able to compare and correlate their behavior. Then you are able to derive deep insights from the data – i.e. do deep learning – which you can play back to the things and enable them to perform better, adapt to new and unknown situations better, 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.
Encouraging results, but still a lot of work to do
Again, the journey towards the IIoT goes beyond optimization – it is a transformation which requires change on all company levels: technology, architecture, processes, people, and business models. Overall, our survey results show that the industry is still in the learning curve in that regard. However, we have to consider that IIoT is an emerging concept, and therefore 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 to talk about a 53% success rate, not a 47% failure rate. The glass is half-full, not half-empty. This also means there’s still a lot of work to do. My appeal to the industry is: embrace and master transformation – and accelerate your journey, because there’s not much time.
Learn more at: https://www.hpe.com/us/en/solutions/industrial-internet-of-things.html
 State of the Industrial IoT, September 2017, based on surveys with 350 managers, directors and C-levels between July 2017 and September 2017. 68 percent of respondents were from Western, Central and Eastern Europe, 14 percent from North America, and 13 percent from Asia/Pacific. Most respondents were participants of one of the Industry of Things World conferences, indicating an above-average IIoT maturity.