The promise of the Industrial Internet of Things

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Previously, I wrote about our Industrial Internet of Things (IIoT) strategy, where we stand on the topic and how we are ensuring the value of what we are doing in this space is clear and relevant to you. Today I want to expand on this topic, move away from talking about products and solutions, and discuss the business opportunities we are seeing emerge from this megatrend.

First, two statistics which seem to be quite telling:

Information compiled by LNS Research, in its eBook Smart Connected Operations: Capturing the Business Value of the Industrial IoT, found that 47 per cent of respondents to its Manufacturing Operations Management (MOM) online survey indicated that they did not expect to invest in IoT technologies in the “foreseeable future”. A further 19 per cent indicated that they did not expect to invest in IoT technologies in the next 12 months.

What is your reaction to these figures?

Frankly I’m not surprised. IIoT seems to bring with it the hype of something that will take a long time to adopt. In some cases I think this can be true. And while we are unclear on what time frame is meant by the term “foreseeable future” referenced above, I believe there are business opportunities that can be capitalised on now and in the medium term. IIoT is more prevalent than we imagine. There are examples and business practices that we often don’t even recognise as being enabled by IIoT – things like increasing industrial performance and augmenting operators are two of the opportunities which can make a difference to your business now.

Increased industrial performance

Using data to improve industrial performance by connecting things to each other – this is happening now. How is it happening? Through wireless technologies, low cost sensors and using advanced analytics. In practice, this is a decision support system for complex manufacturing operations. Let’s take the example of a mine; a mining operation is maybe 10 different mines extracting iron ore. It’s a complex network of a rail system going to a port and ships coming into the port to be loaded up with iron ore for shipment to customers. This is managed as one supply chain. What happens if a train breaks down? What does the operator do? Which orders does he cancel? Can he get the iron ore from another mine? What is the impact on throughput? What is the impact on profitability? Using advanced analytics (based on artificial intelligence) we can make a detailed model, an operator can input the parameters and in a very short amount of time (I mean a reduction from days to minutes) it will explain what he should do and why.

We’ve had “data mining” around for donkey’s years – but it was always after the fact – a forensic approach to past activities. The fundamental change this brings to business practices is to move it into real time, creating a forward looking decision making processes. This requires new ways of thinking and new business models – a seismic shift for some industries!

Augmenting operators

Keeping the example of helping our operator – we know that you need to operate in the most efficient manner possible, while at the same time striving to reach higher safety levels. We also know that in many of the industries we serve there are less and less skilled operators inside the plant. In many cases the demographics are not favourable – we talk about the “great crew change” as a highly skilled but aging workforce moves to retirement. There is often a huge chasm of experience between the old guard and the new people coming in. How do you bridge the gap and augment the workers you have to make them more efficient? In part, the answer is to put information at their fingertips. Younger employees are often digital natives, and comfortable in this environment, so it can be as simple as enabling their mobile phones to scan a QR code to get the information needed to solve the fault flashing on the process drive. It’s not leaving the plant floor and looking in the office cupboard for the operating manual. It also means things like wearables, remote operations and servicing. It’s about making the plant user-centric, not machine-centric.

To conclude, while we can’t deny that some branches of IIoT, like self organizing machines and assets and mass customization, can be considered a mid-to-long-term play due to their complexity, the need to establish new standards and the need to flatten automation hierarchies, there are business opportunities today that will hopefully inspire the 47 per cent of respondents who said they weren’t investing in IIoT in the foreseeable future.