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Industry 4.0, IoT, IIoT, Machine Learning, Deep Learning, Artificial Intelligence, Edge… over the last few years we have been bombarded with these terms and many more. So, what’s behind them? What is real and what is only hype? What sounds like a simple question turns out not to be so simple after all. One reason is that everyone has jumped into the IoT bandwagon. In many cases, what is being labeled as IoT now, is the same thing that just some time ago was called something else.
If you come from the industrial automation space, how is IoT different from SCADA? Or from a DCS system? With those two technologies, we have been connecting distributed assets for decades. What is different now? What has changed?
These are some of the questions I keep hearing during my conversations with our customers and partners around the world. Connecting assets is not new, AI and ML are not new, edge control is not new. So, what is different about IoT or more specifically, IIoT?
I will share some of my thoughts around these critical questions. But the true objective of this post is to hopefully start a conversation and hear what others in the industry think about this very important topic.
In my view, there are at least 4 key differences between IIoT and the solutions we have been using for decades to instrument, monitor and control industrial processes.
The cost of technology is no longer a barrier
It is true that ML and AI are not new, but if you wanted to deploy these technologies at the Edge just a few years ago you would have to setup a full server room. Many of the assets that could benefit from these technologies are in remote areas where it would have been an impossible proposition. Today you can buy an energy efficient industrial Edge Gateway for a few hundred dollars and run powerful machine learning models close to the asset. With the proliferation of platforms to deploy, manage and operate these models on a “pay as you go” plan, anyone can take advantage of these solutions without breaking the bank.
Cyber security awareness
The fact that many of the control networks around the world run on MODBUS tells us all we need to know about how cybersecurity was not a main concern for our industry. We relied on an “air gap” to keep our systems safe, and in many cases, that was the central feature of the security strategy on the control network. Those days are over now. Today’s products need to be secure. The system must be secure. Just as important, the procedures to operate the system must be secure as well.
Knowledge drain
We’ve been hearing for years that a crisis is around the corner due to the retirement of the Baby Boomers from the active workforce. Although I still believe that this is going to be a strain to our industry, the issue that has manifested itself now is not so much the existing workforce retiring but not enough new workers joining the industrial workforce. Especially in the Energy industry. The mix of the cyclical nature of our industry, together with the increasing social shift to a more environmental consciousness, has had a direct impact on young workers not choosing a career in the Oil & Gas industry.
Mix the two issues together and we have a perfect storm of knowledge drain in our industry. The result is the increased interest in Machine Learning and AI as a means to capture and automate the knowledge and experience from the existing workforce.
A focus shift towards efficiency to achieve profitability
In my view, this is what “Digital Transformation” is all about. I guess that is a much sexier way of saying “corporate culture shift supported by the implementation of technology to improve efficiencies and drive costs down to maximize business profitability”. In no other industry is this more important today than in the Oil & Gas industry. During the boom years, efficiency was not a key metric. Profitability was achieved just by producing more hydrocarbons so the industry was volume driven. Today, efficiency and cash flow are key metrics and companies that don’t focus on them are struggling to succeed.
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