Machine and Process Management

Capturing and maintaining knowledge is becoming a critical challenge

Welcome to our four-part technology blog series. I will be covering digital transformation, artificial intelligence, machine-learning and edge computing.

Running artificial intelligence workloads at the industrial edge

Over the last eighteen months, we all have lived through a disruptive time, not only in our personal lives, but also in businesses worldwide.

In many cases, we had to switch to remote operations almost overnight. And with that, sometimes in a very public and painful way, we had to learn about the challenges of keeping our operations secure from cyber-attacks and cyber-threads.

Some of our industrial customers have also had to deal with increased scarcity of knowledge in the market, as more experienced operators and engineers leave the workforce. To make things worse, fewer new workers are joining some industrial segments, like Oil and Gas.

As if this was all not bad enough, we are also living in constant uncertainty of what will happen next. Businesses must react quickly to changing market conditions, supply chain issues, and ever-changing socio-economic conditions.

All of this is happening while in the middle of an ever-accelerating digital transformation push by individuals, corporations, and governments.

Digital Transformation trends in industrial automation

Let’s zoom in on the current digital transformation trends within the industrial automation space.

First, and the most important thing to understand, is that digital transformation is not just about technology. Innovation is happening all around us. It impacts the way we do business, how we interact with each other, and, yes, the technology we use. So digital transformation is about people, processes, and technology.

The IT space has gone through a considerable amount of transformation over the last 10 to 20 years. As a result, today, many organizations don’t own their IT infrastructure but pay to access the services they need on an “on-demand” basis. We are starting to see a similar trend in the OT space.

The emergence of “The Cloud” has also led to an explosion of innovation and new startups. Just think about it; all you need to access infinite processing power and storage these days is just a credit card. The barrier of entry to the industrial automation space is no longer equal to the size of the initial investment required, but the understanding of the processes and a clear vision to realize the value that was previously inaccessible due to cost or other technology limitations.

Another change we are starting to see is the lifecycle expectations around the hardware solutions like RTUs or PLCs. We built systems that were designed to last 15 to 20 years in the field in the past. Pretty much an install-and-forget approach. The problem with this approach is that technology is changing so fast that the controller we install today is obsolete in terms of processing and storage capabilities, just five years later or less.

Today, the decoupling of hardware and software is required to allow for faster hardware lifecycles that enable regular expansion of capabilities without having to re-design and re-commission the control system every time.

With this new speed in innovation and change, no single company can go it alone anymore. An ecosystem of partnerships between technology vendors, system integrators, and customers are required to ensure maximum value.

IT/OT convergence is not a new concept, but we need to consider it more than just technology. If we look back at the radical transformation that the IT space has gone through, we can get clues as to what to expect in the OT space in the coming years.

Watch for my next blog, coming soon, on the renaissance of Artificial Intelligence. In the meantime, read more about AI in our blog selection.


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