You don’t need advanced degrees in computer science or engineering to take advantage of this evolution. 

“If you think Industry 4.0 is just about the technology, you couldn't be further from the truth,” Martin says. 

What it comes down to, he explains, is using data from your machines to make better decisions, faster. 

That sounds pretty straightforward. But Martin says that data collection is a first step many organizations still haven’t taken. Without a solid base of reliable data on, say, how often a piece of equipment is idle or down for maintenance, you don’t have a clear picture of how your machines, people and overall operations are running or where the bottlenecks are. Without that, you can’t make sound business decisions, including, perhaps, to automate or invest in new technology. 

Martin points to a client who did some basic data collection and was shocked to find all of their equipment was running below 50% capacity. That must have been a pretty striking discovery!

When they then looked at their operations, they discovered a process problem that was easy to address. 

If you’re new to all this, Martin suggests you start simply by gathering data, the foundation for Industry 4.0, which is the relationship between your cyber or computer assets, and your physical ones. 

There are many buzzwords in this space, and we define a handful of them at the end of this post, including advanced manufacturing, machine learning, AI, big data and more.  You may have heard of the Internet of Things? In our households, this may be a smart thermostat or door locks that we can control through apps on our phones. Well, the Industrial Internet of Things is the same idea, just in a factory setting. 

Again, Martin makes it easy to understand. He points to the example of a wireless sensor on a tank to measure fuel level. With this information, an operator could build a top-up alert process for when the fuel gets low.  

As you can see, you don’t need to jump in and buy a robot or automate your entire line right away. 

While the ultimate advanced manufacturing model is an autonomous factory that needs minimal human input, this isn’t realistic for most organizations. Martin talked about the Industry 4.0 Maturity Model, a continuum to measure where companies fall in six areas: technology, supply chain, operational excellence, data, manufacturing and people.  

So, along with sensors, hardware and software, Industry 4.0 requires leadership and strong human resources. It’s governance, it’s strategy, and, crucially, it’s change management. As Martin says, if you can’t get your team on board with the shift, “then you're not going to get that far.” 

And, of course, it’s about talent. We need more deep technical skills, and the roles range from engineers, data scientists, process designers, developers, business analysts, entrepreneurs, and more. Unlocking more talent and ensuring we have a steady supply is a priority. Last week’s podcast with two deans of computer science re-iterated they are on a growth plan. It is so exciting to see the threads connecting between different episodes. What is becoming clear is the genuine potential of these emerging trends and technologies to transform our economy. The future for Atlantic Canada is looking bright. 

Terms: 

IoT, or the Internet of Things: A network of physical objects embedded with sensors, software and other technologies so they can connect to and exchange data with other devices and systems over the Internet.

IIoT, or the Industrial Internet of Things: A network of industrial devices connected by communications technologies to create systems to monitor, collect, exchange and analyze data. These insights can then help drive smarter business decisions. 

Machine Learning: A form of AI that uses algorithms to “learn” from data. This way of processing data can quickly draw conclusions that might take humans months or years.  

Artificial Intelligence: Machines process different pieces of data to identify patterns earlier. For instance, AI may be used for predictive analytics for machine failure. 

Big Data/Data Analytics: A field that deals with data sets that are too large or complex to be dealt with by traditional software.

Advanced Manufacturing: Using automation for minimal human intervention. May include equipment, robotics, sensors, and tools such as artificial intelligence and machine learning to make better decisions. 

To listen to our conversation, click here to listen.

Here’s a peek at some of the highlights from this episode:

• [4:25]:  Martin explains why Industry 4.0 is not just about technology and what it actually means. 

• [9:54]: Martin unpacks some terms such as the Internet of Things (IoT), Industrial Internet of Things (IIoT), machine learning, AI, and advanced manufacturing. 

• [23:55]: We talk about the explosion of opportunities made possible by Big Data. 

• [25:45]: Martin describes the JDI Roundtable on Manufacturing Competitiveness and the maturity model of Industry 4.0, where New Brunswick manufacturers can see where they are on the continuum and where they could improve.

• [38:36]: Cathy talks about Digital Boost, a new TechImpact program that connects local tech companies and New Brunswick organizations to accelerate their digital transformation. 

• [40:00]: The show ends on one of Cathy’s favourite subjects: careers!