Centre Stage: Jared Evans
September 5, 2018 | By Kristina Urquhart
September 5, 2018 – We catch up with product manager Jared Evans to find out how much manufacturing execution systems (MES) have changed since MAJiK Systems, the company he co-founded, was last profiled in Manufacturing AUTOMATION.
MA: We last checked in with MAJiK in October 2016. What new technologies have you added to your offering?
JE: We now connect to about 90 per cent of PLCs out there in the market, which means that you can do native deployments in your factory without additional hardware or downtime or reprogramming of PLCs. We connect directly to whatever assets you have, pull the data, and then transform it into what’s needed to show you the monitoring and analytics that you want to view. That’s been really good for our customers because it allows them to add more assets, get a better view of their entire plant floor and fill gaps in their automation. We’ve hired more data scientists and research people and we’re deploying solutions for predictive maintenance and predictive quality, as well as dynamic optimization of production schedules based on real-time conditions on the plant floor.
We’ve streamlined our platform around three areas: monitor, analyze and optimize. “Monitor” is your basic production monitoring downtime, scrap, dashboards, that type of thing. “Analyze” goes into more depth so you can actually root-cause downtime and fault codes, as well as figure out the reasons that you’re having scrap on your lines and then [measure] that with process variables so you can start to see the correlation between, say, temperature and pressure spikes in an operation, and downtime occurring or increased scrap rates. A lot of our customers find that very useful for their continuous improvement officers or their maintenance engineers. Our “Optimize” level is getting into some very interesting stuff. We’ve hired some more data scientists and research people and we’re deploying solutions for predictive maintenance and predictive quality, as well as dynamic scheduling and optimization of production schedules based on real-time conditions on the plant floor.
MA: How have these improvements aligned with how you’ve seen MES evolve over the last few years?
JE: Going back to when we last spoke to MA – almost nobody was talking about Industry 4.0 and digital manufacturing as a concept, about getting information and analysis instantaneously to help make decisions in your operations. Now, people come up to us at trade shows and say, “Can you tell me about Industry 4.0?” or, “Where should I start?”
Our customers have reduced their downtime by over 50 per cent in some cases and increased production by 10 or 20 per cent in other cases. Those things translate directly to bottom-line revenue growth. That’s the real takeaway. I think the next three to five years are going to be explosive as manufacturers start coming on to these platforms, utilize the data and have easier tools to work with to acquire data, to analyze it, and then to optimize their production. I think there’s going to be a very big shift in the manufacturing industry because of that.
MA: Especially in Canada, where we’re seen as falling behind other markets in terms of companies adopting the technology.
JE: I’m hoping that some of the recent supercluster announcements as well as some of the other funding vehicles in Canada are going to help with that. Because you look at how Germany has their full-on Industrie 4.0 program – their universities collaborate with researchers, with industry, with manufacturers. China has its Made in 2025 program. There’s lots of stuff going on in the U.S. – the U.S. is willing to put that investment in because they have that first-to-the-top mentality.
MA: What’s the most common challenge your clients have when they come to you looking for a solution?
JE: We went through a bit of a shift as a company in our thinking on that. We always thought of data integration, cybersecurity and having meaningful data analysis as being very important – that they are the biggest hurdles for our customers. But it turns out it happens much earlier than that, almost before we’re even engaged in a lot of cases. I think the biggest thing is making sure they have a good use case and a strategy for getting a return on investment. And then also ensuring they have the employees capable of understanding and using the technology on their side. Those seem to be the two biggest. Part of the employee issue is that everyone in manufacturing is so busy. The top priority is to make sure that the lines keep running and that you are outputting product, so it doesn’t always leave a lot of time for new training or trying out new systems. We have worked really hard to ensure our platform is very simple to use and intuitive, and also services the right information at the right time for the customer. That helps them with that challenge of having their employees be effective.
For return on investment, I recommend a walk-before-you-run approach that involves doing pilots on key pieces of equipment or at a lead site and having short-term and long-term goals. It’s important because then you can spell out that road map of, “Here’s what we put in, here’s what we got out, here’s what we’re going to continue seeing in the future and this is why it makes sense to move forward with this.”
We’ve implemented a quick-start program for our customers where they can get up and running in less than a month, where they can have one or two pieces of equipment connected to our system and be able to set some goals with one of our field application engineers. The field application engineer will work with them to achieve those goals over the six months of the pilot before they present to their corporate or executive team what they found out from doing that pilot. For any manufacturer, you need to make sure you are getting the full use out of the system and have it become a part of your daily activity. Use the system to find root causes and optimize production before you make a big investment on rolling it out on a larger scale.
MA: What’s next for MAJiK?
JE: We’re really excited about what we’ve been doing with machine learning and artificial intelligence in terms of predicting downtime and scrap issues, as well as schedule optimization. We’re going to be doubling down on that for the next few years and building out a full program and innovation lab to help manufacturers capitalize on this. We started last year with Magna Karmax in Milton on a project that was partially funded through Ontario Centre for Excellence (OCE). It was all about pulling really detailed telemetry data off their stamping lines and then predicting when it would break down for different reasons. We want to provide that to many more customers, and we want to use the grants and researchers available through the government of Canada and the universities – especially University of Waterloo, University of Toronto, McMaster and also Conestoga College – to be able to bring more of these advanced analytics to market. We feel like we do a really good job of the dashboarding and traditional analytics but we see it as a real game changer for manufacturers as a whole if we can continue to develop more models for more types of manufacturing and work with key industry partners to provide those solutions.
A condensed version of this article originally appeared in the September 2018 issue of Manufacturing AUTOMATION.
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