News in the Channel - issue #11

PROFILE: ONROM

in because there's a problem and the line is stopping. “Ten years ago this optical machine was considered very intelligent because it could check a whole board with thousands of pads in a few seconds, but the user interface is poor because it only shows the result of the last board, it doesn't show any history, so when the process engineer comes around again, the only thing he can see is the result of the last board.” Starting the journey Given a scenario of a machine that can’t show engineers patterns, an easier option on paper would have been to invest in a new device that could highlight where consistent faults were occurring. Instead, Omron added a second screen that could show a list of quality checks, colour- coded to red yellow and green to show how the machine is performing, along with a heat map of the boards to demonstrate where products are failing. This end game is naturally what a lot of factory owners will be after, but while finding these problems can be a saving grace, starting the journey is often the biggest hurdle. “Of course, every factory wants to optimise the uptime of the factory, the quality of the products, maximise the resources, and avoid waste but the primary target is always to first be predictable,” Tim says. “In the process of becoming more predictable, it means you also need to improve things. A part of that solution can be to digitise the information that is generated

on the factory floor and to have digital information available. With that information, businesses can get insight. “That is the first step: what do you want to achieve? And how can you get insight into what's going on? Only after that, you might be able to share recommendations, from human to human or in the next step, maybe even automated recommendations.” The right focus Underlying the tour was a focus on data. Without the necessary information, the right decision won’t be made, be it by a human or machine. Tim says this is a key to any journey towards automation. “What I say to customers is we can help you but we cannot start with something like predictive maintenance. We have to go two steps back and start looking at the data that's coming out of the machine together. “From there we can see if there is enough data to start a journey together, and whether there are motivated people available within the organisation who understand what a successful project looks like. This is because adding data science or AI to a factory doesn't work without involving the people who understand the factory. “From there we will move to the next phase, recording data and observing what the machine is doing before diving into predictive maintenance. So as you can see, once businesses start to pull out meaningful data and present it in the correct way, the floor workers can use it to make the factory run more efficiently.”

A part of that solution can be to digitise the information that is generated on the factory floor and to have digital information available. “

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