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Can you better understand the challenges of Industry 4.0 by playing with toy cars? Mentor Graphics thinks so, which is why they set up a racecar track in their booth at productronica. Besides being fun to play with, Mentor Valor Division's Senior Marketing Development Manager Michael Ford explains why it's a perfect analogy for understanding Industry 4.0.
Barry Matties: Michael, tell us what's going on here.
Michael Ford: We wanted people to understand Industry 4.0. One way to illustrate the concept is with our car racing game. The cars that are racing around the track are self-contained automation. They can move left and right and turn around, accelerate, etc. They are analogous to the automated machines such as SMT that we see in production. Of course, the car by itself, where's it going to go? You need to be able to tell it what to do. Here, with this racing game, we have the racing going on where each of the cars is communicating with the racing intelligence app in real time, using Bluetooth. The whole of the racing logic can become very exciting. The cars each have their different personalities and compete against one other in the race.
In production, it's the same. We are taking information directly from the machines in real time, but instead of racing, we're actually trying to get work orders completed in the most efficient way to meet delivery deadlines. Illustrating Industry 4.0 is analogous to computerized racing; the controller of the race is that level of computerization that brings the intelligence to all of the individual processes.
We wanted people to get that Industry 4.0 is not something to be afraid of; it's not just a kind of marketing gimmick. Actually, we can show people what it really means by just playing the racing game.
Matties: And are they getting it?
Ford: Absolutely! It's a lot of fun. The cars normally try to avoid crashing into each other. Sometimes though, they get aggressive and they push each other off the track. It's just like the different and changing priorities that you see in manufacturing. You get a rush order coming in, and you have to meet the customer requirements. Things change as a consequence. It's quite a good illustration of how the whole thing works.
Matties: With big data, the challenge is not realizing that there's big data out there. The challenge, I think, is getting the data that you really need.
Ford: Exactly right. The data that we really need isn't so complicated. These cars, again, are a great example. They just need to know where they are on the track. Then the computerization takes over and provides the intelligence for the race. Automated SMT machines have PCBs to be made, and they have programs to be executed; but to meet the customer requirement today, what is the best way to get my work orders through this and every other process?
We, for example, have taken schedule data from customers where they have 30 days of scheduled production, put it through our Industry 4.0 finite planning solution, and it turns out that they reduce 10 days of that plan. At first, they didn't believe the result because their productivity levels were already 70 or 80%. They said, "How can you get 30% on top of that?" The problem was that their plan itself was inefficient. They were comparing what they were achieving against a flawed plan.
An effective plan has to follow the live customer demand. This is what Industry 4.0 is all about—to make an accurate, finite plan that has no risk of execution, and that's going to be able to make exactly what you want, when you want, in the way that you want it. You need the visibility from the machines. You need to understand what the goal is, in terms of delivery to the customer, and you need to know the resources and materials available. This is about what Industry 4.0 means.
Matties: It's all driven from the customer. That's the point.
Ford: Yeah, exactly right. We saw the example of Industry 4.0, I think, in terms of the soda cans being made and how they were just filled and labeled as the customers ordered them. That's the concept that we need to bring to surface mount manufacturing. The problem is, as the level of complexity increases, as we want to do more changes in surface mount, the productivity goes down big time because we have to change machines over more often. Even if we start to group products together from the engineering perspective, nobody's to say that that's the group of products that was wanted by the customer, so the approach was completely wrong; it actually ends up compromising machine efficiency.
What we are doing instead is sequencing the order of production at the same time as being able to optimize by the grouping of products for the machines. This is the kind of classic Industry 4.0 computerization that exists over the top of automated manufacturing and enables it to be efficient.