In reflecting around the concepts of industry 4.0, you’re thinking about where production systems have come from, and the changes we’ve made to improve throughput and efficiency. It starts off with robotic process automation, where you take some repetitive process and you apply some mechanization to improve the throughput rate of that step.
The interesting thing about this process is that it doesn’t actually make the quality much better, it does certainly make it less variable. But if you’re going to produce 1000 defects with robotic process automation, you’re gonna make 10,000 defects in about the same amount of time. So great for throughput, but not so good for improving your quality. Then we’ve started shifting these paradigms to become more data-led in our production systems, where we draw in production information from our data systems like historians and so on – to inform the analysis that we’re doing on this process.
We come up with better control limits and, ultimately, this just makes the process automation job a little bit harder, because you have to control the process within these finite bounds. It makes the production system a little bit more expensive, but it does have the upside of reducing the number of quality defects that are experienced.
Looking at those two and thinking about where it goes next, we’ve got to think about how we make these adaptive changes as quickly as possible, to understand as much of the system from start to finish as possible. Such that we can improve the final result. That’s the trick because that last step is what really matters the most. And you’ve got to make your production system flexible, to tolerate as much variance in upstream processes as possible, without compromising on the final quality that’s produced at the end result.
So what industry 4.0 does is solves that quality problem. Given the variance of supply of materials, be it raw materials, the input of a foundry through to sort of semi-completed goods from tier-one automotive component manufacturers. You’ve got to take all of that and produce a perfect motor vehicle at the end of the line or a perfect widget – given some of these changes. So where we’re going with industry 4.0 is much more flexible, almost autonomous manufacturing systems that are correcting in as quick as time as possible, even ahead of real-time, and in places to produce the best quality at the lowest cost.
The journey to autonomous manufacturing is quite complicated – it’s not trivial about just turning it on. But in that step, we’re sitting in the guidance space where we’re able to make corrective suggestions to your production team to improve your quality and what this looks like is; go and improve process X, value Y from this value into this range.
We try to make that range as wide as possible to accommodate as much variance in your process as we observe. In that guidance, we make your production system better for two reasons:
I think most plants today are drawing data from the production systems to their engineering or production teams. Folks are following their own inquisitiveness looking through the data to try and come up with some kind of optimization improvement or system improvement. I don’t think that there is a holistic use of data from the start of a process to the end of the process to do overall system improvement. There are two reasons for this:
I think that solving this requires two things:
The really exciting thing about this paradigm of autonomous manufacturing is that it’s specifically designed to work with the existing process. The only thing that changes is becoming more data-led: Use production data and quality data to make the small set of process changes that result in improved quality.
The journey to autonomous manufacturing is, in fact, predicated on having an existing production system there. Although you can work in Greenfield spaces, and then good ways of doing that. But you’ve got a good production system, you’ve just got to make sure that you get enough data out of it to describe the process and then we can have a real substantial impact on your system.
Reactive manufacturing works where you have a quality failure at the end, and you go and make a set of reactive changes to your system. In order to correct that error. There are two things about this:
A prescriptive system is very different. So what we’re saying is to make the small change now to avoid a future defect failure, a future quality failure. So it’s a small set of corrective actions in anticipation of a quality cost that is not ever realized. If you act on prescriptions, you never incur that cost of non-quality throughout the rest of your production line. Whereas reactive you wait for the quality failure, and all those costs, and then you spend extra money in your system to go and correct for those failures.
The three best things that you can do to your production system now to prepare you for autonomous manufacturing are:
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