This podcast explores how manufacturers can manage and utilise data to its full potential and use it to drive productivity.
BK: Welcome to HxGN RADIO. My name is Brian. Thank you for joining us today.
Data is sometimes described as the new gold in manufacturing. But as we continue to capture more and more data, how can we make sure that we are managing it and analysing it to its full potential? Let’s find out today by talking to an expert in statistics, here today, with Dr. Edgar Dietrich, the co-founder of Q-DAS, the Hexagon-owned statistical process control specialist. In today’s episode, we will be exploring the role of statistical analysis in Quality 4.0. Edgar, thank you for joining us.
ED: Thank you.
BK: Great to have you.
ED: Yes, I’m happy to be here.
BK: I’m excited to hear all about this. There’s a lot of information, I know, behind this, and I know it’s kind of a big deal now, you know the whole ‘big data’ idea, Internet of Things (IoT) and Industry 4.0, all of that. So what influence do these trends have on manufacturing production quality?
ED: You mentioned three different topics: big data, IoT and Industry 4.0. Now, the big data, is in everybody’s mouth in this world because we collect more and more data all of the time. In the production area, we had to talk about more smart, big data because it’s important that the data are structured, that we can run automatic analysis and such kind of things. About IoT, in the production I will say IoP, that means Internet of Production, is for me the Industry 4.0 idea. Industry 4.0 was announced five, six years ago in Germany and is the fourth industrial revolution. If it’s the revolution we will see in 10 or 15 years, but all the signals go in this direction because we have the sensors, you have the computing system, you have the networks and everything. So, that means we will have a big change. And this is also a big change for the quality, and for quality what it means is that it’s more and more going direct in the production, and you had to give answers about quality aspects to the machines, to the operators and so on.
BK: Well, tell us a little about digital thread.
ED: Again, to summarise this just in one sentence.
BK: Okay.
ED: The right information, to the right place, at the right time.
BK: That’s very important! Excellent.
ED: In this case, we are talking about different systems; and the system is today called a so-called cyber-physical system. That means such systems are able to receive information, evaluate information, and give results back. So this is more and more important for the future, especially in the production area and the metrology area. I can give you an example. If you produce, these days, a part with 25 characteristics, then it’s clear; if it’s produced and you had to measure the 25 characteristics, this is a fixed procedure. In the future, the part asks another system, which measurement process should I use and how many of the 25 characteristics must be measured based on the capability of the processes, and that means this reduced measurement time, measurement cycle, and, for sure, the cost.
BK: Now I know it takes a while for companies to move into the digitalisation process, it takes time to move everything over, it’s a lot of work, but what’s the progress that companies are making?
ED: This depends on the kind of industries.
BK: Sure.
ED: I would say in the mass production, we are going very fast forward, but this is a little bit different in the industries where have only small sample sizes, like in the aerospace industry, because the parts are more complex, and so it needs more time and takes longer.
BK: What about the importance of structured data?
ED: I mentioned smart data.
BK: Mm-hmm.
ED: If we store more and more data, and you mentioned in the data is a gold of the future, I say the gold is only in the structured data because if you store more and more data, the consequences is also more difficult to get the information out. Yeah. And if the data increase exponentially, it’s more and more complicated. But if you have the data in a structured way, then you can select the data, can evaluate the data, and can do this in an automatic way. And this is the key advantage that you get real-time answers.
BK: Absolutely, you’re right about that. You store more data, and then it just kind of gets piled up, and even if it’s in a digital sense, and you just kind of forget about it, it’s just too much, I don’t have time to find it. But this is great when it’s structured and you can go after it, get exactly what you need when you need it. That’s excellent. So, is the AQDEF, Advanced Quality Data Exchange Format, by the way, is this an industrial standard?
ED: AQDEF is the basis for structured quality data. That’s the basis. And I would say in the automotive industry, it is standard in the mean time because this was developed from the big OEMs like, General Motors, Ford, in Germany VW, BMW, Fiat Chrysler, all of them are involved, and they have annual meetings to take care for this data format. It’s not so well known outside of the automotive industry, but we are working that the AQDEF will be an ISO standard, and we send out this document and it’s approved now. That means we have the chance at maybe middle or end of next year this will be an ISO standard, and then it’s also outside of the automotive industry, automatically well known.
BK: Nice, nice. What’s the advantage of the AQDEF?
ED: Yeah, the standard is always an advantage because the problem is each measurement system, for example, has its own data output. So this is the same; you are talking English, I’m from Germany, and we cannot communicate if we have not the same language. This is not… And the same is for the data. And if the data in the same structure, each measurement system can communicate with the software, and we can make the evaluation. So this is the key advantage of AQDEF.
BK: Good, good. So, security of data, I know it’s under scrutiny. What can we do about all this?
ED: I will start from Germany; Germany has the strongest law about data security and the consequence is that the people in Germany are very sensitive about that. This is in my experience, different in other parts of the world, but it’s, for sure, for everybody an issue. And so, it’s a decision of the company if they have sensitive, very sensitive data or less sensitive data. So we have two options, the sensitive data, you must store in your private cloud, that’s no doubt. Other data you can bring in the public cloud, and then you have to deal with both situations. But for the sensitive data, no doubt, you must store it in the private cloud.
BK: Good to know. Q-DAS, well known for statistical analysis; how important are statistical evaluations for Hexagon customers?
ED: I would say, it’s easy, it’s trust.
BK: Okay.
ED: And maybe if you know the statement, don’t trust any statistics you didn’t force yourself, or I would say, don’t trust any stats you didn’t create yourself. This depends on a lot of influence factors on the evaluation. The reality is that the companies, all the companies, have guidelines how they evaluate their data. They describe this in documents and the Q-DAS software – this is the only one in the world can relate 100% of the requirements with the software. We can do this or the customer can do this on his own. That means, independently, if you make an evaluation in the U.S., in China, in Europe, you get the same results, and so the consequence is that you have validated results and can trust the data, and the results.
BK: Okay, excellent. Now what’s an example of how this would work in mass production?
ED: For us, we are talking about the automotive industry is the typical one – the engine plant or the gearbox plant. The plants produce normally per day, depending from the size, 800 engines, 1,200, more than 2,000. They have the [] Agile manufacturing systems. That means you have parallel machines and produce… have different operations on each machine. And the consequence is that the product goes through the lines and this is not fixed, cannot say at the beginning of the product is just on machine five, six and seven. Next time this is a completely different thing. In this case, we are able to measure the characteristics and to relate this to the process data. And so we can give the operator or the engineers two different kinds of views, the quality view and the process view, and this helps him to improve the processes and especially if you are able to make a correlation between quality characteristics and process parameter. This is the basis for optimisation.
BK: Now what about the smaller samples, like aerospace, for example?
ED: For small samples, most people say, okay, we cannot use statistics. But, especially if you have a complex part, like from the aerospace or from others, these parts have hundreds of characteristics, and some of them have the same specification. So that means, in this case, you can bring all the data with the same specifications together, and then, automatically you have more values and then you can use, also, the statistics in the same way as I described in the mass production.
BK: So does Hexagon have solutions for these tasks?
ED: For sure.
BK: Excellent.
ED: Yeah, we have a tool set, software tool set, to support our customers in all the quality-related data and information, and we call this tool set HxGN SMART Quality.
BK: Okay, so tell me about the main components.
ED: Before I explain the components, I would give a statement about, what is the situation in the companies. If we go to a customer, we cannot expect that he deletes all his software and use only ours. But, the reality is, he has systems. He has a product lifecycle management system, he has an ERP system, he has hundreds of gauges of quality sources in his production. That means we must adapt our software in this existing environment and in the existing IT systems and, therefore, we separated our tool set in three different topics. Number one is connectivity. That means we can connect all the gauges, all the quality sources in our database and store the data, as I mentioned before, in a structured way.
BK: Mm-hmm, mm-hmm.
ED: Number two is statistical analysis because based on this data we can run automatic statistics related with the requirements, with the guidelines of the customers and can give the report. And tools at number three is resource management, that means we can show the status of CMMs of the machines and all such kind of things and try to handle the workflow.
BK: Excellent. Now, what is the target of the workflow management?
ED: Yeah, I guess that’s a very important part.
BK: Absolutely, absolutely.
ED: Therefore, I mention this because in the production we have so many tasks. In a high automatic production, you must lead all these tasks. As an example, normally you start from a CAD drawing, and based on that you can create, with a CAM system, the programs for the controllers for the machines, or you can create also the measurement programs. The advantage is with this kind of workflow, if you have a change in the CAD drawing, then automatically you can change all the other steps and see the consequences. And this reduces the risk to forget something or of something going wrong.
BK: All right. Makes sense. So tell us a little about how you can also integrate quality information from suppliers.
ED: An example for this reality; 2016, or last year, I guess we had in the automotive industry more than 50 million recalls of cars. This cost a lot of money…
BK: Absolutely.
ED: And this damages the image, and the most defects come not from the OEMs, they come from their suppliers. Therefore, in the future, the OEMs will force their suppliers more and more to make their processes transparent. And the answer is AQDEF because if they store their quality data in AQDEF, we can read it, we can evaluate it, the OEM can read it, and so they see what’s going on also on the suppliers’ side, and this helps for sure to find or reduce the number of defects. In the meantime, we have several companies who use this concept, and they allow only to send the product from the supplier to the OEM if they agreed on that and know that they have only good products.
BK: Good, good. Excellent. Well, Edgar, this is fantastic, great information. Anything else you want to add before we wrap up?
ED: I think, at the moment, it’s going to be okay.
BK: Yeah. It’s great. You’ve covered a lot of information, which is fantastic. Okay, well, awesome, thank you very much for your time today, I really appreciate it. And, you can learn more about Q-DAS and statistical process control at HexagonMI.com. Check it out. And also be sure to tune in to more episodes on hxgnradio.com, or we are on iTunes, SoundCloud, or Stitcher Radio. Thanks so much for listening, and enjoy your day.
