Episode Transcript
SD: Welcome everyone to this month’s episode of our Enabling Automation podcast, where we bring expertise from across the ATS group to talk about relevant topics for the automation industry as a whole. This is episode six, which is “Is automation suitable for your business?” I’ll be your host today. My name is Simon Drexler. I’ve been fortunate to be in the automation industry for about 15 years in a variety of different roles, as well as with a variety of different sized companies, both large and small. Doing automation implementation as well as automation, purchasing and technology development. We’re very fortunate today we’re actually being joined by one of the executive leaders here at the ATS Group, Heinrich. And Heinrich, I’d like to turn the mic over to you if you can give an introduction to yourself for our listeners.
HS: Yeah, thank you Simon, for the introduction, I’m Heinrich Seilemann. I’m the president of the IWK Group. I’m educated as a process engineer, and it was my PhD on this one. I worked in different industries many years in automotive, in the process industry and also in the machinery industry where I am today with ATS.
SD: That’s great, Heinrich thank you so much. And what a great background to be able to talk to our listeners about, you know, the suitability of technology and automation for the business. And so why don’t we dive right in? You know, I’m a business operator and I’m listening to this podcast. Where do I start when I’m evaluating if automation is the right choice for me?
HS: Yeah, it’s a good question. It’s also a question who is raising it is it an operator, is it a purchaser, is it a process owner? But let’s look a little bit backwards when we say automation or robotics we’re really starting, I think, at the beginning I think the repetitive work that using the skills for the worker for more complex processes I think was a starting point to say, okay, let’s think about automation to do repetitive work. I think this was probably a starting point when we are also speaking about how we can bring good skills to operators for a different way of work. In other businesses like also in automotive, the elimination of heavy load work was also a question because health and safety was always an important role in the businesses. When you think about, okay, repetitive work, elimination of heavy load work, this is also leading then for sure to a higher output because this kind of work could be accelerated. This is maybe a more from the work environment. And if you’re thinking about, for example, quality questions, it could be also from the from the process owner that is saying, how can I eliminate human errors? If you have repetitive work, if you have heavy load work, or if you have maybe complex work which you can put in automation, then it’s resulting in higher quality and eliminating this is also a big move into reducing the labor cost. Which means resulting in higher profitability and everything what you do in automation is easier to record. It means how can I record the human behavior in front of a process, in front of machine?
HS: But if I have a robot with movement, automation where we have sensors and activators, this data I can record and check also for traceability in case I have maybe a batch issue for a certain batch. So I think there are a lot of things from different aspects from operator perspective, from the process owner perspective, from health and safety perspective. Over time, what we see is automation is ongoing more and more and today the focus is for sure also on optimizing the profitability of the business.
SD: I think, well first and foremost, you touched on a number of key topics there and I think that optimization piece is something that’s highly relevant today. You know, talking about output and value add for the staff that we do have inside the facility becomes a key decision criteria for the business leaders and business operators because of the labor market that we’re experiencing at present. And so you talked about, you know, starting to really focus in on repetitive jobs, focusing on dangerous jobs or heavy lifting jobs. So that we can take that human capital and put that into something more valuable, drive more throughput. Have you seen a recent example of a company who’s done that in a in a unique way, an interesting way, something that’s maybe nonstandard to, you know, the traditional approach to automation.
HS: I would say the experience I had was in automotive because for sure there is a certain mass production behind and you have also heavy lifting work like the chassis or you have the engine parts for some big truck areas. There I saw that the robots and the automation is really helping in several directions. As you mentioned, preventing dangerous operations, heavy lifting and also the precision. If you if you think about welding technologies or placing heavy parts to another part. I saw this in several plants in the automotive industry. In the machinery building industry, that’s more the case if you have also repetitive machines. That means, if I have only a batch size of one, it’s difficult, although everybody wants to have also batch size one. But if you’re in a milling and drilling business, for example, you have machines which you can build in a role or injection molding machines. They have much more repeating machinery designs. When we are now looking at our business, we focusing on our build processes more as a human side, although we have protected lines, when we have seen the complete line approach from the customer’s perspective, we’re using robotics for, for lifting boxes, for cartoning, for making it offline easier to handle. That is where we see the robotics supply. We have also developed a complete new machinery called Flex Line. Where the task was to eliminate change overtime from 2 hours to 15 minutes. And the only way to do this was to use automation. It means we have today developed a system where only one person can do a complete change over in 12 minutes, including a product change, for example, in the toothpaste industry for one toothpaste to another, including different tube designs in terms of diameter, lengths, also capping design and including also different cartons for one tube, for five tubes, for three tubes, two tubes and so on.
HS: So it means all this if you do this in a manual operation, it takes 2 hours, with the robotics and automation we have in place we can repeatedly do this in 12 to 15 minutes. That is a big, big win for our customer because he is gaining nearly two hours more available production time. And this is at the end profitability.
SD: That’s incredible Heinrich, and I think it’s such a good illustration of the advancements in technology, the advancements in automation and how that can provide a benefit to the operator who’s looking at evaluating if automation is for them. In addition to that, I find your approach to evaluating automation quite refreshing because it’s really people focused. You’re starting at, you know, how do we take the tasks away from the operators that can be easily automated so that we can help them do something else, and it’s a different approach than you hear and it’s also part of one of the myths that commonly exist is that that automation is taking people’s jobs or something like that. And that’s rarely the case. We’re looking for more output. We’re looking to automate those repetitive jobs, those dangerous jobs, so that we can drive better business performance.
HS: You are absolutely right. That is one part of the equation. And what we also observe is that our equipment is becoming more and more complex. That means that the skills required to run this equipment is, I think, also now on a higher level than it was required before. On the other end, our customers tell us that it’s not so easy to find skilled workers or even that engineers would like to work in production. So they like to have, say more an office job. And I can understand this. There’s no question about this. This is not only a phenomenon in the industrialized countries like North America, Europe, or others. But also in all other countries where our machinery is delivered to. It means at the end of the day, the automation and the robotics we’re using is also helping the production in the sense that they are not finding the skilled people and the machine can take over the complex processes. Then we see that the labor is reduced and the people can focus in maybe on more on the data information, do we have to adjust something on the processes? And this is typically also when engineering work which is done, but this is one of the feedbacks we receive to find highly skilled workers who would like to work in operation and production is more tricky to find on the market.
SD: I use the analogy I normally use is when computers came to market and the concern was that accounting people would be automated out of a job. But the transition of that whole entire industry became, you know, higher value added operations and data analytics and business operations. And it just changed the work it didn’t replace the work.
HS: That’s a very good comment. It does not mean that people are eliminated. I think there’s only a different skill set of people required. And people will have a different work in the factory. Besides the fact that it’s not easy to find people who would like to work in the company, in production, that is really, the two sides of the coin how to work with this complex equipment and how to find the people who have the skill set to work with it. Coming back to your comment about accounting and computer, it’s an absolutely right view on this. There’s still enough work around to set up the process, to set the environment so that a machine can also work in the automation mode because if the environment is not prepared for it, the machine won’t work.
SD: We’ve touched on complexity a couple of times, and it’s complexity of process, complexity of set up, the complexity of the product driving quality KPIs. And I think that’s a really interesting area for evaluating when to automate or if automation is right for you. Have you seen an interesting example in the last couple of years around, you know, process complexity for a product itself, driving the need for automation, you know, something that has exceeded human capability on the production floor and driving a requirement for automation.
HS: In our case, I would not say that the product itself has changed because working typically in the pharmaceutical or in the cosmetic personal care business where you have a cream, a viscus product. So it means in the sense of the importance a sellable good it was a recipe of the cream of the product itself. What we see is that the complexity comes more from the market itself. It means the demand to our customers is to have some very, very interesting packaging for marketing purposes. So if you maybe if a toothpaste for Valentine’s Day, if you have toothpaste for Saint Nicholas, or maybe even have toothpaste for Independence Day. What I mean is there is this change of the model is very rapidly and then international also different on Halloween you have a black toothpaste for example, and this means they require the marketing changes and also depending on the consumer it could be a child, you have a smaller tube, could be an older person, you have a bigger tube, it could be a family, it has a big tube. So it means the variation in terms of market demand has been increased significantly. If you have before only, say, one tube but you produce it, I don’t know, seven hundred times per minute, that was 10, 15 years ago.
HS: Today, you have small charges where you even don’t know, at the beginning of the day, how much of this to produce, because from the ERP system, it comes from the market. Okay, today I need I don’t know, 10,000 tubes of this one and 20,000 of this one. So that means the MES system of the facility of the production of our customers really pushes the direction what to produce in which time frame. And this means that the complexity is coming from in our case, from the market, not necessarily from the product itself.
SD: And Heinrich, such a good example again and I know our listeners can’t see me but I’m just smiling over here because we often don’t think about how things are produced. Even everyday household goods, you never really think about how they end up in your drawer at home. And we as a marketplace, we’ve become a very demanding bunch. And we want things exactly as we want them, exactly when we want them. And you don’t think about the complexity that that drives at the manufacturer. And that’s a great example of that around the amount of variation that now exists on the manufacturing floor as a business operator to satisfy market demands, drives complexity to our processes, drive complexity in our ERP system, the way that we manage things, which ultimately manifests itself in the potential need for automation.
HS: You are absolutely right. And another trend we should not forget is the sustainability trend. That means how can I use my energy and my product to the to the best extent. How can I eliminate waste? How can I eliminate quality spills? How can I eliminate anything which is not moving to the market? And with the automation, as you mentioned, as I mentioned at the beginning, we have a higher quality level. That means it helps already for productivity. With the automation, we also eliminating waste in terms of set the process is set up in a way that’s, for example from the original product, with a product change only minimum, is not usable for the market because there’s always something that we could eliminate significantly also for the primary product the change the waste in this context because you need also to comply against the hygiene standards you need. So everything having said this the other demand is they are big supermarket chain. They are big say companies which are selling online. I don’t want to call up any brand names, but I think everybody knows who I mean in many North America and Europe. And it’s interesting that our direct customers are impacted heavily by this because they said, for example, before you could sell the package of three toothpaste tubes in single packaging wrapped around with plastic. Today, because of sustainability, they want to have three tubes in one package. One tube is flipped so that you can eliminate space. There is no plastic around anymore. That means the overall packaging size is smaller. They save cardboard. They save plastic. So that means at the end we also supporting the sustainability aspect of the consumer interest and also offsetting the big supermarket and online seller.
SD: That’s such a good example of how technology can help us continue to iterate, you know, drive manufacturers, operators to be better and drive into that sustainability, reduce packaging sizes, be more efficient, which, you know, is something we’re all trying to do and strive to do every day. The final thing that you touched on in our original question, Heinrich, was traceability and recordability of data and so if I’m a listener, I’m listening today, I’m trying to evaluate if automation is right for me. Can you illustrate to them what some of the benefits of data that comes from the automation system are?
HS: I’ll give an example, if you were drilling holes. It means at the end, if you do it on your own, you will drill the holes and you do make it. If a system would do it with automation, the system will record, maybe the energy consumption as an example, the depths of the hole and so on because there’s movement. With your hand, you have a certain feeling of the movement, but the system has a good measurement for movement. So you will always have a repeatability of this one. When you will see, then if you have a driller and you need more energy, and you have maybe limits, when you do it as a as a human, you push a button a little bit more so you have more energy on your driller and this system say, okay, by the way, I’m using more energy. Maybe I have to sharpen the drill or something with my motor. So it means, the data is, first of all for the quality recording so that you have repeatability in your quality. But secondly, also very important that you see deviation to your standard process and deviation to your standard process can drive through much easier to preventive maintenance or predictive maintenance items so that when you do this in advance, the machine will not have a breakdown. You will make your service and it means the breakdown is predictable. So you need maybe 10 minutes to exchange something like a tool. And this is much better than no data, no information and deviations of process, and at the end of the day, there’s really a breakdown in the machinery. So having said this, the data helping you, first of all, on the quality. And second, which also important to prevent any shutdowns which reduces your productivity.
SD: And ultimately, that data really helps them with scaling as well. Am I right? Because you’re starting to really standardize on those processes. You’re starting to understand and quantify the steps of your value chain, whatever that that may be, assembly, packaging or what have you. Is that a fair statement?
HS: That is absolutely right.
SD: And so traceability and data, it’s become a quite interesting topic in the automation world for a couple of different reasons. You know, we’re automating more and more things that are used in personal care. That come in contact with people, and there’s a requirement associated with that. And I think that requirement might be new to some of our listeners. Do you see that in the toothpaste world? Sorry, you mentioned toothpaste as an example. Do you see that in the personal care world?
HS: I think we see it in our business. One of the issues is that really see the skill set of the workers. So recording data have traceability is also helpful to create a code, a smart machine environment. And so over time we have a learning experience when you have the right artificial intelligence implemented that the system knows even with the interaction of the operator, that here is the issue and I have to solve it. But the system learns in interaction with the operator. That means we starting collecting data and connect this with a knowledge base of an operator. And this will lead to a safe, autonomous where the skill set of the operator is transferred more and more, also into the automation or in the delivery of the machine. And so operator can also focus on other complex processes and has not to take care on maybe some standards or some lower complex processes. That does not mean that the operator will be eliminated. It’s more that you moving the operator on a on another skillset level because the machine can do more of the standard work or less complex work and of course adjust itself always in a way that even if there’s a deviation in some signals to the standard that the machine works always in the best operation mode without fail.
SD: That’s the second piece of why data is so important. It’s almost coming back to something you said right from the very beginning, taking that repetitive work and moving that into the realm of artificial intelligence, big data processing, and it’s that next phase of we’ve got robots on the floor taking some of the repetitive strain. Well now, if there’s some repetitive analysis or some repetitive deviations that can be identified by a computer and not a human being, now all of a sudden we’re better for that as well, and driving more throughput and driving more value.
HS: Absolutely.
SD: If I’m looking to evaluate, I’m at the start of the process. I don’t have any pieces of automation. And, you know, often times, at least a lot of the time in my background we have people come in and we say we want to automate everything, you know, we want we want big data. We want industry 4.0. We want to drive all of this value. If the appeal of traceability, deviation, tracking, having that data to have a more intelligent process in my business, where would you tell people to start? How do they evaluate if that’s for them?
HS: I think the first the first step is to collect the data and make data analysis out of it. It’s amazing. Don’t use always the experience engineers. Sometimes it’s good to use a statistical person because these people have a completely different view on data. When you experience to try to always try to get a conclusion out of it because of experience. But some people who have more of the data aspect, the statistical aspect in mind, they look in different, they see clusters, they see trends in a different way we do. So it means, the skills that if you’re doing this, it’s a different one. I mean, just on the starting point. Secondly, you need the data engineer who’s really into actually the data analyst to say, okay, here’s my observation, how do you think about this? And then I think it’s really coming along. You need a different skill set when you have the data collected for the interpretation and to the conclusions, because otherwise you can also bring completely different conclusion to it, which doesn’t help you. So it’s from my perspective, we need to change also our own way of using the data and we have to change our way how to make the conclusion out of it, because otherwise we also do our failures because only because it was working ten years ago so now we have the data so it’s the same as ten years ago. Now it’s a new world with data. It’s a completely different world also in the machinery building business.
SD: Yea, I couldn’t agree with you more and it just speaks to the advancements of technology, the advancements of approaches and like how we continue to push forward as an automation industry as well.
SD: Heinrich, thank you very much for joining us today, sharing your background, your experience, your insights with our listeners around the topic of where do we start if we’re evaluating if automation is right for us. Before we close today, is there any closing thoughts that you had for those listening to the podcast today?
HS: Yeah, maybe. First of all, thank you, Simon. That was really interesting. A lot of fun talking with you. Maybe my last statement is, data is the new gold.
SD: I think that’s such a fantastic way to close the topic because I couldn’t agree with you more. The more data we have, the more understanding we have, the more understanding we have, the more focused we can be on how to get better.
HS: Absolutely, you’re right.
SD: So to our listeners of our Enabling Automation podcast, thank you for joining us today. I sincerely hope that our discussion is helpful in getting you started on the right path to automation, and I would encourage you to join us next month for our seventh episode, which dives into the resources that are required inside of your business to support implementation of automation. Thanks again for joining us today.
