Digital textile production and the impact of COVID19 on market developments


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What is the most efficient way to bring an employee in line with a machine and help them perform at their best away from it? The magic word is digitization. It brings things to light that remain hidden from the analog: It ensures that an employee sits in the right place based on empirical data and performs the work step that he or she is best at.

In the 31st episode of the Industrial IoT Use Case Podcast, host Madeleine Mickeleit asks Joachim Hensch from HUGO BOSS how Industry 4.0 has arrived in the apparel industry and what makes this industry stand out.

Podcast episode summary

In the apparel industry, the focus is more on employees than on machines – that’s the tenor of this podcast episode. The “human resource” should be treated optimally in order to let it act optimally in reverse. Smart employee deployment and workforce optimization are therefore considered the be-all and end-all and determine success. In 2015, HUGO BOSS started creating digital twins – of processes, machines and their employees. The goal: to map more complexity, become more flexible, and gain in quality and profitability. In this context, each employee has received a tablet: “The tablet is the screen to the world for the employee – for himself and also for the whole work process”.

On the one hand, digitization ensures the necessary synchronization between man and machine in manufacturing, and on the other, it provides valuable data on employee performance. This enables them to digitally track and analyze their work performance completely independently. They become the manager of their own performance and also their salary. Because: The apparel industry is very much bonus-driven, output and quality determine the employee’s earnings.

Even in the advancing 21st century, however, there are still work steps in the textile industry that are almost entirely manual – the more individual, the more manual. The insertion of sleeves in the production of jackets, for example, is one of the most complicated work operations and has hardly changed since he began his tailoring apprenticeship in the mid-1980s, notes Joachim Hensch from HUGO BOSS. In cutting, on the other hand, the degree of automation is the highest, as this process is the most standardized.

Exactly how textile manufacturing takes place on a production floor, what employees have to say about tracking their work performance, the role of data protection and the benefits of performance data are some of the other topics covered in this exciting podcast episode.

Podcast interview

Hi Joachim, great to have you on the Industrial IoT Podcast. Can you briefly tell our listeners who you are and what you’ve done so far?


Hello to all listeners, my name is Joachim Hensch. I started a tailoring apprenticeship in 1984 because I always wanted to look behind the curtain. I always wanted to know how things worked. That’s why I started with a men’s tailoring apprenticeship and spent quite a bit of time in the tailor shop. I really sat on the table and sewed suits by hand for hours. A few years later, after my master craftsman’s examination, I went into industry. Actually, only for a short period, because I wanted to learn how to manufacture industrially. But that fascinated me so much that I got stuck there. I joined HUGO BOSS in 1995, although I never wanted to work for a big company. I ended up there anyway and have worked there for the last 25 years – primarily in technical product development, i.e. the conversion from design to production capability. For the last five years, I have been in Izmir, where I was managing director of the largest HUGO BOSS production facility with around 4,000 employees. There, I learned an extremely large amount about production, but above all about how to implement Industry 4.0 in the apparel industry or how to make it viable – in other words, how to take it from PowerPoint to the street, so to speak.


Many listeners may not know such a factory from the inside. Can you give us a visual picture of what a classic production looks like on location and what the challenges are? 


It must be said that the apparel industry as a whole is very labor heavy. That means it has a very, very high percentage of people, whereas in other industries you have a lot more machines. If you look at photos or videos from the automotive, pharmaceutical or food industries, for example, you always see an incredible number of machines and the products rattle through very quickly. In the apparel environment, it’s not like that at all. The machine burr may have changed only minimally in the last 50 years, so it hasn’t really gotten better, but it has become more modern. You have to imagine such a production like this: There is typically a large hall. At the beginning you have the receiving area with raw materials, so big racks with all the fabrics, buttons, zippers and yarns. After that comes automatic cutting with cutters. There are long tables on which the fabric is rolled out and then it goes into cutting. From cutting it then runs into a distribution. The apparel industry is also structured to manufacture components and then assemble the components. A bit similar to the automotive industry. So there’s a line where people sit and sew only the sleeves together, or only sew the front part or pockets. Then things are combined more and more and ironed at the end. So first comes the big production line, then everything is ironed, and then it’s off to quality assurance. It is checked whether everything is in order or possibly threads are cut again. Then it goes to a warehouse and from there to the world. In Izmir, there were two halls, each as large as two soccer fields because of the large number of people. 


Was this only a HUGO BOSS production or did several brands produce something there?


Manufacturing there has also done some work for other brands. In the clothing industry, it always has to do with the product and the category for which you manufacture. Assuming now that we are talking about HUGO BOSS, there are of course other brands that produce at a similar level of complexity – for example, Ralph Lauren or Armani, or any number of other brands that you could name when it comes to the product. Thus, these production cycles fit together and you can also produce such different brands in one production. 


How many people work in such a hall?


There are about 3,500 people in production in total, divided into two shifts. That means you have 700 to 800 people in a hall like that. That’s a lot of people. If you walk through there and say good morning to everyone, you’re going to be busy for quite a while. It was something I did a lot because I liked to be on the production floor a lot. So productions are typically divided into cells or into small groups. These groups are usually between 15 to 25 people in size. This has something to do with how much a first line manager can really oversee in production. How many people he can really support, train or mentor. And then it also has something to do with the number of work operations. For example, a shirt has fewer operations than a jacket or trousers. That’s why sometimes you can bundle groups together. This means a Shirt group” can also be thirty or forty people in size. In the women’s world it is quite different: the products that are produced there are much more complicated. It may happen that a group that sews dresses is only 15 people because the operations are too complicated and take so long. 


In other words, there is a first line manager and then individual groups of employees who manufacture shirts or parts of certain shirts, right?


Exactly. You always have these groups and these group leaders called supervisors. The organization of such a production is actually always the same: You have someone in the back who takes care of the whole cutting area, i.e. everything that has to do with cutting and preparation. This usually includes the warehouse, i.e. the outer fabric. And then you have Sewing division, which is responsible for everything that has to do with sewing. That’s where everything splits into the individual components. And then I have the ironing and finishing divisons, for example, sewing on buttons. Then comes quality assurance, normal quality control, final inspection, and then it’s off to the warehouse. That in itself is the loneliest job in manufacturing. There are five employees running around between 5,000 and 10,000 parts. 


What kind of machines do you find in these process stages – are they sewing machines, cutting machines? How do you have to imagine such an infrastructure on site?


It can be said that at the front of Cutting everything is in long lines. You have to think of it as looking at a table that is 2m wide but 20m long. On this table all the outer fabrics are rolled out in appropriate layers. For example, if I have five size 52 jackets to sew, that’s 5 layers of fabric. Individual jobs are placed there, which are then transported on the table to the cutter. This is a cutting robot that works with vacuum and a knife that moves up and down very quickly. It then cuts out the pattern pieces. At the end of this whole road are people who take out these individual cut pieces and label them so that they can all be brought together again afterwards. Then they are distributed and assigned to the individual product lines. This is where it gets really cramped now. It’s all about transport speed and transport motion. Here I have little transport distance between the individual work operations and operations are combined with each other. The layout can be such that an employee sits in a 90-degree situation, for example, and is always turning back and forth. He does something on one side, then the machine works by itself, makes holes, for example, and in the time that this machine is doing that, this employee is then already doing something else and turns around. This means that the time is quite tight and very well timed. It then goes into the ironing at the back. In a U-shape, there are loud machines that iron individual components of the part. The idea that someone sits there and irons a whole part does not exist in practice. There is someone who irons only the sleeves, the next irons only the torso, the next only the collar. And at the end of this process, there’s a finish ironer, who then really stands there with his iron in a bit of a traditional way. He finishes everything again and checks that there are really no wrinkles at all, and then it goes to quality assurance. 


This is probably also a newer Factory from HUGO BOSS, right? Is the level of automation in this quality segment always that high, that machines like cutting robots are used?


The degree of automation in cutting is actually the highest, because that is also the most standardized. When I cut pattern pieces, I have a very clearly describable process and it doesn’t change. That is, sometimes I have three layers on top of each other, sometimes I have ten layers of fabric on top of each other, but it’s always the same process. I have a large, smooth surface, there’s a cutting pattern and the robot then comes with its cutting head and cuts out the individual parts. It doesn’t matter if that’s a shirt or a blouse or a coat. The work is always the same. That’s why the degree of automation is extremely high here, and that’s also where the most technological progress has been made. It doesn’t matter whether you look in Italy or Bangladesh. But I have to say that in Bangladesh or Pakistan, there is still some edge trimming. But the degree of automation is generally very high in the area of cutting. When it comes to sewing, it changes very, very quickly. There are work operations such as incorporating pockets. They still have a good level of automation. These are the so-called semi-automatic machines. I load the machine with the various components, press the button, and then the machine performs an entire work operation. But then there are other areas, such as the sleeve insertion on a women’s blazer or on a jacket. Sleeve insertion is the most complicated operation in jacket making. It is completely manual. There are a few helpers on the sewing machine, but otherwise it’s just like it was when I started my apprenticeship in 1984. 


Now I mainly talk about digitization issues and industrial IoT in the podcast. What I would be interested in: What are the challenges on site that you can solve with digitization approaches or data-driven approaches?


Because the apparel industry is so labor heavy and more about people than machines, optimizing the use of people is really the name of the game. The machines are already working at top speed today. If you were to go into production in Izmir and compare the noise level in the women’s area, with that in the shirt area, you would notice a significant difference. The machines in shirt production actually just go on and off. These are standardized processes. The employees hold the parts together and then they press the gas once and then the sewing machine goes full throttle. For me, as a trained tailor, it was very adventurous to watch. But I have to say, they do it extremely well. You can no longer increase the sewing machine speed exponentially – you can’t make 20,000 stitches instead of 7,000. It doesn’t work that way. Other industries are much faster. I have to take care of the employee and make sure that this employee is used in the best possible way and see how he performs this work process. And this is where the topic of digitization comes into play very strongly. I can do two things via digitization. First, the synchronization between the machine and the employee. Second, by having information about the employee himself, I can improve his working skills. This means, for example, that I can see from the performance data which operations he is very good at and which he is not. Then I can explicitly organize training programs broken down to each individual. I can make sure the employee is in their right position based on empirical data. For example, it may be that Mr. Müller always sews the sleeves inside. But Mr. Meier would actually be the better one at the position. When I’m a line manager, I have my 20 to 25 employees. That’s when I develop a bit of a gut feeling about who does what well and badly. But whether there’s someone in the line next door who could do it much better, that’s something digitization can find out; I can’t do that via analog channels. 


What data exactly are we talking about in the human-machine interface?


These are different issues. Let’s start with the cutter. The cutter, i.e. the entire cutting process, is the most expensive piece of equipment in a apparel production facility. That’s why the cutter must always be running. It’s very bad if it’s not running. It’s like an earthworm principle and the whole chain immediately starts to wobble. So downtimes have to be avoided, which is why you measure this cutter data very closely, such as vibrations or temperature. So you try to find out with the information as good as possible how the cutter is doing at the moment. You try to schedule maintenance, such as knife changes, in the morning or in the evening or at the weekend. This is data that can be measured and used immediately to prevent downtimes. I once read a report about the rowing eight of the Olympic team. It was about the synchronization of the team. To synchronize these people, the coach holds a microphone in the water that measures sound underwater – the height of the sound and how the oars slide into the water. When I imagine that I have eight people slapping their paddles into the water and I can figure out from the sound if the angle is right and if all 16 paddles went into the water at the same time or not – I find that super exciting. Based on information that comes from somewhere else entirely, I can make a synchronization. I might not see that otherwise. But I can hear it. That’s what fascinated me. I was once in a manufacturing plant listening to a machine that had a beautiful same sound. From a distance, I could see and hear that the woman sitting at it was in a total flow because the machine always sounded the same and the beat was always the same. That was a little bit the philosophical part of it. Now comes the scientific part, the practice. If I can manage to hear and therefore measure an interaction between a machine and the employee, that’s great. Let’s say, for example, in shirt production I have eight lines sewing shirts, then I hear the same thing eight times. I can listen eight times to a seamstress sewing on a shirt collar and I can connect that data, those interactions between the human and the machine to the output from that position. So I can say, what’s the current quality? How many parts per hour? And then I can analyze and figure out which beat, which harmony, which song is the right one. Once I figure that out, then I can train that. That’s actually the idea behind it, if I assume that there are people who have a very natural ability to make a flow out of an operation. That’s fantastic. If you can see that and measure that, then I can train that. Then I can show other employees who may not quite have that flow yet: “Look at this. If you do it like this, you don’t feel at all like you’re being burdened more, you just have a different flow.” And the output determines the employee’s salary. And so he has an intrinsic motivation to do that. 


I’m sure that’s a data protection issue as well. If I now track an employee, then that also means that I have a certain transparency about their actions. What is the legal situation?


European data protection or the General Data Protection Regulation are of course very tough. That is known. And there are also good reasons for it being the way it is. And the Turkish one has more or less followed the European one. There’s not much difference. So that means that when I talk about this employee data now, it is of course something that is not simply rolled out transparently and every manager or employee can look at what the other one is doing. If I now take the line managers who are responsible for their 25 employees, they naturally have access to such data. They are responsible for ensuring that their employees are appropriately supported and challenged. But I’m not in a position where I can just log on to some tablet and say, “Why don’t you let me take a look at what Madeleine is up to on line 6?”. Of course, this is not possible. These systems are, of course, packaged in algorithms, so that even someone in IT can’t simply look at what each employee is doing. Of course, you have to have ways of anonymizing things in this process and making the information visible only to the specific recipient. 


You had said it’s also about incentivization. How do employees feel about transparency and measurability? Do you have any insights in this regard?


Salaries in the apparel industry are very much bonus driven. That is, they have something to do with output – both in quantity and quality. Each company has its own issues. One places more emphasis on output, the other more on quality. Then there are those that appreciate it when an employee knows many work processes, because then he is more valuable to them. Then there are also bonus points for that, for example. Each company has its own requirements or focuses on different things. Now when I’m in Bangladesh in a T-shirt factory, maybe I’m a little more concerned with output. When I sew evening dresses for Armani, I can’t have a bead sewn on in the wrong place. There are simply differences. If you create transparency for an employee, then the employee also has a permanent overview of his or her salary. He is his own manager, so to speak. There is practically one tablet per employee, one PC – the digital twin. If I can always retrieve my own information, then I know where I currently am. We had three professors from Hamburg there who study industrialization and employees, and they interviewed 100 people. This is because a stakeholder dialog resulted in various concerns on this topic. For some, it all sounded far too good to be true. People would certainly feel pressured to see their data all the time. They somehow tried to find out from all corners that the employees must think it’s quite awful after all. But that wasn’t the case. The employees have recognized for themselves: I am my own manager, I am the master of my data. I don’t have to ask a line manager what I’ll be earning soon. I don’t have to go to HR and look at some fancy paperwork. But I can always see where I currently am. I can also see, for example, if I know five operations and I perform two of them at 90 percent, two at 80 and one at 60, where I should improve a little. The next time I’m assigned to this operation with my 60 percent efficiency or quality, I’ll have less on the paycheck at the end of the month. So there’s an intrinsic motivation to educate myself, train myself, and train myself again. 


This means that I, as an employee, can call up my personal info on a particular work step on the tablet, such as the sleeve insertion in the jacket. If I’m good at it, do I see that based on the data from the cloud, and can I be appointed more efficiently, or how does that work exactly?


After all, the idea of Industry 4.0 is to create a digital twin. In 2015, we started doing this, creating a digital twin of everything that came our way – processes, machines or employees. We tried to analyze everything and turn it into 1 and 0 to understand what this information can give us. The aim was to be even more profitable, to achieve higher quality and above all – and this is the most important issue – to become much more flexible, i.e. to be able to map much more complexity without losing profitability. In this context, each employee has received a tablet with information such as performance data, e.g. quality and output. But also information about the operation itself – that means I don’t have to ask anyone if I’ve forgotten anything. I see the complete work instructions. The tablet is, so to speak, the digital twin for all sorts of things. Also if there is any information on Health and Safety features or other news. The tablet is the screen to the world for the employee – for himself and also for the whole work process. 


Now I have to ask a critical question about data protection: Doesn’t that mean that the data is not packaged in an algorithm, but can be viewed in its entirety?


You yourself can always see your data, permanently. You can always see exactly where you stand on the day, week and month. You can see what you are doing right now. That’s why I said: The thing that the employees found good is that they are virtually the managers of their own situation. You can permanently see what you are doing. But now, for example, if I work three machines ahead, I can’t see what you’re doing. I would have to come to you and look at your tablet. 


Exciting, if you look now in other sectors of the industry of today, it would be hard to imagine.


Yes, a lot of things come together. After all, there are different models of piecework. There is the group chord, the line chord or single chord. In Germany, there is quite a lot of focus on group or team chord, at least that is my impression. Let’s say you might have a team of eight people in the automotive field who have a cycle time of three minutes until all the parts have to be attached. Then I have a group performance, but I can also make it just as transparent. In the automobile, it’s relatively simple – the car is passing by all the time and the next one is already coming, the parts are rolling up. If they don’t have their parts attached, the box stops. In apparel, it’s very different: you don’t make a three-year plan for a production line. 6 percent of women’s models even make it into next year. That means you are constantly changing the layout, changing the production lines, adjusting the number of workers and employees and the number of operations. Because of the many changes, the kind of clocking you know from the usual industry is illusory in the apparel industry. Finally, perhaps a few words on the whole story of data transparency: My claim on the subject has always been that we enable employees to take action more independently – for example, when it comes to providing information on work processes without having to ask anyone. That you are simply able to manage everything for yourself and, through digitization, permanently become a better version of yourself. If I can measure which areas I’m good at, which ones I’m not so good at, and which ones I might even be bad at, then I can go and set up virtual trainings, for example. Then, even with 4,000 employees, I can put together my own individual training for every single employee. These are advantages that you also have as a result.


In the end, it’s probably also very much cost-driven, right? Who gets the orders, how is the whole thing distributed, you have to be on your toes, I guess.


Yes, you have to be on your toes. And the problem with our apparel industry is that it’s such a “shifting dune”. That’s also the problem why it hasn’t evolved as much technologically. As long as I can still find a country where I can somehow cut 10 percent of my costs by just buying even cheaper labor, as long as I just take my old sewing machines and just drive them to the next country. Of course, this is unthinkable for an automotive or pharmaceutical industry, because there a factory is assumed to cost a billion or 500 million, but not for apparel. I just keep moving them. And quite honestly, I think that’s terrible.


That would be the perfect starting point if we were to do another follow-up – also in the direction of sustainability and the extent to which digitization can make things measurable here. Thank you very much, Joachim. I’m very excited to see what happens next for you and the apparel industry.

Please do not hesitate to contact me if you have any questions.

Ing. Madeleine Mickeleit

Host & General Manager
IoT Use Case Podcast