Forum Pulire 2016 | Industry 4.0 | Markus Asch

Tony here's Tony thank you very much for inviting me. It's a pleasure and honor to be here and to talk a little bit about how we see the industry, how we see what's happening in our industry, what should be done, and how the industry should move forward. First of all, a little side note: John, I totally agree with your evaluation of Italian food and I totally disagree with your comparison of German to American food. So I already asked a European director to educate a little bit in the rest. We will fix when we see each other in October at the ISSA in Chicago. So first of all, let's quickly: I was asked to talk a little bit about our experience on how we do today, how we digitalize our industry, what are our first experiences, what are our results. And if you don't mind, I would like to set the scene a little bit on a bigger page and say how does this fit into our understanding of Industry 4.0, of digitalization, and where does it basically lead towards.

First of all, who is Interroll? Some of you don't know the company. We have about 60 sales subsidiary, 100 subsidiary in the world. About 95 percent of the world's GDP is covered by owned subsidiary. And we are also really the same size. We only concentrate on cleaning, not more, because only cleaning and value maintenance and we understand it, and we do nothing else. And basically we are a company of about two and a half billion and about eleven and a half thousand people. That's all about us. So why, and that's it, maybe the first question: why do we all talk about industry 4.0?

The question: why do we all talk about industry 4.0? And I thought maybe let's quickly have a look why this has been called industry 4.0. Basically we would divide into four different stages of industrialization. The first real industrial revolution was introduced by the steam engine, because you didn't need a horse anymore to travel from A to B. You had a steam engine or train. You didn't need an animal or person anymore to move you. You could use a steam engine, it was mechanic, mechanized, and so mechanization, you could start all different kinds of production. Then the Second Industrial Revolution was basically what Henry Ford introduced: mass production. So it was the division of labor from a manufacturing point of view into mass production. So you divided basically a total work into single steps and you must produce those steps. Third Industrial Revolution was then when the electronics, electrics were introduced. And basically what you could do: you did not only pick on a fixed schedule produce, but you could introduce robotics, you could introduce all different kinds of technologies, and that helped, you see it especially in automotive production, you see it in electronics production, that helped a substantial chair jump again. Now what we would call, or what's in the industry is called the so-called fourth Industrial Revolution. It's not more electronics, not more data, but the intelligent connection of this data, and that's the most important one. So if you talk about industry 4.0, or if we talk Internet of Clean, or if you talk connected cleaning, or whatever you want to talk, it's combining knowledge that actually could be available but what's not used because it was not available, it could not be drawn from various sources. And combining, we call it cyber physical systems. So that means combining systems, physical systems, this knowledge, and use that knowledge and that combination for the benefit of optimizing situation and optimizing processes.

So and there are tons of studies out there. We just saw another figure at the end. It doesn't matter, by 2020 the main message of this slide is you will have to meet 20, 25, or 30, or 50, or 15; it doesn't make a difference. You will have so many connected devices out there in the market being connected to the Internet and therefore having the capability of providing you with information. That one of the key learnings for us is: in the future, every information that is available will be available for you, for us to be used. So then the question is: what do we do out of it? And before we go into our industry, I would like to open our mind a little bit on quite different industries. What is happening in industries in order to understand how does this apply to our industry, how this applies thinking to our thought process, and especially how could it open up totally different environment. Who of you has their own apartment or their own house? If you have, so one of the challenges is if you want to install a new heating system, there are quite a few stupid things in between. First of all, you have to get a craftsman. He never shows up on time. So it will take one week, two weeks, four weeks, two months. And then the next one: you ask for a quote and sometimes you have to beg him for quotes, sometimes to get the quote fast it will take another four weeks, two weeks, one week. So there's a company called Tomando. What they do: you fill out the form online, what do you need? There are a couple of questions being asked. It takes two pictures and guaranteed within 24 to 48 hours you get a quote with a fixed price. So what are they doing? They are jumping...

So what are they doing? They are jumping today's trading lines. They are combining customer application knowledge directly with the manufacturer. And when you talk to manufacturers today, they are afraid of this company to death. Why? Because now they know exactly the application, they know exactly what is happening out there in the field, and the next generation of heating system they will be able to design. In the past it was manufacturer, channel, distributor, tradesman, customer. So the knowledge of the application at the manufacturer was through at least two or three filters. Now it's one. So why am I showing this? It has nothing to do with cleaning, but what I'm asking you is to open your mind and say: what typical business models could pop up that we don't think of today? If we look at the aircraft industry: on the aircraft industry, I mean you need a plane and you need engines. In the past, usually Airbus and Boeing and other stuff would sell the planes, and Rolls-Royce and others would sell the engines. Today, Rolls-Royce engine our world twice don't sell their engines. They lease their engines, and totally different business models pop up. Why? They have all the data. They equip their engines with tons of sensors. They generate data. Those data are being used first of all for their service and then to optimize the process, but also for charging to the customer. So instead of selling a turbine, they sell the use of the turbine. And by doing so, they can optimize through their sensors. They can optimize the usage. So before the plane actually lands on the ground, they know exactly the status of the turbine, what needs to be done, and they use that to optimize the process. So that's one of the first steps of digitalization: send, put sensors out there, try to get all the data, and then optimize through service and through engineering, but then generate totally different business models. Today, aircrafts or...

Airlines they buy planes and they lease engines. Why? And they just have to pay per hour. And Rolls-Royce knows exactly what needs to be done. If you move to again a totally different industry, and to me I always look a little bit at farming industry. By farming, actually it's very close to cleaning. It's not about a product, it's about a total process. And it's about a process that is so much interlinked with so many different areas that it's not good enough just to look at the machines. It's not good enough just to look at the process around the machines. It's to look at a bigger picture. There are companies out there called Infom. What they do: they offer the farmer one view on his total activities. They combine machines and all kinds of different information. They put together and optimize the total process. And you can believe this is a threat. This is a threat to a John Deere, to a Claas, to anybody else. Because at the end, they don't care about the machine. They look at the overall process. The same you will get from John Deere, the same you will get from Claas. It is always the same. It is moving away from a product, moving into the center, into the heart of the application, and then combining smart products with different solutions. There's different information that is available, and we'll show you a couple of slides on that area because it can be very easily applied to the cleaning industry.

So basically what industry 4.0 or Internet of Things says: you move from a product to a smart product. And that smart product already provides you with some technology and provides you with the capability of producing data. But that's not good enough. From the smart product you need to move to a connected product. Connected product means the data are then being gathered. And then the question was, and then that moves to an integrated system. So in this integrated system, maybe let's say take the example of farming. In the example of farming, you combine tractors with different this ideal information. Where do they have to be? How do they have to be used? For example, through smart tractors, the amount of seeds, crops that are being used have been reduced by 30 percent because you can direct them very ideally. But still there is no connection to weather, there's no connection to the soil. So when it then moves to a system of systems, you then use totally different information as well. That means information of the soil, information of the history: what crops were produced last year. If you combine them with fertilizing, thus the right amount of fertilizing at the right spot, then totally that moves to a system of system, to an entirely integrated system. It takes Claas, take John Deere, you can take any of your example. All of them work. There are tremendous benefits. I don't want to go through all the details of benefits, but basically what you do: you remove waste and you use your energy, your technology, your capabilities, your capacities as precise as possible in order to focus on the result, focus on what is there. So if you take John Deere's example, of course they have smart products. If this play is they have precision farming, this is a smart product. They have guidance control, this is smart product. It then moves to a management system and it moves away from the product into the farm, farm being the center of attention, and then feeding up and enriching those data. There's everything that is required in order to produce most optimum results. So as the first summary of the, let's set up the scene as a first summary: data, we will have any data available anytime, anywhere in the next few years. You will see data popping up. The train maybe describe cleanliness, describe people moving, all that kind of data. The question is not whether we will get the data, the question is what do we do with those data. And if we combine machine and data, then we generate value. We look at the process and we understand it. So let's move to connected cleaning.

Let's move to connected cleaning. First of all, what was very important for us to understand as a company, and it maybe took too long. If you look at the cleaning industry, if you look at your budget, seventy-five to eighty-five percent is labor costs. So there's no point in looking at the machines. There's two points: the major focus is to look at the process, because the process defines the labor cost. So it's no point in getting a better machine. It's looking at the overall process. And if you look at other industries, farming, you name it, those industries that have a process in place, they are the one that are being digitalized, because they are the one that can be tracked, monitored, improved, optimized. That's the most important one. So for us, this is an ideal area. We have the maturity of it: labor cost and labor cost means labor involvement and we're set being in place. How can this be optimized? And in our understanding, innovation starts first with asking a couple of questions. I mean, some of the questions that are being asked: are the rooms cleaned according to schedule? Are the machines operating according to their expectations, to their requirements? Are the machines planned or used exactly as planned to be, generate exactly the same results? Are the staff working according to example we have heard before? Fluctuation, turnover, a big challenge. You don't want to get the call of your customer: 'By the way, your cleaner didn't show up.' You should know it before, but how do you know that in detail? What is my level of cleaning quality? Am I producing what I'm being asked? So, and why it? There are no overall solutions to be able to exactly answer that.

That simple question. If you look at connected cleaning, what we have been launching, and I'm going to be short on that, just to give you a couple of ideas what is happening there, to see the benefit and to see the value. Basically today, machines, people, and information of rooms are being collected and are being processed in the cloud. But as we have learned before, data will be available. Then the question is: what do you do with it? You process those data, you move them in the cloud. Now the question is: what do you do? The task is that for different roles, you need totally different data in order to provide value to the individuals. So maybe if you look at a customer, basically they just want to get the proof of what they asked for. They have tendered for something and they want to get the result. If you look at controlling or management, basically the question is bottom line: what I have invested, does this make sense? Am I doing the right thing? If you look at your facility manager, there the question is about efficiency: Am I using the right resources in the right way? And if you look at your branch managers: Is everything under control? Will I get a call or will be everything under control? So as you can already see, there is no one-size-fits-all. So different based on roles, based on requirements, you need to process totally different data and in a different type. So let's quickly have a look at the content from a top management point of view. You need to know what's happening to your machines. Is everything under control? Is everything running according to schedule? And very interesting: when we talked to many of your colleagues out there in the market, we very often see that assumptions and reality are very often not in line. So you assume that this is happening, and as you don't have any opportunity to check, you have no clue that something else is happening. So you implement notifications. Very simple thing: if a machine needs to start at 7 o'clock, or let's put it the other way: if a job needs to be finished at 8 o'clock in the morning and you know it takes two hours and the machine has not started at 6 o'clock, you're in trouble. So you better let the branch manager or the facility manager know that you're in trouble before your customer tells you you're in trouble. Simple notifications is just connecting intelligence and connecting data.

Or and I will show you a little bit statistics later on. We are shocked how many machines get lost in the Bermuda Triangle. Some of the cleaners say: 'I've changed the location from A to B', and somehow the machines were lost. We don't really know where they are. So today you can track them. And we once had a rental fleet and our service technicians came approached us and said: 'By the way, your system is wrong. Two of the machines are showing up in Morocco.' So we said: 'Sorry, that's what the system says.' So they called up the customer that rented the machines and he had to here to admit they sent the machines to Morocco. So what's happening today? You can track the machine. You can track the machine when they leave. There are certain environments. You can find out what's happening. But that's not good enough. If you look from a branch manager's point of view or from a facility manager's point of view: are machines working according to schedule? Are they doing what they're supposed to be doing? Are they producing the results that you expect them to do? Cleaning crews: are cleaning crews doing what is expected? And there are tremendous surprises popping up when you start tracking and when you start optimizing this. And you can do all kinds of searches, kinds of evaluations, you can do benchmarks, KPIs in order to do so. And that moves to all kinds of details. We don't really have to go into detail. The question is quality. How do you track quality? How do you correlate quality versus the movement of your people and machines? Is there some intelligence in correlating that? Is there some intelligence in finding out: why do I always have a problem in quality in that facility? Does this have to do with the people and with how the people are employed? From an operator's point of view, very easy way of tracking their working time and tracking the way how they do their business. But then looking at the example of Rolls-Royce: Rolls-Royce knows much better than the airline what is happening to their engines. And today we can detect technical issues before they actually are detected by the people. We can let them know what's happening. We can organize service before it's actually required. And what it does at the end: it reduces downtime, it improves availability. So then what is the value? What do you generate out of that?

What do you generate out of that? And I'll just want to concentrate on a couple of them. First of all, it's cost saving. Basically, and this always when you digitalize a process, you look at the waste. We looked at the waste of machines and then you looked at the waste of the process. So really your reduced costs. But not only that, you improve efficiency in terms of reducing waste on time as well. So it's not only putting the right machine and the right people at the right spot, but it's also optimizing the total process, looking at like on the farm, looking at the overall application, and putting people and machines, whatever is required, at the right spot at the right time, perfectly managed, perfectly organized, documented, and tracked. And that at the end generates transparency. Transparency is required for two things: first of all to optimize, but second also to generate trust towards your customers. Because they know what is happening. They cannot tell you 'You have never been there.' You can prove to them what is happening at what point. So customer voices love it. That's one side. I would like to talk to you a little bit about some of our findings. By the end of the year, we will have about 4,000 machines in the market, tens of people, the people tracking rent since many, many years, millions of square meters are connected, and there are a couple of things maybe we're shocked. So we set up the scene based on the contract leader's requirement that machine will run for four hours every day. Reality is two. That's reality. So but again, that's a starter. So that data is not good by itself. Now the question comes: what do we do out of it? Wrong assumption, wrong machine. Right assumption, wrong execution. Then training needs to be required. Or why, where is the problem? Why is it happening? Because obviously you cannot generate a similar result. We have an average: the facilities we have located, at an average they had a loss of machines of about 10% that they're gone in the Bermuda Triangle. You can reduce them because you can track them. You still cannot reduce it to zero, but dramatically you can reduce that. But also you can substantially reduce service calls because some of it you can do preventative, some of it you can do online. You don't even have to send out the service technicians again. It improves availability. And that's another one: you know, if you look at the financial side or the investment side, one of the biggest parts of a scrubber, of a sweeper, is actually the battery. If you do wrong things with the battery, they have to be replaced too soon. We see there are a lot of problems on batteries: that they are wrongly charged in the wrong way because we track it in detail. We look at exactly what is required.

We look at exactly what is required. So basically that's exactly what you expect as soon as you digitalize the whole thing: you see the waste, you document the process, and then you move forward. So when we look at the cleaning point-of-view, this one you can pretty much say under control. But then the next question: how do you localize your movement of your machines but also your people inside? Big, big challenge. Many, many startup companies are working on common platforms, how indoor navigation can look like. That's one of the tasks. Now I would like to take the last five minutes I have to look at the future. Because if you take again the example that I showed you on farming, we are just on connected system. We are not yet on systems of systems. What does it mean? And there are a couple of questions that we have to ask ourselves. If you look at a total building, if you now take the application into the center, a couple of things: why are not machines talking to each other and optimizing their processes? And this in the future will be another possibility: where machines, elevators will be able to tell people how many people are on which level in order to modify cleaning plans, cleaning procedures. Today, basically somebody has to check the machine. I mean the brushes are worn, then you have to do something on primers and on others. You have today's technologies out there that will tell you when what has to be replaced. In our cleaning industry, in the maturity we are not yet there. But let's look at the better. I mean, everybody knows when it's raining you need to clean the entrance. So you can combine weather data with cleaning plans. But today, cleaning plans are static. You clean according to specification. So and in the morning when you, I just arrived this morning also at the airport. If you go to the toilet, you see the schedule: ten o'clock, eleven o'clock, twelve o'clock, one o'clock. Could be between seven and eight, two hundred people on the toilet, but between ten and two, just fifty people on the toilet. So would we find out that knowledge? Maybe in the future, yes. So that means we will have to combine...

Will have to combine knowledge of buildings, knowledge of people movement, knowledge of personal requirements of people. And that has to be combined with cleaning plans that are today totally static. And then we move from today a connected system to systems of systems. That means cleaning on-demand. Why do we clean offices where people have been on holiday? We should be cleaning the meeting rooms where people have, where 20 people have just had the meeting. But today we have no proper communication to facility management or to room reservations. Those systems are not yet properly out there. They need to be implemented because they will help us to adapt to needs that are required. And that's when we move from two days of course a cleaner, that is intelligent when he walks in the room and sees everything is clean, he moves out and moves on. But if the meeting room is not according to his schedule, he doesn't go there, even if it looks horrible. So in the future, if we combine those data of better data of buildings, data of cities, if we combine those with cleaning plans, and if we develop the capability of adapting those requirements or adapting those performances based on requirements, then we see the next improvement. And that doesn't mean you have to cut people. It means we improve quality, it means we improve the performance cleanliness without having to increase people and everything else around it. So communication: men - machine, men - people, people - machine, machine sometime. At one point, robots will tell the cleaners where they have cleaned and where they need to have little help, and vice versa. There we will see substantial changes. Basically it moves from a product to a smart product to a connected product to a connected system, then into a system of systems, and that's where the cleaning industry goes.

I would like to conclude. I have 40, 60, 46 seconds left. Industry 4.0 is a connection of cyber-physical systems. Basically it's connecting machines, connecting technology and data and making use out of it. It is, as our industry, it's very much a process-driven industry. Cleaning is a process. It has a tremendous opportunity. But it means there's a long way to go. We cannot tell today everything is fixed. We have perfect systems out there? No. Today vacuum cleaners are not connected, and many other things are not connected, dispensers are not properly connected, people are not all of them are connected. So it's not yet there. A lot of technological innovation will have to be in place in order to move that to the next level. But as I showed you on farming, on turbine, on Termondo, and others, that will open up totally different business models. Maybe in the future you don't buy cleaning machines or detergents anymore. You pay per square meter. Because exactly the knowledge that is required for that will be available, will be able to improve, to check, and to concentrate. And the topic of this year's conference is basically partnership, and that's where I would like to close. Cleaning is not about a manufacturer and a cleaner and a customer. It's about a highly integrated process. And the time has come, and I'm totally convinced the time has come, where we have to step over those barriers, where we have to look at an overall process. We'd say: what is required? What is required in order to optimize that for the benefit of the customers, of the cleaners, of the manufacturers, for the benefit of mankind? We should never forget: we serve as cleaning the basic need of mankind. Our task is to bring to the attention of the people the value that we generate of cleaning. But in order to do so, we have to move out of our little bit filthy image of the corner into the capabilities that we provide. Services that people need, not only what they need in general, but what they need personally, physically, at that very point of time. And if we move that direction, we will have a fantastic opportunity ahead of us. That's what I'm totally convinced. And I wish all of us and all of you an interesting conference with tens of discussions, hopefully controversial discussions, but discussions that will lead us forward into this direction. Thank you very much.