Alex Schuessler continues the discussion on Paper to Glass

Oh, yeah. Nailed is a good expression. No, not nice. Not nice.

I'm working on that.

Oh yeah, I think it's actually, I mean, that's a great way to put it. You know, many years ago, I think about 15 years ago,17 years ago, I was asked to give a presentation and they really wanted to parade around what we were doing. This was in the early years of SmartEquip. And we were known as one of the companies that would digitize stuff and information was being digitized. And everybody assumed wrongly, as you just hinted at, everybody assumed that by digitizing it, things would become a lot more efficient. And so they invited me to give a keynote address in Europe. And I went in and it was actually called from paper to glass. So that's why that concept hasn't sort of, I haven't been able to shake that off from paper to glass or why digitization makes things worse. And that's exactly what they didn't want to hear.

But I was taking a lot of case studies and saying, look, just like you used to have parts catalogs on in big books, often they were not even unwrapped yet. They were at the service counter until you needed them. And then you. either couldn't find it or had to unwrap it or where there were pages missing because somebody else had used the pages they needed when they did their repair. But just by throwing it from paper onto glass. And if you remember long ago, there was something called the microfiche where all these things were on film, basically, that you then needed a special reader. They were really treating a lot of the computers as modern day microfiche. They use CD-ROMs. They might've been online, the early ones. But for the user, the technician, things actually became harder, not easier. They now had to sort of archive all these little film cards. They had to carry the stuff into the field.

They suddenly had to do things that were easy to do with a book and they couldn't. And so then the question was, well, we're all assuming this is being done to make life easier for the user. And so we started digging in because this was kind of our field and we had a different approach to it, but we needed to understand. why we took that a different approach ourselves before we went too far. And so we started talking to the manufacturers and there's one well-known manufacturer. And we asked him, why is it that your parts book costs $200 a volume or a piece, a unit? And their answer, which surprised us even more was, well, that's because we subsidize it. It actually costs us more to do them. And that's why we were eager to put them on glass. So it really was cost savings for the manufacturer in this case. And it was harder for the user to use.

So it actually, in a way, it was what we might call a zero-sum game, which is one party gains, the other one loses the same amount roughly. So it was just being shifted there. It was really only when the information itself became presented in an intelligent way and allowed you to do things that you couldn't previously do, that things became smart. That's when you had the smart catalogs. and the like. It wasn't because when we built our parts catalogs and started this kind of technology, we said, hey, wait a minute. Once it's machine readable, there are all these things that you can do you couldn't do before. In a book, you would look up a part. You would make sure it's the right drawing. You would make sure it's the right serial number for that drawing since they vary. The drawings vary by the serial number. And then you would start identifying the part. You would write it down. You would go into another system to see if you had it.

Then you would check on your shelf to really make sure you had it, even though the system said you didn't or vice versa and all that. And we said, well, wait a minute, if it's machine readable, click on it and let my other system tell me if I have it or how many of them I have. And if I don't have it here, tell me automatically I have it in another location. So don't think of it as at the display level. which is from paper to glass. Think of it what happens underneath the screen or behind the screen and how you can start chaining those together. And that's where the real efficiencies are. And that's where the real innovation came from. Now, click on that page that's now presented to you. Have it tell me where the parts are. Have it automatically generate my purchase order by putting that information in the purchase order, in my work order.

And then ultimately, and this is where we became very involved, if you're generating all of this information on your system, have it simultaneously create service orders and the like at the dealer or manufacturer where you're actually buying these items and do it to all these systems. So it's really the intelligence that's, that innovation of intelligence is where the efficiency came from, not just by putting it on panels of glass.

From the time

We incorporated March of 2000. We started in January.

Yeah, I think you're touching on two really important things. And, you know, I've always been fascinated. Well, we had to be fascinated by the history of some of this, especially on the technology. And so you're touching on two things. One is the notion of that database or the digitized data and the information and all that. And the other is, as you mentioned, resisting change. And these are two very different things, but obviously they had to collide in all of this and somehow somebody had to make this work. So on the database type, there's a really fascinating history here. It has nothing to do with our industry. It has everything to do with where technology came from and where it was going and what it hoped to achieve. Nobody talks about this much anymore. It's not at all trendy because it's old. But there was a period of time in the 70s and 80s, especially, where a lot of faith was being put into this. NET technology.

Microsoft for a while was big in that, but so were all the ERP systems. And the idea for. NET was that everybody would converge on a standard for data. Everybody would converge on the same kinds of rules. And when they did, they would sort of all be installing fully compatible ERP systems, sort of enterprise systems, and all of that. And they would magically all talk to one another as if there were one. And that convergence never happened.. NET did bring us one beautiful thing, which is it created rules, repeatable rules for how you could access data from other machines. And they're called APIs or application, they're interface layers. And those were done. And so it's much, much easier if a system today will typically always have APIs so that another system can read data from that and write back in. So that came from it. But the intelligence that everybody expected, like these systems would now magically act like one, didn't.

And we came along during that time. And rather than say, hey, we're going to set up some big, huge data exchange in the middle where everybody can park their own parts information, we knew that would never happen. Certainly not in this industry, but as we learned later, it didn't happen in any other industry either. There's a little bit of that actually, oddly enough, in the trucking and transportation side, but that's a very narrow version of this. And we said, we have to find a way to bridge these systems. So if you're a fleet owner, let's go back into our industry. You own a whole bunch of equipment. You have a dealer, and then you've got different manufacturers for your fleet. And all the information that you tend to maintain in your own system, it's called an item master file, which is where do what these are the parts I need. This is the kind of machines I have and so forth. They they need regular updating.

And we said, Well, rule number one, there should be no updating if somebody a john Deere, somebody at Caterpillar, somebody at the case, New Holland, changes, brings brings along a new part for those machines, they should automatically be written into your system. And so that was for us a big deal. We respected that you have what we call a system of record. Your suppliers have a system of record. And all we did is tie them all together that if there's a change that pertains to you, your item master file would self-update when you try to order that part, for example. If it doesn't exist in your file, it'll go and what is it? It'll add it and they'll do it all dynamically. So in a way, we were doing what. NET was promising would happen. But you actually had to build technology in between it to make it happen and use these APIs that it came with. So that was the approach to data.

Everybody that looks at these systems today, at our technology and said, hey, this is pretty easy. It's because you don't see the plumbing in the background of what had to happen to get all of these different systems to talk to one another. So that's one part. The other part, which is in many ways, the more interesting human dimension in all of this is the resisting change side. So here you go, you walk in and you say, hey. we're not just going to put it on glass. We're going to make all this stuff interactive. We're going to allow it to tell one system, tell another system what to do. And then when you come with this sort of vision, you're being very naive because you think, okay, the organization is immediately going to change what they've been doing for generations, and they're just going to hop right in. And that's the wrong way to do it. So there's this whole literature.

Business schools do a lot about it, and we've seen a lot of it, which is they call this the balanced scorecard approach. And what it means is essentially the world it lives in is that you come in with a new technology and you do exactly what you've been doing to date, but you do it faster, more accurately, and with a great deal more efficiency. So let's take an example. A piece of equipment, let's say you own your fleet and you're running it. A piece of equipment comes back from a job. It gets inspected. You say, aha, there's a repair that needs to happen. You create a work order. You identify the parts you need in your own inventory, which you have. And then you say, well, actually, we need 10 parts. We only have two of them. So let's go ahead and order the other eight. Well, who do we buy these parts from? So you then look at your preferred supplier list. You say, well, what region am I currently in? Okay, I'm in Wisconsin.

So my preferred supplier for this is A.A doesn't have those parts. What's my backup supplier? B. So you go there, you call them. A likes it when you look online. B likes it when you call them. C wants you to send a fax, whatever it is. So you really work your way through them. And then when you finally have the right part that you need or the parts. You go back to your own system, you generate a purchase order, you send that purchase order number to them, they give you a sales order number, the stuff arrives. At the end of the day, the fact that you just ordered a $50 part, so $50 are irrelevant. You just spent several hundred dollars of labor time and equipment downtime trying to get all of this. So we came with this new technology, just like a lot of other companies. This is not a SmartEquip specific story. We came and said, hey, we can make all this stuff go really fast and you can skip a lot of steps.

And instead, we saw that the only implementation that worked well was when you put the system in and everybody did exactly what I just described, but they did it electronically. So the equipment would come back, they would click on something, it would generate a work order automatically. They would say, hey, I need these parts. So they would click on the different parts diagram that would pop up, a specific serial number, and the machine or our system would tell them, yeah. Two of those you have in your own stock because we just read your other system. But the other eight you have to order. And by the way, this is your preferred supplier. They have them. And the other one, they have six of them. But the other two you've got to get from your backup supplier. They can be here tomorrow. Here's the correct pricing. We've already put it in your purchase order. We've already sent the PO to them. We've already got a sales order back.

So all this stuff I just described happens electronically and interactively. And you go, wow, this is great. And these guys are happy as can be. Because they can now do in minutes what used to take several hours and was error prone. So that's a big shift. But there's still something that they're not doing. And there's still something that leaves you frustrated. And that is, guys, if we can do all of this, and you wait for them to say, you can't tell them, you wait for them. And we just had this a few years ago, my favorite example, somebody in the Netherlands where we put the system in, some guy came back and says, guys, if we do all of this. Why do we need a work order? So now you're not saying, let's go through the same lines. Now you're saying, let's do it differently altogether. And their boss immediately said, what do you mean? Why do we need a work order? Of course we need a work order.

How are we going to do service work without a work order? And he said, no, but think about it. The reason we have work order in our company, this is a very specific interpretation, is because we want to know what each machine costs to maintain. And we want to see exactly what that machine consumes in parts. We want to assume what it consumes in service units and hours. That's really why we have them. That's the only reason. And we want somebody to look at a piece of paper and say, this is what you need to do. Now we have SmartEquip in here. Again, you can find, I know our example is the best, but this is true for virtually any technology implementation. At some point this happens. And they'll come in and say, hey, if we place the order, The system already knows who the part is being ordered for, meaning which machine it goes to. We can very easily just say, hey, yeah, it took two hours, boom. And we know what our internal rate is.

So let's just bypass work order altogether. And so that's when it gets really exciting. You're not just doing what you've always been doing, but doing it faster. If you think of it geometrically, it's as your innovation curve, or sorry, your process curve, you make it steeper because you're getting better at it. Now you're saying, well, wait a minute, we can do things in a new way and you can just shift it up. And the last thing on all this is if there's one thing that this whole balanced scorecard literature has taught us, trying to leapfrog directly to the outcome by saying, hey, guys, we've got something completely new. Forget everything you've ever done and do this. Almost always fails.

Whereas if you put it in and then have somebody in the organization and good organizations always have sort of their pilot crew and they get involved and they roll up and they know the key performance indicators, they get involved and they come back and they say, guys, guys, guys, we can actually innovate the workflow now. And we can continue to do this on a quarterly, annual, whatever basis and sit down and measure these. And that's when it gets really, really exciting. So that's a big, big shift from, so there are really two shifts we just described. One was get it off the printed page onto Glass and then recognize that that's a bit of a step backwards, at least for the user, not so much for the manufacturer in this case. Second thing is start doing everything that you've been doing before, but you're getting so much faster and better and more accurate at it with the whole process.

But then within that, have a whole different type of innovation happen that was never available before. So that's a long-winded way of describing this and meandering to what. NET role was. But that outcome was always the dream from these technology innovations,. NET and other. But it requires the user to go through all of this. And it also required technology to take a slightly different path than what was originally envisioned.

Oh, it was, I mean. It's brilliant. And the fact that we played a role to have him innovate, that's, a hundred times more effective than us trying to come up with this. I mean, absolutely. Because that's a, so that's a big win. That's a huge win for, for that company, but also for anybody trying to bring a technology in, you know, you said one thing and I want to maybe fine tune it a little bit. It's not a database anymore. That was the original dream. And that's what this whole. NET thing was going to be about, as if they were all part of the same database. You're exactly right. Everybody has different... You mentioned the four main Earth guys. They all have different formats in the way they do things. And it's absolutely unthinkable to tell one of them or to tell three of them, hey, we'd like you to do it the same way that the fourth one is doing it. That's just not going to happen.

The other thing that's, and so therefore people thought, okay, somebody should come along and normalize the data and have it to be a shared database that we just then need to update all the time. And that's not doable either. That's just meaning there's another, and then we're always going to be behind if you do that, especially in something that's as fast moving as parts. So the approach we did is not that we create that database in the middle, but we become what one of our customers called kind of a universal translator. So we understand how. manufacturer A organizes their data and B and C. We understand how they understand serial numbers differently. But from the user's perspective, you no longer need to be able to navigate those differences. Our system will do it and it will present that data for you in a unified form without the backend having to do anything differently. So we're actually doing a service for those four companies.

They don't have to do that effort or maintain multiple standards. And we're doing it for the user. And we do it without keeping our own version of all of this. We just do it on the fly dynamically into your workflow. So that's, I think, very, very important in all of this. But the other thing you mentioned, which is now the big trendy term, and it's really fascinating to watch what's going on there is AI and that kind of stuff. Because now we're getting to a point where we have so much data flying around. and it's becoming more and more exponentially by the moment, that you can start the data volume. It's not just the number of different sources, although that's absolutely critical, but it's also the sheer volume coming from each of those sources that's now enabling you to do something very, very differently. And there, and I apologize in advance, now I'm nerding out not about computer technology, now it's going into statistics.

There, the big break. We all say artificial intelligence. We don't necessarily always know what it really means or refers to, other than that somehow machines miraculously are getting smart. There's something very, very specific. It's a huge break in statistics, which is it used to be that you would have a data set, you would collect it, you'd massage it, you'd sort of organize it nicely, and then you would run statistics on it. But to do that, you would have to have a hypothesis. For example, I believe that the older my oil... gets or the longer the interval between all changes, the more I'll have the following four problems. And that's my hypothesis. And then I go and collect all these service records. You guys ran for 50 hours. You guys ran for 5,000 hours and everything in between. And I try to correlate it with everything else. But you walked in with something called a hypothesis, right?

And the role of statistics was to test that hypothesis. And there it was. And that's really all you could do. And if you were just trying to randomly correlate things, you'd be in deep trouble. You might get results, but these were usually called spurious results. And then AI comes along and what they do is they say there's so much data, so much enormous amounts of data there that it's now the machine that's starting to recognize how things hang together. Now, like I said, you need enormous amounts of data to be able to do that kind of thing. But it is really an enormous break that you no longer need a hypothesis you test. The machine now starts reporting back to you, hey, Ron, you know, in certain circumstances, given certain temperature ranges and dust and whatever and working conditions, here's what you can, here's a life you can expect out of oil.

versus for this kind of machine, maybe different manufacturer, different process, different temperature settings, and so on. So it will start telling you not just the answer, but it also helps define the question for you. And that's a big, big break. And that's where a lot of the changes are happening now.

Well, so I'm a little bit biased because I've, in terms of equipment owners, the ones I've seen up close for the most part were in the rental industry. And, you know, it's such an interesting industry. And I got to know the industry in the mid-90s and I've seen it evolve. And what happened there is, as you know, It really came into being, at least in North America, through massive roll-ups. There was a company once called US Rents. It became United Rentals. And then there was a race between what then were the two largest companies. It was United Rentals and Nations Rent. Both of them grew much larger and much faster so than either of them expected. Nations too much so, then it later became, it went. bankrupt and then came back and then now is part of Sunbelt rental. So there's a lot of stuff that happened there. And the reason I'm mentioning them, there are really two reasons.

One is certain types of rental industry were really ahead of the game because in a way they were a precursor to what we now call the shared economy. A lot of them, and I'm not so much thinking of many of the mom and pop organizations that were there, but there were some that were quite innovative and they said, look, Nobody wants to go and buy a piece of equipment. In fact, they don't even really want to rent a drill, let's say. What they want to do is they want to buy a hole. And renting is the closest you can do to buying that hole and then giving it back and only paying for that output that you really wanted, which is buying that hole. And so I think there is something. more innovation friendly from the user perspective because they're selling innovation to their end users. So I think there was a receptiveness there. But the other thing is a little bit more straightforward.

And that is these companies that came out of nowhere and became very, very large. They knew that a single percent or 2% lift to what they were doing efficiency wise had an enormous impact on the shareholder. And so they were looking for all of this. And they were also turning to technology that the people that started these large rental companies did not come out of rental. They came out of Wall Street. So they were looking at they were kind of agnostic and not kept focused on rental specific stuff. They were looking at throwing anything they could. So I think we see a little bit of it there. The other thing that I think is really interesting now when you look at AI and other innovations, it's the technology companies themselves. There are few companies that come out as pure technology companies. They already all have a flavor of the problems they want to solve, number one. Number two, because everything is now SaaS, you can rent the solution.

You don't have to buy tens of thousands of hundreds of thousands worth of technology. No, you just start renting it. And if it doesn't work, you stop renting it. I mean, it's a subscription model. And so it makes it accessible to pretty much everybody. So some of these new AI companies. Uptake is the one that really stands out in all of that. They're the ones that are showing you this. Let us do it. They said, let us do this. Let us demonstrate it and let us take a cut of the benefits we bring to you. So I think I feel much more optimistic about abilities now to come in and do this on this kind of entrepreneurial type of approach with certain types of customers than I would have, let's say,10 years ago.

I do. No, I do agree. I think it's, it is, there's so much going on right now. And, you know, when we, again, and I keep saying this, I'm thinking back to our examples, but when we came in with our technology, you didn't have a sense of movement around you. You were just starting to see dot-com stuff on the consumer side and the online stuff there. But the idea of actually suggesting that somebody have a screen in their building and set something up in their data center and so on was an uphill battle because it wasn't there. I feel the big difference today is that even though there's, you're absolutely right, there's a great deal of resistance, but the culture that surrounds us is one of constant change on the technology side. It's such a cliche to say this, but the argument today is no longer should we do technology. The pushback is, Are we going to change our best practice? Are we going to change the process flow and so forth?

So at least we've got that going for us, right? And then the very last part, which is the other puzzle that often gets overlooked, you go into a new entity or you go into a company and you offer them a big solution, which is so obviously a technology solution. Again, SmarterCoop is an example. A lot of the telematics stuff is an example and so on. But what you overlook is for the organization itself. It's hardly ever an IT project anymore. If you get Salesforce, that's not an IT project. You need to train everybody, of course, like you do for IT things and so on. But you're not setting up servers. You're not setting up anything. You're now starting next month. We're going to all pay a big license for Salesforce. You pay monthly subscription fees for SmartEquip. If you're a supplier, you pay transaction fees for SmartEquip. But you're not actually doing technology. You're subscribing to this technology that somebody else is doing for you.

And that, too, should be something that takes a lot of the natural resistance out of the equation.

I always enjoy our discussions, and I do think, relative to where we started a few podcasts ago, I think much as some of this today has been more historical and broad and more abstract. uh we're going deeper and deeper down this this path um and i think we've we've we've what's been interesting um thinking about this in between our um get-togethers is just i don't think we've changed direction once where we began i think we put a lot of definition around it but it it's been uh it's been a consistent uh direction yeah