Learning Without Scars
As a third-generation educator, it is easy to say that teaching and training are in the blood for Ron Slee. From his beginnings as a coach, through his time at McGill University, Ron developed a foundation for the work he does today. From working within dealerships, to operating a consulting company, creating a training business and running twenty groups, Ron has been directly involved in this Industry since 1969. Ron has been known as the industry expert for years, and has brought this expertise to bear through his training programs. Today, Ron provides specialized, job function based internet based subject specific classes, job function skills assessments, as well virtual seminars and webinars. These courses are designed for manufacturers and their dealers, as well as independent businesses in the construction equipment, light industrial, on-highway, engine, and agricultural industries through Learning Without Scars (www.LearningWithoutScars.com). This platform is a continuation of the work begun by Quest, Learning Centers which was established in 1996. This training is aimed at improving dealer parts and service operations through qualified people that are knowledgeable in using operational metrics and current market and operational best practice methods.
Learning Without Scars
From Conventional Dealership to AI-Driven Operations: A Conversation with Troy Ottmer
Troy Ottmer brings forty years of dealership experience to a forward-looking conversation about artificial intelligence and its profound impact on the equipment industry. Drawing from his unconventional career path—spanning automotive, medium-duty, construction equipment, agricultural machinery, forestry, and recycling sectors—Troy shares how he's leveraged AI to transform traditional business operations without eliminating jobs.
The conversation takes a fascinating turn as Troy reveals how he quietly began exploring AI applications around 2020, using it to process vast amounts of data from disparate sources. Rather than replacing employees, Troy's approach creates what he calls "augmented personalities"—empowering people with tools that help them work smarter and more effectively. "I'm not trying to displace people," he emphasizes. "What I'm trying to do is give that person an augmented tool set to be more effective at their job, because then they get a happier employee experience and better customer experience."
Particularly compelling is Troy's practical approach to AI implementation in dealerships. He advocates for horizontal integration across departments followed by vertical deepening within each, approaching transformation iteratively rather than attempting overnight change. This measured strategy helps businesses avoid the pitfalls of hasty implementation while still making meaningful progress. Troy shares how these approaches have delivered tangible results, such as improving inventory turns from industry standards of 2-3 to an impressive 6+ through data-driven management.
The discussion also addresses broader implications, including projections that AI and robotics could reduce the American workforce by 50% by 2030. This reality underscores why Troy's human-centered approach to technology matters so much—focusing on augmentation rather than replacement. Whether you're a dealership executive contemplating AI adoption, a parts and service manager looking to improve operational efficiency, or simply curious about how traditional industries are navigating technological transformation, this conversation offers valuable insights into creating a future where technology enhances rather than replaces human potential.
Visit us at LearningWithoutScars.org for more training solutions for Equipment Dealerships - Construction, Mining, Agriculture, Cranes, Trucks and Trailers.
We provide comprehensive online learning programs for employees starting with an individualized skills assessment to a personalized employee development program designed for their skill level.
Aloha and welcome to another Candid Conversation. We're going to continue in our series of discussions and conversations on artificial intelligence. Today we're joined by Troy Otmer, who I've known probably for 25 plus years, who is a particularly talented guy, who unfortunately is now being a consultant, which means he helps too many people. But good afternoon, mr Otner, it's good to have you with me.
Speaker 2:Good to see you, ron, as always, always good to catch up and chat. Just, you know, I can't say how much I appreciate when you do reach out and you want to jar me around a little bit. So I'm, I'm excited, I'm motivated to get after it.
Speaker 1:We, we. Before we started recording this, I said that you know you've started in a in a very normal business industry supply chain. You've seen artificial intelligence arrive, you've been able to work with it, you in the consulting work. Now you start looking forward. So I'd like to try and look at the. You know before what you did and what the problems were, what the opportunities were. Here comes the AI. What did that mean to you and how are you using it? So, with that as as kind of the framework, what year did you start in the industry?
Speaker 2:Oh, 1985 officially. So it's about to approach here next month. Wow, 40 years.
Speaker 1:Amazing right.
Speaker 2:Yeah, so yeah, 40 years, amazing, right. Yeah, so yeah, 1985. And I started in the automotive side, as we've discussed before, and then found myself on the medium duty and school bus repair side and then worked my way into heavy construction equipment and tried like hell to go to work for the cat dealer locally and just never get it done. But John Deere dealer decided to give me a shot and the rest is history, so to speak. So you know, starting out back then, you know computers were on vehicles, on tractors, trucks, at a very limited scale as compared to today, but you know technology has just continued to move forward. So you know, kind of fast forward into the, into the 90s, when I joined into the dealer world, you know a whole nother world opened up. You know, and I would still say I would even call it advanced analytics was shown to me. Now I'm talking AS400, ibm System 36, things like that. Things are crazy. And you're writing old school queries and old school green screen, right, and so it was a whole different world. Green screen, right, and so it was a whole different world.
Speaker 2:Today, you know, as I kind of to current times, I would say that the interest for me as I've gone through my career is how to utilize technology to move the needle forward. Work smarter, not harder. Empower people and I've never done that with the intention of replacing people with technology. And there's no secret that there are moments where you'll find a need to. You know, hey, you can do more with less, meaning less people. But oftentimes that is a mistake. When you look back and go, how did we get here? And you figure out, oh, I don't have enough people. Case in point this would be a forward-looking statement. You know, they predict that we're going to need several hundred thousand electricians in the coming years to deal with all the power generation and infrastructure requirements that AI and other things will need to support them. You know, and that number I've heard scale from three to 500,000 electricians alone. Well then you would add plumbers and technicians and mechanics, etc. Because infrastructure requires construction equipment, it requires heavy trucks, it requires all the above.
Speaker 2:So with that in mind, one of my motivations is again coming into when I decided to do consulting. I've been watching the AI thing for the last four to five years and I scratch my head going okay, which way is this going to go? The dealers it doesn't matter what kind of dealer you are. How are they going to embrace this? The OEMs are obviously going to embrace it, but what are the next steps needed to get to the next place?
Speaker 2:So I wanted to take all my experiences as a conventional dealer operator, but one that would always like to stay outside the bubble and try to challenge, as you used to call it. We're going to kill the sacred cow, right. We're going to have a paradigm shift, you know, and I really want to focus on how do we shift into the next paradigm, whatever that may be, and today I guess we're going to go down the rabbit hole as part of your series, I suspect and talk about what AI looks like from an older person like myself or yourself to how we embrace that and go forward, and I got some nifty ideas I want to unload on you.
Speaker 1:Yeah, no, that's cool. The whole thing with this is that it's uncertain. One of our current contributors his name is Ed Gordon and he's written 20 books. He's got a PhD in economics, another one in history, and one of his books is named Future Shock. That's the first one I read of his, and he says in there that by 2030, artificial intelligence and robotics and other things will shrink the American workforce by 50%. Well, we can argue about the year, but I don't disagree with the premise. And that's 90 million people, Correct.
Speaker 1:So what bothers me is we've spent hundreds of trillions of dollars on technology but hardly anything on sociology. So there's chapter A, Chapter B. In the 1880s, society changed the steam engine to an electric engine and I use this illustration often but it took 20 years, a full generation, before the true capacity of the electric engine could be realized. Because the generation that replaced it change is difficult, so they saved money by changing the tool, but they didn't know what to do with the pool. The next generation said well, why don't we try this, why don't we try that? And then chapter three is starting in 1950. Every 10 years there's been a major, significant, major technological advancement. So 1950 was computers, 60s, database 70s, internet 80s, is GPS, blah blah. And it's come so quickly that people have become a little bit shocked. We've become victims of our world Right.
Speaker 1:And then chapter four parents and grandparents. Again, this is from a book I read. You know the kind of guy I am, I read books incessantly. So in the 80s there was a book called the Fourth Turning, a turning being a 20-year generation, Expectation, is you list? 80 years, and they talked about parenting as a competition. You're trying to protect your children. Grandparents are just love and, amazingly, I was lucky enough to be raised by my grandmother because both my parents worked. My grandmother got a master's degree in 1915. I don't think she ever said no to me. Whenever I was asking silly things, we would talk about it in paragraphs. But my sister comes along and all of a sudden there's two little ones and granny sent me off to school because as soon as my sister was walking cause she couldn't keep up with both of us. So I'm one of the few people you know the repeated kindergarten. So chapter five.
Speaker 1:Then again, because of my weird view of the world, I believe that we are looking at the 22nd century, 21st century form of slavery, that we have a small percentage of people, the aristocracy, in this case, the intellectuals who are going to be bouncing around to the edges. They're curious people, they're going to be trying things, they're going to fail at a whole bunch of stuff. But the rest of us aren't inclined that way. We get into ruts. So that goes back to the parenting, grandparent side. We're taught to be obedient by our parents to protect us. Then we go to school. We're taught to be obedient to learn. Then we leave school whether it's high school, technical university, whatever and we're trained how to do a job and told you know, work on that, get faster at it, make fewer mistakes, You'll be fine.
Speaker 1:And then our life takes off. We find a spouse, male or female, you get married, you get a house, you buy a car, you have a child, you get a bigger house and all. So all. We never have enough money. So we're conscious and careful and all that good stuff. You get to my age. You can't spend all the money you got. So you have to learn how to spend and you don't know how to. But they never look over the wall. They don't have time to look over the wall, Right.
Speaker 1:So here comes artificial intelligence and our purpose at Learning Without Scars is to help people identify their potential individual and professional potential. That's a tough job. I like to see people like you know. I don't know what I'm going to be when I grow up, because I really, you know, there's so many options that you have in life, so how the hell can you choose?
Speaker 1:I left school and, because of my age, I went to university when I was 16. I didn't have a clue what to do. I took mathematics and physics and I realized after about six months I was either going to have to get a master's or doctorate if I wanted to take advantage. And that's not me. I'm too much of a generalist. So here comes artificial intelligence, here comes robotics, here comes the supply chain. So there's the next bump, Right, the number of levels in the supply chain has compressed and COVID has exposed us to different things that we never otherwise would have experienced experienced. So you getting into consulting, you looking at AI for the last four or five years and provoking me with some things that I'm going to find interesting. Neither of us know what the hell's coming up. We have all kinds of things we want to have happen, Right? I don't know what's going to happen. You're not going to be able to see it. I won't.
Speaker 2:Well, don't count yourself out yet, but you know it's happening fast.
Speaker 1:Yeah, I know, I tease people. Only the good die young, so I'm here for a long time. Baby, I know, yeah.
Speaker 2:Well, you know, one of the things you know, going back to how, how you opened, is you know, tell us how you got into business as being a conventional dealer guy, and I know you're you're not pigeonholing me as conventional by any means, you're just simply referencing that, you know. Yeah, because I'm very unconventional in my management style, my leadership style, and I don't I don't normally say management, it's more of a leader focused. You know, my job is to be a tool and everybody that works for me be a tool in their toolbox to empower them to achieve what is needed in the company. And you know what, when they do that, I then arrive and I have done my job and, and that's how I've always led, I've not, I've not always been real popular with with other leadership, because I take that approach. But you know the the financial results of what I've left behind speak for themselves. And I'm also unconventional because I've crossed over from, you know, automotive to truck, to construction, to ag, forestry and also into recycling and aggregate. And you know, nobody talks a lot or much about recycling or aggregate because they just assume it goes with a construction or ag or forestry dealer format.
Speaker 2:But it there's a whole nother level of complexity when you get into, like, for example, covid. You know, paper mills remember the shortage of uh toilet paper and paper towels well, well, in that particular era, I was working with paper mills providing co-gen recycled alternative fuels to help them produce. And paper mills produce, you know, 50 to 60% of their own electricity. Anyway. They don't put it back to the grid. They could, but they typically they're doing that because they're producing steam to make paper, et cetera. But, long story short, you know, covid exposed a lot of weaknesses in not only our infrastructure but our supply chain and how companies do business. And you know, even and I'm thinking back to 2017 18 range people were rattling around about ai. You know. You know, sam altman didn't just wake up one morning here two or three years ago and create open ai and chat, gpt, etc. Yeah, that stuff's been on the books for a while and, uh, people been, you know, working on that.
Speaker 2:So you know, for me, being a conventional dealer guy, I want, I asked myself a few years ago I said, how, how can I improve what I do? And like step function improvement, so similar to what an engineer would be doing as they're trying to go through a process and I thought, okay, I'm going to invest in this AI. I'm not going to tell anybody, I'm just going to read and learn. And, like you, I read crazy. You know I read old books, current books, different. You know.
Speaker 2:I listen to podcasts, I listen to alternative points of view, trying to understand the world around me. So I invested in this and I put it to good use in my last handful of years with Rush, for example, with COVID. It exposed market opportunities as well as market gaps, and I look at gaps as an opportunity rather than a gap. And yeah, so I'm like how do I scale a business that's in a state of decline and this isn't specific to Rush, it would apply to anybody I would have been working for and how do I find these customers? So I have these different data sets over here, and in the past I would use Excel or even Tableau. Today, or Power BI or even, way back, crystal Reports and so on and all these different tools to distill that information down into a workable data set.
Speaker 2:But then what do you do with it? Well, with the advent of AI, you can go through a lot of data. As a matter of fact, my laptop to my left here is working on a client's project. As we speak, it is running several different agents and sub-agents with the specific task it's told to do in these steps. So I look at agents and sub-agents as those are employees to Troy, and so I'm working smarter, not harder.
Speaker 2:Now I still got to read a lot, but you know. So I started investing and doing these things in small incremental steps and I figured out what didn't work. And it's not as simple as hey go to ChatGPT and say, hey, I want to know all the market data in this particular market, and that's all you say, and you should not expect to get anything useful from that type of prompt. Prompt writing, as I've learned over the last three years, is critically important to getting product or good quality data back to get a good product ultimately a product, because you don't just run it through the AI and then give it to your client and say, here you go, pay me. No, you have to proof it out, you have to validate and then there's a process.
Speaker 1:So let me interrupt you there for a second. Sure what I am going to translate that saying you got to get data, yep, you got to have computing power, right. You've got to have computing power right, then you've got to have a mind. Well, you've got to have a goal and then you've got to have a mind that can take you from the data processing to the goal, and we call that today algorithms, and there's very few people that can put together algorithms A, b. We're finding out we don't have enough computing power. Like you mentioned, we need millions of electricians because we're going to have data where nuclear power is going to come back.
Speaker 2:Oh, big time yeah.
Speaker 1:There's all manner of things that we know a hell of a lot more of now than we did then, but the quality of data is terrible.
Speaker 2:Horrible, horrible. It's so noisy, ron, people don't use it. That's like people hire me and you, not us together per se, but people like us to come in, pay us to do X, Y and Z, and then they never act on it.
Speaker 1:Okay so let me give you a little anecdote from when I started into consulting in 1980. And I would do operational reviews of businesses, parts of service. And I was lucky, I was reasonably well-known and I'd worked all around the world. So I got jobs and I would go out and I'd do a review in a month. I'd go for a week kind of finding things. Then I'd go back and I'd process things and put together and then I'd go back and present and test and I always ended up saying okay, have you got somebody that's going to be able to implement this? And everybody said sure. And about three or six months later I'd follow up and nobody did anything. So after about a year and a half I added a couple of sentences at the end of the have you got anybody that can do this for you, or would you like me to do it for you? And all of a sudden I had a five-year backlog because everybody wanted somebody else to do it, because nobody had time to do. Things wanted somebody else to do it because nobody had time to do things. So today we've put profit over people and we've shrunk the headcount. We've done it in every aspect of life, from GDP calculations, which is strictly arithmetic of man hours of work versus output.
Speaker 1:Covid brings us our supply chain gaps. I like the term. I call them warts. I use the illustration of CRM Management in the supply chain got really excited about customer relationship management because it was a control issue of how many calls salesmen got. It had nothing to do with making the job better, it was just the boss knew that you had only 17 calls last month and George had 52. What's the matter with you, troy? Right, and then okay. So that changed and it became a little bit better and out comes a product called Salesforce and I'm not dissing any either of them. Right, right, understood. But here comes Salesforce and they don't talk the same language as CRM, one of the things I say.
Speaker 1:When computers came in, I call it moving from paper to glass, because all we did was we took a paper form and we put it on a computer screen. Instead of writing it, we have to type it into a keyboard. Right, and an acquaintance of mine has a couple of MIT PhDs and he's got, I think, 13 patents pending to control a cursor with his eyeball. Oh boy, and I've seen it. It's scary. Some people and it's not me, I carry the luggage. Some people are unbelievably intelligent. So we have this supply chain, we have these gaps, we have this noisy data. We don't have people that know how to put together algorithms. And when we find the people that have algorithms, we criticize them because they're political as any free speech or blah, blah, blah. That's the new slavery. Yeah, so how the hell does a dealer? Another little observation If you look at business systems today, the people that provide the computer systems and there's a small number of them, maybe eight I haven't found one today that has somebody inside that business that knows the customer. They know their business, they know their systems, they know how to design and operate computer programs, blah, blah, blah. But they don't know what people do with that. So how can they create a system? Well, just making it more elegant, more you know, whatever the hell? The terminal.
Speaker 1:I ran data processing for a while and I cut the staff more than 50%, which is why I was put there. And they were and this is 1970, something early, 73 or so. Their job was not supporting the business. Their job was data processing Correct. Their job was writing programs. Their job was not supporting the business. Their job was data processing Correct, their job was writing programs. Their job was operating programs. They really didn't, so we brought people off the operations side to run the systems analysts and that's how we were able to cut it down in half and change things. So if you look at our world, Russia is a good example 20 groups, yes, where you share process improvement, six Sigma, lean, all this stuff. We got a lot of tools. Did you get any training on Six Sigma?
Speaker 2:I did because I went to school myself. I have a Six Sigma white belt certification.
Speaker 1:Yes, your employer did not do it, though did they?
Speaker 2:No no.
Speaker 1:Did anybody give you continuous improvement training? Did anybody give you that kind of out-of-the-box?
Speaker 2:As a general rule. No, the only company that exposed me to a whole new world was Coppers Inc. Coppers with a K, and they worked with McKinsey and company very closely, so one of the big four, and that's what sparked, you know, that interest in consulting it to a higher level with me, that exposure and that continuous education, lean process or lean Six Sigma I mean 5S, s, the whole thing and and it's relevance in everyday um processing. For example, there's this hokey video of a older gentleman I don't even know if he's alive anymore about making toast and he was one of those six Sigma lean process guys and just quirky. But you watch it and you go he's in his wife or his kitchen making toast and he does it all out of order and it's a cluster, and you know. And they showed this video and I thought this is going to be a waste. I left that class that day that McKinsey put on and I'm going wow, I can make toast now I got it going. Wow, I can make toast now I got it. What's?
Speaker 1:funny is, last week McKinsey announced to the world that they were changing their business yes, that it was no longer them going to be going in finding the data, analyzing the data, bringing it back, because people can do that on their own. Now, right, and you know, I love the story, lou Gerstner, who went? He came from McKinsey, went in to run IBM and he saved them. And the story goes that he went into his first management meeting with his direct reports there's 15 to 20 of them and he asked them to name their top three customers. And they couldn't Ask them to name their top three customers and they couldn't. So after four or five people, he stopped, ended the meeting, said we're going to meet again next week. I want you to come with your top three customers. They did, and after they'd gone around the table with the top three customers, he ended the meeting. And what do you think he did next? He took the top three customers and got on an airplane that right and went to visit every single one of those people.
Speaker 1:And what I used to do when I was working at dealerships is every six months, quarterly, depending on how far along it was. What do I do that you like, I do and you want me to continue. What do I do that you don't like I do, and you want me to stop? And what do I do that really doesn't matter to you? Right, and I'm the same as you. You know, a conductor is the only musician that has his back to the customers, that's right.
Speaker 2:Well, ron, that's a good segue is you know you were talking about? Hey, you don't even know who your customers are. And then this gentleman said well, I'm going to go see these top three customers and figure that out from there. So, going back to what you asked me earlier, what did I do different when I started embracing AI? And here's the key I was doing this before AI anyway. And I realized and you know, you, you, you, you know my career, I'm, I'm an operator, I'm a former technician that stumbled into management and leadership, right, and, and then I had a knack for parts and service, ops and general operations overall. But then somehow the light bulb went off on the sales side. Uh, as you know, it's that opened a whole nother world to me.
Speaker 2:And I'm thinking, wait a second. Sales and parts and service they're usually diametrically opposed to each other. They're fighting each other at every possible turn. If you got a rental department, they're in the mix too. They don't know who they're fighting, you know, and so on.
Speaker 2:So one of the goals when I got into these senior leadership positions and I maintain this all the way up into the vice president roles with several companies is I'm going to go see customers as the manager, parts manager, whatever, and so on. I'm going to go see customers at least one day a week and I'm going to go either with salespeople or I'm going to go solo. In particular, any customer that called and had a complaint, if they were local, they're going to see Troy and they can chew on me all. They need to get it out of their system. But my goal was to go out there and make right whatever they thought wasn't right. And hey, out there and make right whatever they thought wasn't right. And hey, the customer's not always right, but you got to figure it out and I try to instill that in people.
Speaker 2:So, with the AI tool, the way I started using it you know, let's say since 2020, is helping me process massive amounts of data. Quiet the noise, make it make sense, know my customer base, know my customer before I go see the customer. So I'm pulling data from the CRM, from the business system, from parts and service and sales CRM, combining that. Then I'm pulling poke data, uccs, rig dig, whatever other data that's out there figuring out who these customers are, whatever other data that's out there figuring out who these customers are. And hey, we used to sell to these people three years ago. Now we're not selling to them. What happened? Are they out of business? Well, guess what?
Speaker 2:With these new tools I mentioned, troy is now an augmented type personality, because I can do more and be more effective at what I'm doing. I'm not trying to displace people. What I'm trying to do is make the people that have these roles that are, as you said earlier, companies put profit before people, meaning less people, more profits, right, less overhead. I'm just trying to give that person an augmented tool set to be able to go, be more effective and do their job, because then they get happier employee experience is better customer experience, et cetera.
Speaker 2:But I think that is really the directions, and one of the bullet points I put together is why dealerships need AI now. They've needed this for a long time now. You know they've needed this for a long time. But what dealerships really need above an AI or any fancy system? They need to all step back and take a hard look in the mirror pretty much all of them, even the ones that say, no, we got it figured out. You know I do consulting with Bain Company as well, ron, and I do it. You know these are small projects, but I know a lot of people within the organization here in Houston, a lot of former C-suite people that now have retired and work for Bain or partners at Bain, and you know those people have been immensely helpful in guiding me to see the world in a much broader scale. So again, I'm not this is not an egotistical statement but I don't look at it as it can the world around me as a conventional dealer guy anymore.
Speaker 1:So let me let me translate that a little bit. I think, and you know when when was early 2000s. You were first in a class with me, right?
Speaker 2:No 1999.
Speaker 1:Yeah, late 90s Call me a liar for a year, you know, late 90s, yeah. What was interesting is and you know I've had a lot of thousands of people in front of me in the classroom and you remember the ones that are the pain in the ass, and Troy was one I was I mean that in the best possible way, because he pushed back on things, he didn't accept things if he didn't understand it, and I don't think anybody should accept anything they don't understand. So leadership to me is understanding, acceptance and commitment, and what we miss in society is the acceptance. We don't give people a chance to fight about it. Here comes management and they put forward a proclamation from the above and say this is what we're going to do, right, and Helen and George down below say boy, that's stupid. Why the hell do we do that? You know, so it gets nuts.
Speaker 1:So there was a company in COVID outside of Atlanta in Georgia, small company, 40, 50 people. And the owner said okay, everybody's going to work at home, but we're going to have two nights a month where we all get together and I'm going to buy you dinner and we're going to spend three, four hours talking about work. And they did that and you know what's the good news, what's the bad news. What are you having trouble with? What are you doing well at? And they shared and all the rest, and it went on for three years or so, whatever it was, that we were forced to work from home.
Speaker 1:So COVID is over and you can go back to the office. So he has a meeting. He said okay, let's think about this. Do you want to continue the way we've been doing it the last couple of three years, or do you all want to come back to the office? And nobody wanted to go back to the office. So he said okay, if you're sure I'm going to sell all of the office, everything in it, all the rest of the nonsense. I'll tell you what. Whatever money I make from that, I'll split it with you. I'll keep half and we'll put the other half with the rest of you. Do you think there's?
Speaker 2:anybody that's going to leave that company. No, that's perfect. Yeah, that's a unique individual that sees the world in the proper perspective, correct?
Speaker 1:And that's the kind of leadership that we need. You know, joel Barker who's this guy that made the term paradigm common in the English language said that leaders build bridges. And you know, attitude is everything. And allowing people if you don't make mistakes, you're not learning. And allowing people to make mistakes without killing them typically you make a mistake somebody's going to jump your bones.
Speaker 1:I got fired five or six times from the president. I got home once. He said what are you doing at home? And I'm 22 at the time and he's probably 62. And I remember sitting on the side of the Mississippi River on a balcony. He's drinking dark rum and tonic with lime and I'm drinking a beer because what do I know at 22?. And I said what is that? He said this is. Then he tells me well, why do you drink that? He said it's summer, it's refreshing. He said try it. I've been drinking that ever since. But that man, if he was still alive today and the phone rang saying he was in trouble, I'd just ask him where he was and I'm gone. Yep, all of us have people like that, that's right.
Speaker 2:Agreed.
Speaker 1:In a church. Sometimes it's a teacher, sometimes it's a friend, sometimes it's an employee, sometimes it's a competitor. There was two little anecdotes from the Northwest. I was asked to do a survey of business for Seattle, Tacoma so I'm talking to all of the owners of equipment and it was just a small survey and one woman was the chairman of a construction company and she brought back the comment that she hated Napa. I said oh really, why Don't you find that saves you a lot? Oh, yeah, it saves us money, but why don't you like it? Well, here comes this babe driving a truck like she's looking for you know, the hooters, yeah, and everybody stops work and they go to see what she's got and none of them have anything to do with her, but they want to see her. And it drove me crazy. She said another one I'm talking to.
Speaker 1:I kind of a it was a different type of circumstance and I put in female product support salesman and this is back in the late eighties, early nineties and that was really weird. Nobody did that Correct and I got complaints from wives of customers. Your salesman's out with my husband, husband after work in the bar. This is not good Like I don't know. Get over that type of but there's all this stuff you knew intuitively. Your mind operated on problem-solving methodologies, making toast. You know that kind of thing, deming. Why did they have to go to Japan? Why couldn't it happen here? Right, you know data analytics. There's unbelievable power in information. I believe people are afraid that they're not able to hold on to their power position or their income, and that's why they resist change.
Speaker 2:Well, and I think there's also, whether you know, in publicly traded companies you have the pressure to live up to the shareholder's expectation. Continuous shareholder value increases, right on a quarterly basis, right, and that's a lot of pressure. I get that. But in privately held companies it's different. But it's not different because it's at the end of the day. It still gets distilled down to one simple thing You're trying to squeeze as much profit out of the business and reduce your overhead, all at the same time, regardless of what your margin's doing. You could have the best economy and you're just going and you're not really paying attention and you take your eye off the expenses and now you're spending too much, but you don't know. But let's assume you're running your business very efficiently and you're running the business as if you're in a lean operation mode. So you're going to run as lean as needed, but not too lean to create bigger problems. But at the same time you're going to maximize expense burden or reduction where needed, with opportunity to make margin, or reduction where needed, with opportunity to make margin. And oftentimes they start squeezing the first thing companies do they're not looking at price optimization, whether it's for parts or service or sales. They're not calculating the carrying cost of inventory trucks, tractors. The carrying cost of inventory trucks, tractors, and the hidden carrying cost, which isn't always seen in real time, like the floor plan carrying cost of trucks or what have you, is the ugly side of the parts inventory and that's where AI really, I think, comes in from a data analytics, comes in from a data analytics, I think, having these different agents running in real time telling you, you know, you set up the parameters and you're looking, hey, I just we sold 12 of these today. We need to have 12 more on order by the end of the day for tomorrow, because we're selling at least 10 every day, right, or whatever the calculation is, and and that's that's one of the things that I used to do manually. You, you would do it manually, then you figure out the business system. You then you'd set your you know, your reorder points, uh, based on your stock orders. When stock orders were once a week, the world was really different with how you ordered, versus now, stock orders are essentially every day and you're getting them every other day, based on shipping, et cetera, you know. So there's a whole lot of nuances that I think.
Speaker 2:If companies would, you know, for example, you know, I would another bullet point the horizontal integration of, you know, ai from a departmental standpoint, going left to right, cross-departmental collaboration. You would also call that in the conventional method. See, I wrote this in the conventional way as well. Oddly enough, we didn't even practice that. But then from there, once you establish that horizontal integration of where AI is applicable with the right processes as well, integration of where AI is applicable with the right processes as well and the right people, then you go vertical from there in each department and then you start stepping it up. Now you can't eat the whole pie at once, so to speak, but you know, once you go vertical or in the conventional manner, or speak would be deepening within each department, would be deepening within each department, you know. And then I could just keep expanding on these, but you know it's. You have to go through what I would categorize as a strategic transformation.
Speaker 2:Ron, and your two previous podcasts with Ron and it was Nick, right, ron first one and Nick second, yep, and you know they I think they got it right with everything they said. And there's more to say, because you know you're still doing this right, this new industrial revolution, or what have you, and what does it mean? Well, covid flipped the world upside down In some ways. Maybe it needed flipping upside down, but it exposed all these gaps, and again I say gaps are opportunities, yeah. So you then pivot and you go. What do you want that to look like? And that's where I want to help dealers today is, or even other companies and I'm working with some other clients that aren't in the dealer space at all. It's just about transforming how they process business. You know the steps and what that looks like incremental steps and I said look, we're not going to try to change all this overnight. Let's look at this in an iterative manner to where we don't find ourselves in a corner, so to speak.
Speaker 1:What's interesting is you know when did Altman didn't just start chat GPT in the 90s? You know when in America the first public acknowledgement of GPT and AI was?
Speaker 2:Well, it was IBM Watson, wasn't it?
Speaker 1:No, it was 1954 at a symposium at Dartmouth. Okay, well, I wasn't here yet. Oh, I understand that, but that's my point. It's 60 years ago, yeah, true. And so let me come another way. Unless we're going to get trained, unless you're curious and stubborn and you're going to do it yourself like you did, I was sent one year.
Speaker 1:The dealership that I worked for sent people to different parts of the world with specific subjects that they were responsible for. So I was spent to Europe once to look at warehouses and they weren't called distribution centers in those days, but they were warehouses. So I went to Stuttgart, germany, and there's the distribution center for Kodak for Europe, the only one they had and I went to the door with a guy, opened the door and the lights came on. This is 1973-ish. There was nobody working in there. The plant was completely automated no guard dog, nothing. The computers were picking parts, the conveyors were taking them up, they were being packed, they were being strapped onto pallets, they were being shipped out, their trucks would come in and they would take them off the dock and that was the end of it. Wow, yeah. And then, a couple of years later, I'm in Chicago another quote distribution center, and I'll come back Stuttgart was all cranes. Okay, right, in Chicago the guys came to work and they were given their day's work and basically told when you finish that you can go home. So they were picking parts at the 100, 115 line items an hour rate and I'm at the Caterpillar dealer at 15. Right, saying, wait a second, what's wrong with this? And they were also cranes. Then, before I started doing distribution, designs et cetera, I went to Europe again and I saw a company that had cranes, but they were 30 or 40 meters tall, 100 meters long lines, et cetera.
Speaker 1:I went to Europe again and I saw a company that had cranes, but one of the they were 30 or 40 meters tall, a hundred meters long. Oh, wow, computer driven, no people. One aisle was down. I said what that's all about? He said well, you've got a problem with the crane, so what do you do with all the inventory? Well, we have to duplicate it across every line. I said, oh, and what was wrong was there was a deflection on the mast of the crane so that it couldn't move. Ah, okay, caterpillar put their distribution center in with cranes and what that meant was there was a fixed productivity rate. The crane could go from the front to the back in 22 seconds. Okay, it could pick the part in seven seconds. So the best you could do was to a minute. The best you could do then was 120 in an hour and so I went to trucks so they could go out of the aisle and into the aisle. So traffic became my problem and I didn't have to duplicate things.
Speaker 1:Today there's between 800,000 and a million SKUs stock keeping units in a typical distribution construction equipment guy. When I was there was 286 000. Right at that time there were 15 just a little under 15 000 parts that caterpillar sold 12 times in the year in the world or more, and they put it on their price tape. So that's how I know about it. And I said to caterpillar so what the hell do I need anything more than those in my inventory? And they put it on their price tape. So that's how I know about it. And I said to Caterpillar I said why the hell do I need anything?
Speaker 2:more than those in my inventory. They don't like to hear that.
Speaker 1:Of course not. But that's what I went and did. And then what everybody said is your availability is going to go to hell. I said, okay, fine, we'll design this stuff that's under 12. We'll sit down and have a meeting. I sell 10 in a year. How many do you want me to have? And we did that. And then I got into a fight about where are you going to start stocking it? Well, it's two and six, three and 12 type of thing. Right, that's from the 1600s, for goodness sake. Economic order, quantity, the Kern-Noten program, that's 1905. It worked for the last 50 years and we still use it. So we get into the circumstance.
Speaker 1:I went to 12 and higher, had one or two under 12, down to four didn't have anything that was three or less. I charged restocking to the shop. You can't do that. Sales department wants a discount Every month. I published what the amount of money I gave up was and I compared it to the profit they got on their equipment. They didn't like that and they didn't recover it either. Well, they don't cover it. I gave them a discount and they got back a profit.
Speaker 2:Exactly.
Speaker 1:Yeah. Recently I talked to salesmen and sales management and asked the question. It's August now, but I've been doing this since about May. I said what are you going to sell next year? I haven't got a clue. I got to wait till the last quarter. I said really, you got a territory? Yeah, fixed amount of customers. Yeah, you got a territory. Yeah, fixed amount of customers. Yeah, you know the machine population, all those customers yeah, do you know how much you sell in parts and service and rental to these customers? Yeah, do you have life cycle management statistics from your manufacturer? Yeah, well, why can't you tell me that next May you're going to replace this machine for George? And they look at me like I've grown another horn. It's trouble is there's not very many people that ask those questions because we're afraid to look like idiots. I think Something else. I don't know what it is. I'm not smart enough.
Speaker 2:Well, similarly with John Deere, when they rolled out DPM or dealer parts management, my dealer group, along with a handful of other high-performing dealers, we would not go on it because we were outperforming their turns, you know, and their turns they were happy if you had, you know, turns of three, right, and I'll have turns of six, and Rush has always had high. They want you to be, you know, at that time, five plus now, seven plus right, if it are higher in some cases. You, you know certain ones. You're going to have that mix where it's slow, but you're, you know. So we were routinely doing battle with them and I'm like, you know so, the dealer 20 groups. We would brainstorm together about how do we deal with here, you know, because they were pushing on us.
Speaker 1:They asked me to talk at one of their fall meetings in Phoenix and it was probably before that and they wanted me to talk about DPM and I said I can't. I said, why not? For exactly the reason you're talking about. Yeah, the one of the most important measures in a business is return on capital employed. Correct, I'm going to invest a million dollars. How much do I want to get back? Yeah, and inventory turnover is one of those components.
Speaker 1:So when I started, the average turnover in at the Caterpillar network was around two. The average for the AED Associated Equipment Distributors was between one and three quarters and two and a quarter. The gross profit that was given by the manufacturer to the dealer never got touched in the late 60s, early 70s until we got matrix pricing and then different things and I'm one of the idiots that started that. So we'd have 25% gross profit to two time turnover. Your return on capital employed is 50%. So if I give you a million dollars to invest in a business, you're going to give me back 500,000. That doesn't sound like a good plan.
Speaker 2:No.
Speaker 1:So my gig came. Well, I want closer to 35% let's call it 33% and I want to turn over a six. So I'm going to go from 60 to 100 or 50 to 180. I'm going to give you a million. I want you to give me back a million and three quarters. I'll let you have 50 as a bonus. Yeah Well, right, and what's wrong with that? So it it becomes clear, troy, to me in our discussion, we're going to have to do more of this. We're gonna have to do another one. Yes, because I'm going to have to close this up so that I can stay within my time limits and stuff. But what have you thought of this discussion?
Speaker 2:well, I liked it. I. I think there's more obviously more to discuss and when you're ready, just let me know In the next couple of weeks.
Speaker 1:Okay, Not next week, but the week after. Let me know when you've got time what I'm doing.
Speaker 2:I'm not looking to recreate a new chat GPT replica. Actually, the tools are already there, and I'm working with several companies now, some based here in Texas, that are in the data analytics space, not only for marketing but also for, you know, parts service all kinds of different twists and turns and every one of those conversations include AI at a different level. As a matter of fact, I got two meetings on Friday in Austin with two different companies where we have a strategic partnership and me being that unconventional dealer guy you know those are. You know I'm helpful to those guys, being able to bridge that gap efficiently, and so I'm not trying to make a name for myself as some AI specialist. I'm just simply good at bringing all the pieces of the puzzle together and leaving it better than I found it, and that's really what I want to do, ron, is continue down that path.
Speaker 1:And I'm sure you're going to do it and I'm sure you're going to be successful at it, and I want to thank you very much for this time and the next one and for everybody listening. I hope this provoked some thinking and I thank you for being with us and I look forward to being with you for another candid conversation, mahalo.
