August 20, 2025 • 52 mins
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.
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Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Speaker 1 (00:02):
Aloha and welcome to another Candid Conversation.
We're going to continue in ourseries of discussions and
conversations on artificialintelligence.
Today we're joined by TroyOtmer, who I've known probably
for 25 plus years, who is aparticularly talented guy, who
unfortunately is now being aconsultant, which means he helps
(00:24):
too many people.
But good afternoon, mr Otner,it's good to have you with me.
Speaker 2 (00:31):
Good to see you, ron, as always, always good to catch
up and chat.
Just, you know, I can't say howmuch I appreciate when you do
reach out and you want to jar mearound a little bit.
So I'm, I'm excited, I'mmotivated to get after it.
Speaker 1 (00:50):
We, we.
Before we started recordingthis, I said that you know
you've started in a in a verynormal business industry supply
chain.
You've seen artificialintelligence arrive, you've been
able to work with it, you inthe consulting work.
Now you start looking forward.
(01:12):
So I'd like to try and look atthe.
You know before what you didand what the problems were, what
the opportunities were.
Here comes the AI.
What did that mean to you andhow are you using it?
So, with that as as kind of theframework, what year did you
start in the industry?
Speaker 2 (01:32):
Oh, 1985 officially.
So it's about to approach herenext month.
Wow, 40 years.
Speaker 1 (01:43):
Amazing right.
Speaker 2 (01:52):
Yeah, so yeah, 40 years, amazing, right.
Yeah, so yeah, 1985.
And I started in the automotiveside, as we've discussed before
, and then found myself on themedium duty and school bus
repair side and then worked myway into heavy construction
equipment and tried like hell togo to work for the cat dealer
locally and just never get itdone.
But John Deere dealer decidedto give me a shot and the rest
(02:16):
is history, so to speak.
So you know, starting out backthen, you know computers were on
vehicles, on tractors, trucks,at a very limited scale as
compared to today, but you knowtechnology has just continued to
move forward.
So you know, kind of fastforward into the, into the 90s,
when I joined into the dealerworld, you know a whole nother
(02:39):
world opened up.
You know, and I would still sayI would even call it advanced
analytics was shown to me.
Now I'm talking AS400, ibmSystem 36, things like that.
Things are crazy.
And you're writing old schoolqueries and old school green
(03:00):
screen, right, and so it was awhole different world.
Green screen, right, and so itwas a whole different world.
Today, you know, as I kind of tocurrent times, I would say that
the interest for me as I'vegone through my career is how to
utilize technology to move theneedle forward.
Work smarter, not harder.
Empower people and I've neverdone that with the intention of
(03:24):
replacing people with technology.
And there's no secret thatthere are moments where you'll
find a need to.
You know, hey, you can do morewith less, meaning less people.
But oftentimes that is amistake.
When you look back and go, howdid we get here?
And you figure out, oh, I don'thave enough people.
Case in point this would be aforward-looking statement.
(03:47):
You know, they predict thatwe're going to need several
hundred thousand electricians inthe coming years to deal with
all the power generation andinfrastructure requirements that
AI and other things will needto support them.
You know, and that number I'veheard scale from three to
500,000 electricians alone.
(04:07):
Well then you would addplumbers and technicians and
mechanics, etc.
Because infrastructure requiresconstruction equipment, it
requires heavy trucks, itrequires all the above.
So with that in mind, one of mymotivations is again coming into
when I decided to do consulting.
(04:28):
I've been watching the AI thingfor the last four to five years
and I scratch my head goingokay, which way is this going to
go?
The dealers it doesn't matterwhat kind of dealer you are.
How are they going to embracethis?
The OEMs are obviously going toembrace it, but what are the
(04:49):
next steps needed to get to thenext place?
So I wanted to take all myexperiences as a conventional
dealer operator, but one thatwould always like to stay
outside the bubble and try tochallenge, as you used to call
it.
We're going to kill the sacredcow, right.
We're going to have a paradigmshift, you know, and I really
(05:13):
want to focus on how do we shiftinto the next paradigm,
whatever that may be, and todayI guess we're going to go down
the rabbit hole as part of yourseries, I suspect and talk about
what AI looks like from anolder person like myself or
yourself to how we embrace thatand go forward, and I got some
(05:37):
nifty ideas I want to unload onyou.
Speaker 1 (05:40):
Yeah, no, that's cool .
The whole thing with this isthat it's uncertain.
One of our current contributorshis 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 FutureShock.
That's the first one I read ofhis, and he says in there that
(06:02):
by 2030, artificial intelligenceand robotics and other things
will shrink the Americanworkforce by 50%.
Well, we can argue about theyear, but I don't disagree with
the premise.
And that's 90 million people,Correct.
(06:22):
So what bothers me is we'vespent hundreds of trillions of
dollars on technology but hardlyanything on sociology.
So there's chapter A, Chapter B.
In the 1880s, society changedthe steam engine to an electric
engine and I use thisillustration often but it took
(06:45):
20 years, a full generation,before the true capacity of the
electric engine could berealized.
Because the generation thatreplaced it change is difficult,
so they saved money by changingthe 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 isstarting in 1950.
(07:09):
Every 10 years there's been amajor, significant, major
technological advancement.
So 1950 was computers, 60s,database 70s, internet 80s, is
GPS, blah blah.
And it's come so quickly thatpeople have become a little bit
(07:30):
shocked.
We've become victims of ourworld Right.
And then chapter four parentsand grandparents.
Again, this is from a book Iread.
You know the kind of guy I am,I read books incessantly.
So in the 80s there was a bookcalled the Fourth Turning, a
(07:50):
turning being a 20-yeargeneration, Expectation, is you
list?
80 years, and they talked aboutparenting as a competition.
You're trying to protect yourchildren.
Grandparents are just love and,amazingly, I was lucky enough
to be raised by my grandmotherbecause both my parents worked.
My grandmother got a master'sdegree in 1915.
(08:14):
I don't think she ever said noto me.
Whenever I was asking sillythings, we would talk about it
in paragraphs.
But my sister comes along andall of a sudden there's two
little ones and granny sent meoff to school because as soon as
my sister was walking cause shecouldn't keep up with both of
us.
So I'm one of the few peopleyou know the repeated
(08:34):
kindergarten.
So chapter five.
Then again, because of my weirdview of the world, I believe
that we are looking at the 22ndcentury, 21st century form of
slavery, that we have a smallpercentage of people, the
(08:58):
aristocracy, in this case, theintellectuals who are going to
be bouncing around to the edges.
They're curious people, they'regoing to be trying things,
they're going to fail at a wholebunch of stuff.
But the rest of us aren'tinclined that way.
We get into ruts.
So that goes back to theparenting, grandparent side.
We're taught to be obedient byour parents to protect us.
(09:20):
Then we go to school.
We're taught to be obedient tolearn.
Then we leave school whetherit's high school, technical
university, whatever and we'retrained how to do a job and told
you know, work on that, getfaster at it, make fewer
mistakes, You'll be fine.
And then our life takes off.
We find a spouse, male orfemale, you get married, you get
a house, you buy a car, youhave a child, you get a bigger
(09:42):
house and all.
So all.
We never have enough money.
So we're conscious and carefuland all that good stuff.
You get to my age.
You can't spend all the moneyyou got.
So you have to learn how tospend and you don't know how to.
But they never look over thewall.
They don't have time to lookover the wall, Right.
So here comes artificialintelligence and our purpose at
(10:07):
Learning Without Scars is tohelp people identify their
potential individual andprofessional potential.
That's a tough job.
I like to see people like youknow.
I don't know what I'm going tobe when I grow up, because I
really, you know, there's somany options that you have in
life, so how the hell can youchoose?
I left school and, because of myage, I went to university when
I was 16.
I didn't have a clue what to do.
(10:29):
I took mathematics and physicsand I realized after about six
months I was either going tohave to get a master's or
doctorate if I wanted to takeadvantage.
And that's not me.
I'm too much of a generalist.
So here comes artificialintelligence, here comes
robotics, here comes the supplychain.
So there's the next bump, Right, the number of levels in the
(10:51):
supply chain has compressed andCOVID has exposed us to
different things that we neverotherwise would have experienced
experienced.
So you getting into consulting,you looking at AI for the last
four or five years and provokingme with some things that I'm
(11:12):
going to find interesting.
Neither of us know what thehell's coming up.
We have all kinds of things wewant to have happen, Right?
I don't know what's going tohappen.
You're not going to be able tosee it.
I won't.
Speaker 2 (11:28):
Well, don't count yourself out yet, but you know
it's happening fast.
Speaker 1 (11:36):
Yeah, I know, I tease people.
Only the good die young, so I'mhere for a long time.
Baby, I know, yeah.
Speaker 2 (11:43):
Well, you know, one of the things you know, going
back to how, how you opened, isyou know, tell us how you got
into business as being aconventional dealer guy, and I
know you're you're notpigeonholing me as conventional
by any means, you're just simplyreferencing that, you know.
Yeah, because I'm veryunconventional in my management
(12:04):
style, my leadership style, andI don't I don't normally say
management, it's more of aleader focused.
You know, my job is to be atool and everybody that works
for me be a tool in theirtoolbox to empower them to
achieve what is needed in thecompany.
And you know what, when they dothat, I then arrive and I have
(12:28):
done my job and, and that's howI've always led, I've not, I've
not always been real popularwith with other leadership,
because I take that approach.
But you know the the financialresults of what I've left behind
speak for themselves.
And I'm also unconventionalbecause I've crossed over from,
(12:49):
you know, automotive to truck,to construction, to ag, forestry
and also into recycling andaggregate.
And you know, nobody talks alot or much about recycling or
aggregate because they justassume it goes with a
construction or ag or forestrydealer format.
(13:09):
But it there's a whole notherlevel of complexity when you get
into, like, for example, covid.
You know, paper mills rememberthe shortage of uh toilet paper
and paper towels well, well, inthat particular era, I was
working with paper millsproviding co-gen recycled
(13:31):
alternative fuels to help themproduce.
And paper mills produce, youknow, 50 to 60% of their own
electricity.
Anyway.
They don't put it back to thegrid.
They could, but they typicallythey're doing that because
they're producing steam to makepaper, et cetera.
But, long story short, you know, covid exposed a lot of
(13:53):
weaknesses in not only ourinfrastructure but our supply
chain and how companies dobusiness.
And you know, even and I'mthinking back to 2017 18 range
people were rattling aroundabout ai.
You know.
You know, sam altman didn'tjust wake up one morning here
two or three years ago andcreate open ai and chat, gpt,
(14:16):
etc.
Yeah, that stuff's been on thebooks for a while and, uh,
people been, you know, workingon that.
So you know, for me, being aconventional dealer guy, I want,
I asked myself a few years agoI said, how, how can I improve
what I do?
And like step functionimprovement, so similar to what
(14:39):
an engineer would be doing asthey're trying to go through a
process and I thought, okay, I'mgoing 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.
I listen to podcasts, I listento alternative points of view,
(15:03):
trying to understand the worldaround me.
So I invested in this and I putit to good use in my last
handful of years with Rush, forexample, with COVID.
It exposed market opportunitiesas well as market gaps, and I
look at gaps as an opportunityrather than a gap.
And yeah, so I'm like how do Iscale a business that's in a
(15:27):
state of decline and this isn'tspecific to Rush, it would apply
to anybody I would have beenworking for and how do I find
these customers?
So I have these different datasets over here, and in the past
I would use Excel or evenTableau.
Today, or Power BI or even, wayback, crystal Reports and so on
(15:48):
and all these different toolsto distill that information down
into a workable data set.
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 laptopto my left here is working on a
client's project.
As we speak, it is runningseveral different agents and
(16:10):
sub-agents with the specifictask it's told to do in these
steps.
So I look at agents andsub-agents as those are
employees to Troy, and so I'mworking smarter, not harder.
Now I still got to read a lot,but you know.
So I started investing anddoing these things in small
(16:30):
incremental steps and I figuredout what didn't work.
And it's not as simple as heygo to ChatGPT and say, hey, I
want to know all the market datain this particular market, and
that's all you say, and youshould not expect to get
anything useful from that typeof prompt.
Prompt writing, as I've learnedover the last three years, is
(16:54):
critically important to gettingproduct or good quality data
back to get a good productultimately a product, because
you don't just run it throughthe AI and then give it to your
client and say, here you go, payme.
No, you have to proof it out,you have to validate and then
there's a process.
Speaker 1 (17:12):
So let me interrupt you there for a second.
Sure what I am going totranslate that saying you got to
get data, yep, you got to havecomputing power, right.
You've got to have computingpower right, then you've got to
have a mind.
Well, you've got to have a goaland then you've got to have a
mind that can take you from thedata processing to the goal, and
(17:39):
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 haveenough computing power.
Like you mentioned, we needmillions of electricians because
we're going to have data wherenuclear power is going to come
back.
Speaker 2 (17:59):
Oh, big time yeah.
Speaker 1 (18:00):
There's all manner of things that we know a hell of a
lot more of now than we didthen, but the quality of data is
terrible.
Speaker 2 (18:10):
Horrible, horrible.
It's so noisy, ron, peopledon't use it.
That's like people hire me andyou, not us together per se, but
people like us to come in, payus to do X, Y and Z, and then
they never act on it.
Speaker 1 (18:26):
Okay so let me give you a little anecdote from when
I started into consulting in1980.
And I would do operationalreviews of businesses, parts of
service.
And I was lucky, I wasreasonably well-known and I'd
worked all around the world.
So I got jobs and I would goout and I'd do a review in a
month.
I'd go for a week kind offinding things.
(18:48):
Then I'd go back and I'dprocess things and put together
and then I'd go back and presentand test and I always ended up
saying okay, have you gotsomebody that's going to be able
to implement this?
And everybody said sure.
And about three or six monthslater I'd follow up and nobody
did anything.
So after about a year and ahalf I added a couple of
(19:09):
sentences at the end of the haveyou got anybody that can do
this for you, or would you likeme to do it for you?
And all of a sudden I had afive-year backlog because
everybody wanted somebody elseto do it, because nobody had
time to do.
Things wanted somebody else todo it because nobody had time to
do things.
So today we've put profit overpeople and we've shrunk the
(19:33):
headcount.
We've done it in every aspectof life, from GDP calculations,
which is strictly arithmetic ofman hours of work versus output.
Covid brings us our supply chaingaps.
I like the term.
I call them warts.
(19:54):
I use the illustration of CRMManagement in the supply chain
got really excited aboutcustomer relationship management
because it was a control issueof how many calls salesmen got.
It had nothing to do withmaking the job better, it was
(20:15):
just the boss knew that you hadonly 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 alittle bit better and out comes
a product called Salesforce andI'm not dissing any either of
them.
Right, right, understood.
But here comes Salesforce andthey don't talk the same
language as CRM, one of thethings I say.
(20:39):
When computers came in, I callit moving from paper to glass,
because all we did was we took apaper form and we put it on a
computer screen.
Instead of writing it, we haveto type it into a keyboard.
Right, and an acquaintance ofmine has a couple of MIT PhDs
and he's got, I think, 13patents pending to control a
cursor with his eyeball.
Oh boy, and I've seen it.
(20:59):
It's scary.
Some people and it's not me, Icarry the luggage.
Some people are unbelievablyintelligent.
So we have this supply chain,we have these gaps, we have this
noisy data.
We don't have people that knowhow to put together algorithms.
(21:21):
And when we find the peoplethat have algorithms, we
criticize them because they'repolitical as any free speech or
blah, blah, blah.
That's the new slavery.
Yeah, so how the hell does adealer?
Another little observation Ifyou look at business systems
(21:43):
today, the people that providethe computer systems and there's
a small number of them, maybeeight I haven't found one today
that has somebody inside thatbusiness that knows the customer
.
They know their business, theyknow their systems, they know
how to design and operatecomputer programs, blah, blah,
(22:04):
blah.
But they don't know what peopledo with that.
So how can they create a system?
Well, just making it moreelegant, more you know, whatever
the hell?
The terminal.
I ran data processing for awhile and I cut the staff more
than 50%, which is why I was putthere.
And they were and this is 1970,something early, 73 or so.
(22:28):
Their job was not supportingthe business.
Their job was data processingCorrect.
Their job was writing programs.
Their job was not supportingthe business.
Their job was data processingCorrect, their job was writing
programs.
Their job was operatingprograms.
They really didn't, so webrought people off the
operations side to run thesystems analysts and that's how
we were able to cut it down inhalf and change things.
(22:50):
So if you look at our world,Russia is a good example 20
groups, yes, where you shareprocess improvement, six Sigma,
lean, all this stuff.
We got a lot of tools.
Did you get any training on SixSigma?
Speaker 2 (23:06):
I did because I went to school myself.
I have a Six Sigma white beltcertification.
Speaker 1 (23:12):
Yes, your employer did not do it, though did they?
Speaker 2 (23:15):
No no.
Speaker 1 (23:16):
Did anybody give you continuous improvement training?
Did anybody give you that kindof out-of-the-box?
Speaker 2 (23:24):
As a general rule.
No, the only company thatexposed me to a whole new world
was Coppers Inc.
Coppers with a K, and theyworked with McKinsey and company
very closely, so one of the bigfour, and that's what sparked,
you know, that interest inconsulting it to a higher level
(23:45):
with me, that exposure and thatcontinuous education, lean
process or lean Six Sigma I mean5S, s, the whole thing and and
it's relevance in everyday umprocessing.
For example, there's this hokeyvideo of a older gentleman I
(24:05):
don't even know if he's aliveanymore about making toast and
he was one of those six Sigmalean process guys and just
quirky.
But you watch it and you gohe's in his wife or his kitchen
making toast and he does it allout of order and it's a cluster,
and you know.
And they showed this video andI thought this is going to be a
(24:25):
waste.
I left that class that day thatMcKinsey put on and I'm going
wow, I can make toast now I gotit going.
Wow, I can make toast now I gotit.
What's?
Speaker 1 (24:35):
funny is, last week McKinsey announced to the world
that they were changing theirbusiness yes, that it was no
longer them going to be going infinding the data, analyzing the
data, bringing it back, becausepeople can do that on their own
.
Now, right, and you know, Ilove the story, lou Gerstner,
who went?
(24:55):
He came from McKinsey, went into run IBM and he saved them.
And the story goes that he wentinto his first management
meeting with his direct reportsthere's 15 to 20 of them and he
asked them to name their topthree customers.
And they couldn't Ask them toname their top three customers
and they couldn't.
(25:15):
So after four or five people,he stopped, ended the meeting,
said we're going to meet againnext week.
I want you to come with yourtop three customers.
They did, and after they'd gonearound the table with the top
three customers, he ended themeeting.
And what do you think he didnext?
He took the top three customersand got on an airplane that
(25:38):
right and went to visit everysingle one of those people.
And what I used to do when I wasworking at dealerships is every
six months, quarterly,depending on how far along it
was.
What do I do that you like, Ido and you want me to continue.
What do I do that you don'tlike I do, and you want me to
stop?
And what do I do that reallydoesn't matter to you?
Right, and I'm the same as you.
(25:59):
You know, a conductor is theonly musician that has his back
to the customers, that's right.
Speaker 2 (26:06):
Well, ron, that's a good segue is you know you were
talking about?
Hey, you don't even know whoyour customers are.
And then this gentleman saidwell, I'm going to go see these
top three customers and figurethat out from there.
So, going back to what youasked me earlier, what did I do
(26:26):
different when I startedembracing AI?
And here's the key I was doingthis 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 andleadership, right, and, and then
I had a knack for parts andservice, ops and general
operations overall.
(26:47):
But then somehow the light bulbwent off on the sales side.
Uh, as you know, it's thatopened a whole nother world to
me.
And I'm thinking, wait a second.
Sales and parts and servicethey're usually diametrically
opposed to each other.
They're fighting each other atevery possible turn.
If you got a rental department,they're in the mix too.
(27:09):
They don't know who they'refighting, you know, and so on.
So one of the goals when I gotinto these senior leadership
positions and I maintain thisall the way up into the vice
president roles with severalcompanies is I'm going to go see
customers as the manager, partsmanager, whatever, and so on.
I'm going to go see customersat least one day a week and I'm
(27:33):
going to go either withsalespeople or I'm going to go
solo.
In particular, any customerthat called and had a complaint,
if they were local, they'regoing to see Troy and they can
chew on me all.
They need to get it out oftheir system.
But my goal was to go out thereand make right whatever they
thought wasn't right.
And hey, out there and makeright whatever they thought
(27:53):
wasn't right.
And hey, the customer's notalways right, but you got to
figure it out and I try toinstill that in people.
So, with the AI tool, the way Istarted using it you know, let's
say since 2020, is helping meprocess massive amounts of data.
Quiet the noise, make it makesense, know my customer base,
(28:16):
know my customer before I go seethe customer.
So I'm pulling data from theCRM, from the business system,
from parts and service and salesCRM, combining that.
Then I'm pulling poke data,uccs, rig dig, whatever other
data that's out there figuringout who these customers are,
(28:37):
whatever other data that's outthere figuring out who these
customers are.
And hey, we used to sell tothese people three years ago.
Now we're not selling to them.
What happened?
Are they out of business?
Well, guess what?
With these new tools I mentioned, troy is now an augmented type
personality, because I can domore and be more effective at
what I'm doing.
I'm not trying to displacepeople.
What I'm trying to do is makethe people that have these roles
(29:01):
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 thatperson an augmented tool set to
be able to go, be more effectiveand do their job, because then
they get happier employeeexperience is better customer
experience, et cetera.
(29:22):
But I think that is really thedirections, and one of the
bullet points I put together iswhy dealerships need AI now.
They've needed this for a longtime now.
You know they've needed thisfor a long time.
But what dealerships reallyneed above an AI or any fancy
system?
They need to all step back andtake a hard look in the mirror
(29:42):
pretty much all of them, eventhe ones that say, no, we got it
figured out.
You know I do consulting withBain Company as well, ron, and I
do it.
You know these are smallprojects, but I know a lot of
people within the organizationhere in Houston, a lot of former
C-suite people that now haveretired and work for Bain or
(30:03):
partners at Bain, and you knowthose people have been immensely
helpful in guiding me to seethe world in a much broader
scale.
So again, I'm not this is notan egotistical statement but I
don't look at it as it can theworld around me as a
conventional dealer guy anymore.
Speaker 1 (30:22):
So let me let me translate that a little bit.
I think, and you know when whenwas early 2000s.
You were first in a class withme, right?
Speaker 2 (30:33):
No 1999.
Speaker 1 (30:35):
Yeah, late 90s Call me a liar for a year, you know,
late 90s, yeah.
What was interesting is and youknow I've had a lot of
thousands of people in front ofme in the classroom and you
remember the ones that are thepain in the ass, and Troy was
one I was I mean that in thebest possible way, because he
(30:57):
pushed back on things, he didn'taccept things if he didn't
understand it, and I don't thinkanybody should accept anything
they don't understand.
So leadership to me isunderstanding, acceptance and
commitment, and what we miss insociety is the acceptance.
We don't give people a chanceto fight about it.
Here comes management and theyput forward a proclamation from
(31:20):
the above and say this is whatwe're going to do, right, and
Helen and George down below sayboy, that's stupid.
Why the hell do we do that?
You know, so it gets nuts.
So there was a company in COVIDoutside of Atlanta in Georgia,
small company, 40, 50 people.
(31:41):
And the owner said okay,everybody's going to work at
home, but we're going to havetwo nights a month where we all
get together and I'm going tobuy you dinner and we're going
to spend three, four hourstalking about work.
And they did that and you knowwhat's the good news, what's the
bad news.
What are you having troublewith?
What are you doing well at?
(32:02):
And they shared and all therest, and it went on for three
years or so, whatever it was,that we were forced to work from
home.
So COVID is over and you can goback to the office.
So he has a meeting.
He said okay, let's think aboutthis.
Do you want to continue the waywe've been doing it the last
couple of three years, or do youall want to come back to the
office?
And nobody wanted to go back tothe office.
(32:25):
So he said okay, if you're sureI'm going to sell all of the
office, everything in it, allthe 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 putthe other half with the rest of
you.
Do you think there's?
Speaker 2 (32:45):
anybody that's going to leave that company.
No, that's perfect.
Yeah, that's a uniqueindividual that sees the world
in the proper perspective,correct?
Speaker 1 (32:57):
And that's the kind of leadership that we need.
You know, joel Barker who'sthis guy that made the term
paradigm common in the Englishlanguage said that leaders build
(33:19):
bridges.
And you know, attitude iseverything.
And allowing people if youdon't make mistakes, you're not
learning.
And allowing people to makemistakes without killing them
typically you make a mistakesomebody's going to jump your
bones.
I got fired five or six timesfrom the president.
I got home once.
He said what are you doing athome?
And I'm 22 at the time and he'sprobably 62.
And I remember sitting on theside of the Mississippi River on
(33:42):
a balcony.
He's drinking dark rum andtonic with lime and I'm drinking
a beer because what do I knowat 22?
.
And I said what is that?
He said this is.
Then he tells me well, why doyou drink that?
He said it's summer, it'srefreshing.
He said try it.
I've been drinking that eversince.
But that man, if he was stillalive today and the phone rang
(34:03):
saying he was in trouble, I'djust ask him where he was and
I'm gone.
Yep, all of us have people likethat, that's right.
Speaker 2 (34:10):
Agreed.
Speaker 1 (34:16):
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 anecdotesfrom the Northwest.
I was asked to do a survey ofbusiness for Seattle, Tacoma so
I'm talking to all of the ownersof equipment and it was just a
small survey and one woman wasthe chairman of a construction
(34:42):
company and she brought back thecomment that she hated Napa.
I said oh really, why Don't youfind that saves you a lot?
Oh, yeah, it saves us money,but why don't you like it?
Well, here comes this babedriving a truck like she's
looking for you know, thehooters, yeah, and everybody
(35:03):
stops work and they go to seewhat 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'mtalking to.
I kind of a it was a differenttype of circumstance and I put
in female product supportsalesman and this is back in the
(35:25):
late eighties, early ninetiesand that was really weird.
Nobody did that Correct and Igot complaints from wives of
customers.
Your salesman's out with myhusband, husband after work in
the bar.
This is not good Like I don'tknow.
(35:48):
Get over that type of butthere's all this stuff you knew
intuitively.
Your mind operated onproblem-solving methodologies,
making toast.
You know that kind of thing,deming.
Why did they have to go toJapan?
Why couldn't it happen here?
Right, you know data analytics.
There's unbelievable power ininformation.
(36:09):
I believe people are afraidthat they're not able to hold on
to their power position ortheir income, and that's why
they resist change.
Speaker 2 (36:25):
Well, and I think there's also, whether you know,
in publicly traded companies youhave the pressure to live up to
the shareholder's expectation.
Continuous shareholder valueincreases, right on a quarterly
basis, right, and that's a lotof pressure.
(36:45):
I get that.
But in privately held companiesit's different.
But it's not different becauseit's at the end of the day.
It still gets distilled down toone simple thing You're trying
to squeeze as much profit out ofthe business and reduce your
overhead, all at the same time,regardless of what your margin's
doing.
You could have the best economyand you're just going and
(37:09):
you're not really payingattention and you take your eye
off the expenses and now you'respending too much, but you don't
know.
But let's assume you're runningyour business very efficiently
and you're running the businessas if you're in a lean operation
mode.
So you're going to run as leanas needed, but not too lean to
create bigger problems.
(37:30):
But at the same time you'regoing to maximize expense burden
or reduction where needed, withopportunity to make margin, or
reduction where needed, withopportunity to make margin.
And oftentimes they startsqueezing the first thing
companies do they're not lookingat price optimization, whether
it's for parts or service orsales.
They're not calculating thecarrying cost of inventory
(37:56):
trucks, tractors.
The carrying cost of inventorytrucks, tractors, and the hidden
carrying cost, which isn'talways seen in real time, like
the floor plan carrying cost oftrucks or what have you, is the
(38:17):
ugly side of the parts inventoryand that's where AI really, I
think, comes in from a dataanalytics, comes in from a data
analytics, I think, having thesedifferent agents running in
real time telling you, you know,you set up the parameters and
you're looking, hey, I just wesold 12 of these today.
We need to have 12 more onorder by the end of the day for
tomorrow, because we're sellingat least 10 every day, right, or
whatever the calculation is,and and that's that's one of the
(38:38):
things that I used to domanually.
You, you would do it manually,then you figure out the business
system.
You then you'd set your youknow, your reorder points, uh,
based on your stock orders.
When stock orders were once aweek, the world was really
different with how you ordered,versus now, stock orders are
essentially every day and you'regetting them every other day,
(38:59):
based on shipping, et cetera,you know.
So there's a whole lot ofnuances that I think.
If companies would, you know,for example, you know, I would
another bullet point thehorizontal integration of, you
know, ai from a departmentalstandpoint, going left to right,
cross-departmentalcollaboration.
(39:22):
You would also call that in theconventional method.
See, I wrote this in theconventional way as well.
Oddly enough, we didn't evenpractice that.
But then from there, once youestablish that horizontal
integration of where AI isapplicable with the right
processes as well, integrationof where AI is applicable with
the right processes as well andthe right people, then you go
(39:45):
vertical from there in eachdepartment and then you start
stepping it up.
Now you can't eat the whole pieat once, so to speak, but you
know, once you go vertical or inthe conventional manner, or
speak would be deepening withineach department, would be
deepening within each department, you know.
And then I could just keepexpanding on these, but you know
it's.
You have to go through what Iwould categorize as a strategic
(40:08):
transformation.
Ron, and your two previouspodcasts with Ron and it was
Nick, right, ron first one andNick second, yep, and you know
they I think they got it rightwith everything they said.
And there's more to say,because you know you're still
doing this right, this newindustrial revolution, or what
(40:32):
have you, and what does it mean?
Well, covid flipped the worldupside down In some ways.
Maybe it needed flipping upsidedown, but it exposed all these
gaps, and again I say gaps areopportunities, yeah.
So you then pivot and you go.
What do you want that to looklike?
(40:53):
And that's where I want to helpdealers today is, or even other
companies and I'm working withsome other clients that aren't
in the dealer space at all.
It's just about transforminghow they process business.
You know the steps and whatthat looks like incremental
steps and I said look, we're notgoing to try to change all this
(41:13):
overnight.
Let's look at this in aniterative manner to where we
don't find ourselves in a corner, so to speak.
Speaker 1 (41:21):
What's interesting is you know when did Altman didn't
just start chat GPT in the 90s?
You know when in America thefirst public acknowledgement of
GPT and AI was?
Speaker 2 (41:40):
Well, it was IBM Watson, wasn't it?
Speaker 1 (41:42):
No, it was 1954 at a symposium at Dartmouth.
Okay, well, I wasn't here yet.
Oh, I understand that, butthat's my point.
It's 60 years ago, yeah, true.
And so let me come another way.
Unless we're going to gettrained, unless you're curious
(42:02):
and stubborn and you're going todo it yourself like you did, I
was sent one year.
The dealership that I worked forsent people to different parts
of the world with specificsubjects that they were
responsible for.
So I was spent to Europe onceto look at warehouses and they
weren't called distributioncenters in those days, but they
(42:24):
were warehouses.
So I went to Stuttgart, germany, and there's the distribution
center for Kodak for Europe, theonly one they had and I went to
the door with a guy, opened thedoor and the lights came on.
This is 1973-ish.
(42:44):
There was nobody working inthere.
The plant was completelyautomated no guard dog, nothing.
The computers were pickingparts, the conveyors were taking
them up, they were being packed, they were being strapped onto
pallets, they were being shippedout, their trucks would come in
(43:04):
and they would take them offthe dock and that was the end of
it.
Wow, yeah.
And then, a couple of yearslater, I'm in Chicago another
quote distribution center, andI'll come back Stuttgart was all
cranes.
Okay, right, in Chicago theguys came to work and they were
(43:27):
given their day's work andbasically told when you finish
that you can go home.
So they were picking parts atthe 100, 115 line items an hour
rate and I'm at the Caterpillardealer at 15.
Right, saying, wait a second,what's wrong with this?
And they were also cranes.
(43:52):
Then, before I started doingdistribution, designs et cetera,
I went to Europe again and Isaw a company that had cranes,
but they were 30 or 40 meterstall, 100 meters long lines, et
cetera.
I went to Europe again and I sawa company that had cranes, but
one of the they were 30 or 40meters tall, a hundred meters
long.
Oh, wow, computer driven, nopeople.
One aisle was down.
I said what that's all about?
(44:14):
He said well, you've got aproblem with the crane, so what
do you do with all the inventory?
Well, we have to duplicate itacross every line.
I said, oh, and what was wrongwas there was a deflection on
the mast of the crane so that itcouldn't move.
Ah, okay, caterpillar put theirdistribution center in with
(44:34):
cranes and what that meant wasthere was a fixed productivity
rate.
The crane could go from thefront to the back in 22 seconds.
Okay, it could pick the part inseven seconds.
So the best you could do was toa minute.
The best you could do then was120 in an hour and so I went to
(45:07):
trucks so they could go out ofthe aisle and into the aisle.
So traffic became my problemand I didn't have to duplicate
things.
Today there's between 800,000and a million SKUs stock keeping
units in a typical distributionconstruction equipment guy.
When I was there was 286 000.
Right at that time there were15 just a little under 15 000
parts that caterpillar sold 12times in the year in the world
(45:31):
or more, and they put it ontheir price tape.
So that's how I know about it.
And I said to caterpillar sowhat the hell do I need anything
more than those in my inventory?
And they put it on their pricetape.
So that's how I know about it.
And I said to Caterpillar Isaid why the hell do I need
anything?
Speaker 2 (45:45):
more than those in my inventory.
They don't like to hear that.
Speaker 1 (45:48):
Of course not.
But that's what I went and did.
And then what everybody said isyour availability is going to
go to hell.
I said, okay, fine, we'lldesign this stuff that's under
12.
We'll sit down and have ameeting.
I sell 10 in a year.
How many do you want me to have?
And we did that.
And then I got into a fightabout where are you going to
(46:09):
start stocking it?
Well, it's two and six, threeand 12 type of thing.
Right, that's from the 1600s,for goodness sake.
Economic order, quantity, theKern-Noten program, that's 1905.
It worked for the last 50 yearsand we still use it.
So we get into the circumstance.
(46:29):
I went to 12 and higher, had oneor two under 12, down to four
didn't have anything that wasthree or less.
I charged restocking to theshop.
You can't do that.
Sales department wants adiscount Every month.
I published what the amount ofmoney I gave up was and I
(46:49):
compared it to the profit theygot on their equipment.
They didn't like that and theydidn't recover it either.
Well, they don't cover it.
I gave them a discount and theygot back a profit.
Speaker 2 (46:57):
Exactly.
Speaker 1 (46:58):
Yeah.
Recently I talked to salesmenand sales management and asked
the question.
It's August now, but I've beendoing this since about May.
I said what are you going tosell next year?
I haven't got a clue.
I got to wait till the lastquarter.
I said really, you got aterritory?
Yeah, fixed amount of customers.
Yeah, you got a territory.
Yeah, fixed amount of customers.
Yeah, you know the machinepopulation, all those customers
(47:19):
yeah, do you know how much yousell in parts and service and
rental to these customers?
Yeah, do you have life cyclemanagement statistics from your
manufacturer?
Yeah, well, why can't you tellme that next May you're going to
replace this machine for George?
And they look at me like I'vegrown another horn.
It's trouble is there's notvery many people that ask those
(47:42):
questions because we're afraidto look like idiots.
I think Something else.
I don't know what it is.
I'm not smart enough.
Speaker 2 (47:50):
Well, similarly with John Deere, when they rolled out
DPM or dealer parts management,my dealer group, along with a
handful of other high-performingdealers, we would not go on it
because we were outperformingtheir turns, you know, and their
turns they were happy if youhad, you know, turns of three,
(48:12):
right, and I'll have turns ofsix, and Rush has always had
high.
They want you to be, you know,at that time, five plus now,
seven plus right, if it arehigher in some cases.
You, you know certain ones.
You're going to have that mixwhere it's slow, but you're, you
know.
So we were routinely doingbattle with them and I'm like,
(48:33):
you know so, the dealer 20groups.
We would brainstorm togetherabout how do we deal with here,
you know, because they werepushing on us.
Speaker 1 (48:42):
They asked me to talk at one of their fall meetings
in Phoenix and it was probablybefore that and they wanted me
to talk about DPM and I said Ican't.
I said, why not?
For exactly the reason you'retalking about.
Yeah, the one of the mostimportant measures in a business
is return on capital employed.
Correct, I'm going to invest amillion dollars.
(49:02):
How much do I want to get back?
Yeah, and inventory turnover isone of those components.
So when I started, the averageturnover in at the Caterpillar
network was around two.
The average for the AEDAssociated Equipment
Distributors was between one andthree quarters and two and a
(49:22):
quarter.
The gross profit that was givenby the manufacturer to the
dealer never got touched in thelate 60s, early 70s until we got
matrix pricing and thendifferent things and I'm one of
the idiots that started that.
So we'd have 25% gross profitto two time turnover.
(49:42):
Your return on capital employedis 50%.
So if I give you a milliondollars to invest in a business,
you're going to give me back500,000.
That doesn't sound like a goodplan.
Speaker 2 (49:53):
No.
Speaker 1 (49:54):
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 to100 or 50 to 180.
I'm going to give you a million.
I want you to give me back amillion and three quarters.
I'll let you have 50 as a bonus.
Yeah Well, right, and what'swrong with that?
(50:18):
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 anotherone.
Yes, because I'm going to haveto close this up so that I can
stay within my time limits andstuff.
But what have you thought ofthis discussion?
Speaker 2 (50:34):
well, I liked it.
I.
I think there's more obviouslymore to discuss and when you're
ready, just let me know In thenext couple of weeks.
Speaker 1 (50:43):
Okay, Not next week, but the week after.
Let me know when you've gottime what I'm doing.
Speaker 2 (51:03):
I'm not looking to recreate a new chat GPT replica.
Actually, the tools are alreadythere, and I'm working with
several companies now, somebased here in Texas, that are in
the data analytics space, notonly for marketing but also for,
you know, parts service allkinds of different twists and
turns and every one of thoseconversations include AI at a
different level.
(51:23):
As a matter of fact, I got twomeetings on Friday in Austin
with two different companieswhere we have a strategic
partnership and me being thatunconventional dealer guy you
know those are.
You know I'm helpful to thoseguys, being able to bridge that
gap efficiently, and so I'm nottrying to make a name for myself
(51:46):
as some AI specialist.
I'm just simply good atbringing all the pieces of the
puzzle together and leaving itbetter than I found it, and
that's really what I want to do,ron, is continue down that path
.
Speaker 1 (51:58):
And I'm sure you're going to do it and I'm sure
you're going to be successful atit, and I want to thank you
very much for this time and thenext one and for everybody
listening.
I hope this provoked somethinking and I thank you for
being with us and I look forwardto being with you for another
candid conversation, mahalo.
Aloha and welcome to another Candid Conversation.
We're going to continue in ourseries of discussions and
conversations on artificialintelligence.
Today we're joined by TroyOtmer, who I've known probably
for 25 plus years, who is aparticularly talented guy, who
unfortunately is now being aconsultant, which means he helps
(00:24):
too many people.
But good afternoon, mr Otner,it's good to have you with me.
Speaker 2 (00:31):
Good to see you, ron, as always, always good to catch
up and chat.
Just, you know, I can't say howmuch I appreciate when you do
reach out and you want to jar mearound a little bit.
So I'm, I'm excited, I'mmotivated to get after it.
Speaker 1 (00:50):
We, we.
Before we started recordingthis, I said that you know
you've started in a in a verynormal business industry supply
chain.
You've seen artificialintelligence arrive, you've been
able to work with it, you inthe consulting work.
Now you start looking forward.
(01:12):
So I'd like to try and look atthe.
You know before what you didand what the problems were, what
the opportunities were.
Here comes the AI.
What did that mean to you andhow are you using it?
So, with that as as kind of theframework, what year did you
start in the industry?
Speaker 2 (01:32):
Oh, 1985 officially.
So it's about to approach herenext month.
Wow, 40 years.
Speaker 1 (01:43):
Amazing right.
Speaker 2 (01:52):
Yeah, so yeah, 40 years, amazing, right.
Yeah, so yeah, 1985.
And I started in the automotiveside, as we've discussed before
, and then found myself on themedium duty and school bus
repair side and then worked myway into heavy construction
equipment and tried like hell togo to work for the cat dealer
locally and just never get itdone.
But John Deere dealer decidedto give me a shot and the rest
(02:16):
is history, so to speak.
So you know, starting out backthen, you know computers were on
vehicles, on tractors, trucks,at a very limited scale as
compared to today, but you knowtechnology has just continued to
move forward.
So you know, kind of fastforward into the, into the 90s,
when I joined into the dealerworld, you know a whole nother
(02:39):
world opened up.
You know, and I would still sayI would even call it advanced
analytics was shown to me.
Now I'm talking AS400, ibmSystem 36, things like that.
Things are crazy.
And you're writing old schoolqueries and old school green
(03:00):
screen, right, and so it was awhole different world.
Green screen, right, and so itwas a whole different world.
Today, you know, as I kind of tocurrent times, I would say that
the interest for me as I'vegone through my career is how to
utilize technology to move theneedle forward.
Work smarter, not harder.
Empower people and I've neverdone that with the intention of
(03:24):
replacing people with technology.
And there's no secret thatthere are moments where you'll
find a need to.
You know, hey, you can do morewith less, meaning less people.
But oftentimes that is amistake.
When you look back and go, howdid we get here?
And you figure out, oh, I don'thave enough people.
Case in point this would be aforward-looking statement.
(03:47):
You know, they predict thatwe're going to need several
hundred thousand electricians inthe coming years to deal with
all the power generation andinfrastructure requirements that
AI and other things will needto support them.
You know, and that number I'veheard scale from three to
500,000 electricians alone.
(04:07):
Well then you would addplumbers and technicians and
mechanics, etc.
Because infrastructure requiresconstruction equipment, it
requires heavy trucks, itrequires all the above.
So with that in mind, one of mymotivations is again coming into
when I decided to do consulting.
(04:28):
I've been watching the AI thingfor the last four to five years
and I scratch my head goingokay, which way is this going to
go?
The dealers it doesn't matterwhat kind of dealer you are.
How are they going to embracethis?
The OEMs are obviously going toembrace it, but what are the
(04:49):
next steps needed to get to thenext place?
So I wanted to take all myexperiences as a conventional
dealer operator, but one thatwould always like to stay
outside the bubble and try tochallenge, as you used to call
it.
We're going to kill the sacredcow, right.
We're going to have a paradigmshift, you know, and I really
(05:13):
want to focus on how do we shiftinto the next paradigm,
whatever that may be, and todayI guess we're going to go down
the rabbit hole as part of yourseries, I suspect and talk about
what AI looks like from anolder person like myself or
yourself to how we embrace thatand go forward, and I got some
(05:37):
nifty ideas I want to unload onyou.
Speaker 1 (05:40):
Yeah, no, that's cool .
The whole thing with this isthat it's uncertain.
One of our current contributorshis 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 FutureShock.
That's the first one I read ofhis, and he says in there that
(06:02):
by 2030, artificial intelligenceand robotics and other things
will shrink the Americanworkforce by 50%.
Well, we can argue about theyear, but I don't disagree with
the premise.
And that's 90 million people,Correct.
(06:22):
So what bothers me is we'vespent hundreds of trillions of
dollars on technology but hardlyanything on sociology.
So there's chapter A, Chapter B.
In the 1880s, society changedthe steam engine to an electric
engine and I use thisillustration often but it took
(06:45):
20 years, a full generation,before the true capacity of the
electric engine could berealized.
Because the generation thatreplaced it change is difficult,
so they saved money by changingthe 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 isstarting in 1950.
(07:09):
Every 10 years there's been amajor, significant, major
technological advancement.
So 1950 was computers, 60s,database 70s, internet 80s, is
GPS, blah blah.
And it's come so quickly thatpeople have become a little bit
(07:30):
shocked.
We've become victims of ourworld Right.
And then chapter four parentsand grandparents.
Again, this is from a book Iread.
You know the kind of guy I am,I read books incessantly.
So in the 80s there was a bookcalled the Fourth Turning, a
(07:50):
turning being a 20-yeargeneration, Expectation, is you
list?
80 years, and they talked aboutparenting as a competition.
You're trying to protect yourchildren.
Grandparents are just love and,amazingly, I was lucky enough
to be raised by my grandmotherbecause both my parents worked.
My grandmother got a master'sdegree in 1915.
(08:14):
I don't think she ever said noto me.
Whenever I was asking sillythings, we would talk about it
in paragraphs.
But my sister comes along andall of a sudden there's two
little ones and granny sent meoff to school because as soon as
my sister was walking cause shecouldn't keep up with both of
us.
So I'm one of the few peopleyou know the repeated
(08:34):
kindergarten.
So chapter five.
Then again, because of my weirdview of the world, I believe
that we are looking at the 22ndcentury, 21st century form of
slavery, that we have a smallpercentage of people, the
(08:58):
aristocracy, in this case, theintellectuals who are going to
be bouncing around to the edges.
They're curious people, they'regoing to be trying things,
they're going to fail at a wholebunch of stuff.
But the rest of us aren'tinclined that way.
We get into ruts.
So that goes back to theparenting, grandparent side.
We're taught to be obedient byour parents to protect us.
(09:20):
Then we go to school.
We're taught to be obedient tolearn.
Then we leave school whetherit's high school, technical
university, whatever and we'retrained how to do a job and told
you know, work on that, getfaster at it, make fewer
mistakes, You'll be fine.
And then our life takes off.
We find a spouse, male orfemale, you get married, you get
a house, you buy a car, youhave a child, you get a bigger
(09:42):
house and all.
So all.
We never have enough money.
So we're conscious and carefuland all that good stuff.
You get to my age.
You can't spend all the moneyyou got.
So you have to learn how tospend and you don't know how to.
But they never look over thewall.
They don't have time to lookover the wall, Right.
So here comes artificialintelligence and our purpose at
(10:07):
Learning Without Scars is tohelp people identify their
potential individual andprofessional potential.
That's a tough job.
I like to see people like youknow.
I don't know what I'm going tobe when I grow up, because I
really, you know, there's somany options that you have in
life, so how the hell can youchoose?
I left school and, because of myage, I went to university when
I was 16.
I didn't have a clue what to do.
(10:29):
I took mathematics and physicsand I realized after about six
months I was either going tohave to get a master's or
doctorate if I wanted to takeadvantage.
And that's not me.
I'm too much of a generalist.
So here comes artificialintelligence, here comes
robotics, here comes the supplychain.
So there's the next bump, Right, the number of levels in the
(10:51):
supply chain has compressed andCOVID has exposed us to
different things that we neverotherwise would have experienced
experienced.
So you getting into consulting,you looking at AI for the last
four or five years and provokingme with some things that I'm
(11:12):
going to find interesting.
Neither of us know what thehell's coming up.
We have all kinds of things wewant to have happen, Right?
I don't know what's going tohappen.
You're not going to be able tosee it.
I won't.
Speaker 2 (11:28):
Well, don't count yourself out yet, but you know
it's happening fast.
Speaker 1 (11:36):
Yeah, I know, I tease people.
Only the good die young, so I'mhere for a long time.
Baby, I know, yeah.
Speaker 2 (11:43):
Well, you know, one of the things you know, going
back to how, how you opened, isyou know, tell us how you got
into business as being aconventional dealer guy, and I
know you're you're notpigeonholing me as conventional
by any means, you're just simplyreferencing that, you know.
Yeah, because I'm veryunconventional in my management
(12:04):
style, my leadership style, andI don't I don't normally say
management, it's more of aleader focused.
You know, my job is to be atool and everybody that works
for me be a tool in theirtoolbox to empower them to
achieve what is needed in thecompany.
And you know what, when they dothat, I then arrive and I have
(12:28):
done my job and, and that's howI've always led, I've not, I've
not always been real popularwith with other leadership,
because I take that approach.
But you know the the financialresults of what I've left behind
speak for themselves.
And I'm also unconventionalbecause I've crossed over from,
(12:49):
you know, automotive to truck,to construction, to ag, forestry
and also into recycling andaggregate.
And you know, nobody talks alot or much about recycling or
aggregate because they justassume it goes with a
construction or ag or forestrydealer format.
(13:09):
But it there's a whole notherlevel of complexity when you get
into, like, for example, covid.
You know, paper mills rememberthe shortage of uh toilet paper
and paper towels well, well, inthat particular era, I was
working with paper millsproviding co-gen recycled
(13:31):
alternative fuels to help themproduce.
And paper mills produce, youknow, 50 to 60% of their own
electricity.
Anyway.
They don't put it back to thegrid.
They could, but they typicallythey're doing that because
they're producing steam to makepaper, et cetera.
But, long story short, you know, covid exposed a lot of
(13:53):
weaknesses in not only ourinfrastructure but our supply
chain and how companies dobusiness.
And you know, even and I'mthinking back to 2017 18 range
people were rattling aroundabout ai.
You know.
You know, sam altman didn'tjust wake up one morning here
two or three years ago andcreate open ai and chat, gpt,
(14:16):
etc.
Yeah, that stuff's been on thebooks for a while and, uh,
people been, you know, workingon that.
So you know, for me, being aconventional dealer guy, I want,
I asked myself a few years agoI said, how, how can I improve
what I do?
And like step functionimprovement, so similar to what
(14:39):
an engineer would be doing asthey're trying to go through a
process and I thought, okay, I'mgoing 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.
I listen to podcasts, I listento alternative points of view,
(15:03):
trying to understand the worldaround me.
So I invested in this and I putit to good use in my last
handful of years with Rush, forexample, with COVID.
It exposed market opportunitiesas well as market gaps, and I
look at gaps as an opportunityrather than a gap.
And yeah, so I'm like how do Iscale a business that's in a
(15:27):
state of decline and this isn'tspecific to Rush, it would apply
to anybody I would have beenworking for and how do I find
these customers?
So I have these different datasets over here, and in the past
I would use Excel or evenTableau.
Today, or Power BI or even, wayback, crystal Reports and so on
(15:48):
and all these different toolsto distill that information down
into a workable data set.
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 laptopto my left here is working on a
client's project.
As we speak, it is runningseveral different agents and
(16:10):
sub-agents with the specifictask it's told to do in these
steps.
So I look at agents andsub-agents as those are
employees to Troy, and so I'mworking smarter, not harder.
Now I still got to read a lot,but you know.
So I started investing anddoing these things in small
(16:30):
incremental steps and I figuredout what didn't work.
And it's not as simple as heygo to ChatGPT and say, hey, I
want to know all the market datain this particular market, and
that's all you say, and youshould not expect to get
anything useful from that typeof prompt.
Prompt writing, as I've learnedover the last three years, is
(16:54):
critically important to gettingproduct or good quality data
back to get a good productultimately a product, because
you don't just run it throughthe AI and then give it to your
client and say, here you go, payme.
No, you have to proof it out,you have to validate and then
there's a process.
Speaker 1 (17:12):
So let me interrupt you there for a second.
Sure what I am going totranslate that saying you got to
get data, yep, you got to havecomputing power, right.
You've got to have computingpower right, then you've got to
have a mind.
Well, you've got to have a goaland then you've got to have a
mind that can take you from thedata processing to the goal, and
(17:39):
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 haveenough computing power.
Like you mentioned, we needmillions of electricians because
we're going to have data wherenuclear power is going to come
back.
Speaker 2 (17:59):
Oh, big time yeah.
Speaker 1 (18:00):
There's all manner of things that we know a hell of a
lot more of now than we didthen, but the quality of data is
terrible.
Speaker 2 (18:10):
Horrible, horrible.
It's so noisy, ron, peopledon't use it.
That's like people hire me andyou, not us together per se, but
people like us to come in, payus to do X, Y and Z, and then
they never act on it.
Speaker 1 (18:26):
Okay so let me give you a little anecdote from when
I started into consulting in1980.
And I would do operationalreviews of businesses, parts of
service.
And I was lucky, I wasreasonably well-known and I'd
worked all around the world.
So I got jobs and I would goout and I'd do a review in a
month.
I'd go for a week kind offinding things.
(18:48):
Then I'd go back and I'dprocess things and put together
and then I'd go back and presentand test and I always ended up
saying okay, have you gotsomebody that's going to be able
to implement this?
And everybody said sure.
And about three or six monthslater I'd follow up and nobody
did anything.
So after about a year and ahalf I added a couple of
(19:09):
sentences at the end of the haveyou got anybody that can do
this for you, or would you likeme to do it for you?
And all of a sudden I had afive-year backlog because
everybody wanted somebody elseto do it, because nobody had
time to do.
Things wanted somebody else todo it because nobody had time to
do things.
So today we've put profit overpeople and we've shrunk the
(19:33):
headcount.
We've done it in every aspectof life, from GDP calculations,
which is strictly arithmetic ofman hours of work versus output.
Covid brings us our supply chaingaps.
I like the term.
I call them warts.
(19:54):
I use the illustration of CRMManagement in the supply chain
got really excited aboutcustomer relationship management
because it was a control issueof how many calls salesmen got.
It had nothing to do withmaking the job better, it was
(20:15):
just the boss knew that you hadonly 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 alittle bit better and out comes
a product called Salesforce andI'm not dissing any either of
them.
Right, right, understood.
But here comes Salesforce andthey don't talk the same
language as CRM, one of thethings I say.
(20:39):
When computers came in, I callit moving from paper to glass,
because all we did was we took apaper form and we put it on a
computer screen.
Instead of writing it, we haveto type it into a keyboard.
Right, and an acquaintance ofmine has a couple of MIT PhDs
and he's got, I think, 13patents pending to control a
cursor with his eyeball.
Oh boy, and I've seen it.
(20:59):
It's scary.
Some people and it's not me, Icarry the luggage.
Some people are unbelievablyintelligent.
So we have this supply chain,we have these gaps, we have this
noisy data.
We don't have people that knowhow to put together algorithms.
(21:21):
And when we find the peoplethat have algorithms, we
criticize them because they'repolitical as any free speech or
blah, blah, blah.
That's the new slavery.
Yeah, so how the hell does adealer?
Another little observation Ifyou look at business systems
(21:43):
today, the people that providethe computer systems and there's
a small number of them, maybeeight I haven't found one today
that has somebody inside thatbusiness that knows the customer
.
They know their business, theyknow their systems, they know
how to design and operatecomputer programs, blah, blah,
(22:04):
blah.
But they don't know what peopledo with that.
So how can they create a system?
Well, just making it moreelegant, more you know, whatever
the hell?
The terminal.
I ran data processing for awhile and I cut the staff more
than 50%, which is why I was putthere.
And they were and this is 1970,something early, 73 or so.
(22:28):
Their job was not supportingthe business.
Their job was data processingCorrect.
Their job was writing programs.
Their job was not supportingthe business.
Their job was data processingCorrect, their job was writing
programs.
Their job was operatingprograms.
They really didn't, so webrought people off the
operations side to run thesystems analysts and that's how
we were able to cut it down inhalf and change things.
(22:50):
So if you look at our world,Russia is a good example 20
groups, yes, where you shareprocess improvement, six Sigma,
lean, all this stuff.
We got a lot of tools.
Did you get any training on SixSigma?
Speaker 2 (23:06):
I did because I went to school myself.
I have a Six Sigma white beltcertification.
Speaker 1 (23:12):
Yes, your employer did not do it, though did they?
Speaker 2 (23:15):
No no.
Speaker 1 (23:16):
Did anybody give you continuous improvement training?
Did anybody give you that kindof out-of-the-box?
Speaker 2 (23:24):
As a general rule.
No, the only company thatexposed me to a whole new world
was Coppers Inc.
Coppers with a K, and theyworked with McKinsey and company
very closely, so one of the bigfour, and that's what sparked,
you know, that interest inconsulting it to a higher level
(23:45):
with me, that exposure and thatcontinuous education, lean
process or lean Six Sigma I mean5S, s, the whole thing and and
it's relevance in everyday umprocessing.
For example, there's this hokeyvideo of a older gentleman I
(24:05):
don't even know if he's aliveanymore about making toast and
he was one of those six Sigmalean process guys and just
quirky.
But you watch it and you gohe's in his wife or his kitchen
making toast and he does it allout of order and it's a cluster,
and you know.
And they showed this video andI thought this is going to be a
(24:25):
waste.
I left that class that day thatMcKinsey put on and I'm going
wow, I can make toast now I gotit going.
Wow, I can make toast now I gotit.
What's?
Speaker 1 (24:35):
funny is, last week McKinsey announced to the world
that they were changing theirbusiness yes, that it was no
longer them going to be going infinding the data, analyzing the
data, bringing it back, becausepeople can do that on their own
.
Now, right, and you know, Ilove the story, lou Gerstner,
who went?
(24:55):
He came from McKinsey, went into run IBM and he saved them.
And the story goes that he wentinto his first management
meeting with his direct reportsthere's 15 to 20 of them and he
asked them to name their topthree customers.
And they couldn't Ask them toname their top three customers
and they couldn't.
(25:15):
So after four or five people,he stopped, ended the meeting,
said we're going to meet againnext week.
I want you to come with yourtop three customers.
They did, and after they'd gonearound the table with the top
three customers, he ended themeeting.
And what do you think he didnext?
He took the top three customersand got on an airplane that
(25:38):
right and went to visit everysingle one of those people.
And what I used to do when I wasworking at dealerships is every
six months, quarterly,depending on how far along it
was.
What do I do that you like, Ido and you want me to continue.
What do I do that you don'tlike I do, and you want me to
stop?
And what do I do that reallydoesn't matter to you?
Right, and I'm the same as you.
(25:59):
You know, a conductor is theonly musician that has his back
to the customers, that's right.
Speaker 2 (26:06):
Well, ron, that's a good segue is you know you were
talking about?
Hey, you don't even know whoyour customers are.
And then this gentleman saidwell, I'm going to go see these
top three customers and figurethat out from there.
So, going back to what youasked me earlier, what did I do
(26:26):
different when I startedembracing AI?
And here's the key I was doingthis 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 andleadership, right, and, and then
I had a knack for parts andservice, ops and general
operations overall.
(26:47):
But then somehow the light bulbwent off on the sales side.
Uh, as you know, it's thatopened a whole nother world to
me.
And I'm thinking, wait a second.
Sales and parts and servicethey're usually diametrically
opposed to each other.
They're fighting each other atevery possible turn.
If you got a rental department,they're in the mix too.
(27:09):
They don't know who they'refighting, you know, and so on.
So one of the goals when I gotinto these senior leadership
positions and I maintain thisall the way up into the vice
president roles with severalcompanies is I'm going to go see
customers as the manager, partsmanager, whatever, and so on.
I'm going to go see customersat least one day a week and I'm
(27:33):
going to go either withsalespeople or I'm going to go
solo.
In particular, any customerthat called and had a complaint,
if they were local, they'regoing to see Troy and they can
chew on me all.
They need to get it out oftheir system.
But my goal was to go out thereand make right whatever they
thought wasn't right.
And hey, out there and makeright whatever they thought
(27:53):
wasn't right.
And hey, the customer's notalways right, but you got to
figure it out and I try toinstill that in people.
So, with the AI tool, the way Istarted using it you know, let's
say since 2020, is helping meprocess massive amounts of data.
Quiet the noise, make it makesense, know my customer base,
(28:16):
know my customer before I go seethe customer.
So I'm pulling data from theCRM, from the business system,
from parts and service and salesCRM, combining that.
Then I'm pulling poke data,uccs, rig dig, whatever other
data that's out there figuringout who these customers are,
(28:37):
whatever other data that's outthere figuring out who these
customers are.
And hey, we used to sell tothese people three years ago.
Now we're not selling to them.
What happened?
Are they out of business?
Well, guess what?
With these new tools I mentioned, troy is now an augmented type
personality, because I can domore and be more effective at
what I'm doing.
I'm not trying to displacepeople.
What I'm trying to do is makethe people that have these roles
(29:01):
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 thatperson an augmented tool set to
be able to go, be more effectiveand do their job, because then
they get happier employeeexperience is better customer
experience, et cetera.
(29:22):
But I think that is really thedirections, and one of the
bullet points I put together iswhy dealerships need AI now.
They've needed this for a longtime now.
You know they've needed thisfor a long time.
But what dealerships reallyneed above an AI or any fancy
system?
They need to all step back andtake a hard look in the mirror
(29:42):
pretty much all of them, eventhe ones that say, no, we got it
figured out.
You know I do consulting withBain Company as well, ron, and I
do it.
You know these are smallprojects, but I know a lot of
people within the organizationhere in Houston, a lot of former
C-suite people that now haveretired and work for Bain or
(30:03):
partners at Bain, and you knowthose people have been immensely
helpful in guiding me to seethe world in a much broader
scale.
So again, I'm not this is notan egotistical statement but I
don't look at it as it can theworld around me as a
conventional dealer guy anymore.
Speaker 1 (30:22):
So let me let me translate that a little bit.
I think, and you know when whenwas early 2000s.
You were first in a class withme, right?
Speaker 2 (30:33):
No 1999.
Speaker 1 (30:35):
Yeah, late 90s Call me a liar for a year, you know,
late 90s, yeah.
What was interesting is and youknow I've had a lot of
thousands of people in front ofme in the classroom and you
remember the ones that are thepain in the ass, and Troy was
one I was I mean that in thebest possible way, because he
(30:57):
pushed back on things, he didn'taccept things if he didn't
understand it, and I don't thinkanybody should accept anything
they don't understand.
So leadership to me isunderstanding, acceptance and
commitment, and what we miss insociety is the acceptance.
We don't give people a chanceto fight about it.
Here comes management and theyput forward a proclamation from
(31:20):
the above and say this is whatwe're going to do, right, and
Helen and George down below sayboy, that's stupid.
Why the hell do we do that?
You know, so it gets nuts.
So there was a company in COVIDoutside of Atlanta in Georgia,
small company, 40, 50 people.
(31:41):
And the owner said okay,everybody's going to work at
home, but we're going to havetwo nights a month where we all
get together and I'm going tobuy you dinner and we're going
to spend three, four hourstalking about work.
And they did that and you knowwhat's the good news, what's the
bad news.
What are you having troublewith?
What are you doing well at?
(32:02):
And they shared and all therest, and it went on for three
years or so, whatever it was,that we were forced to work from
home.
So COVID is over and you can goback to the office.
So he has a meeting.
He said okay, let's think aboutthis.
Do you want to continue the waywe've been doing it the last
couple of three years, or do youall want to come back to the
office?
And nobody wanted to go back tothe office.
(32:25):
So he said okay, if you're sureI'm going to sell all of the
office, everything in it, allthe 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 putthe other half with the rest of
you.
Do you think there's?
Speaker 2 (32:45):
anybody that's going to leave that company.
No, that's perfect.
Yeah, that's a uniqueindividual that sees the world
in the proper perspective,correct?
Speaker 1 (32:57):
And that's the kind of leadership that we need.
You know, joel Barker who'sthis guy that made the term
paradigm common in the Englishlanguage said that leaders build
(33:19):
bridges.
And you know, attitude iseverything.
And allowing people if youdon't make mistakes, you're not
learning.
And allowing people to makemistakes without killing them
typically you make a mistakesomebody's going to jump your
bones.
I got fired five or six timesfrom the president.
I got home once.
He said what are you doing athome?
And I'm 22 at the time and he'sprobably 62.
And I remember sitting on theside of the Mississippi River on
(33:42):
a balcony.
He's drinking dark rum andtonic with lime and I'm drinking
a beer because what do I knowat 22?
.
And I said what is that?
He said this is.
Then he tells me well, why doyou drink that?
He said it's summer, it'srefreshing.
He said try it.
I've been drinking that eversince.
But that man, if he was stillalive today and the phone rang
(34:03):
saying he was in trouble, I'djust ask him where he was and
I'm gone.
Yep, all of us have people likethat, that's right.
Speaker 2 (34:10):
Agreed.
Speaker 1 (34:16):
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 anecdotesfrom the Northwest.
I was asked to do a survey ofbusiness for Seattle, Tacoma so
I'm talking to all of the ownersof equipment and it was just a
small survey and one woman wasthe chairman of a construction
(34:42):
company and she brought back thecomment that she hated Napa.
I said oh really, why Don't youfind that saves you a lot?
Oh, yeah, it saves us money,but why don't you like it?
Well, here comes this babedriving a truck like she's
looking for you know, thehooters, yeah, and everybody
(35:03):
stops work and they go to seewhat 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'mtalking to.
I kind of a it was a differenttype of circumstance and I put
in female product supportsalesman and this is back in the
(35:25):
late eighties, early ninetiesand that was really weird.
Nobody did that Correct and Igot complaints from wives of
customers.
Your salesman's out with myhusband, husband after work in
the bar.
This is not good Like I don'tknow.
(35:48):
Get over that type of butthere's all this stuff you knew
intuitively.
Your mind operated onproblem-solving methodologies,
making toast.
You know that kind of thing,deming.
Why did they have to go toJapan?
Why couldn't it happen here?
Right, you know data analytics.
There's unbelievable power ininformation.
(36:09):
I believe people are afraidthat they're not able to hold on
to their power position ortheir income, and that's why
they resist change.
Speaker 2 (36:25):
Well, and I think there's also, whether you know,
in publicly traded companies youhave the pressure to live up to
the shareholder's expectation.
Continuous shareholder valueincreases, right on a quarterly
basis, right, and that's a lotof pressure.
(36:45):
I get that.
But in privately held companiesit's different.
But it's not different becauseit's at the end of the day.
It still gets distilled down toone simple thing You're trying
to squeeze as much profit out ofthe business and reduce your
overhead, all at the same time,regardless of what your margin's
doing.
You could have the best economyand you're just going and
(37:09):
you're not really payingattention and you take your eye
off the expenses and now you'respending too much, but you don't
know.
But let's assume you're runningyour business very efficiently
and you're running the businessas if you're in a lean operation
mode.
So you're going to run as leanas needed, but not too lean to
create bigger problems.
(37:30):
But at the same time you'regoing to maximize expense burden
or reduction where needed, withopportunity to make margin, or
reduction where needed, withopportunity to make margin.
And oftentimes they startsqueezing the first thing
companies do they're not lookingat price optimization, whether
it's for parts or service orsales.
They're not calculating thecarrying cost of inventory
(37:56):
trucks, tractors.
The carrying cost of inventorytrucks, tractors, and the hidden
carrying cost, which isn'talways seen in real time, like
the floor plan carrying cost oftrucks or what have you, is the
(38:17):
ugly side of the parts inventoryand that's where AI really, I
think, comes in from a dataanalytics, comes in from a data
analytics, I think, having thesedifferent agents running in
real time telling you, you know,you set up the parameters and
you're looking, hey, I just wesold 12 of these today.
We need to have 12 more onorder by the end of the day for
tomorrow, because we're sellingat least 10 every day, right, or
whatever the calculation is,and and that's that's one of the
(38:38):
things that I used to domanually.
You, you would do it manually,then you figure out the business
system.
You then you'd set your youknow, your reorder points, uh,
based on your stock orders.
When stock orders were once aweek, the world was really
different with how you ordered,versus now, stock orders are
essentially every day and you'regetting them every other day,
(38:59):
based on shipping, et cetera,you know.
So there's a whole lot ofnuances that I think.
If companies would, you know,for example, you know, I would
another bullet point thehorizontal integration of, you
know, ai from a departmentalstandpoint, going left to right,
cross-departmentalcollaboration.
(39:22):
You would also call that in theconventional method.
See, I wrote this in theconventional way as well.
Oddly enough, we didn't evenpractice that.
But then from there, once youestablish that horizontal
integration of where AI isapplicable with the right
processes as well, integrationof where AI is applicable with
the right processes as well andthe right people, then you go
(39:45):
vertical from there in eachdepartment and then you start
stepping it up.
Now you can't eat the whole pieat once, so to speak, but you
know, once you go vertical or inthe conventional manner, or
speak would be deepening withineach department, would be
deepening within each department, you know.
And then I could just keepexpanding on these, but you know
it's.
You have to go through what Iwould categorize as a strategic
(40:08):
transformation.
Ron, and your two previouspodcasts with Ron and it was
Nick, right, ron first one andNick second, yep, and you know
they I think they got it rightwith everything they said.
And there's more to say,because you know you're still
doing this right, this newindustrial revolution, or what
(40:32):
have you, and what does it mean?
Well, covid flipped the worldupside down In some ways.
Maybe it needed flipping upsidedown, but it exposed all these
gaps, and again I say gaps areopportunities, yeah.
So you then pivot and you go.
What do you want that to looklike?
(40:53):
And that's where I want to helpdealers today is, or even other
companies and I'm working withsome other clients that aren't
in the dealer space at all.
It's just about transforminghow they process business.
You know the steps and whatthat looks like incremental
steps and I said look, we're notgoing to try to change all this
(41:13):
overnight.
Let's look at this in aniterative manner to where we
don't find ourselves in a corner, so to speak.
Speaker 1 (41:21):
What's interesting is you know when did Altman didn't
just start chat GPT in the 90s?
You know when in America thefirst public acknowledgement of
GPT and AI was?
Speaker 2 (41:40):
Well, it was IBM Watson, wasn't it?
Speaker 1 (41:42):
No, it was 1954 at a symposium at Dartmouth.
Okay, well, I wasn't here yet.
Oh, I understand that, butthat's my point.
It's 60 years ago, yeah, true.
And so let me come another way.
Unless we're going to gettrained, unless you're curious
(42:02):
and stubborn and you're going todo it yourself like you did, I
was sent one year.
The dealership that I worked forsent people to different parts
of the world with specificsubjects that they were
responsible for.
So I was spent to Europe onceto look at warehouses and they
weren't called distributioncenters in those days, but they
(42:24):
were warehouses.
So I went to Stuttgart, germany, and there's the distribution
center for Kodak for Europe, theonly one they had and I went to
the door with a guy, opened thedoor and the lights came on.
This is 1973-ish.
(42:44):
There was nobody working inthere.
The plant was completelyautomated no guard dog, nothing.
The computers were pickingparts, the conveyors were taking
them up, they were being packed, they were being strapped onto
pallets, they were being shippedout, their trucks would come in
(43:04):
and they would take them offthe dock and that was the end of
it.
Wow, yeah.
And then, a couple of yearslater, I'm in Chicago another
quote distribution center, andI'll come back Stuttgart was all
cranes.
Okay, right, in Chicago theguys came to work and they were
(43:27):
given their day's work andbasically told when you finish
that you can go home.
So they were picking parts atthe 100, 115 line items an hour
rate and I'm at the Caterpillardealer at 15.
Right, saying, wait a second,what's wrong with this?
And they were also cranes.
(43:52):
Then, before I started doingdistribution, designs et cetera,
I went to Europe again and Isaw a company that had cranes,
but they were 30 or 40 meterstall, 100 meters long lines, et
cetera.
I went to Europe again and I sawa company that had cranes, but
one of the they were 30 or 40meters tall, a hundred meters
long.
Oh, wow, computer driven, nopeople.
One aisle was down.
I said what that's all about?
(44:14):
He said well, you've got aproblem with the crane, so what
do you do with all the inventory?
Well, we have to duplicate itacross every line.
I said, oh, and what was wrongwas there was a deflection on
the mast of the crane so that itcouldn't move.
Ah, okay, caterpillar put theirdistribution center in with
(44:34):
cranes and what that meant wasthere was a fixed productivity
rate.
The crane could go from thefront to the back in 22 seconds.
Okay, it could pick the part inseven seconds.
So the best you could do was toa minute.
The best you could do then was120 in an hour and so I went to
(45:07):
trucks so they could go out ofthe aisle and into the aisle.
So traffic became my problemand I didn't have to duplicate
things.
Today there's between 800,000and a million SKUs stock keeping
units in a typical distributionconstruction equipment guy.
When I was there was 286 000.
Right at that time there were15 just a little under 15 000
parts that caterpillar sold 12times in the year in the world
(45:31):
or more, and they put it ontheir price tape.
So that's how I know about it.
And I said to caterpillar sowhat the hell do I need anything
more than those in my inventory?
And they put it on their pricetape.
So that's how I know about it.
And I said to Caterpillar Isaid why the hell do I need
anything?
Speaker 2 (45:45):
more than those in my inventory.
They don't like to hear that.
Speaker 1 (45:48):
Of course not.
But that's what I went and did.
And then what everybody said isyour availability is going to
go to hell.
I said, okay, fine, we'lldesign this stuff that's under
12.
We'll sit down and have ameeting.
I sell 10 in a year.
How many do you want me to have?
And we did that.
And then I got into a fightabout where are you going to
(46:09):
start stocking it?
Well, it's two and six, threeand 12 type of thing.
Right, that's from the 1600s,for goodness sake.
Economic order, quantity, theKern-Noten program, that's 1905.
It worked for the last 50 yearsand we still use it.
So we get into the circumstance.
(46:29):
I went to 12 and higher, had oneor two under 12, down to four
didn't have anything that wasthree or less.
I charged restocking to theshop.
You can't do that.
Sales department wants adiscount Every month.
I published what the amount ofmoney I gave up was and I
(46:49):
compared it to the profit theygot on their equipment.
They didn't like that and theydidn't recover it either.
Well, they don't cover it.
I gave them a discount and theygot back a profit.
Speaker 2 (46:57):
Exactly.
Speaker 1 (46:58):
Yeah.
Recently I talked to salesmenand sales management and asked
the question.
It's August now, but I've beendoing this since about May.
I said what are you going tosell next year?
I haven't got a clue.
I got to wait till the lastquarter.
I said really, you got aterritory?
Yeah, fixed amount of customers.
Yeah, you got a territory.
Yeah, fixed amount of customers.
Yeah, you know the machinepopulation, all those customers
(47:19):
yeah, do you know how much yousell in parts and service and
rental to these customers?
Yeah, do you have life cyclemanagement statistics from your
manufacturer?
Yeah, well, why can't you tellme that next May you're going to
replace this machine for George?
And they look at me like I'vegrown another horn.
It's trouble is there's notvery many people that ask those
(47:42):
questions because we're afraidto look like idiots.
I think Something else.
I don't know what it is.
I'm not smart enough.
Speaker 2 (47:50):
Well, similarly with John Deere, when they rolled out
DPM or dealer parts management,my dealer group, along with a
handful of other high-performingdealers, we would not go on it
because we were outperformingtheir turns, you know, and their
turns they were happy if youhad, you know, turns of three,
(48:12):
right, and I'll have turns ofsix, and Rush has always had
high.
They want you to be, you know,at that time, five plus now,
seven plus right, if it arehigher in some cases.
You, you know certain ones.
You're going to have that mixwhere it's slow, but you're, you
know.
So we were routinely doingbattle with them and I'm like,
(48:33):
you know so, the dealer 20groups.
We would brainstorm togetherabout how do we deal with here,
you know, because they werepushing on us.
Speaker 1 (48:42):
They asked me to talk at one of their fall meetings
in Phoenix and it was probablybefore that and they wanted me
to talk about DPM and I said Ican't.
I said, why not?
For exactly the reason you'retalking about.
Yeah, the one of the mostimportant measures in a business
is return on capital employed.
Correct, I'm going to invest amillion dollars.
(49:02):
How much do I want to get back?
Yeah, and inventory turnover isone of those components.
So when I started, the averageturnover in at the Caterpillar
network was around two.
The average for the AEDAssociated Equipment
Distributors was between one andthree quarters and two and a
(49:22):
quarter.
The gross profit that was givenby the manufacturer to the
dealer never got touched in thelate 60s, early 70s until we got
matrix pricing and thendifferent things and I'm one of
the idiots that started that.
So we'd have 25% gross profitto two time turnover.
(49:42):
Your return on capital employedis 50%.
So if I give you a milliondollars to invest in a business,
you're going to give me back500,000.
That doesn't sound like a goodplan.
Speaker 2 (49:53):
No.
Speaker 1 (49:54):
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 to100 or 50 to 180.
I'm going to give you a million.
I want you to give me back amillion and three quarters.
I'll let you have 50 as a bonus.
Yeah Well, right, and what'swrong with that?
(50:18):
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 anotherone.
Yes, because I'm going to haveto close this up so that I can
stay within my time limits andstuff.
But what have you thought ofthis discussion?
Speaker 2 (50:34):
well, I liked it.
I.
I think there's more obviouslymore to discuss and when you're
ready, just let me know In thenext couple of weeks.
Speaker 1 (50:43):
Okay, Not next week, but the week after.
Let me know when you've gottime what I'm doing.
Speaker 2 (51:03):
I'm not looking to recreate a new chat GPT replica.
Actually, the tools are alreadythere, and I'm working with
several companies now, somebased here in Texas, that are in
the data analytics space, notonly for marketing but also for,
you know, parts service allkinds of different twists and
turns and every one of thoseconversations include AI at a
different level.
(51:23):
As a matter of fact, I got twomeetings on Friday in Austin
with two different companieswhere we have a strategic
partnership and me being thatunconventional dealer guy you
know those are.
You know I'm helpful to thoseguys, being able to bridge that
gap efficiently, and so I'm nottrying to make a name for myself
(51:46):
as some AI specialist.
I'm just simply good atbringing all the pieces of the
puzzle together and leaving itbetter than I found it, and
that's really what I want to do,ron, is continue down that path
.
Speaker 1 (51:58):
And I'm sure you're going to do it and I'm sure
you're going to be successful atit, and I want to thank you
very much for this time and thenext one and for everybody
listening.
I hope this provoked somethinking and I thank you for
being with us and I look forwardto being with you for another
candid conversation, mahalo.
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