Latest / The Inspired Stories Podcast / Tomasz Jamroz's People-Process-Technology Framework: Transforming Roadrunner from Bottom to #2 LTL Carrier

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Tomasz Jamroz's People-Process-Technology Framework: Transforming Roadrunner from Bottom to #2 LTL Carrier · The Inspired Stories Podcast

0:04
Welcome to another edition of the Inspired Stories podcast,
0:08
where leaders share their experiences so we can learn from
0:11
their successes and be inspired by how they've overcome
0:14
adversity. My name is Anthony Cotespotti
0:17
and today's guest is Tamash Jamros, the head of operations
0:21
at Roadrunner Freight. They are a truck transportation
0:23
company founded in 1984 that focuses on providing high
0:28
quality, scalable Lt. ATL freight services across the
0:32
US. They specialize in delivering
0:35
freight with next day options and encourage a culture of
0:38
accountability through their motto, Ship it Like You Own It.
0:43
With a background in streamlining processes, Tamash
0:46
has LED key initiatives to upgrade technology and improve
0:50
delivery times. He was also recognized for
0:52
fueling the company's expansion into new lanes and forging
0:56
strategic partnerships for continued growth.
0:59
In recognition of his exceptional contributions to the
1:02
industry, Tamash won the 2023 Pros to No Award.
1:08
He has played a vital role in strengthening Red Runner
1:11
Freight's mission to make freight shipping reliable,
1:14
efficient and customer focus. Now before we get into all that
1:18
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AD back benefits.com. All right, back to our guest
2:00
today, President and COO at Roadrunner.
2:02
Tamash, thanks for making the time to Share your story today.
2:05
Thank you, Anthony. Thank you for having me.
2:08
So Tash, you grew up in Canada and you had some great roles at
2:12
terrific companies in Canada. I'm curious to hear what have
2:16
you found to be some of the biggest differences in business
2:19
culture between the US and Canada?
2:23
So. 11 Little clarification, I actually was born and raised in
2:27
Poland. I emigrated to to Canada when I
2:33
was 20. Pretty much came here from my
2:36
university. But going back to your question,
2:44
I think there is a Canada is a bit more structured and I would
2:53
not necessarily say it in in in the positive sense.
2:57
It's a bit of, it has the number of invisible borders that you
3:03
kind of need to overcome when you kind of work in, in Canada,
3:08
US. On the other side, I think it's
3:11
really this American dream is that country that you can come
3:15
and you can start on the dock and you can become ACEO of that
3:20
company. And, and, and it's kind of very
3:23
deeply rooted in, in the DNA of, of everybody.
3:30
And I kind of had a chance to see the difference because I was
3:35
working for large multinationals and I had an opportunity to, to
3:40
work across the globe, mostly Canada and US.
3:45
And I remember from the fairly early in my career, I kind of,
3:51
I, I kind of like working in US, US was kind of a place that you
3:56
were able to question more people were kind of a naturally
4:01
very open to, to somebody coming.
4:04
And as long as the idea made sense, there was, I would say,
4:08
less of a judgement of, you know, so many years of
4:12
experience you have or were, you know, where, where you gained
4:17
the knowledge. Is that what you mean by the
4:18
invisible borders? That's what I felt.
4:21
That's how I felt in Canada. It's felt a bit like people were
4:26
less risk takers and they kind of like hiding behind like,
4:30
well, I don't know if we should do it.
4:32
And I don't know if this is the right idea.
4:36
And, and they kind of were trying to create this kind of,
4:39
well, maybe, you know, maybe maybe you, you don't know this
4:42
because you haven't had a, a chance to be working really long
4:48
in that industry or something. So I, it was very, very
4:51
interesting because I was kind of operating within similar
4:53
industries across the border and the same point of view was
4:59
really differently perceived North and South of the border.
5:04
So I think that's, that's, that's the biggest difference.
5:08
I would still choose Canada over Europe because that is too
5:13
regulated and overcomplicated. But definitely US seem to be a
5:21
bit more, I would say business friendly than Canada is and kind
5:27
of on the spectrum. You know, I've never, I've never
5:29
really had a chance to. I work in Asia, but I haven't
5:34
worked enough that I would have the same level of exposure to
5:38
all the regulations and all the rules that apply.
5:42
So it's hard for me to speak. But if I were if I were to poke
5:46
like a Europe and any place in Europe, because it's fairly
5:50
similar, especially through the European Union concept and
5:54
compare European Union, Canada and and Usus really would be on
6:00
top of a place is that it's just an easy to do business.
6:03
Is this I think is deeply rooted in that DNA of American dream
6:09
that did you get anything is possible and I I I absolutely
6:13
love it working in US from from the first time I I had an
6:18
opportunity to to work and advise my my clients at the
6:22
time. So I want to talk a little bit
6:25
about some of your background before we get to present day,
6:30
because you're a tech guy at heart.
6:32
And I don't often see sort of this transition from tech into
6:37
like a president role. But before we kind of get into
6:41
that transition there, you know, there's a company that you
6:44
worked at called Scale AI. And nowadays everybody's talking
6:50
about AI, right? It's on the tip of our tongues
6:53
because ChatGPT, open AI, large language models have brought it
6:57
to, you know, the forefront of everybody's awareness.
7:00
But you were that there back in starting in 2019 before most
7:05
people we're really using even any of that vernacular.
7:09
Can you tell us about the work that you were doing there at the
7:12
time and maybe tie into how you're still able to leverage
7:17
some of that experience in the work that you're doing with
7:19
Roadrunner today of? Course, so it it was I actually
7:26
stepped into the what we call today AI back in 20/20/15.
7:38
Even earlier. It was, it was connected to my
7:43
background, educational background.
7:45
So I, I actually, I was kind of a person that explored quite a
7:50
bit. So I've been in Med school.
7:52
I did biotechnology engineering back in Europe.
7:56
Then I came to Canada, I did neuro behavioural psychology and
7:59
I did computer science. And as I was doing those kind
8:03
of, let's say, interdisciplinary start studies where I was doing
8:08
the research in neuro behavioral psychology, I start kind of
8:12
applying in very kind of advanced and sophisticated
8:15
models, which kind of opened me to learn more about computer
8:21
science and ultimately get, get part of a degree in computer
8:30
science. But the most important work that
8:32
I, I had a chance at the time to be exposed to it was actually
8:36
working with people that at the time were already experimenting.
8:40
So the, the machine learning and artificial intelligence, this
8:44
concept is, is since 50s actually.
8:48
So this is not new. That would really change was
8:51
that at one point in time we had a cloud computing which gave us
8:58
amazing opportunity to scale. We had a lot of data because
9:05
everybody start using social network and everything else.
9:08
So there was a vast amount of information that you could get
9:14
access to and and you know, ability to compute such a vast
9:21
amount of data enable that those those models and those concept
9:26
that kind of existed for a long time, they suddenly started to
9:30
get traction. So I was very lucky to be in
9:34
kind of, I would say at the early stage of machine learning
9:41
becoming kind of a major, major movement in technology that that
9:48
required, required people who had a good understanding about
9:53
the algorithms and fairly sophisticated mathematical
9:58
models. And it happened that I had, I
10:02
had that I had that experience that I had a chance to kind of
10:05
experiment it with with it at the time when I was still doing
10:10
my research at school. And that naturally kind of moved
10:15
me when I was working first for Accenture, then for CGI.
10:21
That's why I had a chance to work with scale AI when when
10:27
people were looking like who can, who can we move to the new
10:31
form division that you know, everybody kind of identified
10:35
that this is the future. Like for me, when I raised my
10:39
hand was was such a natural thing because I had AI had a
10:42
very good understanding. So what kind of work were you
10:45
guys doing there at scale and and who were you doing it for?
10:48
So, so for scale AI, it's it's a very interesting initiative.
10:52
So that was a governmental Canada came up with $4 billion
11:03
investment and, and the goal of that investment was that we, we
11:09
had to identify very tangible use cases.
11:18
And through the partnership between the logistic companies,
11:23
small startups and, and the key multinationals like, you know,
11:30
Accenture, Deloitte, IBM, all the kind of a big, big
11:37
consulting firms. We we had to deliver and fix
11:43
some of the some of the major challenges that exist utilize
11:50
utilizing machine learning. So, so there was number of
11:55
interesting initiative. You know, I would say, you know,
11:58
those things that you know, you, you go today and, and you see in
12:04
Amazon stores like I think Amazon go, those are kind of the
12:09
initiatives that we were working and, and figuring out how would
12:14
you do it? How would you go?
12:15
How would you do do it? That you can go and figure that
12:18
somebody touch, grab something from a shelf and you'll be
12:22
automatically able to to charge that customer.
12:25
We work on on you know much more sophisticated models that led to
12:32
a lot of optimization that you can see in pick up and delivery
12:36
when you have a travelling salesman algorithm.
12:40
And so a lot of initiatives. The key of everything we work on
12:46
was that it had to really be a tangible use case that has
12:53
returned on investment. And I think that was the very,
12:56
that was the key point and very interesting actually, because as
12:59
a technologist, you often get excited about the technology.
13:04
Oh yeah. And you lose that, you lose the
13:08
reason why you're doing it for and that and and the very and
13:13
scaly. I had absolutely amazing, very
13:17
robust framework and it absolutely required that you
13:27
have that return on investment. And even in the middle of a
13:31
project, you suddenly there was no opportunity to go further
13:38
because suddenly like, no, you cannot prove the case that
13:41
you're going to be able to, to make money or save money.
13:44
We had to kind of stop. So it was a very interesting
13:47
experience. Yeah, it sounds like a very
13:50
practical approach, and I want to come back to your comfort in
13:54
your understanding of AI and potentially how you're able to
13:58
insert that into Roadrunner today.
14:01
But first, explain to us how the opportunity to join Roadrunner
14:04
came about. So for me joining the Roadrunner
14:10
was was it was months, if not years in in works.
14:19
So I, I really enjoy, I really enjoy the work that I've been
14:22
doing by utilizing AI data and technology.
14:29
I, I kind of established myself as a fixer.
14:33
I was able to come to the company and fix one of the most
14:38
fundamental and challenging problems they face often, often
14:45
the challenges that the leadership of that organization
14:48
was just just failing to address.
14:52
And, and I, I really, really like it.
14:57
My brother who, who is kind of a amazing, amazing leader who
15:04
specialized in turning around logistics businesses.
15:09
He has, he has a, you know, he generated billions of dollars
15:13
for shareholders in, in turnarounds that he, he was
15:17
successful in, in driving. He'd been asked to, to go and
15:23
fix Roadrunner. And when he, he enter
15:30
Roadrunner, he kind of realized that at one point this is a very
15:35
different problem than the ones he had in the past.
15:39
And this company was fundamentally broken.
15:45
Roadrunner was when Chris was telling me when he kind of
15:53
engaged, he was not sure if that company going to survive 90
15:58
days. So in and, and, you know, there
16:01
was a, there was a bit of a, a trick that he pulled on me.
16:11
I was actually sailing in, in Greece.
16:13
He came, we went on, we went on the yacht and he brought our mom
16:17
and then he told our mom that like, wouldn't be great if two
16:21
of your sons work together. And I really didn't want to join
16:26
it because it's, it's, it's I, I love what I did.
16:29
I, I was really at the, the most advanced.
16:33
I was at the edge of working with the Fortune 100 and
16:37
government and the most advanced technology and then.
16:43
Yeah, and logistics isn't that sexy.
16:45
No, I just think, isn't that sexy tracking?
16:49
Yeah, it's, it's even less so, you know, it's, it's utilizing a
16:57
bit of like unfair advantage of, of our mum who we absolutely
17:02
laugh and adore. And she is, you know, not
17:06
somebody you can fight with because she, she so you.
17:09
Did this to make mom happy. I bet yes.
17:13
And to be honest, I'm very happy because I think this is this is
17:16
this is a challenge that a challenge and opportunity that I
17:21
would not step into otherwise. And it enabled me to you, it's
17:33
almost like, I don't know if you ever wonder, but like I always
17:37
wondered if I suddenly move back in time, would I be successful?
17:44
You know, like what? Would I, How would I do?
17:46
Is technology the only thing that you're good at?
17:48
Yeah. Yeah, yeah, kind of.
17:50
But I kind of felt that what happened because I was, you
17:53
know, in such a, you know, advanced tier of what's
17:59
happening, almost like it feels like a different Galaxy.
18:03
And then somebody brought me back to like 70s or 80s where
18:07
technology was and, and you kind of have to go and catch up and
18:11
bring yourself up to speed. So it was kind of, I think it
18:15
was a very interesting challenge to be part of.
18:18
So you were brought in initially as the CIO and so no, no.
18:22
No, no, no. So initially I was abroad as
18:26
the, as a kind of an advisor and I've been, I've been asked to
18:36
kind of, you know, Chris asked me like, can you, can you help
18:39
me understand where the challenges are and how
18:43
technology can help? And I came and I remember the
18:51
first thing I was amazed was the amount of data that is
18:59
completely not used but exists in tracking and logistics.
19:04
Give us an example. Oh no, when you think about it,
19:09
you have in in tracking, I have I have information about
19:12
everything. I have an information from ELD
19:15
and telematics of of the engine I have.
19:20
I have a years and decades of all the movements of freight I
19:26
have. But how would you?
19:27
Use that. How would you put that to use in
19:29
a useful way? Oh, you, you can, you can, you
19:32
can, you can create and understand what is the most
19:35
efficient way for you moving freight.
19:37
You can understand what are the patterns, you can, you can see
19:41
what almost like you can start predicting what might happen,
19:45
what, what, how the seasonality plays out.
19:49
And if you're going to go and you're not going to start kind
19:52
of going and collecting that information and, and looking at,
19:58
you know, your cost and, and trying to combine everything,
20:04
you're starting to have a very clear understanding of.
20:09
Where are the opportunities for you to be more efficient, more
20:14
effective What's what way of moving freight might be much
20:21
better for you from a perspective of of I call it
20:27
total cost of ownership TCO. What I mean by that, you know,
20:31
initially we when I came, we're looking and I remember I was
20:35
talking to the executives that I've met and they told me rail
20:38
is the best for us. I'm like, OK, great.
20:41
I'm like true. It seems it seems that rate
20:46
pellet per mile is pretty, pretty low.
20:49
So rail is great. But then when I kind of, you
20:53
know, brought all the data together, then you start to
20:55
realize, OK, but the drayage is actually not that cheap and
21:00
managing everything. And especially if you if you
21:03
miss your window from a drayage, you go into the penalties of.
21:09
Storage, basically. What storage?
21:11
Yeah, Drayage, yes, basically storage, but also you need to
21:16
bring that container back to terminal and then you kind of
21:20
look, OK, so let's look at all the shipments that travelled
21:23
through rail and you're trying to utilize them and compare them
21:28
against any other shipment. You're looking at the ratio of
21:32
claims and you realize, oh, wow, that's, that's something
21:37
interesting happening because your claims are it's just, it's,
21:40
it's just uncomparable. It's just, it's different,
21:45
completely different, different ratios that that you experience.
21:50
So then you start looking at it from A-Z and you actually
21:53
realize it's, it's in our case, it was the most expensive form
22:00
of moving freight. So you can, you can get excited
22:03
because you see just part of it. Oh my God, cost per miles is so
22:07
small, but not really because you can get the full picture
22:10
there. Yeah, if you look for a full
22:12
picture of picking up from a customer to delivering to a
22:14
customer. And you know, if, if you, if
22:16
you're going to go on top of it and apply a factor of
22:22
unhappiness from a customers that you might have, you start
22:26
realizing it is not the best. And listen, don't get me wrong,
22:30
it depends what you're shipping. And in certain instances, rail
22:37
is absolutely phenomenal way to, to transport goods.
22:41
But the goods that we were transporting as a Roadrunner, it
22:46
was, it was the worst, it was the worst for us.
22:48
So, but, but you would never ever be able to get to that
22:53
point of understanding unless you you brought all the data
22:57
together. And that's what kind of
22:59
happened. So when I came, I kind of
23:01
realized there is so much data, but everybody has an opinion,
23:06
but nobody provides me data-driven answers.
23:10
And I'm very empirical. I'm kind of a our, our board of
23:18
directors has a nickname for me. They called me Dada dog because
23:22
I am like, I really, I, I really it, it really doesn't matter
23:26
what you tell me if you don't have a way to show me and tell
23:30
me the story through the numbers.
23:32
I just even if I would like, I won't be able to convince myself
23:37
and and I'm very honest to myself always.
23:39
You don't get emotionally attached to who's right or who's
23:42
wrong. Just show me the data.
23:44
Let's make the decision based on that.
23:47
So, yeah, so, so that, so this is pretty much how my story, you
23:50
know, I started playing with it. So I went and built a very
23:53
sophisticated a data infrastructure for Roadrunner.
23:59
And as I was doing it naturally because of my technology, I
24:02
started to realize like, OK, the issue he he is here, the issue
24:07
is there. So I start kind of, you know,
24:09
going and I would say expanding by going and taking other ACT
24:17
areas because, you know, the the data is one, but the importance
24:21
of data governance and quality of the data goes back to the
24:27
system of records. So I need to set up that I have
24:29
that there are guardrails that when people use the system, they
24:33
cannot put the The Dirty or misleading data because the
24:39
system doesn't have verification.
24:41
So then I kind of went after all the systems because system
24:46
becomes my input to the old data leg that that we have built and
24:50
make sure that that those systems work and are integrated
24:54
with each other that they give the data sanity.
24:58
So when we make that decision and we building those algorithms
25:01
on top of the data that we don't make wrong decisions because
25:06
garbage in, garbage out. This is very important that that
25:12
you did you take it very, very good care of the quality of the
25:16
data I have in your system. And naturally, it's kind of, you
25:21
know, once I start touching those systems, I started having,
25:25
let's say, seeing opportunities of how we can improve and then
25:29
when I kind of stepped into the CIO role at Roadrunner.
25:35
So you started out as a consultant.
25:38
You start building this technology infrastructure to
25:40
help you guys make better decisions.
25:42
So you've got easier, quicker, better access to the mountains
25:45
of data that had been collected over the years.
25:48
Then you really start to get an idea of the big picture, where
25:50
the opportunities are. You move into the CIO role.
25:54
From there, you're continuing, I'm guessing, to solve problems
25:59
using data and technology first. Eventually you migrate into the
26:05
role of COO and president. Now you don't leave your tech
26:09
behind, right? Tech runs in your blood.
26:12
That's how that's that's the big lever that you pull for solving
26:15
problems. But I'm curious to hear,
26:17
especially as you've made that transition, what percentage of
26:21
the problems do you find you can't resolve with tech?
26:25
You know it. An organization your size.
26:27
People are a big part of what either works or doesn't work.
26:31
Just as an example. So yeah, you're absolutely
26:37
right. So my migration happened because
26:41
once I I established and I understood now I have a
26:45
technology, I have all the data. Naturally, I wanted to work as a
26:49
consultant to the business and and advise people to maybe
26:54
modify the decision that they make and show them a different
26:58
way. And I was faced with a lot of on
27:03
doing it for 25 years. And what do you know about it?
27:06
You're new to the industry. And that for me is like, it's
27:11
like you're going to show the the red piece of cloth to the
27:16
ball. Like you told me, you make
27:19
statement like this. My psychologist count comes in.
27:23
OK, so you're defensive now. You're defensive because I'm
27:29
showing you something that you have absolutely no clue what I'm
27:32
talking about it and you're hiding yourself behind that
27:35
statement that you're trying to establish your position, but you
27:41
basically don't know what I'm talking about.
27:43
And I and I, and that for me became the path that like I need
27:46
to step in more because I just, we basically need to make sure
27:51
that we have, we have a right team and we make decisions
27:56
differently because the way how company was making the decision
27:59
was, was just questionable and at best.
28:01
So how, how, how do I make those decisions and how, how many are
28:08
not technology driven? I would say all of them and
28:12
none. And, and I'll, I would explain
28:15
it. I don't believe, I don't believe
28:20
in technology as, as a magic solution.
28:22
I believe in the people, process and technology, in that order.
28:28
If you have the right people that really know what they're
28:31
doing, you can survive without the processes and you can
28:36
survive without technology. If if you have a people that
28:40
maybe need some improvement, well, then you definitely need
28:47
procedures and processes. But if you really, really want
28:52
to have an organization that that let's say is safeguarded
28:57
and it's and really operates efficiently and you don't have
29:02
to worry, you need to make sure that you take care of the third
29:05
one, which is technology. So I don't believe just
29:07
technology can fix it because I can put the best technology in
29:11
front of somebody. They don't want to use it and
29:13
they not going to use it. Nothing's going to change, so
29:17
you need to make sure that you have a process of how you kind
29:22
of implement the technology and teach other people that are open
29:26
for using it. Today.
29:29
I really, and this is something, it's not me.
29:32
This is how I was brainwashed by Accenture.
29:35
That's that's where they'd kind of made me display everything is
29:40
people, process and technology. And it's actually it's true.
29:43
It's I find that this is this is this is where you're going to be
29:46
the most effective. That's where you're going to
29:48
have the best, the best return on investment.
29:54
If you really find that the perfect synergy between those
29:59
three key elements to success. So for me today, it's very
30:05
important that we identify and fix problems.
30:11
Fixing through technology is not the right way.
30:13
The first thing, make sure you have the right people.
30:16
And that's the first thing I'll go and remove and replace.
30:19
The second thing I'm gonna go is I'm going to make sure we have a
30:22
process and see if this can work because technology is expensive.
30:26
Technology can take time to build.
30:29
And the last things you want to be doing is you want to be
30:31
building technology for a broken processes or for the wrong
30:35
people, because what you're going to do, you're just going
30:37
to fail faster. And this is this is this is,
30:40
this is not, this is not the right way to do it.
30:43
So. So if I'm understanding
30:46
correctly, at some point is you're moving up through sort of
30:49
the leadership roles, you come to understand that there are
30:52
probably some of the wrong people in the wrong seats and
30:55
you need to make some changes, pretty big changes.
31:00
Was this A a painful transition for you or for the company to go
31:03
through a? Bit unfortunate so there is not
31:13
a single head of a department as I was going through departments
31:21
and I kind of you know, I start I start bringing the data and
31:28
it's kind of starting to have an understanding we identify and
31:34
this is now when kind of I look at it almost feels maybe I
31:38
should have been even faster in making those decisions because I
31:42
have to admit the one thing that because it was such a big group
31:48
of people that throw thrown at me that Oh yeah, I have you know
31:54
decades of experience and so on. I am kind of a person that I
31:59
like to re evaluate I'm very analytical so I'm like what if I
32:03
am making a mistake? What if there is something I
32:06
don't see and? So you were open to the
32:07
possibility that maybe you were in the wrong here, OK.
32:11
Always. And I think, I think they
32:13
managed to, to plant the seeds of doubt in, in my, my approach,
32:22
which kind of slowed me down a bit because like, what if, what
32:27
if there's something I, I, I don't and listen, it's, I'll be
32:32
stupid thinking that, you know, I know everything because it's
32:35
impossible. And, and that's, I know that
32:40
this is a wrong assumption to operate under.
32:43
So that was something that definitely affected me.
32:48
But at the end of the day, there is no single head of a
32:53
department that state because when we went.
32:57
But what what was really amazing was that we had a lot wrong
33:01
leaders. I think there was just this,
33:03
this is just what it was, but delayer anger.
33:08
We had a very good. So let's call it the senior
33:11
management. We had a perfect people.
33:13
They're exist working for wrong people.
33:16
So what I actually managed to do, I managed to elevate that
33:19
senior. Senior was really the one people
33:22
that were like holding that organization or that department,
33:28
but the heads were kind of, you know, not really understanding
33:31
the big picture or, and I'll be honest, it's, it wasn't easy.
33:37
And I, I don't want to like go and say, you know, they were
33:40
wrong people. They're just, they were wrong
33:42
people for what needed to happen.
33:44
And what needed to happen is major overhaul, major
33:48
transformation. You need, you need to
33:52
experience, you need to be part of the organization.
33:54
Least once did you go through major transformation to really
33:59
understand how you make the decision, what kind of decisions
34:03
you need to make, what kind of speed of decision making is
34:07
required in the process like that.
34:09
And, and most of the people, they would never, ever be in
34:13
those situations. I was very lucky that in my
34:17
career I was both as A at Accenture and CGII went through
34:27
multiple major transformation for different businesses.
34:31
And I remember that when I was coming and working with those
34:34
businesses, there were people working there for 30-40 years
34:39
and through the period that I was there for a year or a bit
34:42
longer, it was the largest change they ever experienced in
34:46
all of their career because it was a fundamental kind of
34:50
reorganization change complete. So you.
34:54
Had quite a bit of experience in change management by the time
34:57
you got the Roadrunner. Change management, a lot of
35:00
transformation. That's why that's why I was a
35:01
fixer for me was so I it became a bit like, you know, you, you
35:07
could be a phenomenal Dr., but not every phenomenal Dr. or
35:14
surgeon in and you're going to put them in the middle of Iraq
35:18
or Afghanistan, in the middle of a war theater where you have to
35:23
operate under, you know, bombs and things and machine guns
35:28
shooting and so on. Not all of them is going to be
35:31
good. And I happen to be that kind of
35:34
a surgeon that operate in that war theater.
35:38
So for me that that environment was and I think that was a big
35:42
difference. And, and I think that people
35:44
struggle because they were used to operate in, in the good
35:50
companies and, and, you know, they just happened to be a
35:53
Roadrunner, which Roadrunner, Roadrunner had at the time
35:56
fundamental challenges and, and, and they didn't really know how
36:03
to handle it. They know how to, you know, how
36:05
to run a good company. They didn't know how to
36:08
transform and completely overhaul something that
36:13
absolutely is required, so. What really needed to be, what
36:17
really needed to be overhauled though, I mean, we talked about,
36:20
you know, you were able to use data to help make better
36:22
decisions and we used the example of rail is good in some
36:26
cases, but not always because they weren't factoring in
36:29
drayage but kind of helpless understand.
36:31
So rail was fundamentally good. I I listen, Anthony, it would be
36:37
easier to say what what we didn't have to change was the
36:39
name of a company. OK, it was, it was that broken,
36:44
that flawed. Everything sort of needed to be
36:46
rebuilt from the ground up. Everything from from from that
36:53
was prior to me and Chris joining, but you know, there
36:56
there is, there was DOJ investigation into accounting
37:01
fraud and Roadrunner in 2018 I believe or something.
37:05
So there was there was it was really amazing company.
37:13
Rhoda used to be absolutely amazing company.
37:17
I believe up to it was as you mentioned was created in 1984
37:21
until 2010. It was phenomenal business,
37:26
phenomenal trucking, LDL then through some kind of a weird
37:33
decisions that and and ownership changes, Roadrunner suddenly
37:41
thought that is going to become a bit of everything.
37:44
So they bought the airline, they bought the cold storage,
37:49
warehousing, tracking brokerage, you name it, they they would go
37:56
after it. And it was, it was a completely
38:00
unmanaged pursue of acquisition with 0 integration and logic how
38:09
this actually helped the organization that led to almost
38:16
the collapse of the business. It went through serious troubles
38:21
as I mentioned due to DOJ investigation, DOJ investigation
38:26
and some of executive actually serving prison sentences.
38:32
So it was quite something. And then another ownership
38:37
change occur in, in somewhere around 2019, I think.
38:49
And that's where, you know, first Chris was brought, then he
38:54
kind of I, I kind of joined the party a bit later.
38:58
We had, we had a bit of our own team there and we started kind
39:03
of peeling the onion. And I, I, I have to admit that
39:08
we had to go pretty close to, I call it open heart surgery that
39:15
we really had to break the business and go to, to the
39:19
points that we had to question everything.
39:20
So if you know, you talk about the rail today, we do zero rail.
39:25
We don't touch IT0. If you look about, yeah, we had
39:30
to when we came, we, we had to close a significant number of
39:35
markets because we were, we're not able to serve them.
39:39
We're just and, and, and make a lot of a lot of a lot of changes
39:45
operationally, financially cost wise, bring a lot of new people.
39:53
And, and I would say question everything.
39:57
We we literally question every decision that has ever been made
40:02
and. And we manage in fourth.
40:07
Well, it'll be it'll be five years now turn it around like we
40:12
super proud transfer logistics. They run this massive, massive
40:21
survey with shippers in United States and shippers voted us as
40:26
a second best LTL carrier and United States.
40:30
So when you think about it, when we came and we are absolutely
40:34
hated by our customers and we were like bottom of a bottom of
40:39
a barrel. Like really there was we were
40:41
like we're horrible, absolutely horrible.
40:47
And today customer comments and I have a customers reaching out
40:54
to us and say, listen, I do something.
40:57
I only have all Dominion that is doing it.
41:01
But there is only one company that I would be willing to to
41:04
move this type of business to if you guys agree to do it.
41:09
So, you know, putting Roadrunner, I don't know how
41:11
familiar you are, but all Dominion is this amazing
41:16
Northern Star in LTL, but nobody for years managed to, to, to
41:25
really compete with them or even give them any hard time.
41:28
And, and I, I had, I had some people from Old Dominion
41:31
reaching out. It's like, how did you sneak out
41:34
on us? So like this, like, how did you
41:36
do it? Like 4 years ago, you like
41:38
everybody was laughing at, about you and thinking like, what's
41:42
going to happen? And on top of it, they brought
41:44
the people that are not from a tracking company.
41:46
So they probably going to kill this business.
41:49
And then like four or five years later, it's like, holy cow.
41:53
Now we have to watch out for you like how that happened.
41:55
But it is, it is a kind of a fun journey of, of being focused
42:02
and, and unapologetic and very driven and, and really believing
42:12
in, in the mission and, and we strongly believe that Roadrunner
42:17
will be the best. Yeah.
42:19
So I mean, what I'm sort of putting together here, as you
42:23
were saying the company was broken.
42:24
I mean, there there were illegal things going on, people doing
42:27
time, there was a DOJ investigation.
42:32
It also sounds like, and maybe this is related or not, but you
42:35
know, there were some people at the helm who's maybe eyes were
42:38
bigger than their stomachs, like, hey, business is going
42:40
well, let's grow in all of these different areas at the same time
42:44
without really a a well thought out plan.
42:47
And so this put the company on the verge of collapse and you
42:50
outsiders come in with this fresh perspective, You know,
42:54
lots of change management experience, lots of data
42:57
experience, and you were able to figure out how to rebuild this
43:00
thing. And today you're successful #2
43:03
in the country in that, you know, customer satisfaction
43:06
survey. Are there any other numbers that
43:07
you can share with us that, hey, when we came in, this is where
43:11
we were, here's where we are now.
43:13
Now I think it's a privately owned company.
43:14
So I I don't know what you're comfortable sharing, but that
43:17
would be interesting. The, the thing I think the, you
43:22
know, the, the data that is kind of available there, I can tell
43:26
you were where I came. Our claims were at 5% and to be
43:35
honest, they were worse than 5% because there was a lot of data
43:42
manipulation. So 5 is already horrible when
43:45
you think about it, 5%. I really think it was more like
43:48
20%. But the people were, people were
43:51
really manipulating the data to look better, which was one of
43:55
the big things that one of the biggest challenges I, I actually
44:01
face is how to bring the operation side and make sure
44:04
that what we see is what it is, not what people want us to
44:10
believe. And that required, they really
44:15
required a lot of a lot of late nights to figure how we're going
44:19
to do it. The, our, our ratio of claim is
44:25
0.2 today. And I can assure you is very
44:30
valid because the, the, the, the data that we develop and the
44:35
visibility that we have. I can even tell you which
44:38
forklift operator touched that freight and broke it because we
44:42
have algorithm running and and I have machine learning vision
44:48
models analyzing the pictures and things to kind of understand
44:52
when things might have happened and so on.
44:54
So I know that this this is a valid number.
44:57
I can tell you that when we came, our service was 50%.
45:05
What is that in your service? Like so each service.
45:09
Means tracking service. Means, how accurate are your
45:15
deliveries to the times that you committed so you're only
45:20
getting? Half of those.
45:22
On time, yes, at the best and but this is kind of interesting.
45:28
This is the very interesting for the whole industry because
45:32
everybody is lying on this number.
45:35
Everybody, even even even even my admirable all dominion, they,
45:39
they always kind of over advertise it because they
45:41
there's a bunch of things that you're not going to.
45:44
It took me a while to understand it, but there is a bunch of
45:47
metrics that you're not going to count it.
45:49
So for example, if you deliver something to Florida and, and
45:56
sadly in Florida, it's a hurricane and you're not going
45:59
to dispatch your truck there. Well, you're going to count it
46:03
as you don't have to count the service if you if you're going
46:08
to go and do it because it was snowing in Dallas.
46:11
And if if, if there is a frost in Texas, the state is closed.
46:17
So anybody who is from Midwest doesn't really understand it
46:20
because we operate with the snow.
46:21
But that is a very specific is different.
46:25
Everything, everything is perspective.
46:28
So in in those southern states, when you get a bit of a snow it
46:32
it's a very different story than OK, you wouldn't do.
46:35
And then I realized that operations come up with bunch of
46:42
other reasons why are we not going to count the service.
46:45
So it's like, you know, you're judging yourself.
46:47
Are you judging yourself? And, and, and you kind of make
46:54
it more favourable to look. So what I, what I did when I
46:58
came, and this is, it was interesting because some of our
47:02
executive in operations actually left after I decided to
47:07
implement this logic. I said you know what I gonna do
47:10
CPOV customer point of view and I really don't care what the
47:16
reason is. You can put the reason I just
47:19
not going to count it, which people were shocked operations
47:23
hated it at first because you know, certain instances when you
47:28
think about it is unfair to them because it's not their fault
47:32
that that I don't know that and you cannot and you know it's not
47:38
their fault. That's like they came to work,
47:40
they want to work and it happened that I don't know a
47:43
truck broke and and etcetera. But I explained them like we are
47:48
in customer business so it doesn't matter.
47:52
It's fair to us or not. When I, I gave this analogy of
47:56
a, of a toothbrush and it somehow would work pretty well
47:59
with our, our team and, and I'm incredibly proud of my
48:05
operations because today they live by it.
48:08
And you tell them that CPOV is wrong.
48:12
Oh my God, like, watch out, watch out.
48:15
I mean, I, I would not go there. It took us a while to get
48:18
together where we are, but they came along.
48:21
But but the the example of a toothbrush I gave was so if I
48:28
forgot my toothbrush and I go and buy it online and somebody
48:34
going to tell me you're going to have it on Thursday morning.
48:38
The only thing I care is will I have a fresh or bad breath on
48:46
Thursday when I go and meet my customer?
48:50
Because that's what really matters.
48:51
Everything else doesn't matter. It's it's outside of my control.
48:54
I come to the company, company commits to me that they're going
48:58
to go and deliver something and do they deliver or not.
49:03
And I, I get it. There's a bunch of reasons that
49:07
something might have happened in between that prevented it.
49:10
Some of them are good, some of them are not, but it doesn't
49:14
change. At the end of the day, that
49:16
customer perspective is what we really need to be focusing on.
49:20
So, so we introduced this customer point of view, which
49:24
was a shock to to my operations. As I said today, I'm very proud
49:32
of them how they kind of embrace it and customers were shocked
49:38
because when I came to customers and I show them and that's how
49:41
customer measure you actually, which is fun and story I didn't
49:44
know. So when I came and I showed that
49:46
my numbers are lower than how they were measuring me, they
49:49
really were surprised. Who is that new Roadrunner?
49:53
Like everybody comes and has a better numbers and you come to
49:56
me and you tell me the numbers are worse than the one I see it,
50:00
but are actually pretty good. And you tell me that they're
50:03
actually worse that they are because you have a more drastic
50:07
way because it's through the through the, you know, the
50:10
months and everything we came up how to make this logic even
50:13
harder to it just to kind of, you know.
50:17
You gained a lot of credibility through that process.
50:19
Yeah, they take your bar higher, not lower.
50:22
Like it's it's almost like I almost used the another analogy.
50:26
It's like, you know, it's telling me like, Oh yeah, I can
50:29
still fit my pants, but I just keep opening my bath like no,
50:33
no, like tighten it up and that's and try to tighten even
50:36
more and then then use the same statement.
50:39
So it was it was really I think it was one of the first signs
50:43
customer realizing that. So maybe that Roadrunner 2 point
50:48
O is not just a marketing gimmick, but there is something
50:53
really different and. So tell us more about Roadrunner
50:58
2 point O or even 3 point O. What are you most excited about
51:02
that's coming for the future of Roadrunner?
51:07
Still the two point O is what we call the the best comeback in
51:13
transportation industry because the Roadrunner as I said up to
51:18
2010 was absolutely loved amazing company.
51:25
Then it's a bit of a, you know, few sense happen between
51:32
20/10/2020 and since 2020 we really kind of stepped on that
51:36
transformation of how to make Roadrunner a premium carrier,
51:44
how to bring it back, how to how to regain the trust, how to
51:50
establish and establish ourselves with the the best
51:57
peers in class. And kind of happy to report that
52:04
we get there. We, we are we are already there.
52:07
So, you know, through through quest, through Mastio survey, we
52:11
see that through our customers, we already get there.
52:16
Now that which which was a lot of changes was, you know, if
52:21
you, if you look at it, the number of people we change the
52:24
process we installed technology, we've changed data, we utilize.
52:30
It's, it's just, it's day and night today.
52:35
I can tell you I have, I have a report, I can see a daily hourly
52:40
level of 1000 metrics at the executive level.
52:46
And I'd probably utilize 10% of them.
52:48
But I like having the the other 900 because if something looks
52:53
weird, I can go and check what are could be the others
52:57
influencing variables. And, and it's, it's for me,
53:02
it's, it's a great kind of opportunity to kind of analyse
53:06
and understand the business and go really to the tiny detail.
53:09
But that means that every team has a real time visibility and
53:15
they can understand what's happening.
53:16
And I think this is, this is absolutely critical in, in that
53:21
2.0 transformation. 3.0 We kind of slowly stepping on this is
53:30
really reinvention of LTL and tracking.
53:35
So we, we cut up, we cut up with the guys that are that are on
53:41
top and consider premium carriers.
53:45
And now we want to come and bring a lot of innovation, a lot
53:51
of outside way of thinking, questioning status quo and
53:56
influencing the future of, of that industry by being that
54:02
innovation leader, being that somebody that's going to be
54:05
doing things differently. And we showed already through
54:08
CPOB and few other things that at the smaller scale that we
54:14
attempted. Now we want to go and really
54:16
make it, make it and show that, you know, if, if my predecessor
54:24
is on the call and he ever talks to you, he doesn't have to say
54:30
that he, he moved back in time because he went into tracking.
54:35
I want somebody to come. And it's like, Oh my God, this
54:38
is so cool and tracking is so cool.
54:40
Actually, I, I, I should, I should say it for the last four
54:44
years, five years, I absolutely fell in love in LTLI love LTL.
54:50
It's such a fascinating, unnecessarily complicated
54:57
industry, but it's very fascinating and, and just, it's
55:01
something romantic and sexy about moving freight.
55:06
And I I was working in a lot of industries, but I've never had
55:10
the same amount of passion for any industry I work in as as I
55:15
have for tracking. Do you think that's because
55:18
trucking historically has been so far behind and so you see
55:22
just this tremendous gap that you can help the industry fill?
55:28
Definitely part of it, but I manage through the years,
55:37
attract people from other industries and when they can.
55:43
I don't know something addictive about there is something about
55:46
the concept that you there is no way that any day is going to be
55:53
the same because it's always something happens and you look
55:57
at it. Now we have a tariffs who think
55:59
about it. You will have to go and and
56:01
navigate around and then you COVID happens and and you know,
56:05
COVID happens and you know, great, you can go and work from
56:08
home. I actually joined in COVID,
56:10
which was even more, was maybe even more exciting to join
56:13
logistics. But you know, people like, oh,
56:16
you can work from home and, and you're going to have a mask and
56:19
you're going to be on zoom calls and so on.
56:21
What, what you know what, that doesn't apply to logistics
56:25
because if that happened, we would literally die because we
56:28
would have no food and, and we wouldn't get nothing.
56:32
So we wouldn't get those laptops if we needed them to be on those
56:35
calls because somebody needs to deliver them.
56:38
So it's kind of you kind of living the the challenges and
56:42
whatever happens, you have to navigate and and there is never
56:47
an option that there's no like, oh, OK, I cannot do it.
56:52
No, there's just impossible in this industry, in logistics,
56:56
it's you always need to find a solution.
56:59
Doesn't matter what the problem is, doesn't doesn't matter how
57:03
impossible it seems seems at first the failure is not an
57:07
option because that theoretically the whole economy
57:10
would collapse. So it's it's kind of I think
57:14
that that that's the other part definitely for me, because being
57:17
a techie and and having an opportunity to to go and bring
57:23
bring a company to to more modern era is 1.
57:28
But I would not, I would not undermine the, the impact and
57:34
the influence of actually how important is the job that you do
57:38
every day. And, and really in COVID that
57:41
that kind of made you realize that you are the essential
57:45
service. You are the, the most critical
57:52
chain of that supply chain and the tracking And what you do is,
57:56
is, is fundamentally, you know, you're responsible for
58:02
well-being of many people. So I think that sense of
58:05
responsibility is also a part of it.
58:08
The country needs you. Yeah, Tamash, I just have one
58:11
more question, but before I ask it, I'm going to do 2 things.
58:14
First, everyone listening, pause for just a second.
58:16
Go to that podcast app you're using right now.
58:18
Go ahead and hit the follow or Subscribe button so you continue
58:21
to get more great content like we've had today with Tamash from
58:23
Roadrunner. I'm also going to tell you the
58:25
best way to get in touch with Tamash, and that is his LinkedIn
58:29
page and we'll include a link to that in the show notes.
58:31
But for those of you listening to the audio version, Tamash is
58:35
spelled Tomasz and his last name, Jamroz is JAMROZ.
58:42
And for Roadrunner to get in touch with the company directly,
58:45
it is shiproadrunnerfreight.com. So last question for you,
58:51
Tamash. You and I reconnect a year from
58:54
today and you're celebrating something big.
58:56
You're very excited. What's that big thing you're
58:58
celebrating one year from now? One year from now, I'm
59:08
celebrating brows to to now. What's that now close to now,
59:18
I'd I'm I'm celebrating a lot of things.
59:22
So you kind of put me. That's a weird one for you, huh?
59:26
It is the weird one, I've admit that.
59:28
You put me I'm I'm computing celebrating here from now.
59:40
I don't know please. Let's let's pick a different one
59:43
then. So how about something as you
59:46
look at your past that seemed like a mistake at the time?
59:50
Something in your career, whether it was with this
59:52
business or another one that now you look back on with gratitude
59:56
because it helped to propel you in a different direction or help
59:59
to teach you something that was an important lesson.
1:00:06
Sure. The early my career, I, I made
1:00:16
the pivot to go into the data and analytics space very early
1:00:30
and for number of reasons how I fail it and I failed it because
1:00:41
I had a very different expectation what the work would
1:00:44
be. So I was working for a big, big
1:00:47
consulting company. I was in strategy and, and
1:00:51
technology and I felt like I want to go in the analytics.
1:00:57
It was kind of early. It was, I don't know, early
1:01:02
2010, 2011, but I was really kind of I felt that I should go
1:01:06
into that data analytics, but I couldn't really get in.
1:01:11
I within the organization. I couldn't really find somebody
1:01:19
who in order for you to change when company at the time I was
1:01:23
back center, you need to have a sponsor to go.
1:01:25
And I was trying to and, and, and I was pitching and I was
1:01:29
trying to find somebody who's going to, who's going to help
1:01:32
me, who's going to help me to, to make the change because I
1:01:38
thought that I going to love that data and analytics field
1:01:44
and, and it didn't, it didn't work out.
1:01:49
I even try, I went for some training.
1:01:51
I kind of like, I didn't even like it.
1:01:55
And, and I remember at the time in instead of being like a
1:02:01
frustrated, so like, OK, well, it's different, different than
1:02:05
what I thought. I, I thought that I would be
1:02:06
great in, in data analytics and years later turned out that I'm
1:02:11
pretty good in it. But my first attempt was
1:02:14
absolute. Failure.
1:02:15
What was lacking at the time? I think I was lucky that nobody
1:02:23
wanted to take me because I find a lot of people in in analytics
1:02:31
or in advanced analytics or anybody working, a lot of the
1:02:34
data is they don't understand the business and they understand
1:02:41
the numbers and they getting over excited about numbers and
1:02:45
they paying attention to things that are not meaningful because
1:02:48
data shows you something. But in reality, it's not
1:02:51
meaningful at all because that's just how business operates.
1:02:54
And I think I was lucky because my my failure to join that
1:03:03
analytics teams earlier resulted in me being part of a major
1:03:08
transformation. Me seeing how businesses really
1:03:12
function and I build that, that knowledge that then later on
1:03:17
when this opportunity presented itself, that advance machine
1:03:24
learning opportunity were starting to pop out.
1:03:28
There were no people who had any background in the area and I had
1:03:34
all the background from mathematical point of view.
1:03:37
But then I always had, but now I brought that business acumen.
1:03:44
So that was kind of, it was an interesting opportunity for me
1:03:47
that I had a good instinct. I thought I'm going to be good
1:03:52
at it turned out I am very good at it, but but I failed at 1st
1:03:58
and and that failure helped me kind of develop the, the, the
1:04:04
skill set that I would never learn if, if I went there right
1:04:08
away because I, I would. That is so fascinating that you
1:04:13
can get lost in it. And I learned a lot about
1:04:16
business. I learned a lot about big
1:04:18
transformation, learned a lot of technologies, implementing a
1:04:23
challenges, operationalizing it and everything that comes with
1:04:27
it. That when I was kind of
1:04:29
reintroduced to that field within the same company
1:04:32
actually, because it happened in Accenture, I was a very I had a
1:04:38
very different perspective. And I think that perspective is
1:04:43
that helped me to be successful with, you know, scale AICGI now
1:04:49
with Roadrunner and you know, at one point with Ascent as though
1:04:54
something that was also working on in the last few years.
1:05:00
So this is this, this is something, don't get discouraged
1:05:04
if you, if something didn't work out at Ferris, if you're really,
1:05:09
if you're really good at it, if you're really motivated, just
1:05:13
let follow the path, take the pivot and then there might be
1:05:18
another opportunity. And when you have that
1:05:20
opportunity, just take it. Just take it.
1:05:23
Don't, don't overthink it and it could be great.
1:05:27
It worked out for me. So that would be the advice.
1:05:31
Yeah, I love that lesson, Tamash.
1:05:32
And I think, you know, a big part for you, it sounds like was
1:05:35
needing to go and get some other skill sets first, some other
1:05:38
experiences, so that when you came back and you were looking
1:05:41
at the data, you had a different lens at which to see it. 100
1:05:45
percent, 100%, that was just I, I really, I develop a point of
1:05:51
view that I would, I would not be even capable of comprehending
1:05:57
if, if I made that, if that earlier pivot was successful.
1:06:04
Tamash Yamraz from Roadrunner, I want to be the first to thank
1:06:07
you for sharing both your time and your story with us today.
1:06:10
I really appreciate it. Thank you, Anthony.
1:06:13
It was a pleasure, a pleasure talking to you, talking to your
1:06:17
audience. Thank you for, for thinking
1:06:20
about Roadrunner and giving us an opportunity to, to tell the
1:06:26
story because there is a lot of, a lot of interesting and amazing
1:06:30
people behind that story. I, I had a pleasure of, you
1:06:36
know, talking about Roadrunner, but there's many more
1:06:39
Roadrunners out there that that are really amazing, amazing,
1:06:43
amazing job to, to make as best in class.
1:06:48
Love it folks. That's a wrap on another episode
1:06:51
of the Inspired Stories podcast. Thanks for learning with us
1:06:53
today.