November 27, 2025 • 57 mins
What if your warehouse could flex labor across sites on demand, cut safety risk, and feed cleaner data into your WMS—all without a massive retrofit? We sit down with Brian Nachtigall, VP & GM of Vaux at ArcBest, to unpack smart autonomy that blends autonomous forklifts with remote teleoperation for a fast, pragmatic path to automation. No gimmicks, no buzzword soup—just a clear look at how deep learning, digital twins, and disciplined operations deliver measurable wins right now.
Brian traces his journey from Bain to Cognex to Boston Dynamics, sharing what he learned about scaling machine vision, building reliable autonomy stacks, and applying management systems that actually fit the culture. We break down the Vaux suite: robotic forklifts that map your facility, fleet management that coordinates traffic safely, and a human-in-the-loop model that lets operators “dial in” only when tasks get tricky. That pooled remote labor becomes a powerful lever for load balancing across DCs, smoothing demand spikes and reducing stockouts, late fees, and excess safety stock that quietly erode margins.
We also explore warehouse digitization and why data integrity is the real multiplier. By capturing accurate pallet locations and on-forklift dimensions with Vaux Vision, your WMS makes better calls, slotting improves, and the bullwhip effect softens. Beyond forklifts, Vaux's freight movement platforms accelerate shuttle runs, protect sensitive electronics for data center logistics, and enable scaffolding to move non-stackable freight efficiently. Throughout, Brian’s stance is grounded: automate what you can, keep people in the loop for edge cases, and compound gains over time rather than chasing lights-out promises.
If you care about safer operations, faster deployments, and resilient supply chains, this conversation offers a blueprint. Subscribe, share with your ops team, and leave a review with the one bottleneck you’d automate first—we’ll feature our favorite responses in a future episode.
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Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
SPEAKER_04 (00:08):
I want to clarify that this podcast is distinct
from my responsibilities as aprofessor in the Stan M.
Walton College of Business.
Nonetheless, it aligns with myaspiration to provide practical
insights to professionals andbusiness by showcasing companies
and people that can enhance yourability to manage, lead, and
strategize and marketeffectively in the retail value
(00:30):
chain.
And now without further ado,let's get into the exciting
episode.
I have with me today BrianNoctigall, and he is heading up
Vox for ArcBest.
He has an incredible backgroundin consulting and automation
that really positioned himperfectly for the kind of role
(00:53):
that he's in right now.
But today we're going to betalking about smart autonomy and
the future of warehouseinnovation.
I'm really excited about this.
Brian's background, like I say,is amazing.
So, Brian, would you mean mindtelling us a little bit about
how you wound up in thisposition and your journey here?
SPEAKER_01 (01:11):
Yeah, sure, man.
And thank you very much forhaving me on the on the podcast.
Pleasure to be here.
Yeah, pleasure to be here.
Uh it's been pleasure kind ofgetting to know you here in this
role.
But how I've gotten here, I'mreally just thrilled to be a
part of ArcBest and to be havethe opportunity to be leading
the Vox group and kind of takingthe building blocks that you
know that the team has createdover the over the past years and
(01:33):
being able to build a businessout of it.
It's uh it's it's an excitingtime, exciting time in the
industry, the evolution oftechnology and applying
technology to warehouseautomation, and uh and really
just thrilled to be here havinghaving a good time, you know,
building this business.
I would say uh, you know, how Igot here, I it it just was an
opportunity that I couldn't passup.
(01:55):
Uh it was it was too good of afit with you know my background
in industrial automation and andcombining the sort of the early
career and strategy and and uhkind of being able to put all of
you know the um the sort of thelessons that I've learned in my
past and uh to to work to kindof you know build this
(02:17):
opportunity.
SPEAKER_04 (02:19):
Yeah, you know, um when I first read about your
joining um ArcBest, leading thisinitiative, I was thrilled,
especially because of yourbackground.
Um, you know, you have severalyears of experience uh as a
consultant with Baying Company,which is one of the best
consulting firms in the world.
(02:40):
Uh you have incrediblebackground with machine vision,
robotics, automation technologycompanies.
And even earlier in your careeras an investment uh analyst,
that's a unique background forsomething like this.
But I would imagine that itreally informs your current
role.
Is that right?
SPEAKER_01 (03:00):
Yeah, it's funny.
Uh it is funny how you you pickthis bits and pieces.
I imagine everybody does this intheir careers, but you pick bits
and pieces of you know yourbackground and lessons you've
learned and applying them.
And uh what's exciting for me isI'm in a role now where where
I'm actually getting to apply, Ithink, lessons from really
almost nearly every one of myexperiences.
(03:22):
And so I guess you know, goingback, uh as you mentioned, uh,
you know, I have I have about 15years of of industrial
automation experience, but kindof going all the way back, yeah,
I I graduated from PrincetonUniversity, and my first job out
of college was with the thegroup that invests the endowment
at Princeton, the called Princo.
(03:42):
Uh it was a small group of ofabout 14 people and with about
$10 billion to invest.
And so just a really interestingmile-high view of the world of
finance, you know, for uh forsomebody coming out of out of
school and kind of learning howthe world works.
But uh to me, uh, it was it wasprobably about 20 years ago that
(04:03):
I that I made the decision tokind of get out of pure
investing and wanted to learn alittle bit more about how you
know how how what decisions aremade and what goes into those
decisions to actually createvalue in society and in the
economy.
And you know, had the had thejust great opportunity to get to
(04:24):
to go to Bain and Company.
And didn't you go to Duke firstfor your MBA?
So I got I got my MBA at Duke uhwhile working at at Bain.
So I spent a couple of years atBain uh out in uh out in
California, and then kind ofreturned to the East Coast and
went to Duke uh to get my MBA atthe Fuca School of Business, uh,
(04:45):
and then returned to Bain.
And that's what that's uh whatbrought me up to Boston,
Massachusetts.
And I you know spent about 15years in in uh in Boston, kind
of working at Bain initially fora few more years.
And then uh when I when I leftBain, it was sort of that that
itch again to roll up my sleevesa little further and really dig
in and and uh help buildbusinesses.
(05:08):
And uh just had the had thegreat opportunity of getting
onto this trend of you knowmachine vision and robotics and
industrial automation that youknow worked at Cognex
Corporation, who's a a globalleader in the industrial machine
vision space and manufacturingand warehouse settings, had the
opportunity at Boston Dynamicsto lead their uh their warehouse
(05:31):
automation effort uh with theirrobot that they call stretch and
uh kind of create the other thebusiness model out of out of
stretch and build the teamthere.
And uh, and then here, you know,at Box uh within ArcBest, sort
of the just just as I mentionedearlier, the the thrill,
thrilled to be able to take thebuilding blocks that they've
(05:55):
created and really turn it intoa business and set ourselves up
for growth going forward.
SPEAKER_04 (06:01):
You know, um my first academic research study um
that got published was publishedin 1990.
And it was the first academicresearch I'd really done.
Um it was on flexibleautomation.
This was 1990.
(06:23):
And um and I did a few otherstudies um uh for a few years on
flexible automation.
Back then, the flexiblemanufacturing systems was it was
a hot topic in academics.
It and then it faded for a longtime.
But it's been interesting to meto see how you know automation
(06:46):
and robotics in the industryback in 1990, I think we thought
we would be further along by nowthan we are, but you got into it
really at a great time, and nowit's just taking off beyond
imagination.
And and I can't imagine.
(07:07):
Um did you did you see that ordid you just were were you just
interested in that?
SPEAKER_01 (07:13):
Uh you know, a little bit of both.
Um, you know, I grew up I grewup in a blue-collar town,
manufacturing town, and uh sothat the idea of using you know
using technology to make you tomake manufacturing more
efficient was always somethingthat was kind of interesting to
me.
And then really just had youknow a couple of pieces fall
(07:38):
into place, but uh had aconsulting project at Bain and
Company where we were helping aclient understand how to how to
you know track their productthrough their supply chain.
And we would talk about howthere are technologies to read
you know barcodes and datamatrix codes and and to be able
to track product through.
And and but you know, as astrategy consultant, you kind of
(08:01):
get to the point where you say,well, there are a ways to do
this.
And then you you scratch alittle deeper under the surface,
you say, Well, okay, who are thecompanies?
What are the solutions that dothis?
And you get some shoulder shrugsand some, well, you know, they
they, you know, in quotes, theycan do this.
Well, who's they?
And and you found that thetechnology wasn't necessarily
(08:24):
there yet, or there were itwasn't clear who was providing
these types of solutions.
And this is back, you know,2010, you know, timeframe or so.
And so when I came across Cognixand the opportunity to join um
to you know, Rob Willett, whowas the you know the CEO at the
time at Cognix, and um and seewhat Cognix was doing, I said,
(08:49):
okay, here's a company that isdedicated to machine vision and
you know, doing things like youknow inspection using cameras
and robotic guidance, but thenalso the the reading of barcodes
using cameras and the 2D codesthat uh that are used now.
Um, I said, okay, the thiscompany is onto something.
(09:09):
There's a need here, and here'sa and here's a business that is
you know innovating and focusedon building a solution for this
space.
And so that was what kind ofmade me think, okay, Cognix is a
good place to put my energy.
And sure enough, it was anexciting time for Cognix.
The the five years that I spentwith them, you know, they went
(09:30):
from about$250 million ofrevenue to$750 million of
revenue during that time.
There's just a an exciting time,you know, when the industry was
adopting some of thesetechnologies, both in the
manufacturing setting and inlogistics space.
Um, and uh to your point aboutflexible manufacturing, it was a
(09:50):
time when people were realizinghow to use machine vision to do
things like reduce batch sizesand identify defects and then
the track and trace things whereyou know the automotive industry
is figuring out how to, youknow, when they find a problem,
you know, the the recalls in theautomotive industry are so much
smaller than they used to bebecause they can track a problem
(10:12):
back through the supply chainback to where they um to where
that defect actually happened.
And they can all they can recalljust the cars that um you know
that that were affected by youknow a problem that they were
able to diagnose with much morespecificity.
Um so exciting time there.
(10:34):
And then uh, but but really evenat that time, you know, 10, 5,
15 years ago, a lot of it wasrules-based.
So you use uh you use machinevision, you you program rules
around, you know, what should arobot do or what should a
manufacturing system do based onthe input that it's getting from
a machine vision system.
(10:54):
And what's what's exciting nowis really over the last five or
10 years is this idea of deeplearning.
And so you go from a rules-basedapproach of, you know, I'm I'm
looking for something specificto, you know, I'm looking for a
specific thing in a specificplace, uh, to I'm looking for,
(11:15):
you know, something somewhereand and the and the system can
kind of find something and uhand uh that that you don't
necessarily specifically programit to do.
And so as industry gets morecomfortable with this deep
learning approach, and then youknow, I think the deep learning
(11:35):
approach has led to all the thegenerative AI stuff that's been
that's been you know that it'sbeen really game-changing over
the last couple of years, um,and how that leads to you know
simulation technologies thatallow these robotic systems to
learn so much faster than theycould before.
It's it's a really aninteresting time and an exciting
(11:56):
time.
SPEAKER_04 (11:57):
Well, I have to say I'm impressed with your ability
to describe things like thatwithout using a bunch of jargon.
That was that was impressive.
Because I I was thinking of thejargon as you were explaining
it, and uh that's it's good.
But you know, the other thingthat was really neat about your
experience there is you got tosee what it's like to scale up
(12:17):
big time.
I mean, going from 250,000 to700 million, was it?
Yeah.
That's a big scale up.
SPEAKER_01 (12:25):
Yeah, and uh so so one of the things, you know, you
think about management systemsand building a business.
So I'm and I mentioned I had theopportunity to work under Rob
Willett at Cognex.
So Rob had in his background, hehad sold a business, a family
business to Danaher Corporationand then spent 10 years leading,
you know, running a businesswithin Danaher and also kind of
(12:48):
learning that that Danaherbusiness system and and the the
the management philosophy andthe and the approach to
measuring uh a business andmanaging a business that way.
And then and then he brought,when he had the opportunity to
lead Cognix, he brought some ofthose lessons.
And I say some of those lessonsbecause um because in a lot of
(13:12):
ways, you know, sometimes thatthat business system, you know,
at Danaher, it is part of theirDNA.
And they are they are justrelentless in how they how they
how they implement thatmanagement system.
And so when you come into uhanother business where you know
you you have to take, you haveto pick and choose which tools
(13:33):
to apply.
And I think Rob did a reallyeffective job of that at the
time at Cognix.
And and I take a lot of lessonsaway from that in terms of how
when I go into a new situation,uh, I don't just overlay those
management tools, but you know,I've had the opportunity over
this past uh you know half ayear or so at ArcBest and with
(13:58):
the team at Vox to really kindof observe and talk to people
and and and see you know whatare our products, who is our
team, what are our what are whatis our culture, you know, how
how do how does this group dothings?
What puzzle thesis do we have?
And now what you know, whataspects of this management
system do we apply to thisspecific situation?
(14:21):
And we're in the process ofdoing that, you know, right now
and kind of changing the team.
And um uh, you know,fortunately, I inherited a
really strong team, you know,ArcBest and with the Vox team,
they did an amazing job atpulling together, you know,
really talented individuals.
They've built a roboticsengineering team and a
(14:43):
mechanical engineering team tobuild their products.
And so from an engineeringperspective and a product
management perspective, like alot of the people were there.
Also, ArcBest, they've beenthey've been investing in
innovation for a long time.
And they have this pool ofreally talented people doing you
know data analytics.
(15:04):
Uh, it shows up in things liketheir pricing strategies and
their, you know, that in theirand uh and their you know yield
analytics to to to help ArcBestkind of be both competitive and
price competitive for their youknow to win business in the
trucking industry, but also youknow, selective in their
customers and making sure thatthey're profitable.
(15:26):
And it also comes in things likeyou know their route
optimization, you know, uh torun the ArcBest trucking
business more efficiently andeffectively and more profitably.
So we have you know this greatpool of talent at ArcBest, and
and they've just done a greatjob at put at putting those
those pieces together.
(15:46):
And now I think what I'mbringing to the table is you
know how to organize thosegroups, organize those puzzle
pieces in a way that we can umwe can then then kind of overlay
the the commercial side of itand the sales side of it.
How do we how do we really focusour attention on the right on
the right markets, theattractive markets where we can
(16:07):
make the big biggest impact withcustomers and and have the most
commercial success?
And how do we streamline ourdecision making to uh to be to
be moving fast and and agilewith uh with building this
business?
SPEAKER_04 (16:22):
You know, I've met a lot of these people you're
talking about, and I'm superimpressed with I even know some
very recent hires uh in thatarea, data science and so forth.
But um I think the culture therehelps with recruiting top
talent, you know, because peoplewant to work somewhere where
(16:42):
they're gonna enjoy it and theyhear of other people enjoying
their work.
Um but um but in addition tothat, uh you know, one of the
things that I've always said,and uh the listeners know we
have Northwest Arkansas has anunusually high concentration of
logistics and supply chaintalent.
(17:02):
Not just not just um uh uhpeople that are actually in
supply chain, but even peoplethat are not.
For example, if you go to thesesupplier teams in town, their
salespeople can talk aboutlogistics quite a bit.
Which if you go to the stay withthe same CPG company, you go to
a sales team in another uh citylike Cincinnati or Minneapolis,
(17:26):
et cetera, they don't have thatability.
Um there's just a lot ofinterest in logistics and supply
chain here.
So for me, I am particularlythrilled that Vox is doing this
in Northwest Arkansas.
To think that we're uh NorthwestArkansas is developing um
(17:46):
advanced um uh automation andtechnology around logistics,
entral logistics in particular,here is exciting.
And I think it's uh it'snoteworthy.
Uh I'd like to shift gears justa second to um you know to set
the stage, if you wouldn't minddescribing a little bit about uh
(18:06):
the Vox suite um as well as uhthe mission of Vox within within
the ArcBest mission.
SPEAKER_01 (18:15):
Right, right.
So yeah, so Vox uh you know, Voxwithin ArcBest, uh it kind of
represents some of the strategicpillars of ArcBest in general.
So ArcBest, you know, ArcBestmission and has three strategic
pillars.
It's you know it's growth,efficiency, and innovation.
And they really take thatinnovation piece seriously, as I
(18:36):
as I mentioned earlier.
Um, and you know, with thingslike you know, the pricing and
the route optimization thingsare are some of the under the
hood uh tools that they build.
Uh, but then you have you know,Vox is really just a tangible
illustration of that innovationthat that is is is part of that
ArcBest DNA.
(18:56):
And and I really can't, youknow, and you you you touched on
you know the culture piece, butI also can't overstate how
important that is as well, youknow, of the you know, the the
culture of ArcBest, the strengthof character of the leadership
team.
Absolutely.
And, you know, the the um youknow, just just two of their of
(19:20):
their sort of core values areyou know, integrity and
excellence, and how but how thatthat strength of culture just
flows down through theorganization, helps us in a lot
in a lot of ways.
Um so so getting in, you youasked about, you know, so what
is the Vox suite of technology?
So to kind of give a you know,you we're really kind of
(19:40):
bucketed into you know threeproduct groups.
So I'll start with uh theautonomous forklifts.
So you know, Vox is building arobotic forklift solution.
And we believe that you know oursolution is the fastest way for
customers who are interested inautomating their warehouse
operations to apply and beginreducing their operating costs
(20:00):
uh you know of their warehouseas fast as possible.
So our approach is to automatethe forklift operations as much
as possible.
And then we have ahuman-in-the-loop approach where
the the portions of the tasksthat are you know too
challenging to automate, aperson can remotely dial into
(20:21):
those forklifts and performthose tasks via you know remote
operation or teleoperation.
And so by doing that, we we havea a low upfront infrastructure
uh investment required.
So uh we we can get you, we cancome in and within you know four
to six weeks be up and runningat a customer.
(20:42):
And uh a customer doesn't reallyneed to change their
infrastructure for us.
We can operate within acustomer's you know process.
And so that's our that's our uhautomated forklift business.
We call it smart autonomy.
Uh another piece of our businessis on the uh it's really a
(21:03):
relatively low tech, althoughthere's there's smarts, you
know, uh it's a it's anintelligent solution to it, but
it's our we call it freightmovement system, but these are
these large staging platformsthat allow customers to stage
freight, and then it can quicklyand smoothly roll that freight
into and out of trailers.
(21:24):
So that we have some patentedtechnology into and how these
rollers engage so that it cancarry heavy freight, but then
still roll around a warehouse orinto into and out of trailers.
And we're finding that's reallyeffective for um situations
where customers have either uhfreight that can't be stacked.
(21:45):
So we have a scaffolding systemwhere you could you can make two
or three layers of freight in atrailer that otherwise couldn't
be stacked.
So you can cut down on your onyour number of uh number of
trucking routes, you know, thatway, or number of runs on your
routes that way.
We're also finding it's reallyuseful in in applications where
(22:05):
uh where you have shuttle runs,where the the loading and
unloading time is high relativeto the travel distance.
So that's that's particularlyeffective in things like the
automotive sector, where youhave you know what parts
warehouses relatively close tothe assembly plants, and you're
doing a lot of shuttle runs backand forth um in a situation like
(22:26):
that.
And then we're also kind ofstumbled on uh the this
opportunity in in the datacenter space, where we found
that it's really valuable forfor you know high-value
electronics, um, where it's youknow high value and sensitive
electronics, where you know ourstaging platform allows it to be
(22:46):
touched fewer times, and we canhandle kind of the some of the
vibration sensitivity over theroads.
And so we're talking to playersin the data center space that
are that could be huge.
SPEAKER_04 (22:57):
That's such a big and growing business right now
with uh all the AI and evencompanies taking data out of the
uh cloud and back to their uhnative uh servers.
SPEAKER_01 (23:11):
Yeah, it's uh it's obviously a huge trend.
It's uh it's it's it's on a lotof people's mind.
There's you know the this buildout of the data center space,
and you know, we're uh we'redoing our best to do our part to
kind of help that industry moveforward.
There's a lot of these,especially the hyperscalers
they're working on.
Um they're working on kind ofpre-fabricating or
(23:32):
pre-manufacturing as much of theserver infrastructure so that it
can just be kind of rolled intoplace and plugged in as much as
possible uh to make kind of thethe uh the startup of the data
centers as you know as as fastand as streamlined as possible.
And we're we're helpingcustomers in that space.
SPEAKER_04 (23:51):
You know, um, I know part of what Vox is doing is
trying to make autonomouswarehousing available to all
businesses.
Could you talk a little bitabout that from a deployment
type perspective?
SPEAKER_01 (24:05):
Yeah.
So I mentioned that you know, wewe believe that we're the we're
the fastest way to automate yourwarehouse or your forklifts
operations and pallet movement.
And basically what I mean bythat is we have a relatively
low-cost method of outfittingkind of a network communications
infrastructure in a warehouse,which is important because you
(24:28):
need seamless connectivity whenyou're driving connected
forklifts around in a warehouse.
We have uh the technology toremote in and do this teleop
approach.
So, you know, the drivers of theforklifts, you know, we can, you
know, from a from a safetyperspective, we get people off
the floor and you know, workingmore of an office environment as
(24:49):
opposed to you know out on thewarehouse floor.
And um and we can um you know wewe can implement that quickly in
a customer's current process.
So we're not asking a customerto apply or to change their
process or put a lot ofadditional infrastructure so you
(25:11):
know we can come in and um beginworking quickly with the
customer.
SPEAKER_04 (25:16):
Brian, you know, of course, ArcBest has over a
hundred year history.
They made it throughderegulation, the Motor Carrier
Act of 1980 successfully, whichwas not easy to do.
Very few firms have done that.
Um and of course, after theMotor Carrier Act of 1980, the
LTL industry started uh formingand um and because um ArcBest
(25:44):
has been, I mean, they're in allkinds of areas of logistics now,
but you know, after that theyreally specialize to um LTL.
But LTL requires different typesof trucks for line haul versus
pickup and delivery, it requirescross-docking uh terminals, um,
many things that uh other kindsof uh trucking companies don't
(26:08):
need, um like uh full truckloadtrucking companies.
But uh but since then, ofcourse, uh ArcBest has expanded
into many different other areasof logistics.
So Vox uh has an advantage, andI'd love to hear what you think
it is by being a part of thisrich history and cap the
(26:32):
capability set as compared tosay a startup in the same space.
SPEAKER_01 (26:38):
Yeah, I I think you know being a part of ArcBest is
in a way it's it's sort of oursuperpower, right?
Our hidden superpower.
So we you know, we benefit froma in a lot of ways, just that
inherent knowledge that the theother employees at ArcBest have
(26:59):
about how real-world operationswork.
So when we have an idea of howto how to improve a process, or
we or we talk to people aboutways to improve their processes,
we can we can kind of test thoseideas or those hypotheses with
people who can say, yeah, youknow, this is how a person on a
warehouse floor would operatethat or would do that.
(27:22):
And so we can quickly kind ofnarrow down how, you know, you
know, how do we create asolution that can be effective
in a real world environment?
How do we handle real worldfreight and the challenges that
come with that?
Um, it's more than just havingaccess to the to the test
environment, which is wonderful,uh, by the way, to be able to
(27:46):
take a take a prototype or asolution and test it in a real
world environment and get thatfeedback.
Uh, that's a that's a big that'sa big benefit.
But it's also just a team thatinherently knows what's
important and what it takes todo something in in such an
industrial environment.
(28:06):
So one example is is just thethe IT team.
And I mentioned how ArcBestinvests a lot in innovation and
and IT, but there's a whole teamof people that understands the
importance of a reliable ITnetwork and uh in a secure IT
network when we're putting uhyou know advanced robotic
(28:29):
solutions into facilities, thatthe cybersecurity topic is a is
an important one.
And we have a team of peoplethat knows you know what what's
necessary to have a goodsolution and something that our
customers will be um will benodding their heads in the right
direction, you know, when youknow, when we discuss, you know,
the things that we're doing withrespect to cybersecurity, with
(28:51):
respect to safety uh and howmuch we invest and and the team
that we have to to be looking atat safety and uh with our with
our robotic solutions and and soforth, things like that.
SPEAKER_04 (29:03):
That is interesting.
Um I hadn't thought of thesafety aspect of it, but you're
right.
I mean, with with ArcBest havingto operate these terminals and
all the transportation equipmentthey have in the terminals,
outside of the terminals, onover the road, et cetera, et
cetera.
And I know safety has been a bigpart of ArcBest's um approach,
(29:30):
as long as I've known them.
Um but I hadn't thought aboutthat.
That you know, that's somethinga startup wouldn't necessarily
have access to that richknowledge and background.
SPEAKER_01 (29:40):
Um Yeah, and it's it's also just um it's it's part
of, you know, clearly for foralmost any industrial company,
safety has to be a part of theirDNA, right?
It's it's paramount, right?
Um it's more than just a talkingpoint.
It's a it's the importance ofyou know, your employees have
families to go home to at theend of the day.
And so you have to do, you know,your job, or we have to have
(30:05):
processes that that make it, youknow, uh, you know, that that
that show that importance, youknow, that that our employees
can can can go home at the endof the day.
And um and and and sort of on ontop of that, so clearly when
when we're applying roboticsolutions, the importance of
putting safety systems in placeuh is is critical, right?
(30:28):
And we make sure that that isthat is of utmost importance.
Um, but then also theunderstanding of you know, for
our customers, the cost ofsafety.
So um, you know, when we talkabout getting uh getting kind of
people off the floor, part of itis is also reducing the
liability of a company, youknow, the and the the risk of
(30:52):
you know, companies are payinginsurance premiums based on the
the the risk of a safetyincident happening.
And if you get people off thefloor, and so our our systems
are not just automating as muchas possible, but the with the
human teleop approach, thepeople that are then remoting in
and driving those forklifts arein an office environment as
(31:15):
opposed to on the on thewarehouse floor.
And so um, so you're you'reyou're further reducing that
liability cost as well.
That is really a great point.
SPEAKER_04 (31:26):
So I'd like to dig in a little bit more to the Vox
smart autonomy.
Um and um I was really intriguedby that.
Um and and of course, as I saidearlier, it's it's exciting to
know this kind of technology isbeing um developed here in
Northwest Arkansas, but it'sreally already developed.
(31:47):
It's not like it's developing,it's it's uh it's a product
that's usable today.
But um, but you're still tryingto develop it further.
Um but if you wouldn't mind tellus a little bit about the
building blocks of that.
Yeah, sure.
SPEAKER_01 (32:04):
So um I would say at a at a yeah, at a basic level,
it's it's that it's that networkcommunications infrastructure
that we go in.
We you know, uh we we willinstall that at a customer.
It's the IT integration so thatwe're can we can both you know
connect our remote um our kindof the the remote teleoperation
(32:25):
labor.
Um and then the um and then onthe on the robotic forklifts
themselves.
Well, before I get to therobotic forklifts, it's you have
the communications network, andwe'll go in and we'll map a
warehouse.
So we've effectively we'recreating a digital twin of of a
warehouse.
(32:46):
And you know what you have thatmap of a of a warehouse and you
we apply the robotic forkliftsor the automated forklifts to
that to that map uh to operatein that setting.
So then the the additional kindof technology stack is the the
fleet management.
So when you have multiple uhvehicles operating in a
(33:11):
warehouse and the and the sortof the traffic management of
those vehicles is one layer ofthe stack.
There's the safety layer of thestack that you know I touched on
earlier to to make sure that thevehicles are are operating
safely.
They have all the sensors andthe awareness of what's around
them to uh to operate.
Uh and then it's that thatteleop uh setup as well.
(33:34):
So we can operate you know umautonomously as much as
possible.
And we have we have the thesensor suite and the autonomy
stack to do that.
And then the um theteleoperation interface so a
team a you know a uh someone cankind of see when a when a
(33:54):
forklift kind of raises its handand says, hey, I you know, I
have you know below thresholdconfidence in a in a particular
task and raises hand, and ateleoperator can can dial in,
can see that, dial into that uhto that forklift and perform the
task and then turn it back overto to the autonomous mode.
SPEAKER_04 (34:13):
So uh earlier Brian mentioned digital twins, and you
can think of it as um if youthink back to just regular
discrete event simulation whereyou simulate something to figure
out how you should maybe laysomething out, design something.
Um digital twins are a littledifferent in the sense that it
(34:36):
is simulation, it's simulatingit, but it's doing it over and
over in real time.
So every time something changes,it's simulating the process.
And um, as it simulates, then uhdecisions can be made based on
the output.
Uh it's behind the scenes,people don't see it, uh, but uh
(34:56):
but that's basically uh what itis.
SPEAKER_01 (34:59):
Yeah, there's the there's the simulation piece of
of testing and building ourtechnology, making sure that it
works when it goes out into youknow into the world.
Um, and that that that's youknow part of what's helping us
as well as a lot of a lot ofcompanies across the across the
robotics ecosystem to you knowto push these things forward.
Um and then you know, when wetalk about digital twins, you
(35:22):
know, one of the other thingsthat I'm I'm just excited,
really excited about is how weare putting these technologies
together to improve the dataintegrity for um you know for
for for a lot in a lot of waysfor for other systems.
So uh our customers and a lot ofbusinesses out there, they have
(35:43):
you know WMS systems, warehousemanagement systems.
Those that WMS software relieson good data about where the
freight is.
So a WMS system can can provideguidance on, you know, move this
freight from from here to there,here's how the flow of freight
should go.
But uh sometimes when you havemanual operation within a
(36:05):
warehouse, that those palletsget put you know in different
places that that you know, youuh it's easy to lose sight of
exactly where your freight is.
When you have a robotic or um oran automated approach to moving
pallets around, you know wherethat where that freight is.
And so I see kind of at a youknow, at a if you step back, a
(36:30):
lot of what we're doing here ispart of this warehouse
digitization trend of ofimproving sort of the amount of
data that a business has aboutwhere its freight is in the
warehouse, which just excel kindof kind of amplifies the value
(36:51):
of things like WMS systems.
SPEAKER_04 (36:55):
That is a great point.
Um Yeah, one of the things thatstruck me, and you know this, um
when I first learned about Voxand what was going on is uh a
problem came to my mind, andthat is that you know companies
have distribution centers indifferent regions, and uh
(37:17):
sometimes the demands on thoseregions vary somewhat
unpredictably.
You might be able to know thetotal demand, like if you have
if you have 20 distributioncenters across the country, uh
if you look at the total demand,it's fairly predictable.
But if you get down to aspecific DC, uh it may be that
(37:41):
the demand on that DC is lowwhen the demand on another DC is
high.
And this can be happeningbecause of many reasons.
It can be happening because of,you know, seasons shift over
time, naturally.
You know, you may need moregardening type supplies in one
region earlier than you thoughtyou would.
(38:01):
And you may have it there, butyou've got a constraint of labor
um there.
Uh and you've got labor inanother facility that you wish
you could teleport them there touse it, but you can't.
Um and then you've got, youknow, distribution centers
where, well, it's um they'reopening on the east coast before
(38:24):
they are in the other parts ofthe country, and then they're
closing on the west coast uh wayafter uh other parts of the
country have closed.
And and then there's just randomvariation that you can't really
explain, but it exists.
And so when when I first heardabout this, I thought this idea
(38:44):
of low uh you know loadbalancing would be a big benefit
to many industries, especiallyretail and certain areas of
consumer goods.
Would you mind speaking to that?
SPEAKER_01 (38:56):
Yeah, absolutely.
And um, you know, we couldprobably we could we could
probably nerd out for a whileand talk about you know
stochastic analysis and you knowthe the the quantitative
analysis on on how do you handleyou know these the the
volatility and these things, butum, and and I even draw upon you
know we talked about earlierabout you know previous
(39:18):
experience, you know, it's hassimilarities to things like
portfolio theory and yes and uhyou know spreading spreading the
risk around by diversification.
So so yeah, a a customer thathas multiple warehouses, say
across the country, and theyhave you know uncertain demand,
and they're trying to matchtheir supply of labor for for in
(39:38):
in our specific situation, we'retalking about forklift drivers
labor, and they're trying tomatch that planned labor to the
demand level.
And the demand level may varybased on seasonality, uh based
on the uncertainty in their umin their labor, you know, people
you know, they have to be theyhave to account for whether
(40:01):
people may call in sick or youknow, be be available.
And if you have multiple sitesacross the country, if you can
pull that demand, you uh you'rebasically smoothing out the um
smoothing out the you know youryour volatility in demand.
(40:21):
And so we're enabling you topull that demand.
So things like you know, theavailability of labor kind of
gets smoothed out, then also thetiming variability there.
So if a season out a seasonalbusiness that, let's say, you
know, in this in the springtime,you you know, based on the
weather pattern, if when it getswarmer, people start to want to
(40:44):
get out and do their springcleaning or get out and and do
their yard work and or you know,do their DIY projects a little
sooner.
So, you know, the buildingsupply or the home improvement
stores want to, you know, gettheir product onto the shelves
sooner if you have a relativelywarm you know end of winter.
(41:04):
And um, and they might, they mayor may not plan for that in
their labor planning.
And what we do is by poolingthat labor together, they could
turn that labor on quicklyacross their network of
warehouses uh rather than haveto plan ahead of time, either
either, either overstaffing, uh,which costs money, or
(41:25):
understaffing, which means thatthey end up with some you know
stock out problems uh and nothaving the product on the
shelves or available in their intheir supply chain uh as early
as they might at, you know, theymight have it exactly when they
planned to have it months agobefore they knew how the weather
would plan out, would pan out,but you know, too late relative
(41:46):
to you know a relatively warmspring in that example.
SPEAKER_04 (41:50):
You know, I I think that when people first hear
about this kind of technology,they think, okay, we reduce
labor cost.
And it's true.
Um as you pointed out, you canreduce liability costs and
injuries and things like this.
But this ability to loadbalance, as you're describing so
(42:12):
clearly, yeah, it reduces stockouts.
That in some cases winds upbeing more valuable than
anything.
Because you know, especially ifyou're a supplier to the retail
channel, if you wind up havinglate deliveries, not only do you
miss sales, which is expensive,but sometimes you get charged
(42:32):
for being late, and that'sexpensive.
Um but the other thing is you uhif you can smooth that out, you
don't need as much safety stock.
So you need uh less safetystock.
And I think it reduces bullwhip.
And this is something for thoseof you that don't know, uh bull
whip is the idea that smallperturbations in demand or uh
(42:58):
requirements usage at the bottomechelon of a supply chain get
amplified as they move up thesupply chain, which causes
additional problems likestockouts and safety stock.
It becomes a vicious cycle.
So anytime you can pool likethis and st and really get to
the root problem of these kindsof things, the whole supply
(43:20):
chain operates more efficiently.
SPEAKER_01 (43:22):
And really just it's it's reducing that volatility,
which reduces that bulleteffect.
But then all and then also thethe greater data integrity of
knowing what's in your warehouseis another, it's just another
source of volatility.
Uh, you know, sometimes theyhave to you know overstock
because they don't necessarilyknow exactly what's in their
supply chain.
(43:42):
So both the data integrity andthe ability to make decisions
later in the cycle, you know,all reduces that, you know, that
bull bull with the factory.
SPEAKER_04 (43:52):
Yeah, I hadn't thought of that, but you're
right.
Um that information accuracysolves a lot of problems.
And and it makes people moreconfident when they're making
decisions to know that the datais accurate.
Brian, could you share anexample of how Vox has moved the
needle for a customer?
SPEAKER_01 (44:13):
Yeah, I think the kind of the the clearest one in
my mind is a customer that weworked with who was a little
uncertain about using our remotelabor.
They love the idea of automatingthe forklifts, getting their
getting their drivers off thefloor, and but they wanted to
use their own, you know, theirown labor in in their facility.
(44:35):
And so we were working with themon that.
We were we were up and running,we were helping them, you know,
with our robotic forklifts intheir facility, and their labor
was just in an office settingoff you know to the side of
their warehouse floor.
But then, you know, you know,unfortunately, they they had a
week where, for a number ofdifferent reasons, the the
(44:56):
people that they had staffed tobe manning their forklifts, you
know, remotely operating but onsite, were unable to be there.
And they came to us and said,hey, you know, we we've already
integrated our IT system.
Uh, we're kind of in a bindhere.
We have product that needs tomove, and our guys aren't here
this week.
(45:17):
Uh, can we, you know, is thereany way that we could turn on,
you know, Vox's remote labor tohelp us move this freight?
And we were able to, you know,within about four hours, get you
know, our our guys up andrunning, connected in to the to
the forklifts operating in thiscustomer's facility, and we were
(45:37):
moving their freight for themthat day.
And uh, and you know, uh, youknow, shortly after, you know, I
was talking with the head ofstrategy at this business, and
you know, he was just saying,yeah, that's a, you know, that
this idea of being able toleverage, you know, a pulled
group of remote labor is isreally an area where where our
(46:00):
feelings on this on this topichave matured over time or
evolved over time because uhbecause it was so seamless of a
transition to using this laborthat you know is really you know
very far away.
And just but but connecting inand you know and operating in
our facility and it worked andit worked really well.
(46:20):
And so um it was just a it was avery uh poignant example of
where you know you kind of sawthe kind of the light turn on
for a customer and how and howthey saw well, wow, this this
can be even more valuable thanwe even thought.
Very impressive.
SPEAKER_04 (46:41):
Where do you see Vox going over the next say three to
five years?
SPEAKER_01 (46:45):
Yeah.
So in some ways, I'm reallyexcited at just further
penetrating the market.
We have a solution that worksand just getting the word out
and working with more customers,solving more customers'
challenges and you know, it withgetting this solution out there
and and applying it and rollingit out and making a difference
(47:06):
for more and more customers.
I think, you know, I think aboutsome of some of the research and
development that we're doing, inaddition to continue continuing
to to improve our product andour and our solution, but um
some of the the the realgame-changing things are this,
you know, this furthering theidea of digitizing the warehouse
and and providing even more dataon you know the location of
(47:31):
freight and um and to to enabletheir um to enable you know
things like their WMS systemsand their visibility of their
operations and their workflows.
And um, I see that as a way thatwe can really um have a really
(47:51):
large impact on, you know, it'snot just you know, while we can
reduce their cost on things likemoving freight on our, you know,
on our MPs, you know, in and outof trucks, and we can reduce
their operating costs in theirforklift operations.
But when we really show them thegreat, the the amount of uh the
greater data integrity, theamount of information that they
(48:14):
can have about you know thelocation of their freight in
their facility with the you knowthe digital twin technology and
things like that, you know,taking moving that forward is is
something that's got is gonna begame-changing in the industry.
SPEAKER_04 (48:30):
So I know I've been around ArcBest for 30 years, and
I know they've got a veryhuman-centered approach to
business.
And um and I know it goes wayback before I was ever
introduced to ArcBest.
And Vox is focused on roboticsand automation.
(48:54):
So I'm just curious, um, how doyou balance that um with the
human-centered approach?
SPEAKER_01 (49:02):
Yeah.
So so one of our leadersrecently said, you know, in an
interview, you know, technologydrives change, but it's the
people that make it happen.
I like that.
And it's it's kind of, you know,if you take a step back to like
how ARC best treats itstechnology in general, right?
We're not turning systems overto AI, you know, agents.
We're using technology as toolsfor uh for our people to help
(49:29):
our customers more and more.
And so uh and and Vox is youknow, what we're doing here is
we're we're bringing tools andsolutions to our customers that
allow you know people to um tobe more efficient, to use those
tools to to just be moreproductive.
And um, and I think that's a lotwhat we're you know, what we're
(49:51):
doing.
SPEAKER_04 (49:52):
So Vox is um you know creating a lot more
visibility and informationthat's useful to systems like
WMS, ERP, et cetera.
What are you thinking aboutthere and how do they link in
and help?
SPEAKER_01 (50:09):
Yeah, I think that you know, a lot of that there's
a lot of potential.
And there are companies outthere that are selling the dream
of of what can be done, youknow, in a future world where
you know there's a there's aperfect digital twin for
everything.
And then you could you couldhave a simulated kind of uh you
(50:30):
know, a simulated version of ofwhat of what's going to happen
and have you know a lot ofinformation about that before
you actually do things in thereal world.
And um the reality is that thedata in the real world is very
imperfect, and but the the moredata that you can provide and
collect and have that dataintegrity, the more you can
(50:53):
simulate kind of decisionmaking, you know, simulate
decisions going forward orsimulate workflows or provide
guidance on uh so in our world,it's how to flow freight around
in a warehouse.
Um and there are a lot of othercompanies that are in all walks
of life talking about how to howto create these these digital
twins that can simulate decisionmaking, whether it be in the
(51:15):
defense industry or orotherwise.
Um but we are we are doing ourpart of this, uh, you know, in
you know, intralogistics or inthe warehouse.
Uh so Art Buzz Box, we'refocused on, you know, how are we
moving, moving freight around ormoving pallets around in a
(51:36):
warehouse and providing betterdata uh for that.
Things like, you know, and Ididn't even haven't even really
spoken about it yet, but our BoxVision product, which is which
is a new product we're workingwith early adopter customers at
this point, but it's anon-the-forklift dimensioning
capability where you knowcameras and sensors that can be
(51:57):
installed onto a forklift willcapture the dimensions of
freight.
It's used, you know, it'simportant to anywhere you have
multiple parties that arepassing freight around, whether
it's a 3PL moving freight foranother customer or a vendor
sending freight through a B2Bdistributor where you know they
they want kind of good dataintegrity around what the
(52:20):
dimensions are of that freight,which helps them move the
freight, you know, either eitherprice the price their services
more effectively or moreaccurately, or move the freight
more efficiently through theirsupply chain.
So we're excited about that andbringing that solution as well.
SPEAKER_04 (52:36):
That's great.
So what advice would you havefor, say, a leader in a company
that's trying to future-prooftheir um intralogistics
capabilities and operations?
SPEAKER_01 (52:54):
Yeah, what I would say is you know, from my
experience in the roboticsspace, I'm I'm a real believer
in what we're doing with thehuman-in-the-loop approach, to
not try to fully automateeverything and make that large
and upfront investment ininfrastructure to have a fully
automated lights out system.
(53:15):
Um, you know, I'm I'm a believerin applying automation where you
can.
And but the the human in theloop approach that that we have
is is what I feel like is theright way to say, let's automate
what we can.
Let's have a human kind of dialin and do what's challenged
what's currently challengingwith existing state-of-the-art
(53:37):
technology, but we can continueto push and push the ball
forward.
I think it's the right balanceto make a real impact on real
world operations, reducingoperating costs, um, but kind of
moving the ball forward withautomating and improving.
You know, it's it's all aboutcontinuous improvement.
(53:59):
And, you know, we're getting toa world where, you know, we'll
have a lights out warehouse, andbut that's that's a pretty far
down the road.
Uh too far for me to make, youknow, a uh to justify a business
case for building a business.
Um and I really too far for Ithink for our customers to be
(54:21):
justifying their spend onrobotics.
But I I really believe, youknow, the approach of saying
let's automate what we can, andthis this human-in-the-loop
approach to uh you know to tohandle you know the cases that
are challenging, it's uh it's atleast my advice on how to how to
(54:42):
make improvements and make yourinvestment in automation uh pay
off quickly.
SPEAKER_04 (54:49):
You know, um of course I think if ArcBest could
have said, okay, we're gonnadesign someone's experience so
that they're perfectly suitedfor this job, you you would fit
that.
But my question to you is, andyou've already moved the needle.
Um I've heard just your uhability to articulate what Vox
(55:14):
is about, where you're going, uhis impressive.
But what are you most excitedabout in leading Vox in the
future?
SPEAKER_01 (55:23):
Yeah, I mean, I'm I'm excited to be a part of Art,
part of ArcBest.
I mentioned the culturalelements of that.
It's a it's a group, it's a teamthat I'm just proud to be a part
of, I think is one.
Um I'm excited to be making, youknow, have a an opportunity to
build this business that canreally make a difference in how,
you know, where how ourcustomers and are are performing
(55:47):
their warehouse operations andmaking a difference in how you
know, kind of the the efficiencyof the supply chain.
We're doing our part, you know,to to make the supply chain more
efficient and and lower cost andand everything.
And uh, and it's exciting to beto be helping customers in that
way.
And you know, and honestly, themore the more customers we help,
(56:07):
the more um the more jobs wecreate for for our own team.
So I believe, I believe we'vegot a great culture and we've
got a great business and a greatteam to be to be building.
And so, you know, creating jobsthat are uh that are rewarding
both emotionally and financiallyfor for our team members and our
employees is is what kind of isa big part of what it's about
(56:31):
and what what makes me gets meexcited about about things.
And so um really having having areally good time, you know,
basically with the people I'mworking with, with the customers
I'm able to work with and thetechnology uh being at the
forefront of this technology umis uh is great.
SPEAKER_04 (56:49):
Well, Brian, this has been so much fun.
I I really feel like I'velearned a lot through this and
about what Vox is doing, whereyou're going, the benefits.
Um thank you for taking time toto share with us about this.
We really appreciate it.
Thank you.
SPEAKER_01 (57:04):
Thank you, Matt.
It's really uh really a pleasureto be here with you.
I want to clarify that this podcast is distinct
from my responsibilities as aprofessor in the Stan M.
Walton College of Business.
Nonetheless, it aligns with myaspiration to provide practical
insights to professionals andbusiness by showcasing companies
and people that can enhance yourability to manage, lead, and
strategize and marketeffectively in the retail value
(00:30):
chain.
And now without further ado,let's get into the exciting
episode.
I have with me today BrianNoctigall, and he is heading up
Vox for ArcBest.
He has an incredible backgroundin consulting and automation
that really positioned himperfectly for the kind of role
(00:53):
that he's in right now.
But today we're going to betalking about smart autonomy and
the future of warehouseinnovation.
I'm really excited about this.
Brian's background, like I say,is amazing.
So, Brian, would you mean mindtelling us a little bit about
how you wound up in thisposition and your journey here?
SPEAKER_01 (01:11):
Yeah, sure, man.
And thank you very much forhaving me on the on the podcast.
Pleasure to be here.
Yeah, pleasure to be here.
Uh it's been pleasure kind ofgetting to know you here in this
role.
But how I've gotten here, I'mreally just thrilled to be a
part of ArcBest and to be havethe opportunity to be leading
the Vox group and kind of takingthe building blocks that you
know that the team has createdover the over the past years and
(01:33):
being able to build a businessout of it.
It's uh it's it's an excitingtime, exciting time in the
industry, the evolution oftechnology and applying
technology to warehouseautomation, and uh and really
just thrilled to be here havinghaving a good time, you know,
building this business.
I would say uh, you know, how Igot here, I it it just was an
opportunity that I couldn't passup.
(01:55):
Uh it was it was too good of afit with you know my background
in industrial automation and andcombining the sort of the early
career and strategy and and uhkind of being able to put all of
you know the um the sort of thelessons that I've learned in my
past and uh to to work to kindof you know build this
(02:17):
opportunity.
SPEAKER_04 (02:19):
Yeah, you know, um when I first read about your
joining um ArcBest, leading thisinitiative, I was thrilled,
especially because of yourbackground.
Um, you know, you have severalyears of experience uh as a
consultant with Baying Company,which is one of the best
consulting firms in the world.
(02:40):
Uh you have incrediblebackground with machine vision,
robotics, automation technologycompanies.
And even earlier in your careeras an investment uh analyst,
that's a unique background forsomething like this.
But I would imagine that itreally informs your current
role.
Is that right?
SPEAKER_01 (03:00):
Yeah, it's funny.
Uh it is funny how you you pickthis bits and pieces.
I imagine everybody does this intheir careers, but you pick bits
and pieces of you know yourbackground and lessons you've
learned and applying them.
And uh what's exciting for me isI'm in a role now where where
I'm actually getting to apply, Ithink, lessons from really
almost nearly every one of myexperiences.
(03:22):
And so I guess you know, goingback, uh as you mentioned, uh,
you know, I have I have about 15years of of industrial
automation experience, but kindof going all the way back, yeah,
I I graduated from PrincetonUniversity, and my first job out
of college was with the thegroup that invests the endowment
at Princeton, the called Princo.
(03:42):
Uh it was a small group of ofabout 14 people and with about
$10 billion to invest.
And so just a really interestingmile-high view of the world of
finance, you know, for uh forsomebody coming out of out of
school and kind of learning howthe world works.
But uh to me, uh, it was it wasprobably about 20 years ago that
(04:03):
I that I made the decision tokind of get out of pure
investing and wanted to learn alittle bit more about how you
know how how what decisions aremade and what goes into those
decisions to actually createvalue in society and in the
economy.
And you know, had the had thejust great opportunity to get to
(04:24):
to go to Bain and Company.
And didn't you go to Duke firstfor your MBA?
So I got I got my MBA at Duke uhwhile working at at Bain.
So I spent a couple of years atBain uh out in uh out in
California, and then kind ofreturned to the East Coast and
went to Duke uh to get my MBA atthe Fuca School of Business, uh,
(04:45):
and then returned to Bain.
And that's what that's uh whatbrought me up to Boston,
Massachusetts.
And I you know spent about 15years in in uh in Boston, kind
of working at Bain initially fora few more years.
And then uh when I when I leftBain, it was sort of that that
itch again to roll up my sleevesa little further and really dig
in and and uh help buildbusinesses.
(05:08):
And uh just had the had thegreat opportunity of getting
onto this trend of you knowmachine vision and robotics and
industrial automation that youknow worked at Cognex
Corporation, who's a a globalleader in the industrial machine
vision space and manufacturingand warehouse settings, had the
opportunity at Boston Dynamicsto lead their uh their warehouse
(05:31):
automation effort uh with theirrobot that they call stretch and
uh kind of create the other thebusiness model out of out of
stretch and build the teamthere.
And uh, and then here, you know,at Box uh within ArcBest, sort
of the just just as I mentionedearlier, the the thrill,
thrilled to be able to take thebuilding blocks that they've
(05:55):
created and really turn it intoa business and set ourselves up
for growth going forward.
SPEAKER_04 (06:01):
You know, um my first academic research study um
that got published was publishedin 1990.
And it was the first academicresearch I'd really done.
Um it was on flexibleautomation.
This was 1990.
(06:23):
And um and I did a few otherstudies um uh for a few years on
flexible automation.
Back then, the flexiblemanufacturing systems was it was
a hot topic in academics.
It and then it faded for a longtime.
But it's been interesting to meto see how you know automation
(06:46):
and robotics in the industryback in 1990, I think we thought
we would be further along by nowthan we are, but you got into it
really at a great time, and nowit's just taking off beyond
imagination.
And and I can't imagine.
(07:07):
Um did you did you see that ordid you just were were you just
interested in that?
SPEAKER_01 (07:13):
Uh you know, a little bit of both.
Um, you know, I grew up I grewup in a blue-collar town,
manufacturing town, and uh sothat the idea of using you know
using technology to make you tomake manufacturing more
efficient was always somethingthat was kind of interesting to
me.
And then really just had youknow a couple of pieces fall
(07:38):
into place, but uh had aconsulting project at Bain and
Company where we were helping aclient understand how to how to
you know track their productthrough their supply chain.
And we would talk about howthere are technologies to read
you know barcodes and datamatrix codes and and to be able
to track product through.
And and but you know, as astrategy consultant, you kind of
(08:01):
get to the point where you say,well, there are a ways to do
this.
And then you you scratch alittle deeper under the surface,
you say, Well, okay, who are thecompanies?
What are the solutions that dothis?
And you get some shoulder shrugsand some, well, you know, they
they, you know, in quotes, theycan do this.
Well, who's they?
And and you found that thetechnology wasn't necessarily
(08:24):
there yet, or there were itwasn't clear who was providing
these types of solutions.
And this is back, you know,2010, you know, timeframe or so.
And so when I came across Cognixand the opportunity to join um
to you know, Rob Willett, whowas the you know the CEO at the
time at Cognix, and um and seewhat Cognix was doing, I said,
(08:49):
okay, here's a company that isdedicated to machine vision and
you know, doing things like youknow inspection using cameras
and robotic guidance, but thenalso the the reading of barcodes
using cameras and the 2D codesthat uh that are used now.
Um, I said, okay, the thiscompany is onto something.
(09:09):
There's a need here, and here'sa and here's a business that is
you know innovating and focusedon building a solution for this
space.
And so that was what kind ofmade me think, okay, Cognix is a
good place to put my energy.
And sure enough, it was anexciting time for Cognix.
The the five years that I spentwith them, you know, they went
(09:30):
from about$250 million ofrevenue to$750 million of
revenue during that time.
There's just a an exciting time,you know, when the industry was
adopting some of thesetechnologies, both in the
manufacturing setting and inlogistics space.
Um, and uh to your point aboutflexible manufacturing, it was a
(09:50):
time when people were realizinghow to use machine vision to do
things like reduce batch sizesand identify defects and then
the track and trace things whereyou know the automotive industry
is figuring out how to, youknow, when they find a problem,
you know, the the recalls in theautomotive industry are so much
smaller than they used to bebecause they can track a problem
(10:12):
back through the supply chainback to where they um to where
that defect actually happened.
And they can all they can recalljust the cars that um you know
that that were affected by youknow a problem that they were
able to diagnose with much morespecificity.
Um so exciting time there.
(10:34):
And then uh, but but really evenat that time, you know, 10, 5,
15 years ago, a lot of it wasrules-based.
So you use uh you use machinevision, you you program rules
around, you know, what should arobot do or what should a
manufacturing system do based onthe input that it's getting from
a machine vision system.
(10:54):
And what's what's exciting nowis really over the last five or
10 years is this idea of deeplearning.
And so you go from a rules-basedapproach of, you know, I'm I'm
looking for something specificto, you know, I'm looking for a
specific thing in a specificplace, uh, to I'm looking for,
(11:15):
you know, something somewhereand and the and the system can
kind of find something and uhand uh that that you don't
necessarily specifically programit to do.
And so as industry gets morecomfortable with this deep
learning approach, and then youknow, I think the deep learning
(11:35):
approach has led to all the thegenerative AI stuff that's been
that's been you know that it'sbeen really game-changing over
the last couple of years, um,and how that leads to you know
simulation technologies thatallow these robotic systems to
learn so much faster than theycould before.
It's it's a really aninteresting time and an exciting
(11:56):
time.
SPEAKER_04 (11:57):
Well, I have to say I'm impressed with your ability
to describe things like thatwithout using a bunch of jargon.
That was that was impressive.
Because I I was thinking of thejargon as you were explaining
it, and uh that's it's good.
But you know, the other thingthat was really neat about your
experience there is you got tosee what it's like to scale up
(12:17):
big time.
I mean, going from 250,000 to700 million, was it?
Yeah.
That's a big scale up.
SPEAKER_01 (12:25):
Yeah, and uh so so one of the things, you know, you
think about management systemsand building a business.
So I'm and I mentioned I had theopportunity to work under Rob
Willett at Cognex.
So Rob had in his background, hehad sold a business, a family
business to Danaher Corporationand then spent 10 years leading,
you know, running a businesswithin Danaher and also kind of
(12:48):
learning that that Danaherbusiness system and and the the
the management philosophy andthe and the approach to
measuring uh a business andmanaging a business that way.
And then and then he brought,when he had the opportunity to
lead Cognix, he brought some ofthose lessons.
And I say some of those lessonsbecause um because in a lot of
(13:12):
ways, you know, sometimes thatthat business system, you know,
at Danaher, it is part of theirDNA.
And they are they are justrelentless in how they how they
how they implement thatmanagement system.
And so when you come into uhanother business where you know
you you have to take, you haveto pick and choose which tools
(13:33):
to apply.
And I think Rob did a reallyeffective job of that at the
time at Cognix.
And and I take a lot of lessonsaway from that in terms of how
when I go into a new situation,uh, I don't just overlay those
management tools, but you know,I've had the opportunity over
this past uh you know half ayear or so at ArcBest and with
(13:58):
the team at Vox to really kindof observe and talk to people
and and and see you know whatare our products, who is our
team, what are our what are whatis our culture, you know, how
how do how does this group dothings?
What puzzle thesis do we have?
And now what you know, whataspects of this management
system do we apply to thisspecific situation?
(14:21):
And we're in the process ofdoing that, you know, right now
and kind of changing the team.
And um uh, you know,fortunately, I inherited a
really strong team, you know,ArcBest and with the Vox team,
they did an amazing job atpulling together, you know,
really talented individuals.
They've built a roboticsengineering team and a
(14:43):
mechanical engineering team tobuild their products.
And so from an engineeringperspective and a product
management perspective, like alot of the people were there.
Also, ArcBest, they've beenthey've been investing in
innovation for a long time.
And they have this pool ofreally talented people doing you
know data analytics.
(15:04):
Uh, it shows up in things liketheir pricing strategies and
their, you know, that in theirand uh and their you know yield
analytics to to to help ArcBestkind of be both competitive and
price competitive for their youknow to win business in the
trucking industry, but also youknow, selective in their
customers and making sure thatthey're profitable.
(15:26):
And it also comes in things likeyou know their route
optimization, you know, uh torun the ArcBest trucking
business more efficiently andeffectively and more profitably.
So we have you know this greatpool of talent at ArcBest, and
and they've just done a greatjob at put at putting those
those pieces together.
(15:46):
And now I think what I'mbringing to the table is you
know how to organize thosegroups, organize those puzzle
pieces in a way that we can umwe can then then kind of overlay
the the commercial side of itand the sales side of it.
How do we how do we really focusour attention on the right on
the right markets, theattractive markets where we can
(16:07):
make the big biggest impact withcustomers and and have the most
commercial success?
And how do we streamline ourdecision making to uh to be to
be moving fast and and agilewith uh with building this
business?
SPEAKER_04 (16:22):
You know, I've met a lot of these people you're
talking about, and I'm superimpressed with I even know some
very recent hires uh in thatarea, data science and so forth.
But um I think the culture therehelps with recruiting top
talent, you know, because peoplewant to work somewhere where
(16:42):
they're gonna enjoy it and theyhear of other people enjoying
their work.
Um but um but in addition tothat, uh you know, one of the
things that I've always said,and uh the listeners know we
have Northwest Arkansas has anunusually high concentration of
logistics and supply chaintalent.
(17:02):
Not just not just um uh uhpeople that are actually in
supply chain, but even peoplethat are not.
For example, if you go to thesesupplier teams in town, their
salespeople can talk aboutlogistics quite a bit.
Which if you go to the stay withthe same CPG company, you go to
a sales team in another uh citylike Cincinnati or Minneapolis,
(17:26):
et cetera, they don't have thatability.
Um there's just a lot ofinterest in logistics and supply
chain here.
So for me, I am particularlythrilled that Vox is doing this
in Northwest Arkansas.
To think that we're uh NorthwestArkansas is developing um
(17:46):
advanced um uh automation andtechnology around logistics,
entral logistics in particular,here is exciting.
And I think it's uh it'snoteworthy.
Uh I'd like to shift gears justa second to um you know to set
the stage, if you wouldn't minddescribing a little bit about uh
(18:06):
the Vox suite um as well as uhthe mission of Vox within within
the ArcBest mission.
SPEAKER_01 (18:15):
Right, right.
So yeah, so Vox uh you know, Voxwithin ArcBest, uh it kind of
represents some of the strategicpillars of ArcBest in general.
So ArcBest, you know, ArcBestmission and has three strategic
pillars.
It's you know it's growth,efficiency, and innovation.
And they really take thatinnovation piece seriously, as I
(18:36):
as I mentioned earlier.
Um, and you know, with thingslike you know, the pricing and
the route optimization thingsare are some of the under the
hood uh tools that they build.
Uh, but then you have you know,Vox is really just a tangible
illustration of that innovationthat that is is is part of that
ArcBest DNA.
(18:56):
And and I really can't, youknow, and you you you touched on
you know the culture piece, butI also can't overstate how
important that is as well, youknow, of the you know, the the
culture of ArcBest, the strengthof character of the leadership
team.
Absolutely.
And, you know, the the um youknow, just just two of their of
(19:20):
their sort of core values areyou know, integrity and
excellence, and how but how thatthat strength of culture just
flows down through theorganization, helps us in a lot
in a lot of ways.
Um so so getting in, you youasked about, you know, so what
is the Vox suite of technology?
So to kind of give a you know,you we're really kind of
(19:40):
bucketed into you know threeproduct groups.
So I'll start with uh theautonomous forklifts.
So you know, Vox is building arobotic forklift solution.
And we believe that you know oursolution is the fastest way for
customers who are interested inautomating their warehouse
operations to apply and beginreducing their operating costs
(20:00):
uh you know of their warehouseas fast as possible.
So our approach is to automatethe forklift operations as much
as possible.
And then we have ahuman-in-the-loop approach where
the the portions of the tasksthat are you know too
challenging to automate, aperson can remotely dial into
(20:21):
those forklifts and performthose tasks via you know remote
operation or teleoperation.
And so by doing that, we we havea a low upfront infrastructure
uh investment required.
So uh we we can get you, we cancome in and within you know four
to six weeks be up and runningat a customer.
(20:42):
And uh a customer doesn't reallyneed to change their
infrastructure for us.
We can operate within acustomer's you know process.
And so that's our that's our uhautomated forklift business.
We call it smart autonomy.
Uh another piece of our businessis on the uh it's really a
(21:03):
relatively low tech, althoughthere's there's smarts, you
know, uh it's a it's anintelligent solution to it, but
it's our we call it freightmovement system, but these are
these large staging platformsthat allow customers to stage
freight, and then it can quicklyand smoothly roll that freight
into and out of trailers.
(21:24):
So that we have some patentedtechnology into and how these
rollers engage so that it cancarry heavy freight, but then
still roll around a warehouse orinto into and out of trailers.
And we're finding that's reallyeffective for um situations
where customers have either uhfreight that can't be stacked.
(21:45):
So we have a scaffolding systemwhere you could you can make two
or three layers of freight in atrailer that otherwise couldn't
be stacked.
So you can cut down on your onyour number of uh number of
trucking routes, you know, thatway, or number of runs on your
routes that way.
We're also finding it's reallyuseful in in applications where
(22:05):
uh where you have shuttle runs,where the the loading and
unloading time is high relativeto the travel distance.
So that's that's particularlyeffective in things like the
automotive sector, where youhave you know what parts
warehouses relatively close tothe assembly plants, and you're
doing a lot of shuttle runs backand forth um in a situation like
(22:26):
that.
And then we're also kind ofstumbled on uh the this
opportunity in in the datacenter space, where we found
that it's really valuable forfor you know high-value
electronics, um, where it's youknow high value and sensitive
electronics, where you know ourstaging platform allows it to be
(22:46):
touched fewer times, and we canhandle kind of the some of the
vibration sensitivity over theroads.
And so we're talking to playersin the data center space that
are that could be huge.
SPEAKER_04 (22:57):
That's such a big and growing business right now
with uh all the AI and evencompanies taking data out of the
uh cloud and back to their uhnative uh servers.
SPEAKER_01 (23:11):
Yeah, it's uh it's obviously a huge trend.
It's uh it's it's it's on a lotof people's mind.
There's you know the this buildout of the data center space,
and you know, we're uh we'redoing our best to do our part to
kind of help that industry moveforward.
There's a lot of these,especially the hyperscalers
they're working on.
Um they're working on kind ofpre-fabricating or
(23:32):
pre-manufacturing as much of theserver infrastructure so that it
can just be kind of rolled intoplace and plugged in as much as
possible uh to make kind of thethe uh the startup of the data
centers as you know as as fastand as streamlined as possible.
And we're we're helpingcustomers in that space.
SPEAKER_04 (23:51):
You know, um, I know part of what Vox is doing is
trying to make autonomouswarehousing available to all
businesses.
Could you talk a little bitabout that from a deployment
type perspective?
SPEAKER_01 (24:05):
Yeah.
So I mentioned that you know, wewe believe that we're the we're
the fastest way to automate yourwarehouse or your forklifts
operations and pallet movement.
And basically what I mean bythat is we have a relatively
low-cost method of outfittingkind of a network communications
infrastructure in a warehouse,which is important because you
(24:28):
need seamless connectivity whenyou're driving connected
forklifts around in a warehouse.
We have uh the technology toremote in and do this teleop
approach.
So, you know, the drivers of theforklifts, you know, we can, you
know, from a from a safetyperspective, we get people off
the floor and you know, workingmore of an office environment as
(24:49):
opposed to you know out on thewarehouse floor.
And um and we can um you know wewe can implement that quickly in
a customer's current process.
So we're not asking a customerto apply or to change their
process or put a lot ofadditional infrastructure so you
(25:11):
know we can come in and um beginworking quickly with the
customer.
SPEAKER_04 (25:16):
Brian, you know, of course, ArcBest has over a
hundred year history.
They made it throughderegulation, the Motor Carrier
Act of 1980 successfully, whichwas not easy to do.
Very few firms have done that.
Um and of course, after theMotor Carrier Act of 1980, the
LTL industry started uh formingand um and because um ArcBest
(25:44):
has been, I mean, they're in allkinds of areas of logistics now,
but you know, after that theyreally specialize to um LTL.
But LTL requires different typesof trucks for line haul versus
pickup and delivery, it requirescross-docking uh terminals, um,
many things that uh other kindsof uh trucking companies don't
(26:08):
need, um like uh full truckloadtrucking companies.
But uh but since then, ofcourse, uh ArcBest has expanded
into many different other areasof logistics.
So Vox uh has an advantage, andI'd love to hear what you think
it is by being a part of thisrich history and cap the
(26:32):
capability set as compared tosay a startup in the same space.
SPEAKER_01 (26:38):
Yeah, I I think you know being a part of ArcBest is
in a way it's it's sort of oursuperpower, right?
Our hidden superpower.
So we you know, we benefit froma in a lot of ways, just that
inherent knowledge that the theother employees at ArcBest have
(26:59):
about how real-world operationswork.
So when we have an idea of howto how to improve a process, or
we or we talk to people aboutways to improve their processes,
we can we can kind of test thoseideas or those hypotheses with
people who can say, yeah, youknow, this is how a person on a
warehouse floor would operatethat or would do that.
(27:22):
And so we can quickly kind ofnarrow down how, you know, you
know, how do we create asolution that can be effective
in a real world environment?
How do we handle real worldfreight and the challenges that
come with that?
Um, it's more than just havingaccess to the to the test
environment, which is wonderful,uh, by the way, to be able to
(27:46):
take a take a prototype or asolution and test it in a real
world environment and get thatfeedback.
Uh, that's a that's a big that'sa big benefit.
But it's also just a team thatinherently knows what's
important and what it takes todo something in in such an
industrial environment.
(28:06):
So one example is is just thethe IT team.
And I mentioned how ArcBestinvests a lot in innovation and
and IT, but there's a whole teamof people that understands the
importance of a reliable ITnetwork and uh in a secure IT
network when we're putting uhyou know advanced robotic
(28:29):
solutions into facilities, thatthe cybersecurity topic is a is
an important one.
And we have a team of peoplethat knows you know what what's
necessary to have a goodsolution and something that our
customers will be um will benodding their heads in the right
direction, you know, when youknow, when we discuss, you know,
the things that we're doing withrespect to cybersecurity, with
(28:51):
respect to safety uh and howmuch we invest and and the team
that we have to to be looking atat safety and uh with our with
our robotic solutions and and soforth, things like that.
SPEAKER_04 (29:03):
That is interesting.
Um I hadn't thought of thesafety aspect of it, but you're
right.
I mean, with with ArcBest havingto operate these terminals and
all the transportation equipmentthey have in the terminals,
outside of the terminals, onover the road, et cetera, et
cetera.
And I know safety has been a bigpart of ArcBest's um approach,
(29:30):
as long as I've known them.
Um but I hadn't thought aboutthat.
That you know, that's somethinga startup wouldn't necessarily
have access to that richknowledge and background.
SPEAKER_01 (29:40):
Um Yeah, and it's it's also just um it's it's part
of, you know, clearly for foralmost any industrial company,
safety has to be a part of theirDNA, right?
It's it's paramount, right?
Um it's more than just a talkingpoint.
It's a it's the importance ofyou know, your employees have
families to go home to at theend of the day.
And so you have to do, you know,your job, or we have to have
(30:05):
processes that that make it, youknow, uh, you know, that that
that show that importance, youknow, that that our employees
can can can go home at the endof the day.
And um and and and sort of on ontop of that, so clearly when
when we're applying roboticsolutions, the importance of
putting safety systems in placeuh is is critical, right?
(30:28):
And we make sure that that isthat is of utmost importance.
Um, but then also theunderstanding of you know, for
our customers, the cost ofsafety.
So um, you know, when we talkabout getting uh getting kind of
people off the floor, part of itis is also reducing the
liability of a company, youknow, the and the the risk of
(30:52):
you know, companies are payinginsurance premiums based on the
the the risk of a safetyincident happening.
And if you get people off thefloor, and so our our systems
are not just automating as muchas possible, but the with the
human teleop approach, thepeople that are then remoting in
and driving those forklifts arein an office environment as
(31:15):
opposed to on the on thewarehouse floor.
And so um, so you're you'reyou're further reducing that
liability cost as well.
That is really a great point.
SPEAKER_04 (31:26):
So I'd like to dig in a little bit more to the Vox
smart autonomy.
Um and um I was really intriguedby that.
Um and and of course, as I saidearlier, it's it's exciting to
know this kind of technology isbeing um developed here in
Northwest Arkansas, but it'sreally already developed.
(31:47):
It's not like it's developing,it's it's uh it's a product
that's usable today.
But um, but you're still tryingto develop it further.
Um but if you wouldn't mind tellus a little bit about the
building blocks of that.
Yeah, sure.
SPEAKER_01 (32:04):
So um I would say at a at a yeah, at a basic level,
it's it's that it's that networkcommunications infrastructure
that we go in.
We you know, uh we we willinstall that at a customer.
It's the IT integration so thatwe're can we can both you know
connect our remote um our kindof the the remote teleoperation
(32:25):
labor.
Um and then the um and then onthe on the robotic forklifts
themselves.
Well, before I get to therobotic forklifts, it's you have
the communications network, andwe'll go in and we'll map a
warehouse.
So we've effectively we'recreating a digital twin of of a
warehouse.
(32:46):
And you know what you have thatmap of a of a warehouse and you
we apply the robotic forkliftsor the automated forklifts to
that to that map uh to operatein that setting.
So then the the additional kindof technology stack is the the
fleet management.
So when you have multiple uhvehicles operating in a
(33:11):
warehouse and the and the sortof the traffic management of
those vehicles is one layer ofthe stack.
There's the safety layer of thestack that you know I touched on
earlier to to make sure that thevehicles are are operating
safely.
They have all the sensors andthe awareness of what's around
them to uh to operate.
Uh and then it's that thatteleop uh setup as well.
(33:34):
So we can operate you know umautonomously as much as
possible.
And we have we have the thesensor suite and the autonomy
stack to do that.
And then the um theteleoperation interface so a
team a you know a uh someone cankind of see when a when a
(33:54):
forklift kind of raises its handand says, hey, I you know, I
have you know below thresholdconfidence in a in a particular
task and raises hand, and ateleoperator can can dial in,
can see that, dial into that uhto that forklift and perform the
task and then turn it back overto to the autonomous mode.
SPEAKER_04 (34:13):
So uh earlier Brian mentioned digital twins, and you
can think of it as um if youthink back to just regular
discrete event simulation whereyou simulate something to figure
out how you should maybe laysomething out, design something.
Um digital twins are a littledifferent in the sense that it
(34:36):
is simulation, it's simulatingit, but it's doing it over and
over in real time.
So every time something changes,it's simulating the process.
And um, as it simulates, then uhdecisions can be made based on
the output.
Uh it's behind the scenes,people don't see it, uh, but uh
(34:56):
but that's basically uh what itis.
SPEAKER_01 (34:59):
Yeah, there's the there's the simulation piece of
of testing and building ourtechnology, making sure that it
works when it goes out into youknow into the world.
Um, and that that that's youknow part of what's helping us
as well as a lot of a lot ofcompanies across the across the
robotics ecosystem to you knowto push these things forward.
Um and then you know, when wetalk about digital twins, you
(35:22):
know, one of the other thingsthat I'm I'm just excited,
really excited about is how weare putting these technologies
together to improve the dataintegrity for um you know for
for for a lot in a lot of waysfor for other systems.
So uh our customers and a lot ofbusinesses out there, they have
(35:43):
you know WMS systems, warehousemanagement systems.
Those that WMS software relieson good data about where the
freight is.
So a WMS system can can provideguidance on, you know, move this
freight from from here to there,here's how the flow of freight
should go.
But uh sometimes when you havemanual operation within a
(36:05):
warehouse, that those palletsget put you know in different
places that that you know, youuh it's easy to lose sight of
exactly where your freight is.
When you have a robotic or um oran automated approach to moving
pallets around, you know wherethat where that freight is.
And so I see kind of at a youknow, at a if you step back, a
(36:30):
lot of what we're doing here ispart of this warehouse
digitization trend of ofimproving sort of the amount of
data that a business has aboutwhere its freight is in the
warehouse, which just excel kindof kind of amplifies the value
(36:51):
of things like WMS systems.
SPEAKER_04 (36:55):
That is a great point.
Um Yeah, one of the things thatstruck me, and you know this, um
when I first learned about Voxand what was going on is uh a
problem came to my mind, andthat is that you know companies
have distribution centers indifferent regions, and uh
(37:17):
sometimes the demands on thoseregions vary somewhat
unpredictably.
You might be able to know thetotal demand, like if you have
if you have 20 distributioncenters across the country, uh
if you look at the total demand,it's fairly predictable.
But if you get down to aspecific DC, uh it may be that
(37:41):
the demand on that DC is lowwhen the demand on another DC is
high.
And this can be happeningbecause of many reasons.
It can be happening because of,you know, seasons shift over
time, naturally.
You know, you may need moregardening type supplies in one
region earlier than you thoughtyou would.
(38:01):
And you may have it there, butyou've got a constraint of labor
um there.
Uh and you've got labor inanother facility that you wish
you could teleport them there touse it, but you can't.
Um and then you've got, youknow, distribution centers
where, well, it's um they'reopening on the east coast before
(38:24):
they are in the other parts ofthe country, and then they're
closing on the west coast uh wayafter uh other parts of the
country have closed.
And and then there's just randomvariation that you can't really
explain, but it exists.
And so when when I first heardabout this, I thought this idea
(38:44):
of low uh you know loadbalancing would be a big benefit
to many industries, especiallyretail and certain areas of
consumer goods.
Would you mind speaking to that?
SPEAKER_01 (38:56):
Yeah, absolutely.
And um, you know, we couldprobably we could we could
probably nerd out for a whileand talk about you know
stochastic analysis and you knowthe the the quantitative
analysis on on how do you handleyou know these the the
volatility and these things, butum, and and I even draw upon you
know we talked about earlierabout you know previous
(39:18):
experience, you know, it's hassimilarities to things like
portfolio theory and yes and uhyou know spreading spreading the
risk around by diversification.
So so yeah, a a customer thathas multiple warehouses, say
across the country, and theyhave you know uncertain demand,
and they're trying to matchtheir supply of labor for for in
(39:38):
in our specific situation, we'retalking about forklift drivers
labor, and they're trying tomatch that planned labor to the
demand level.
And the demand level may varybased on seasonality, uh based
on the uncertainty in their umin their labor, you know, people
you know, they have to be theyhave to account for whether
(40:01):
people may call in sick or youknow, be be available.
And if you have multiple sitesacross the country, if you can
pull that demand, you uh you'rebasically smoothing out the um
smoothing out the you know youryour volatility in demand.
(40:21):
And so we're enabling you topull that demand.
So things like you know, theavailability of labor kind of
gets smoothed out, then also thetiming variability there.
So if a season out a seasonalbusiness that, let's say, you
know, in this in the springtime,you you know, based on the
weather pattern, if when it getswarmer, people start to want to
(40:44):
get out and do their springcleaning or get out and and do
their yard work and or you know,do their DIY projects a little
sooner.
So, you know, the buildingsupply or the home improvement
stores want to, you know, gettheir product onto the shelves
sooner if you have a relativelywarm you know end of winter.
(41:04):
And um, and they might, they mayor may not plan for that in
their labor planning.
And what we do is by poolingthat labor together, they could
turn that labor on quicklyacross their network of
warehouses uh rather than haveto plan ahead of time, either
either, either overstaffing, uh,which costs money, or
(41:25):
understaffing, which means thatthey end up with some you know
stock out problems uh and nothaving the product on the
shelves or available in their intheir supply chain uh as early
as they might at, you know, theymight have it exactly when they
planned to have it months agobefore they knew how the weather
would plan out, would pan out,but you know, too late relative
(41:46):
to you know a relatively warmspring in that example.
SPEAKER_04 (41:50):
You know, I I think that when people first hear
about this kind of technology,they think, okay, we reduce
labor cost.
And it's true.
Um as you pointed out, you canreduce liability costs and
injuries and things like this.
But this ability to loadbalance, as you're describing so
(42:12):
clearly, yeah, it reduces stockouts.
That in some cases winds upbeing more valuable than
anything.
Because you know, especially ifyou're a supplier to the retail
channel, if you wind up havinglate deliveries, not only do you
miss sales, which is expensive,but sometimes you get charged
(42:32):
for being late, and that'sexpensive.
Um but the other thing is you uhif you can smooth that out, you
don't need as much safety stock.
So you need uh less safetystock.
And I think it reduces bullwhip.
And this is something for thoseof you that don't know, uh bull
whip is the idea that smallperturbations in demand or uh
(42:58):
requirements usage at the bottomechelon of a supply chain get
amplified as they move up thesupply chain, which causes
additional problems likestockouts and safety stock.
It becomes a vicious cycle.
So anytime you can pool likethis and st and really get to
the root problem of these kindsof things, the whole supply
(43:20):
chain operates more efficiently.
SPEAKER_01 (43:22):
And really just it's it's reducing that volatility,
which reduces that bulleteffect.
But then all and then also thethe greater data integrity of
knowing what's in your warehouseis another, it's just another
source of volatility.
Uh, you know, sometimes theyhave to you know overstock
because they don't necessarilyknow exactly what's in their
supply chain.
(43:42):
So both the data integrity andthe ability to make decisions
later in the cycle, you know,all reduces that, you know, that
bull bull with the factory.
SPEAKER_04 (43:52):
Yeah, I hadn't thought of that, but you're
right.
Um that information accuracysolves a lot of problems.
And and it makes people moreconfident when they're making
decisions to know that the datais accurate.
Brian, could you share anexample of how Vox has moved the
needle for a customer?
SPEAKER_01 (44:13):
Yeah, I think the kind of the the clearest one in
my mind is a customer that weworked with who was a little
uncertain about using our remotelabor.
They love the idea of automatingthe forklifts, getting their
getting their drivers off thefloor, and but they wanted to
use their own, you know, theirown labor in in their facility.
(44:35):
And so we were working with themon that.
We were we were up and running,we were helping them, you know,
with our robotic forklifts intheir facility, and their labor
was just in an office settingoff you know to the side of
their warehouse floor.
But then, you know, you know,unfortunately, they they had a
week where, for a number ofdifferent reasons, the the
(44:56):
people that they had staffed tobe manning their forklifts, you
know, remotely operating but onsite, were unable to be there.
And they came to us and said,hey, you know, we we've already
integrated our IT system.
Uh, we're kind of in a bindhere.
We have product that needs tomove, and our guys aren't here
this week.
(45:17):
Uh, can we, you know, is thereany way that we could turn on,
you know, Vox's remote labor tohelp us move this freight?
And we were able to, you know,within about four hours, get you
know, our our guys up andrunning, connected in to the to
the forklifts operating in thiscustomer's facility, and we were
(45:37):
moving their freight for themthat day.
And uh, and you know, uh, youknow, shortly after, you know, I
was talking with the head ofstrategy at this business, and
you know, he was just saying,yeah, that's a, you know, that
this idea of being able toleverage, you know, a pulled
group of remote labor is isreally an area where where our
(46:00):
feelings on this on this topichave matured over time or
evolved over time because uhbecause it was so seamless of a
transition to using this laborthat you know is really you know
very far away.
And just but but connecting inand you know and operating in
our facility and it worked andit worked really well.
(46:20):
And so um it was just a it was avery uh poignant example of
where you know you kind of sawthe kind of the light turn on
for a customer and how and howthey saw well, wow, this this
can be even more valuable thanwe even thought.
Very impressive.
SPEAKER_04 (46:41):
Where do you see Vox going over the next say three to
five years?
SPEAKER_01 (46:45):
Yeah.
So in some ways, I'm reallyexcited at just further
penetrating the market.
We have a solution that worksand just getting the word out
and working with more customers,solving more customers'
challenges and you know, it withgetting this solution out there
and and applying it and rollingit out and making a difference
(47:06):
for more and more customers.
I think, you know, I think aboutsome of some of the research and
development that we're doing, inaddition to continue continuing
to to improve our product andour and our solution, but um
some of the the the realgame-changing things are this,
you know, this furthering theidea of digitizing the warehouse
and and providing even more dataon you know the location of
(47:31):
freight and um and to to enabletheir um to enable you know
things like their WMS systemsand their visibility of their
operations and their workflows.
And um, I see that as a way thatwe can really um have a really
(47:51):
large impact on, you know, it'snot just you know, while we can
reduce their cost on things likemoving freight on our, you know,
on our MPs, you know, in and outof trucks, and we can reduce
their operating costs in theirforklift operations.
But when we really show them thegreat, the the amount of uh the
greater data integrity, theamount of information that they
(48:14):
can have about you know thelocation of their freight in
their facility with the you knowthe digital twin technology and
things like that, you know,taking moving that forward is is
something that's got is gonna begame-changing in the industry.
SPEAKER_04 (48:30):
So I know I've been around ArcBest for 30 years, and
I know they've got a veryhuman-centered approach to
business.
And um and I know it goes wayback before I was ever
introduced to ArcBest.
And Vox is focused on roboticsand automation.
(48:54):
So I'm just curious, um, how doyou balance that um with the
human-centered approach?
SPEAKER_01 (49:02):
Yeah.
So so one of our leadersrecently said, you know, in an
interview, you know, technologydrives change, but it's the
people that make it happen.
I like that.
And it's it's kind of, you know,if you take a step back to like
how ARC best treats itstechnology in general, right?
We're not turning systems overto AI, you know, agents.
We're using technology as toolsfor uh for our people to help
(49:29):
our customers more and more.
And so uh and and Vox is youknow, what we're doing here is
we're we're bringing tools andsolutions to our customers that
allow you know people to um tobe more efficient, to use those
tools to to just be moreproductive.
And um, and I think that's a lotwhat we're you know, what we're
(49:51):
doing.
SPEAKER_04 (49:52):
So Vox is um you know creating a lot more
visibility and informationthat's useful to systems like
WMS, ERP, et cetera.
What are you thinking aboutthere and how do they link in
and help?
SPEAKER_01 (50:09):
Yeah, I think that you know, a lot of that there's
a lot of potential.
And there are companies outthere that are selling the dream
of of what can be done, youknow, in a future world where
you know there's a there's aperfect digital twin for
everything.
And then you could you couldhave a simulated kind of uh you
(50:30):
know, a simulated version of ofwhat of what's going to happen
and have you know a lot ofinformation about that before
you actually do things in thereal world.
And um the reality is that thedata in the real world is very
imperfect, and but the the moredata that you can provide and
collect and have that dataintegrity, the more you can
(50:53):
simulate kind of decisionmaking, you know, simulate
decisions going forward orsimulate workflows or provide
guidance on uh so in our world,it's how to flow freight around
in a warehouse.
Um and there are a lot of othercompanies that are in all walks
of life talking about how to howto create these these digital
twins that can simulate decisionmaking, whether it be in the
(51:15):
defense industry or orotherwise.
Um but we are we are doing ourpart of this, uh, you know, in
you know, intralogistics or inthe warehouse.
Uh so Art Buzz Box, we'refocused on, you know, how are we
moving, moving freight around ormoving pallets around in a
(51:36):
warehouse and providing betterdata uh for that.
Things like, you know, and Ididn't even haven't even really
spoken about it yet, but our BoxVision product, which is which
is a new product we're workingwith early adopter customers at
this point, but it's anon-the-forklift dimensioning
capability where you knowcameras and sensors that can be
(51:57):
installed onto a forklift willcapture the dimensions of
freight.
It's used, you know, it'simportant to anywhere you have
multiple parties that arepassing freight around, whether
it's a 3PL moving freight foranother customer or a vendor
sending freight through a B2Bdistributor where you know they
they want kind of good dataintegrity around what the
(52:20):
dimensions are of that freight,which helps them move the
freight, you know, either eitherprice the price their services
more effectively or moreaccurately, or move the freight
more efficiently through theirsupply chain.
So we're excited about that andbringing that solution as well.
SPEAKER_04 (52:36):
That's great.
So what advice would you havefor, say, a leader in a company
that's trying to future-prooftheir um intralogistics
capabilities and operations?
SPEAKER_01 (52:54):
Yeah, what I would say is you know, from my
experience in the roboticsspace, I'm I'm a real believer
in what we're doing with thehuman-in-the-loop approach, to
not try to fully automateeverything and make that large
and upfront investment ininfrastructure to have a fully
automated lights out system.
(53:15):
Um, you know, I'm I'm a believerin applying automation where you
can.
And but the the human in theloop approach that that we have
is is what I feel like is theright way to say, let's automate
what we can.
Let's have a human kind of dialin and do what's challenged
what's currently challengingwith existing state-of-the-art
(53:37):
technology, but we can continueto push and push the ball
forward.
I think it's the right balanceto make a real impact on real
world operations, reducingoperating costs, um, but kind of
moving the ball forward withautomating and improving.
You know, it's it's all aboutcontinuous improvement.
(53:59):
And, you know, we're getting toa world where, you know, we'll
have a lights out warehouse, andbut that's that's a pretty far
down the road.
Uh too far for me to make, youknow, a uh to justify a business
case for building a business.
Um and I really too far for Ithink for our customers to be
(54:21):
justifying their spend onrobotics.
But I I really believe, youknow, the approach of saying
let's automate what we can, andthis this human-in-the-loop
approach to uh you know to tohandle you know the cases that
are challenging, it's uh it's atleast my advice on how to how to
(54:42):
make improvements and make yourinvestment in automation uh pay
off quickly.
SPEAKER_04 (54:49):
You know, um of course I think if ArcBest could
have said, okay, we're gonnadesign someone's experience so
that they're perfectly suitedfor this job, you you would fit
that.
But my question to you is, andyou've already moved the needle.
Um I've heard just your uhability to articulate what Vox
(55:14):
is about, where you're going, uhis impressive.
But what are you most excitedabout in leading Vox in the
future?
SPEAKER_01 (55:23):
Yeah, I mean, I'm I'm excited to be a part of Art,
part of ArcBest.
I mentioned the culturalelements of that.
It's a it's a group, it's a teamthat I'm just proud to be a part
of, I think is one.
Um I'm excited to be making, youknow, have a an opportunity to
build this business that canreally make a difference in how,
you know, where how ourcustomers and are are performing
(55:47):
their warehouse operations andmaking a difference in how you
know, kind of the the efficiencyof the supply chain.
We're doing our part, you know,to to make the supply chain more
efficient and and lower cost andand everything.
And uh, and it's exciting to beto be helping customers in that
way.
And you know, and honestly, themore the more customers we help,
(56:07):
the more um the more jobs wecreate for for our own team.
So I believe, I believe we'vegot a great culture and we've
got a great business and a greatteam to be to be building.
And so, you know, creating jobsthat are uh that are rewarding
both emotionally and financiallyfor for our team members and our
employees is is what kind of isa big part of what it's about
(56:31):
and what what makes me gets meexcited about about things.
And so um really having having areally good time, you know,
basically with the people I'mworking with, with the customers
I'm able to work with and thetechnology uh being at the
forefront of this technology umis uh is great.
SPEAKER_04 (56:49):
Well, Brian, this has been so much fun.
I I really feel like I'velearned a lot through this and
about what Vox is doing, whereyou're going, the benefits.
Um thank you for taking time toto share with us about this.
We really appreciate it.
Thank you.
SPEAKER_01 (57:04):
Thank you, Matt.
It's really uh really a pleasureto be here with you.
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