August 21, 2025 • 36 mins
Workplace injuries cost U.S. companies over $160 billion a year - and much of that risk hides in plain sight. Traditional safety programs rely on after-the-fact reporting and limited oversight, leaving logistics teams reactive rather than proactive.
This week, host Ellen Wood speaks with Jack Braniff of Voxel AI about how logistics and warehouse operators are using computer vision and existing security camera infrastructure to spot risks in real time. Jack shares how Voxel's platform helps teams flag unsafe behavior, coach smarter, and redesign processes to prevent injuries before they happen—all without invading worker privacy.
In this episode:
This week, host Ellen Wood speaks with Jack Braniff of Voxel AI about how logistics and warehouse operators are using computer vision and existing security camera infrastructure to spot risks in real time. Jack shares how Voxel's platform helps teams flag unsafe behavior, coach smarter, and redesign processes to prevent injuries before they happen—all without invading worker privacy.
In this episode:
- How Voxel AI turns video into real-time risk alerts
- Why ergonomic risk is often an invisible threat
- The safety ROI of smarter, faster interventions
- How data-led coaching improves operations and morale
Mark as Played
Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Speaker 1 (00:00):
Generally speaking, a efficient facility as a safe facility and
vice versa. So as we work with customers, a lot
of the use cases that we support sort of bridge
the gas between efficient operations and making sure that they
have a safe work department.
Speaker 2 (00:14):
Hello and welcome to speaking of supply chain, where we
explore trends, current events, and innovations impacting the logistics and
supply chain industries. I'm your host, Ellen Wood. Large machinery,
heavy lifting, and high traffic are inherent risks in warehousing
and logistics, and ensuring employees safety is critical. Companies that
proactively invest in robust workplace safety programs not only protect
(00:36):
their workforce, but also enhance productivity, reduce operational disruptions, and
foster a culture of trust and engagement. Joining me for
today's discussion is Jack Brannef from VAUXHALLAI.
Speaker 1 (00:48):
Welcome Jack, Thanks Allen, great to be here.
Speaker 2 (00:51):
Great to have you. So tell our listeners a little
bit about what VAUXHALAI is and how it works.
Speaker 1 (00:59):
Vauxhall is found in twenty twenty in San Francisco with
the mission to protect people who make the world essentially work.
So we are a computer vision AI company that leverages
existing security camera infrastructure and relays it with our AI
and computer vision models to essentially flag risk in real
time and allow our customers to be a lot more
(01:20):
proactive as they're mitigating risk and driving safety culture within
their facilities.
Speaker 2 (01:24):
So you said twenty twenty, and the question begs, was
it a reaction to what was happening in twenty twenty
and slightly before that, or did you was this already
in the works as just part of the logistics industry.
Speaker 1 (01:39):
So I actually love our origin story. So our founders
are some very impressive people, espe on the technical side.
They didn't necessarily go into it thinking that they were
building technology for the purpose of workplace safety. They were
more than anything, taking some of their experience, you know,
working at places and institutions like Carnegie, Mellon, Google, Uber,
(02:01):
a few of them coming together sort of playing with
really innovative, breaking edge tech at this time when everyone
had a little bit more time on their hands or
figure it out and realize that there was really a
lot of opportunity in the computer vision space. And you know,
ultimately we're able to work with some advisors and for
a few of our funders pull on some really personal
(02:22):
stories and find a neat way to take this innovative
tech and tie it to an area of opportunity that
you know, both allows us to build AI with a
really positive mission, which ultimately is to ensure that people
get home and return safely to their families each night.
There's also a massive market opportunity. So I think as
they were getting in and realizing that the type of
(02:42):
product fit and the existing infrastructure that really was in
place in a lot of these facilities, you know, there's
over one hundred and sixty seven billion dollars in injury
related claims each year, and so thinking about the market
opportunity there, purely from a business standpoint, there was there
was a lot of run. But again I think that
the initial reason for making the correlation was a lot
(03:04):
more on sort of the much more personal side of
what we're doing, which again is ensuring that folks that
really drive our economy, drive our country, you know, are
they are able to work on or return safely to
their homes each night.
Speaker 2 (03:15):
That's amazing. Now I've interviewed others in this same general
field of you know, workplace safety, and you know the
types of it's usually a wearable tech that allows to
collect those biometric markers and understand what's happening as people
are moving physically within their job and their function. So
(03:41):
you said yours integrates with the existing security systems. So
the security cameras and just watching an overview, how does
that work exactly with the overlay? Is it just identifying
movement patterns? I mean, is it able to get close
enough and granular enough to see some of the smaller
tasks that are happening, especially when we're talking about a
(04:03):
security system, because I don't know, I've seen some security
system video and it's not always the best quality, So
I'm interested to understand a bit more of.
Speaker 1 (04:13):
How that works. Yeah. So, I think first major observation
from our end is the vast majority of facilities that
we work with, whether it's a manufacturing site, you know,
a e commerce or retail facility, a logistics hub like
a quarter, or a terminal, they really do have a
lot of the camera infrastructure in place. And really, even
over the last few years, we've seen more and more
(04:33):
companies you invest in having sort of robust cameras that
really cover a vast majority of the areas that we
want to see. There's certainly you know instances where we
work with customers to optimize the camera views, to maybe
move them around, add a couple cameras, to really focus
in on some of the use cases that are going
to drive value. But honestly, I would say that sort
(04:53):
of a minority at this point. I'm at least from
my seat, my perspective, you know, a lot of the
warehouse or again these different these different sites that I'll
go visit when we're seeing our customers have the infrastructure
in place. In terms of actually deploying our software, our technology,
it's it's really quite seamless. There's a small hardware component
we do ship a box out to the sites that
(05:16):
compress the videos and make sure we're not putting any
strain on their network infrastructure. But generally speaking, you know,
we can be live with our customers in a matter
of days, given the way we're able to again deploy
on top of the cameras that they have in place,
and in some cases, you know, a week from working
with the customer initially, we're already at a point where
we're looking at live data from their facility that has
(05:38):
been you know, essentially run through our models and we're
we're flagging that risk and having starting to have those
conversations around how do we use this to actually drive
impact drive change management with our customers sort of in
that timeframe, which is pretty awesome and sort of a
different experience for me coming from a lot more enterprise
technology that may take weeks to months at deploy. Being
able to see this sort of almost in real time time,
(06:01):
you know, drive action for our customers is pretty remarkable.
Speaker 2 (06:04):
Yeah, that is a really quick turnaround time. So what
types of things is the software designed to capture and
collect and study in terms of you know, workplace safety, ergonomics,
and movement.
Speaker 1 (06:17):
Yeah, so really, you know, the use cases we support
really align or generally aligned to sort of the largest
leading indicators of injuries and so, you know, just to
give a handful of examples, you know, a large focus
on ergonomics, so overreaching bending, you know, are we seeing
are we seeing movements that essentially you know, could lead to,
(06:38):
especially if repeated over and over, pretty serious injuries. A
strong focus on vehicles, so you know, proximity vehicle to vehicle,
vehicle to pedestrian speeding. Speeding has been a really interesting
use case that we've been working with a lot of
our logistics customers on even some outdoor views looking at
semis sort of ripping through facilities. And then another general
(07:02):
category of large categories area controls. You know, as a
person somewhere that they're not supposed to be, is an
object sitting somewhere that it's not supposed to be blocking
an exit blocking a fire extinguisher is a is a
door left open for too long, which you know, in
some cases if you're in a cold storage facility, could
be a big issue from you know, from an energy
(07:22):
efficiency standpoint. So you know, we're very focused on safety,
and you know, a lot of it's the LEA again
the leading indicators that ultimately could lead to individuals getting injured.
But there's a really interesting sort of operational component too
as you're thinking about you know, some of the things
I different I mentioned, you know, generally speaking, a efficient
facility as a safe facility and vice versa. So as
(07:45):
we as we work with customers, a lot of the
use cases that we support sort of bridge the gas
between efficient operations and making sure that they have a
safe work environment.
Speaker 2 (07:53):
So I'm interested in some of the tangible benefits, especially
in this case since we're using a structure and an
infrastructure that the company already had, which is security cameras.
We're assuming that they have them and they're going to
be pointed at the right things. So, you know, if
a company already has a security camera focused on these
(08:14):
logistic areas, you know they should be able to see
using this footage, if if someone's speeding through the area,
if someone's wearing their ppe, if someone is you know,
not stacking things the way they're supposed to, or if they're
bending too often. But obviously the AI helps identify those
things and those patterns a little bit more quickly. So
(08:36):
what are some of the tangible results that your clients
are able to receive in these these benefits? What are
they able to find more easily and what are they
able to correct more quickly?
Speaker 1 (08:48):
I love this question. This is this is really sort
of where my role in my team focuses with our customers.
How do we ensure that we are driving tangible benefit,
you know, while at the same time, you know, a
lot of times sort of driving more of the qualitative
benefit of uh, you know, our customers feeling like they
are they're having a much safer facility, But tangible benefit
is critical to ensure that you know, customers want to
(09:10):
continue working with us, that we're able to expand, and
that frankly, we know that we're being successful. And so
first starting with maybe sort of the tackle morgnity gritty
in terms of how we measure it. You know, one
of the one of the primary ways is looking at
claims information, and so you know, as we work into
a facility, maybe running three years of their historical data
(09:30):
where injuries occurring, you know, what area of the facility,
what type of actions are actually driving injuries they're driving
sort of lost time, you know, things that you know,
both impact operations, but also you know, again to sort
of our mission, you are leading to unsafe environments, and
so we'll use that data to really drive focus. And
it definitely changes from site to site. You know, working
(09:54):
with a customer pretty pretty heavily. Right now that's very
focused on you know, ergonomics from a very sort of
manual packing and unpacking processes, and so they're using our data,
you know, around understanding exactly where in the facility they're
getting the most ergo hits, sort of the time, what
shifts it's happening. They're running heat maps, so able to
sort of see exactly where in a camera view these
(10:16):
actions are occurring and then using that to drive sort
of better coaching. You know, how do they build a
much sort of more robust safety program to actually coach
to the behaviors that are occurring and be very targeted.
But I think very you know, in terms of extremely
tangible changes that they're able to make without driving, you know,
complete change in human behavior. They able to invest in
(10:38):
the infrastructure that makes the job a lot more efficient,
so you know, new tables, changing sort of the configuration
of conveyor lines and belts and the heights at which
so you know, those risky behaviors aren't happening in the
first place. So I guess sort of in summary, you know,
regardless of its ergo vehicle, you know, area controls, you know,
(11:01):
compliance with certain article closing like bump caps or safety vests.
You know, we really see two core ways of tackling it.
You know, using the information to drive a lot more
efficient management of processes and safety based programs, but then
also our customers taking a lot more initiative of how
do we use sort of a data first approach to
(11:21):
configuring our facilities to really optimize for a much safer
work environment.
Speaker 2 (11:26):
And you've mentioned a couple of different aspects of what
you're able to measure, to record and measure, and we're
talking about logistics in general. You mentioned, you know, ports
and logistics facilities three pls is what comes to my mind,
you know, doing logistics from multiple different brands manufacturing, how
(11:48):
does the platform adapt to the unique challenges of those
because those are not exactly interchangeable. There's distinct differences between
those types of facilities and the work that's done within them.
Speaker 1 (11:59):
A couple lots here. First and foremost, our platform is
quite flexible and our models are quite flexible, especially the
use cases that we've really decided to sort of prioritize
in terms of the offerings we provide to our entire
customer base, regardless if it's a if it's a terminal
or inside a warehouse or within a back of a store.
(12:22):
You know, a lot of times those same sort of
behaviors around organomic risk vehicles moving around, they exist sort
of across these different facilities. So, first and foremost, the
platform is quite flexible in that regard. But I think
one thing that's really unique about our approach in terms
of how we developed our software and our AI is
we're constantly training based off of site level data and
(12:45):
then you know, essentially optimizing our models based off the
insights that we're seeing customer by customer, site by site,
use gates by use case, and so you know, how
do we make sure that you know, we are capturing
you know, vehicle proximity when it's a tugger versus you know,
a forklift. You know, it's constantly sort of taking those
those data streams from different sites in and making sure
(13:07):
that you know, those use cases are are tangible and
work for the individual operations that you know, slightly differ
between the different types of customers that we support.
Speaker 2 (13:16):
Interesting and one other thing that comes to mind right now,
and it's it's unique because we're in this age where
we're seeing a lot of autonomous vehicles and those interacting
with human lines of transport within these facilities. How does
that factor in when you're when you're dealing with I mean, obviously,
(13:40):
this this autonomous vehicle is following a track, it's following
a path that it has been programmed to do. But
then you're having to deal with the human element of
don't walk in front of the train as it's coming
at you, or it's going to cause a disruption or
potentially cause an injury. So how does that factor into
what you're able to measure or does it even.
Speaker 1 (14:02):
Make a difference. Well, I think it's I think it's
a really interesting point and I think we're you know,
it's an extremely exciting time just for this our industry
in general, and I think, you know, particularly computer vision
and what we're doing, we're going to continue to see
so much sort of rapid iteration as AI continues to
drive you know, sort of more and more workflow within
industrial facilities. That being said, you know, we have strong
(14:24):
conviction that there's you're never going to completely take away
the human element and so being able to have the
you know, really you know, tangible, unbiased data to understand
when human behavior introduced risks, you know, is you know,
something that we're always going to be able to do.
And I think as we see more and more innovation,
there's going to be you know, continued investment in more
(14:47):
camera views you know, uh, facilities built out really with
the focus of having technology like ours and many other
places in place, and that's just only going to expand
the use cases that we can support and sort of
the interaction between more automation and humans that really are
going to ensure that these facilities are always operating as
they need to be.
Speaker 2 (15:06):
I think that's one thing that we see at Meebach,
especially when we're looking at a lot of these new
facility designs that we're working with companies on, is you know,
we have to take into account that there are these
autonomous vehicles and they are going to work one way
and people are going to work a different way, and
having those two differences in the way the work gets accomplished,
(15:27):
managed efficiently within the space and without having so much
over I mean, there's there's going to be overlap, but
without having too much interference with the other because if
you're having to cross lines then it's not efficient. And
you know, the more it can work in tandem and parallel,
the better things flow throughout the warehouse. So I imagine
(15:48):
that that's also what you're seeing when you're looking at
some of these you know, maybe more holistic views from
these video feeds is how this works and maybe where
things need to be optimized differently in order to make
it safer. Is that an accurate statement?
Speaker 1 (16:06):
I think that's spot on. I think just you know,
sort of as a side note impiling onto your comment,
I toured a terminal facility. The other data is introduced
an immense amount of automation with massive, massive amounts of machinery,
and frankly it's you know, it's changing their operations, but
it's introducing entirely new factors around safety protocols and protocols
(16:26):
in general that you know, sort of an unbiased data
set like ours, and understanding how that automation is functioning
with the way things have done traditionally and the way
that there's always going to need to be intervention with
other third parties potentially come on these facilities again opens
up sort of a really interesting area for the intersection
of technology and understanding, you know, what's working and where
(16:48):
there areas and continued areas to continue growing.
Speaker 2 (16:51):
Yeah, especially in these brown field type facilities where they've
been doing something a certain way for so long now
that the automation has come in and making that adjustment.
It's not just adjusting to the fact that there is
an automation component to that facility, but also that the
human element has to adapt with it. To your change
management point, earlier that you know, there's there's a lot
(17:13):
of training and understanding and making sure that people know
what their role is in the facility and along with
all of this automation in order to remain safe and
to your point, make at home and uninjured at the
end of the day.
Speaker 1 (17:30):
Oh, absolutely so.
Speaker 2 (17:32):
With having video component and using the security systems, that
raises a lot of questions, especially regarding privacy. It's an
ethical issue of you know, are we invading the individual's
privacy with this much visualization of every detail of what
they're doing throughout the day. So what does vauxhel Ai
(17:57):
do to ensure employee privacy when you are doing this
kind of engagement where you're watching every move. Yeah, it's
it's not a like topic.
Speaker 1 (18:06):
It's something we take extremely seriously and you know, again
sticking through our mission, it's a lot more around driving
safe behaviors holistically than any sort of tracking of individuals
or you know, data being leveraged for any sort of
punitive action. So, you know a few things that we do,
you know, across the board that are you know, absolutely
fundamental to our technology. We do not do any biometric tracking,
(18:30):
so it takes the individual out of it completely. Sure,
we're seeing an individual performed behaviors, but you're not able
to track that person across the facility or you know,
sort of any of the big brother you know, to
fears that might arise from having a technology around this.
It's behavioral based. What are we seeing that ultimately, you know,
done repeated over and over across different individuals and different
(18:52):
areas facility are going to are going to lead, you know,
to potential risk that ultimately could lead to harm. On
top of that, you know, we take it a step
further of even taking the person out of it, so
enabling blurring in the sense where you actually have no
idea what who is performing what action, rather that a
person performed that behavior in this area, and you know,
(19:15):
sort of across both those examples, you know, you can
see individual clips that we're flagging where those behaviors occur,
but really where our customers are effective using our information
is looking at the holistic data set. So how do
we understand observation trends over time? How do we understand
sort of the proximity of incidents you know, within certain
areas of facility and then using that data to ultimately
(19:38):
drive larger change management instead of saying, you know, we
need to change behavior with this one individual person at
this in one exact time.
Speaker 2 (19:45):
So what upcoming features, technologies, what else is Vauxhel looking
to further enhance this tool in order to you know,
make workplaces safer? Are there any new feature in the pipeline.
Speaker 1 (20:01):
We have some really really exciting plans future. I think first,
first and foremost, you know, in June we closed our
Series B which really gives us, you know, the capital
to really go and invest and expand what we're doing
on behalf of our customers. So I think extremely exciting
time from that perspective. But you know a few of
the things that we're focused on, first and foremost putting
(20:23):
a little bit more sort of prescriptive insights in front
of our customers. So we have a huge amount of
data that you know, is sort of flagging the certain
behaviors that ultimately could lead to change. How do we
continue to invest more and more to really decipher that
information and allow it to be very bite sized insights
that customers can use to drive those right behaviors. That's
(20:46):
something that we do with our customers today based off
our support model and my team really of safety professionals
working hand in hand with our customers to take a
data set and understand how we translate it to action.
But I think there's a massive opportunity to productize that
and invest in leveraging AI to really drive some more
of those insights to make it easier for our customers
to drive action on their side. On top of that,
(21:09):
you know, I think continuing to expand the use cases
that we support, and so you know, we're working with
really innovative companies today that you know, sort of see
the application of computer vision at a partner like Box
on their facilities is sort of a gateway to you know,
so many different use cases, whether it's around quality control,
you know, understanding even more risk parameters that may exist.
(21:35):
You know, so I think just working hand in hand
with our customers to really understand, you know, what are
those what are those use cases that are going to
drive value, whether it's you know, just a couple of
quick examples working underneath loads that are extended over your head,
or more proximity and understanding area controls outside of a facility,
(21:56):
Are there people that are interacting in a in a
doctor that should be there embedding analytics that allow us
to review this information in the form of heat maps
directly in the platform and access that data in real time.
So I think quite a different way a few ways
that we're thinking about sort of investment in the future
and extending sort of the value proposition and the level
(22:17):
of stickiness and engagement that we're able to drive with
our customers.
Speaker 2 (22:21):
What kind of limitations does this technology have in assessing
the safety of a particular activity or a particular area,
Because what we're doing is you're looking, so it's visual,
but there's other aspects of the work in that area.
Perhaps the temperature that may or may not be able
(22:44):
to be recorded from that or included in that data set.
You know, if you're working in a warehouse and it's
ninety degrees that's a safety concern. That's not just a
environmental comfort level, it's dangerous. Or in cold chain warehouses
where it's in a freezing or refrigerated and using the
(23:05):
correct obviously the PPE, but the amount of time that's
spent there. So what are some other aspects that need
to be included or considered when using this tool and
looking at some of this information, because it's looking at
well looking first.
Speaker 1 (23:23):
Just as a Have you ever been in a negative
forty degree freezer in a cold chain facility?
Speaker 2 (23:28):
Yes?
Speaker 1 (23:28):
Yes, I have actually one lesson where thick socks whenever
you do that, because you always get big jacket to
our facility like that, but they don't give you socks.
That's something something that I've learned, you know, on the
job here. But you know, back to sort of the
question at hand. You know, I think one of the
really exciting aspects of this type of technology is the
(23:50):
amount of flexibility there is. And so you know, to
your point, we might not be able to capture the
actual temperature from a camera view, but what are leading
indicators that might give us an indication of temperature being
an issue? So, you know, are are we seeing consistent
usage of coats within a cold coldure facility? You know,
(24:11):
in a facility that's too hot? Are we able to
monitor and use our visions to make sure that there's
water stations approkenately set up? And these are just examples
that I'm throwing out, But you know, I think we
look at a space in a very optimistic light in
the sense that you know, there's really interesting ways to
take all this live data to understand holistically, what is
happening in a facility, you know, and tie it back
(24:32):
to our mission at hand, which is making sure that
folks have the tools and the support they need to
be safe in their roles.
Speaker 2 (24:38):
And it's interesting you say that because I remember when
when we first met and we were talking about warehouse safety.
It was it was one of the things I remember
very vividly from my uh from when I was a
child and my father was working in a cold storage
warehouse with the freezer rooms, and I remember going in
there occasionally with him. But one of the things he
(25:01):
did as the vice president of this facility and operationally,
he was the person who made sure that the refrigeration
kept going and whenever it was I don't know how
the refrigeration works, that's not my forte, but I know
that there was ammonia involved in the refrigeration. And when
(25:23):
there was an ammonia leak, ems would show up the
fire department, you know, environmental, and they would go in
and they're trying to find the source of the ammonia leak.
My dad would go in with no ppe just he'd
tell them he had to go in and breathe the
air in order to identify and to feel the cold
(25:43):
to understand where the late link leak was. So it
was it was incredibly unsafe. But you know that type
of that type of measurement of these I mean, obviously
that's a one off situation that's not daily erganoma and
daily work. But you know, making these facilities safer is
(26:04):
you know, there's there's lots of aspects to it. There's
there's the smell, there's the temperature, there's there's all sorts
of other environmental aspects. So I'm eager to see what
Vauxel comes up with in the future to incorporate some
of those other data sets to bring in and have
a more and more comprehensive picture. What are their plans
(26:27):
to expand your services? So right now you're only in
the US. Is that that correct?
Speaker 1 (26:33):
We're actually quite global. Oh okay, we're live on four
different continent or deployed on four continents I think a
fifth very shortly. Really it's customers that are driving us there,
and so it hasn't been you know, sort of our
intention to make a big splash going international. Previously, we
(26:55):
were taking international different facilities by customers that wanted to
deploy our technology generally starting in the US and seeing
an opportunity expand it out pretty rapidly, we were able
to support that. But in June of this year, we
did announce our intention to really go international and really
continue to sort of invest in that segment of our
(27:16):
business as there is just a huge amount of opportunity,
again driven by our customers, to extend our footprint.
Speaker 2 (27:22):
Do you find that the different safety regulations in different
geographies has an impact on what it is that you're
looking for. I mean, obviously not every geography has the
same safety standards or regulations, But if we're looking just
to make sure that everyone gets home safely, the regulation
(27:45):
really doesn't matter. I mean, it does at the end
of the day, and there are companies that are looking
to make sure that they stay within those regulations. But
does that factor into any of the measurement or data
comprehension that you're working with.
Speaker 1 (28:00):
So I think there's there's continued opportunities and things that
we're doing with customers to you know, sort of make
the data set at their site or their their their
geo more relevant to metrics that matter most for them.
But I think you know to your question, you know,
I don't. We don't really too hung up on you know,
we're going to forecast risk based off of this regulation
(28:20):
in this country. In this country, We're going to look
at the behaviors that you know, we have a strong
conviction and working with the large amount of customers that
we we have in the massive data set that we
have to trein our models of behaviors that actually, you know,
put humans at risk, and so regardless of where that
facility is, we'll be able to provide those those insights.
Speaker 2 (28:39):
You know.
Speaker 1 (28:39):
Back to your original point, though, there's probably some some
opportunity as we you know, really work with more and
more customers in these areas to even fine tune the
type of insights that we provide a bit more. You know,
how does how does a company in the United States, Uh,
you know, sort of stay in compliance with ocean regulations
relative to another country. Sure, there's there's probably more to
do there, something exciting, But when we think about forecasting risk,
(29:02):
it's really much more around the human element versus you
know what what what regulations may exist in certain markets.
Speaker 2 (29:10):
Well, and I love this, this dichotomy of using AI
to ensure human safety. I think that's that's a fantastic
use for the technology. So what do you see the
role of AI in the workplace safety evolving over the
next five ten years.
Speaker 1 (29:28):
Yeah, I think one one side note first, and just
sort of that that theme of as we like to
call it, you know, within Vaxal AI for good, it
attracts you know, sort of really motivated folks. If I
look across some of the technical members and our team
that are really excited about the mission of building AI
for that purpose, for that intent, something that's extremely talent, tangible,
(29:49):
and you know, ultimately has those extremely positive outcomes. It's
not here to uh, to outsource or you know, to
do any of those. It's it's here to get people
long safe. And so that that mission is you know,
just as a sort of again a side note, you know,
really drives a lot of people to what we're building here.
You know, I think there's a lot of folks buy
(30:10):
better better than me to forecast what the future looks
in terms of in terms of AI. I think it's
just it's pretty incredible to see, you know, just how
rapidly you know, things are iterating and progressing and maturing.
You know, it's when I think about our business it's
you know, how do we you know, even more seamlessly
sort of deploy use cases, you know, even quicker and quicker?
(30:32):
How do we surface you know, more tangible insights from
ourder data sets and relay that information to customers in
a way that's extremely easy to digest and lead to action.
And so, you know, I can only can only imagine
what we're going to be seeing five years from now.
I imagine I can't even comprehend it right now, but
it's it's an extremely exciting time to be in this space.
Speaker 2 (30:51):
You know what, if we had said five years ago
that this is where we were going to be today,
we wouldn't have believed it. The world is changing so
fast in our industry, especially now that it's front and
center of everyone's attention. So I'd say, you know, I
guess one of my last questions is what is probably
the best customer use case story, the best result that
(31:15):
you've been able to achieve for one of your customers
using this technology and helping them improve their employees safety.
I mean, there's got to be someone who had some
really big aha moments and was able to really tighten
up and make things better for their employees.
Speaker 1 (31:35):
Yeah, I think I'll mention two examples, you know, one
very tangibly measured and extremely safety focused. You know, we
were working with a customer that had some pretty catastrophic injuries,
you know, had pretty high exposure from a claim's perspectives
as a result, and you know, generally speaking to this
was something that they had to invest in and sort
(31:56):
of through our data and you know, they had a
good sense this beforehand, but our data really you know,
drove home exactly what they need to focus on. It
really was around you know, sort of vehicle uh, vehicle
related incidents and use cases within within a warehouse environment,
blowing through no stops, you know, not clear drive lanes.
(32:17):
You know, the proximity monitoring was sort of was was
was spiking and so you know, we had a customer
that really over indexed and you know, sort of re
engineering their facility to have clear drive lanes. You know,
instilled a very you know focused safety program on you know,
training the right ways to use and even had people
(32:38):
out on the floor that were monitoring no stops you know,
on sort of rotating shifts. Over invested in making sure
that making sure that that vehicles were operating the way
they're supposed to. And you know, year over year they
were able to drive their claims data down in one
location alone by over two million dollars. And so you
know that's obviously from an ROI standpoint, you know, based
(32:59):
off of you know, them investing our technology, quite positive.
But more importantly, those represent you know, what would have
been claims, which means something had happened that wasn't supposed to.
So you know, that example alone, you know, was just
able to show sort of what is possible with the
right change management and focus using technology like ours. One
other one, I'll keep this example a little quicker, but
(33:20):
I love to focus on sort of the intersection of
operations and safety. We had a customer that noticed a
huge number of ergonomic incidents spiking about three hours into
a shift on two conveyor lines tied to two different doctors.
They're sort of looking at the data as an anomaly,
what's going on? Why is ergo just happening right here?
It took a step back and they realized, hey, we're
(33:41):
actually directing trucks with all of our heavy product skews
to these two doctors only, and every other doctor is
essentially moving pillows at the light step of a better example.
And so, you know, two hours in every shift, the
folks that were working these conveyors were just completely fatigued, strained,
and it was leading to a huge increase in you know,
(34:01):
improper motions that our leading indicators of risk. And so
they sort of took this data and they said, okay,
let's figure out better scheduling around our inbound full truckloads
and redistribute them across a few different doctors in the facility.
And doing so, you know, knock the ergo risks sort
of that we were flagging out of the picture, but
also increase their throughput quite a bit. You know, they
(34:23):
were a lot more efficient in terms of how they
were unloading product and moving product through their facility. And
so again it's sort of really interesting intersection of operations
and safety. And I'll say it again, a safe environment
is an efficient environment. An efficient environment is a safe environment.
So those type of insights are really interesting to see
as well.
Speaker 2 (34:40):
I love that because that's not something that most people
would even think about or consider. Is you know, you're
receiving all day long, and you're receiving all kinds of things,
but what are you receiving and who is responsible for
receiving it and how are they able to do it
or are they able to continuously do it throughout the shift.
That's incredible insight that they may have spent years trying
(35:01):
to figure out dealing with injuries and you know, claims
and employees out and having to you know, higher temporary
staff or higher additional staff. I mean there's there's a
huge operational opportunity costs there that you're saving them from
having to spend.
Speaker 1 (35:19):
Yeah, absolutely, absolutely, all.
Speaker 2 (35:22):
Right, Well, thank you so much, Jack for joining us
today and telling us more about your company's product and
the safety that you're able to help companies achieve for
their employees. Thank you so much for joining us.
Speaker 1 (35:37):
No, thanks for having me, Alan, It's great to be here.
Speaker 2 (35:39):
So it's quite the step away from zero days in
the workplace without an injury. Stuck on a wall poster
to our listeners. If you found today's episode valuable, be
sure to follows speaking of supply chain wherever you get
your podcasts and share it with someone who's shaping the
future of operations.
Speaker 3 (36:02):
Meeboch Consulting is one of the largest and most globally
recognized supply chain consulting, engineering, and advisory firms for nearly
fifty years. We've helped clients achieve supply chain excellence and
sustainable competitive advantage across the entire spectrum of the supply
chain by delivering improvements and innovation strategically, tactically, and digitally.
(36:23):
To learn more of visit meeboch dot com. You've been
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latest in supply chain information. Thanks for listening. Until next time.
Generally speaking, a efficient facility as a safe facility and
vice versa. So as we work with customers, a lot
of the use cases that we support sort of bridge
the gas between efficient operations and making sure that they
have a safe work department.
Speaker 2 (00:14):
Hello and welcome to speaking of supply chain, where we
explore trends, current events, and innovations impacting the logistics and
supply chain industries. I'm your host, Ellen Wood. Large machinery,
heavy lifting, and high traffic are inherent risks in warehousing
and logistics, and ensuring employees safety is critical. Companies that
proactively invest in robust workplace safety programs not only protect
(00:36):
their workforce, but also enhance productivity, reduce operational disruptions, and
foster a culture of trust and engagement. Joining me for
today's discussion is Jack Brannef from VAUXHALLAI.
Speaker 1 (00:48):
Welcome Jack, Thanks Allen, great to be here.
Speaker 2 (00:51):
Great to have you. So tell our listeners a little
bit about what VAUXHALAI is and how it works.
Speaker 1 (00:59):
Vauxhall is found in twenty twenty in San Francisco with
the mission to protect people who make the world essentially work.
So we are a computer vision AI company that leverages
existing security camera infrastructure and relays it with our AI
and computer vision models to essentially flag risk in real
time and allow our customers to be a lot more
(01:20):
proactive as they're mitigating risk and driving safety culture within
their facilities.
Speaker 2 (01:24):
So you said twenty twenty, and the question begs, was
it a reaction to what was happening in twenty twenty
and slightly before that, or did you was this already
in the works as just part of the logistics industry.
Speaker 1 (01:39):
So I actually love our origin story. So our founders
are some very impressive people, espe on the technical side.
They didn't necessarily go into it thinking that they were
building technology for the purpose of workplace safety. They were
more than anything, taking some of their experience, you know,
working at places and institutions like Carnegie, Mellon, Google, Uber,
(02:01):
a few of them coming together sort of playing with
really innovative, breaking edge tech at this time when everyone
had a little bit more time on their hands or
figure it out and realize that there was really a
lot of opportunity in the computer vision space. And you know,
ultimately we're able to work with some advisors and for
a few of our funders pull on some really personal
(02:22):
stories and find a neat way to take this innovative
tech and tie it to an area of opportunity that
you know, both allows us to build AI with a
really positive mission, which ultimately is to ensure that people
get home and return safely to their families each night.
There's also a massive market opportunity. So I think as
they were getting in and realizing that the type of
(02:42):
product fit and the existing infrastructure that really was in
place in a lot of these facilities, you know, there's
over one hundred and sixty seven billion dollars in injury
related claims each year, and so thinking about the market
opportunity there, purely from a business standpoint, there was there
was a lot of run. But again I think that
the initial reason for making the correlation was a lot
(03:04):
more on sort of the much more personal side of
what we're doing, which again is ensuring that folks that
really drive our economy, drive our country, you know, are
they are able to work on or return safely to
their homes each night.
Speaker 2 (03:15):
That's amazing. Now I've interviewed others in this same general
field of you know, workplace safety, and you know the
types of it's usually a wearable tech that allows to
collect those biometric markers and understand what's happening as people
are moving physically within their job and their function. So
(03:41):
you said yours integrates with the existing security systems. So
the security cameras and just watching an overview, how does
that work exactly with the overlay? Is it just identifying
movement patterns? I mean, is it able to get close
enough and granular enough to see some of the smaller
tasks that are happening, especially when we're talking about a
(04:03):
security system, because I don't know, I've seen some security
system video and it's not always the best quality, So
I'm interested to understand a bit more of.
Speaker 1 (04:13):
How that works. Yeah. So, I think first major observation
from our end is the vast majority of facilities that
we work with, whether it's a manufacturing site, you know,
a e commerce or retail facility, a logistics hub like
a quarter, or a terminal, they really do have a
lot of the camera infrastructure in place. And really, even
over the last few years, we've seen more and more
(04:33):
companies you invest in having sort of robust cameras that
really cover a vast majority of the areas that we
want to see. There's certainly you know instances where we
work with customers to optimize the camera views, to maybe
move them around, add a couple cameras, to really focus
in on some of the use cases that are going
to drive value. But honestly, I would say that sort
(04:53):
of a minority at this point. I'm at least from
my seat, my perspective, you know, a lot of the
warehouse or again these different these different sites that I'll
go visit when we're seeing our customers have the infrastructure
in place. In terms of actually deploying our software, our technology,
it's it's really quite seamless. There's a small hardware component
we do ship a box out to the sites that
(05:16):
compress the videos and make sure we're not putting any
strain on their network infrastructure. But generally speaking, you know,
we can be live with our customers in a matter
of days, given the way we're able to again deploy
on top of the cameras that they have in place,
and in some cases, you know, a week from working
with the customer initially, we're already at a point where
we're looking at live data from their facility that has
(05:38):
been you know, essentially run through our models and we're
we're flagging that risk and having starting to have those
conversations around how do we use this to actually drive
impact drive change management with our customers sort of in
that timeframe, which is pretty awesome and sort of a
different experience for me coming from a lot more enterprise
technology that may take weeks to months at deploy. Being
able to see this sort of almost in real time time,
(06:01):
you know, drive action for our customers is pretty remarkable.
Speaker 2 (06:04):
Yeah, that is a really quick turnaround time. So what
types of things is the software designed to capture and
collect and study in terms of you know, workplace safety, ergonomics,
and movement.
Speaker 1 (06:17):
Yeah, so really, you know, the use cases we support
really align or generally aligned to sort of the largest
leading indicators of injuries and so, you know, just to
give a handful of examples, you know, a large focus
on ergonomics, so overreaching bending, you know, are we seeing
are we seeing movements that essentially you know, could lead to,
(06:38):
especially if repeated over and over, pretty serious injuries. A
strong focus on vehicles, so you know, proximity vehicle to vehicle,
vehicle to pedestrian speeding. Speeding has been a really interesting
use case that we've been working with a lot of
our logistics customers on even some outdoor views looking at
semis sort of ripping through facilities. And then another general
(07:02):
category of large categories area controls. You know, as a
person somewhere that they're not supposed to be, is an
object sitting somewhere that it's not supposed to be blocking
an exit blocking a fire extinguisher is a is a
door left open for too long, which you know, in
some cases if you're in a cold storage facility, could
be a big issue from you know, from an energy
(07:22):
efficiency standpoint. So you know, we're very focused on safety,
and you know, a lot of it's the LEA again
the leading indicators that ultimately could lead to individuals getting injured.
But there's a really interesting sort of operational component too
as you're thinking about you know, some of the things
I different I mentioned, you know, generally speaking, a efficient
facility as a safe facility and vice versa. So as
(07:45):
we as we work with customers, a lot of the
use cases that we support sort of bridge the gas
between efficient operations and making sure that they have a
safe work environment.
Speaker 2 (07:53):
So I'm interested in some of the tangible benefits, especially
in this case since we're using a structure and an
infrastructure that the company already had, which is security cameras.
We're assuming that they have them and they're going to
be pointed at the right things. So, you know, if
a company already has a security camera focused on these
(08:14):
logistic areas, you know they should be able to see
using this footage, if if someone's speeding through the area,
if someone's wearing their ppe, if someone is you know,
not stacking things the way they're supposed to, or if they're
bending too often. But obviously the AI helps identify those
things and those patterns a little bit more quickly. So
(08:36):
what are some of the tangible results that your clients
are able to receive in these these benefits? What are
they able to find more easily and what are they
able to correct more quickly?
Speaker 1 (08:48):
I love this question. This is this is really sort
of where my role in my team focuses with our customers.
How do we ensure that we are driving tangible benefit,
you know, while at the same time, you know, a
lot of times sort of driving more of the qualitative
benefit of uh, you know, our customers feeling like they
are they're having a much safer facility, But tangible benefit
is critical to ensure that you know, customers want to
(09:10):
continue working with us, that we're able to expand, and
that frankly, we know that we're being successful. And so
first starting with maybe sort of the tackle morgnity gritty
in terms of how we measure it. You know, one
of the one of the primary ways is looking at
claims information, and so you know, as we work into
a facility, maybe running three years of their historical data
(09:30):
where injuries occurring, you know, what area of the facility,
what type of actions are actually driving injuries they're driving
sort of lost time, you know, things that you know,
both impact operations, but also you know, again to sort
of our mission, you are leading to unsafe environments, and
so we'll use that data to really drive focus. And
it definitely changes from site to site. You know, working
(09:54):
with a customer pretty pretty heavily. Right now that's very
focused on you know, ergonomics from a very sort of
manual packing and unpacking processes, and so they're using our data,
you know, around understanding exactly where in the facility they're
getting the most ergo hits, sort of the time, what
shifts it's happening. They're running heat maps, so able to
sort of see exactly where in a camera view these
(10:16):
actions are occurring and then using that to drive sort
of better coaching. You know, how do they build a
much sort of more robust safety program to actually coach
to the behaviors that are occurring and be very targeted.
But I think very you know, in terms of extremely
tangible changes that they're able to make without driving, you know,
complete change in human behavior. They able to invest in
(10:38):
the infrastructure that makes the job a lot more efficient,
so you know, new tables, changing sort of the configuration
of conveyor lines and belts and the heights at which
so you know, those risky behaviors aren't happening in the
first place. So I guess sort of in summary, you know,
regardless of its ergo vehicle, you know, area controls, you know,
(11:01):
compliance with certain article closing like bump caps or safety vests.
You know, we really see two core ways of tackling it.
You know, using the information to drive a lot more
efficient management of processes and safety based programs, but then
also our customers taking a lot more initiative of how
do we use sort of a data first approach to
(11:21):
configuring our facilities to really optimize for a much safer
work environment.
Speaker 2 (11:26):
And you've mentioned a couple of different aspects of what
you're able to measure, to record and measure, and we're
talking about logistics in general. You mentioned, you know, ports
and logistics facilities three pls is what comes to my mind,
you know, doing logistics from multiple different brands manufacturing, how
(11:48):
does the platform adapt to the unique challenges of those
because those are not exactly interchangeable. There's distinct differences between
those types of facilities and the work that's done within them.
Speaker 1 (11:59):
A couple lots here. First and foremost, our platform is
quite flexible and our models are quite flexible, especially the
use cases that we've really decided to sort of prioritize
in terms of the offerings we provide to our entire
customer base, regardless if it's a if it's a terminal
or inside a warehouse or within a back of a store.
(12:22):
You know, a lot of times those same sort of
behaviors around organomic risk vehicles moving around, they exist sort
of across these different facilities. So, first and foremost, the
platform is quite flexible in that regard. But I think
one thing that's really unique about our approach in terms
of how we developed our software and our AI is
we're constantly training based off of site level data and
(12:45):
then you know, essentially optimizing our models based off the
insights that we're seeing customer by customer, site by site,
use gates by use case, and so you know, how
do we make sure that you know, we are capturing
you know, vehicle proximity when it's a tugger versus you know,
a forklift. You know, it's constantly sort of taking those
those data streams from different sites in and making sure
(13:07):
that you know, those use cases are are tangible and
work for the individual operations that you know, slightly differ
between the different types of customers that we support.
Speaker 2 (13:16):
Interesting and one other thing that comes to mind right now,
and it's it's unique because we're in this age where
we're seeing a lot of autonomous vehicles and those interacting
with human lines of transport within these facilities. How does
that factor in when you're when you're dealing with I mean, obviously,
(13:40):
this this autonomous vehicle is following a track, it's following
a path that it has been programmed to do. But
then you're having to deal with the human element of
don't walk in front of the train as it's coming
at you, or it's going to cause a disruption or
potentially cause an injury. So how does that factor into
what you're able to measure or does it even.
Speaker 1 (14:02):
Make a difference. Well, I think it's I think it's
a really interesting point and I think we're you know,
it's an extremely exciting time just for this our industry
in general, and I think, you know, particularly computer vision
and what we're doing, we're going to continue to see
so much sort of rapid iteration as AI continues to
drive you know, sort of more and more workflow within
industrial facilities. That being said, you know, we have strong
(14:24):
conviction that there's you're never going to completely take away
the human element and so being able to have the
you know, really you know, tangible, unbiased data to understand
when human behavior introduced risks, you know, is you know,
something that we're always going to be able to do.
And I think as we see more and more innovation,
there's going to be you know, continued investment in more
(14:47):
camera views you know, uh, facilities built out really with
the focus of having technology like ours and many other
places in place, and that's just only going to expand
the use cases that we can support and sort of
the interaction between more automation and humans that really are
going to ensure that these facilities are always operating as
they need to be.
Speaker 2 (15:06):
I think that's one thing that we see at Meebach,
especially when we're looking at a lot of these new
facility designs that we're working with companies on, is you know,
we have to take into account that there are these
autonomous vehicles and they are going to work one way
and people are going to work a different way, and
having those two differences in the way the work gets accomplished,
(15:27):
managed efficiently within the space and without having so much
over I mean, there's there's going to be overlap, but
without having too much interference with the other because if
you're having to cross lines then it's not efficient. And
you know, the more it can work in tandem and parallel,
the better things flow throughout the warehouse. So I imagine
(15:48):
that that's also what you're seeing when you're looking at
some of these you know, maybe more holistic views from
these video feeds is how this works and maybe where
things need to be optimized differently in order to make
it safer. Is that an accurate statement?
Speaker 1 (16:06):
I think that's spot on. I think just you know,
sort of as a side note impiling onto your comment,
I toured a terminal facility. The other data is introduced
an immense amount of automation with massive, massive amounts of machinery,
and frankly it's you know, it's changing their operations, but
it's introducing entirely new factors around safety protocols and protocols
(16:26):
in general that you know, sort of an unbiased data
set like ours, and understanding how that automation is functioning
with the way things have done traditionally and the way
that there's always going to need to be intervention with
other third parties potentially come on these facilities again opens
up sort of a really interesting area for the intersection
of technology and understanding, you know, what's working and where
(16:48):
there areas and continued areas to continue growing.
Speaker 2 (16:51):
Yeah, especially in these brown field type facilities where they've
been doing something a certain way for so long now
that the automation has come in and making that adjustment.
It's not just adjusting to the fact that there is
an automation component to that facility, but also that the
human element has to adapt with it. To your change
management point, earlier that you know, there's there's a lot
(17:13):
of training and understanding and making sure that people know
what their role is in the facility and along with
all of this automation in order to remain safe and
to your point, make at home and uninjured at the
end of the day.
Speaker 1 (17:30):
Oh, absolutely so.
Speaker 2 (17:32):
With having video component and using the security systems, that
raises a lot of questions, especially regarding privacy. It's an
ethical issue of you know, are we invading the individual's
privacy with this much visualization of every detail of what
they're doing throughout the day. So what does vauxhel Ai
(17:57):
do to ensure employee privacy when you are doing this
kind of engagement where you're watching every move. Yeah, it's
it's not a like topic.
Speaker 1 (18:06):
It's something we take extremely seriously and you know, again
sticking through our mission, it's a lot more around driving
safe behaviors holistically than any sort of tracking of individuals
or you know, data being leveraged for any sort of
punitive action. So, you know a few things that we do,
you know, across the board that are you know, absolutely
fundamental to our technology. We do not do any biometric tracking,
(18:30):
so it takes the individual out of it completely. Sure,
we're seeing an individual performed behaviors, but you're not able
to track that person across the facility or you know,
sort of any of the big brother you know, to
fears that might arise from having a technology around this.
It's behavioral based. What are we seeing that ultimately, you know,
done repeated over and over across different individuals and different
(18:52):
areas facility are going to are going to lead, you know,
to potential risk that ultimately could lead to harm. On
top of that, you know, we take it a step
further of even taking the person out of it, so
enabling blurring in the sense where you actually have no
idea what who is performing what action, rather that a
person performed that behavior in this area, and you know,
(19:15):
sort of across both those examples, you know, you can
see individual clips that we're flagging where those behaviors occur,
but really where our customers are effective using our information
is looking at the holistic data set. So how do
we understand observation trends over time? How do we understand
sort of the proximity of incidents you know, within certain
areas of facility and then using that data to ultimately
(19:38):
drive larger change management instead of saying, you know, we
need to change behavior with this one individual person at
this in one exact time.
Speaker 2 (19:45):
So what upcoming features, technologies, what else is Vauxhel looking
to further enhance this tool in order to you know,
make workplaces safer? Are there any new feature in the pipeline.
Speaker 1 (20:01):
We have some really really exciting plans future. I think first,
first and foremost, you know, in June we closed our
Series B which really gives us, you know, the capital
to really go and invest and expand what we're doing
on behalf of our customers. So I think extremely exciting
time from that perspective. But you know a few of
the things that we're focused on, first and foremost putting
(20:23):
a little bit more sort of prescriptive insights in front
of our customers. So we have a huge amount of
data that you know, is sort of flagging the certain
behaviors that ultimately could lead to change. How do we
continue to invest more and more to really decipher that
information and allow it to be very bite sized insights
that customers can use to drive those right behaviors. That's
(20:46):
something that we do with our customers today based off
our support model and my team really of safety professionals
working hand in hand with our customers to take a
data set and understand how we translate it to action.
But I think there's a massive opportunity to productize that
and invest in leveraging AI to really drive some more
of those insights to make it easier for our customers
to drive action on their side. On top of that,
(21:09):
you know, I think continuing to expand the use cases
that we support, and so you know, we're working with
really innovative companies today that you know, sort of see
the application of computer vision at a partner like Box
on their facilities is sort of a gateway to you know,
so many different use cases, whether it's around quality control,
you know, understanding even more risk parameters that may exist.
(21:35):
You know, so I think just working hand in hand
with our customers to really understand, you know, what are
those what are those use cases that are going to
drive value, whether it's you know, just a couple of
quick examples working underneath loads that are extended over your head,
or more proximity and understanding area controls outside of a facility,
(21:56):
Are there people that are interacting in a in a
doctor that should be there embedding analytics that allow us
to review this information in the form of heat maps
directly in the platform and access that data in real time.
So I think quite a different way a few ways
that we're thinking about sort of investment in the future
and extending sort of the value proposition and the level
(22:17):
of stickiness and engagement that we're able to drive with
our customers.
Speaker 2 (22:21):
What kind of limitations does this technology have in assessing
the safety of a particular activity or a particular area,
Because what we're doing is you're looking, so it's visual,
but there's other aspects of the work in that area.
Perhaps the temperature that may or may not be able
(22:44):
to be recorded from that or included in that data set.
You know, if you're working in a warehouse and it's
ninety degrees that's a safety concern. That's not just a
environmental comfort level, it's dangerous. Or in cold chain warehouses
where it's in a freezing or refrigerated and using the
(23:05):
correct obviously the PPE, but the amount of time that's
spent there. So what are some other aspects that need
to be included or considered when using this tool and
looking at some of this information, because it's looking at
well looking first.
Speaker 1 (23:23):
Just as a Have you ever been in a negative
forty degree freezer in a cold chain facility?
Speaker 2 (23:28):
Yes?
Speaker 1 (23:28):
Yes, I have actually one lesson where thick socks whenever
you do that, because you always get big jacket to
our facility like that, but they don't give you socks.
That's something something that I've learned, you know, on the
job here. But you know, back to sort of the
question at hand. You know, I think one of the
really exciting aspects of this type of technology is the
(23:50):
amount of flexibility there is. And so you know, to
your point, we might not be able to capture the
actual temperature from a camera view, but what are leading
indicators that might give us an indication of temperature being
an issue? So, you know, are are we seeing consistent
usage of coats within a cold coldure facility? You know,
(24:11):
in a facility that's too hot? Are we able to
monitor and use our visions to make sure that there's
water stations approkenately set up? And these are just examples
that I'm throwing out, But you know, I think we
look at a space in a very optimistic light in
the sense that you know, there's really interesting ways to
take all this live data to understand holistically, what is
happening in a facility, you know, and tie it back
(24:32):
to our mission at hand, which is making sure that
folks have the tools and the support they need to
be safe in their roles.
Speaker 2 (24:38):
And it's interesting you say that because I remember when
when we first met and we were talking about warehouse safety.
It was it was one of the things I remember
very vividly from my uh from when I was a
child and my father was working in a cold storage
warehouse with the freezer rooms, and I remember going in
there occasionally with him. But one of the things he
(25:01):
did as the vice president of this facility and operationally,
he was the person who made sure that the refrigeration
kept going and whenever it was I don't know how
the refrigeration works, that's not my forte, but I know
that there was ammonia involved in the refrigeration. And when
(25:23):
there was an ammonia leak, ems would show up the
fire department, you know, environmental, and they would go in
and they're trying to find the source of the ammonia leak.
My dad would go in with no ppe just he'd
tell them he had to go in and breathe the
air in order to identify and to feel the cold
(25:43):
to understand where the late link leak was. So it
was it was incredibly unsafe. But you know that type
of that type of measurement of these I mean, obviously
that's a one off situation that's not daily erganoma and
daily work. But you know, making these facilities safer is
(26:04):
you know, there's there's lots of aspects to it. There's
there's the smell, there's the temperature, there's there's all sorts
of other environmental aspects. So I'm eager to see what
Vauxel comes up with in the future to incorporate some
of those other data sets to bring in and have
a more and more comprehensive picture. What are their plans
(26:27):
to expand your services? So right now you're only in
the US. Is that that correct?
Speaker 1 (26:33):
We're actually quite global. Oh okay, we're live on four
different continent or deployed on four continents I think a
fifth very shortly. Really it's customers that are driving us there,
and so it hasn't been you know, sort of our
intention to make a big splash going international. Previously, we
(26:55):
were taking international different facilities by customers that wanted to
deploy our technology generally starting in the US and seeing
an opportunity expand it out pretty rapidly, we were able
to support that. But in June of this year, we
did announce our intention to really go international and really
continue to sort of invest in that segment of our
(27:16):
business as there is just a huge amount of opportunity,
again driven by our customers, to extend our footprint.
Speaker 2 (27:22):
Do you find that the different safety regulations in different
geographies has an impact on what it is that you're
looking for. I mean, obviously not every geography has the
same safety standards or regulations, But if we're looking just
to make sure that everyone gets home safely, the regulation
(27:45):
really doesn't matter. I mean, it does at the end
of the day, and there are companies that are looking
to make sure that they stay within those regulations. But
does that factor into any of the measurement or data
comprehension that you're working with.
Speaker 1 (28:00):
So I think there's there's continued opportunities and things that
we're doing with customers to you know, sort of make
the data set at their site or their their their
geo more relevant to metrics that matter most for them.
But I think you know to your question, you know,
I don't. We don't really too hung up on you know,
we're going to forecast risk based off of this regulation
(28:20):
in this country. In this country, We're going to look
at the behaviors that you know, we have a strong
conviction and working with the large amount of customers that
we we have in the massive data set that we
have to trein our models of behaviors that actually, you know,
put humans at risk, and so regardless of where that
facility is, we'll be able to provide those those insights.
Speaker 2 (28:39):
You know.
Speaker 1 (28:39):
Back to your original point, though, there's probably some some
opportunity as we you know, really work with more and
more customers in these areas to even fine tune the
type of insights that we provide a bit more. You know,
how does how does a company in the United States, Uh,
you know, sort of stay in compliance with ocean regulations
relative to another country. Sure, there's there's probably more to
do there, something exciting, But when we think about forecasting risk,
(29:02):
it's really much more around the human element versus you
know what what what regulations may exist in certain markets.
Speaker 2 (29:10):
Well, and I love this, this dichotomy of using AI
to ensure human safety. I think that's that's a fantastic
use for the technology. So what do you see the
role of AI in the workplace safety evolving over the
next five ten years.
Speaker 1 (29:28):
Yeah, I think one one side note first, and just
sort of that that theme of as we like to
call it, you know, within Vaxal AI for good, it
attracts you know, sort of really motivated folks. If I
look across some of the technical members and our team
that are really excited about the mission of building AI
for that purpose, for that intent, something that's extremely talent, tangible,
(29:49):
and you know, ultimately has those extremely positive outcomes. It's
not here to uh, to outsource or you know, to
do any of those. It's it's here to get people
long safe. And so that that mission is you know,
just as a sort of again a side note, you know,
really drives a lot of people to what we're building here.
You know, I think there's a lot of folks buy
(30:10):
better better than me to forecast what the future looks
in terms of in terms of AI. I think it's
just it's pretty incredible to see, you know, just how
rapidly you know, things are iterating and progressing and maturing.
You know, it's when I think about our business it's
you know, how do we you know, even more seamlessly
sort of deploy use cases, you know, even quicker and quicker?
(30:32):
How do we surface you know, more tangible insights from
ourder data sets and relay that information to customers in
a way that's extremely easy to digest and lead to action.
And so, you know, I can only can only imagine
what we're going to be seeing five years from now.
I imagine I can't even comprehend it right now, but
it's it's an extremely exciting time to be in this space.
Speaker 2 (30:51):
You know what, if we had said five years ago
that this is where we were going to be today,
we wouldn't have believed it. The world is changing so
fast in our industry, especially now that it's front and
center of everyone's attention. So I'd say, you know, I
guess one of my last questions is what is probably
the best customer use case story, the best result that
(31:15):
you've been able to achieve for one of your customers
using this technology and helping them improve their employees safety.
I mean, there's got to be someone who had some
really big aha moments and was able to really tighten
up and make things better for their employees.
Speaker 1 (31:35):
Yeah, I think I'll mention two examples, you know, one
very tangibly measured and extremely safety focused. You know, we
were working with a customer that had some pretty catastrophic injuries,
you know, had pretty high exposure from a claim's perspectives
as a result, and you know, generally speaking to this
was something that they had to invest in and sort
(31:56):
of through our data and you know, they had a
good sense this beforehand, but our data really you know,
drove home exactly what they need to focus on. It
really was around you know, sort of vehicle uh, vehicle
related incidents and use cases within within a warehouse environment,
blowing through no stops, you know, not clear drive lanes.
(32:17):
You know, the proximity monitoring was sort of was was
was spiking and so you know, we had a customer
that really over indexed and you know, sort of re
engineering their facility to have clear drive lanes. You know,
instilled a very you know focused safety program on you know,
training the right ways to use and even had people
(32:38):
out on the floor that were monitoring no stops you know,
on sort of rotating shifts. Over invested in making sure
that making sure that that vehicles were operating the way
they're supposed to. And you know, year over year they
were able to drive their claims data down in one
location alone by over two million dollars. And so you
know that's obviously from an ROI standpoint, you know, based
(32:59):
off of you know, them investing our technology, quite positive.
But more importantly, those represent you know, what would have
been claims, which means something had happened that wasn't supposed to.
So you know, that example alone, you know, was just
able to show sort of what is possible with the
right change management and focus using technology like ours. One
other one, I'll keep this example a little quicker, but
(33:20):
I love to focus on sort of the intersection of
operations and safety. We had a customer that noticed a
huge number of ergonomic incidents spiking about three hours into
a shift on two conveyor lines tied to two different doctors.
They're sort of looking at the data as an anomaly,
what's going on? Why is ergo just happening right here?
It took a step back and they realized, hey, we're
(33:41):
actually directing trucks with all of our heavy product skews
to these two doctors only, and every other doctor is
essentially moving pillows at the light step of a better example.
And so, you know, two hours in every shift, the
folks that were working these conveyors were just completely fatigued, strained,
and it was leading to a huge increase in you know,
(34:01):
improper motions that our leading indicators of risk. And so
they sort of took this data and they said, okay,
let's figure out better scheduling around our inbound full truckloads
and redistribute them across a few different doctors in the facility.
And doing so, you know, knock the ergo risks sort
of that we were flagging out of the picture, but
also increase their throughput quite a bit. You know, they
(34:23):
were a lot more efficient in terms of how they
were unloading product and moving product through their facility. And
so again it's sort of really interesting intersection of operations
and safety. And I'll say it again, a safe environment
is an efficient environment. An efficient environment is a safe environment.
So those type of insights are really interesting to see
as well.
Speaker 2 (34:40):
I love that because that's not something that most people
would even think about or consider. Is you know, you're
receiving all day long, and you're receiving all kinds of things,
but what are you receiving and who is responsible for
receiving it and how are they able to do it
or are they able to continuously do it throughout the shift.
That's incredible insight that they may have spent years trying
(35:01):
to figure out dealing with injuries and you know, claims
and employees out and having to you know, higher temporary
staff or higher additional staff. I mean there's there's a
huge operational opportunity costs there that you're saving them from
having to spend.
Speaker 1 (35:19):
Yeah, absolutely, absolutely, all.
Speaker 2 (35:22):
Right, Well, thank you so much, Jack for joining us
today and telling us more about your company's product and
the safety that you're able to help companies achieve for
their employees. Thank you so much for joining us.
Speaker 1 (35:37):
No, thanks for having me, Alan, It's great to be here.
Speaker 2 (35:39):
So it's quite the step away from zero days in
the workplace without an injury. Stuck on a wall poster
to our listeners. If you found today's episode valuable, be
sure to follows speaking of supply chain wherever you get
your podcasts and share it with someone who's shaping the
future of operations.
Speaker 3 (36:02):
Meeboch Consulting is one of the largest and most globally
recognized supply chain consulting, engineering, and advisory firms for nearly
fifty years. We've helped clients achieve supply chain excellence and
sustainable competitive advantage across the entire spectrum of the supply
chain by delivering improvements and innovation strategically, tactically, and digitally.
(36:23):
To learn more of visit meeboch dot com. You've been
listening to Speaking of Supply Chain, a meboch podcast. Keep
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latest in supply chain information. Thanks for listening. Until next time.
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