February 20, 2025 • 38 mins
With the warehouse and distribution landscape rapidly evolving, operations face significant challenges such as labor shortages and rising operational costs. Could your business be operating more efficiently? Discover how AI can help streamline operations, reduce expenses, and improve workforce utilization.
This week, host Ellen Wood is joined by Lee Rector, founder of LaborAI, to discuss the pressing challenges facing warehouses and distribution operations, and how these challenges extend beyond the industry. Lee explains how AI can optimize labor efficiency with real-time operational planning and gamification, offering practical solutions that reduce costs, enhance accuracy, and boost employee engagement. The conversation also highlights successful AI implementations that not only improved operations but also contributed to sustainability goals.
In this Episode:
This week, host Ellen Wood is joined by Lee Rector, founder of LaborAI, to discuss the pressing challenges facing warehouses and distribution operations, and how these challenges extend beyond the industry. Lee explains how AI can optimize labor efficiency with real-time operational planning and gamification, offering practical solutions that reduce costs, enhance accuracy, and boost employee engagement. The conversation also highlights successful AI implementations that not only improved operations but also contributed to sustainability goals.
In this Episode:
- How Labor AI’s gamification features can optimize workforce efficiency
- Moving beyond the average aiming metrics set by traditional WMS systems to improve overall operations
- Leveraging AI for sustainability efforts, including opportunities for ESG grants
- Learn how to connect with LaborAI and Miebach at ProMat
Mark as Played
Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Speaker 1 (00:06):
You're listening to Speaking of Supply Chain, a meeboch podcast.
This is a show for logistics professionals looking to learn
more about the latest innovations in supply chain. Each episode
will feature a conversation on topics such as mitigating supply
chain disruption and reducing risk, current automation trends, sustainability initiatives,
and more. Let's dive right in.
Speaker 2 (00:31):
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. In today's
rapidly evolving warehouse and distribution landscape, operations grapple with significant changes,
including labor shortages and escalating operational costs. Recent statistics reveal
(00:52):
that labor expenses can constitute up to sixty five percent
of a warehouse's total budget. To address these pressing issues,
labor management solutions like labor ai offer advanced tools designed
to optimize labor efficiency and productivity. In this episode, we're
going to explore how this tool can help warehouses navigate
the complexities of modern operations. And to join me for
(01:15):
the discussion is laborai founder Lee Rector. Welcome Lee, great
to have you.
Speaker 3 (01:21):
Thank you Ellen.
Speaker 2 (01:22):
All right, So let's get to our topic today, which
is your tool labor AI. So can you share the
origin story? How did this all come about?
Speaker 3 (01:31):
Well, I'd been in the industry for well over thirty
years and over the last we'll call it fifteen years.
Part of the problem in the industry from a technical
point of view, is that we were building we'll call
it management systems, whether it's WS or LMS, labor to
manage labor, and the cost of implementing those systems was
(01:55):
actually outstripping the benefit, and.
Speaker 4 (01:58):
So essentially the services were four to five to one
for the cost of the software, which made it punitive
for any company to actually try and install it, and
then to try and get an ROI on.
Speaker 3 (02:10):
That was very difficult, and only one percent of the
companies could actually afford that risk. Going back to twenty fifteen,
I had already envisioned a simpler system by building the
engineering sort of all the services already into the software. So,
(02:30):
rather than pay for a system that enabled an engineer
to put in values, what if we were to pre
populate a solution with engineered values. So we started that model.
I started, took a hiatus on that through acquisitions and
companies growing and things like that, and in twenty twenty
two I decided that I would revisit that and build
(02:53):
it as a new way of handling labor. And our
model was to build it, make it affordable and applicable
to essentially any company on the planet, whether you are
a small mother and poshop or you know you're one
of the largest companies on the planet. Make it scalable.
And the only way to do that was to make
engineering essentially at a base rate that it didn't matter.
(03:18):
So if you take a look at our standards, we
actually have almost one thousand standards in our database. Now.
The standard for stepping on the fork is stepping off
the fork, which is as low as you can get
in terms of a task. But at one point six seconds,
whether I'm in the US, I'm in Asia, or I'm
in Europe. You know, if I'm asure then maybe they're
(03:41):
a little bit shorter, it's one point six to two seconds,
and in Germany, because they're a little bit taller, it's
one point five to eight. At one point six seconds,
that change that differential is so nominal that we've taken
out sort of all the swings and averages from side
to side around the globe. And so by building a
(04:01):
database that is at that level of elemental side and
we build things up, we are now able to translate
that to whether you're a company that's got a full
blown LMS or you are just you know, you're running
almost like a garage type operation. You've got three people
in the back, and we can then calculate the tasks
on that. So the idea was to make a universal solution,
(04:25):
but then make it scalable so that it's attractive enough
to the top one hundred or two hundred companies on
the planet that it would replace their their thought process
of building a full LMS. So we've now got to
the point where labor Ai is enterprise as well as standalone.
(04:45):
And for a company, you know, a large global operation,
they could get up and running once the API is
built in five days so and get calibrated results. We've
just recently done a blind study with another company that
has an LMS system. They pay well over six figures
(05:07):
for that operation, and within a day and a half
we calibrated our solution to their solution. Their standards are
set at eighty five percent, and our software came back
and calculated at eighty three point two. So having something
that's accurate and quick to get up and running is
far more applicable in today's market than something that's being
(05:29):
precise that takes you months and months and months of
services to get up and running to get to the
same answer.
Speaker 1 (05:36):
Right.
Speaker 2 (05:37):
So that reminds me of a saying that one of
my mentors used to say, good enough today is better
than perfect tomorrow.
Speaker 3 (05:45):
Yeah, and Hunderd touch cheaper.
Speaker 2 (05:48):
It sounds yeah, it sounds like your solution is quick
to implement, which I can see essentially a great benefit
to that. But I know that there are you know,
individuals out there who are going to question, if it's
that easy, what are we missing with that increasing complexity
(06:11):
with those operations? You know, when you scale up, we're
experiencing labor shortages right now, you know, across across the industry,
all over the place. So how can it assist these
warehouse managers forecasting those labor needs if it's so easy
and quick to do it? Like, how accurate are we
are we able to get?
Speaker 3 (06:32):
How accurate? I always use the term it's how long
it's a piece of string because there's really no answer
to how accurate. But if we're within two percent of actual,
you can make a management decision on that. And how
easy is it? Well, typically our model you would run
the software, You would run a model today for tomorrow's
(06:54):
work profile, so we would tell you how many hours
of labor you have where those people need to work.
So you know, if we look at typical, where else
I've got people. In receiving, I've got people and put away, picking,
replan so and then all that we'll call it the
subcategories of that. I'm picking units, versus cases, versus pallettes
and things like that. We would then come back and
(07:15):
say you need one hundred and sixty four hours worth
of labor tomorrow, and in that you need twenty four
hours in receiving, you need thirty two hours and thirty
three point seven hours in picking and such, so that
you could build your labor plan for tomorrow now, because
we're using AI in the software as well. The AI
(07:36):
the more models you run, So if you run this
daily over the next six months, Let's say we set
our target standard at seventy five percent, so a modest level,
and then what would happen is our software would learn
as you approach to be continuably at seventy five percent,
so in other words, I'm not using more or less.
Then the software then would actually micro size raise your
(08:00):
your levels. Like flying an airplane. You make small moves
so that at the end of the year, your standard
that you're actually working toward is considerably higher than where
it was. Maybe it's three percent, maybe it's thirteen percent.
But what happens is by making small changes to the
way you do things and when you're ordering people, you
(08:20):
then can forecast your day's business and then intra day,
which is a very big thing, is if I said
I needed to do one hundred units today and at
eleven o'clock in the morning, I've already done sixty units,
I don't need to have those people. Conversely, if I
get the one o'clock in the afternoon and I've only
(08:42):
done forty percent, I'm actually working at a lower standard.
I've had something that's caused me the software and then say, well,
you're probably looking at fourteen hours of overtime? How am
I going to handle that? So we're not fixing the
labor problem, but what we're doing is that critical operational
planning of not having waste right, or if I said, oh,
(09:04):
by the way, you're going to need one hundred and
thirty four hours of overtime today because somebody dropped in
a massive order for you, it's in your best interest
not to pay overtime and to go in and hire
thirty tempts to handle that and to mitigate your cost.
So these are the things that would call critical operational
planning that you can do inside of our tool. All right.
Speaker 2 (09:29):
So recognizing that, you know, one of the one of
the critical aspects of labor managing labor managing people is
employee engagement satisfaction in order to reduce that turnover. So
how does labor AI contribute to, you know, creating that
work environment that supports and motivates the warehouse you.
Speaker 3 (09:53):
Know, part and parcel of this will refer to as
the gamification inside the building. Use an analogy, if you
give me two minutes on this. If you're driving down
in Chicago to Nashville and I say to you, how
long is that gonna take? You're gonna say to me, well,
you know, it usually takes four and a half hours,
And I say, okay, great. But if you're gonna leave
(10:14):
now and you're gonna go to Nashville. What's the first
thing you do. You go to some mapping application, either
Google or Wave, and it says it's gonna take you
because there's traffic on sixty five. You're probably gonna go,
you know, I eighty and around the corner or whatever
and go sixty seven whatever the numbers are right, and
(10:36):
it says, oh, if you're gonna take this, you choose
I'm gonna go sixty five, and it says it's gonna
take you four hours and twenty one minutes. Okay, so
that's the standard. Now you get in your car and
you're going four four hours and twenty one minutes. Do
you drive with the thought process that I'm gonna go
four hours and twenty minutes or do you save yourself? No,
(10:57):
I'm gonna do that in four hours. I'm gonna be
that state. And that's the gamification. Now, if I do
that same thing inside the warehouse and intra day, I'm
showing people that we think that we need to do
all this work by five o'clock today, and we can
do this many orders, and you gamify that and people
then start to see that we are getting better, then
(11:20):
you get engagement, and you also have things where the
company then can do shift type incentives. If we do
this work today, we'll have a barbecue on Friday. And
because it's no longer subjective, my warehouse manager says, we
either did or we didn't, the staff can work as
a team. We got to speed up today because you know,
(11:41):
we got to get all this stuff done, and or
you know what, there's a hockey game on tonight. I
want to get home. I don't want to have to
stay late. I want to do things better. And so
the gamification is whether I work inside of a lot
of businesses. We have different business units. I have E Commerce,
I have B to be, I have commercial or whatever.
(12:01):
You can then compare performance from business unit to business
unit using different metrics but the same ideal performance rate.
So what it does is it allows the organization and
the people on the floor to actually see their performance
in a measurable model versus all those guys got the
easy orders, that's the easy part of the business. We're
(12:23):
never going to approach them. Or if I'm comparing site
to site, you know, all those guys they've got a
little tiny building, they're always going to be faster and
stop looking at comparing activity as a function of productivity. Okay,
so those are the things that will in turn get
(12:44):
better engagement. And if I get more people, I don't
have the requirement for temporary people. I'll be more likely
to hire the right people and not have to swell by.
You know, I'll use you know, some of the big
we'll call it consumers of labor. These days, they order
labor in the morning like it's and all you can
(13:05):
eat buffet. They don't know if they need it, but
they bring it all on. And what they do is
they're driving up the price of labor. They're driving up
the scarcity of labor, even though they don't use it,
and that's one of our major problems in the market
these days. The other problem is we don't build warehouses
(13:26):
where people live, so they have to get there. And
because we're building warehouses further and further out, then we
have a transport issue, right, so that makes labor even
more scarce. So getting the right size labor the day
before is going to be far more efficient than actually
trying to order you know, all you can eat buffet
(13:47):
and hope that I've got the right people right, we've
heard this our whole lives hope is not a plan.
Speaker 2 (13:55):
That's true, absolutely true, And I love that you you
likened that to the challenge accepted of the time on
when you're navigating somewhere, because I know I do that
when I map out somewhere and it gives me, you know,
my destination time, it's like, all right, I think I
can beat it by two minutes, you know, even if
(14:16):
it's a short drive and I think, oh, well, you know,
if I hit all the lights right, or I make
this other turn or take, I can get there faster.
I think that's a great way to really engage your
team in trying to you know, continuous improvement, make sure
that things are getting better all the time, but not
only improving, just informing and letting the employees and the warehouse,
(14:40):
you know, whether they're temporary or whether they're they're full employees,
have some stake in the game and really feel like
the work that they're doing is important and it's valuable
and it's making a difference toward the company's bottom line.
Because nothing is more demoralizing than feeling like the work
you're doing is just time spent doing something and it's
(15:04):
not important. That's a terrible feeling that my contribution isn't
worth anything to the bottom line.
Speaker 3 (15:11):
It's not a dichotomy, but it's a misconception in the market.
Most people, and you've just pointed out, don't aspire to
be average. They want to beat the average, right, But
most operations, all they deal in is trying to be average.
Most WMS systems out there set the target as the
(15:31):
average of what we've done. Right. So one of my
larger customers, like a global supply chain group, the chairman
of that company says, we've spent millions of dollars over
the last five years figuring out everything we've done, and
he says, you have how much money we've spent on
what we should have done? Zero? So what happens is
(15:55):
we have people in the buildings that want to aspire
to be better than average, and yet all all we
do from a corporation is try to be average. Right.
So that's part of the gamification side. Right. If I'm
doing fifty four cases an hour, is that good? Is
that thirty percent of the standard? Is it ninety? Most
(16:16):
companies don't know, so they're saying, well, we measure ourselves
we did thirty four cases an hour this week, we
did thirty five hours. Next week, we're ten percent better. Great,
but then product mix changes and we go down to
twenty seven cases an hour, and then all of a sudden, well,
we're not performing at the right standard because the average
is as you know, there's such a huge swing and
(16:36):
average that that doesn't do anything, and that really demoralizes
the employee because the manager's looking at averages and depending
on your luck of the draw, you may get a
crappy day or a crappy assignment and you're being compared
to average. Yeah, but your order wasn't an average. Those
are the things that bother people.
Speaker 2 (16:57):
So that brings up an interesting point. So considering you know,
the the challenges of integrating something like this. You were
just talking about the customer who spent millions of dollars,
you know, trying to find these answers to bring in
the technology that will help them have this level of
visibility given all the effort that's put into it. How
(17:17):
does labor Ai you know, number one, address some of
that resistance that we know happens with any type of implementation.
But you know that the level of customization that's available
to address that issue of having averages that aren't necessarily accurate.
Speaker 3 (17:35):
So because we calibrate a standard, it's unique for every
single operation. So part of the process of implementation is
tell me the size of the building you've got, what
type of equipment are you running, how do your orders
come out? You know, what is your process. These are
all things that are like tickboxes in our application, and
(17:55):
then it chooses which standards are apply to that particular task.
So we take out all of this customization. So typically
our interface is at the inventory level and at the
order profile level, the order itself level, so we're not
talking about a massive integration. So we're still not talking
(18:16):
about an execution level solution, so we're talking about from
a planning solution. So we get a dump of the
activity file and then we run that through our standards
and within thirty seconds you've got your standards for the
timeframe that you're looking at. If you want to change things,
you can rerun the standard. So let's say you're no
(18:38):
longer shipping toothpaste this week, you're shipping cases of laundry detergent.
Now a case is not a case. You get two
hundred and sixteen cases a toothpaste on a pallet, you
get thirty six of Lindry detergent. So now my work
content's considerably higher. I could change my standards to accommodate
that work standard for that period versus looking at a line.
(19:03):
Oh what's a line again? You know when companies compare lines,
it's how long is a piece of string? Right? I
got the line that had one unit two or one
line two units, and I got the other one that was, oh,
it's twenty seven pallets. So my work content is two
and a half hours on one and thirty seconds on
the other. So we want to take those anomalies out.
Speaker 2 (19:25):
So dialing in to get super specific. But also you know,
having all of those variables as part of the system,
that you're able to have that agility to switch it
up when something has changed that you want to measure
accurately against what's actually happening. It's apples to apples instead
(19:48):
of apples to oranges.
Speaker 3 (19:51):
Yeh one, And that's exactly you know the point. You know,
we still talk about we want to be agile. The
only way the solution makes it versal is that if
it's simple to use, simple to implement, and agile to change.
So you're not calling me to redo your samples or
your standards. You just rerun a model. Right, So you
(20:13):
hit a button. If you want AI to make our recommendation,
you hit the AI button. What does AI recommend based
on our current operation? Oh, maybe you should look at
reducing your distance between skews by three inches. That will
then increase your profitability by you know, seven percent. So
(20:34):
these are the small things. We're not telling you to
go out and buy automation, but if you buy automation,
you could actually model that inside the software. How will
it affect us?
Speaker 2 (20:46):
So that's that's an interesting point because that could be
something where people use the tool to say, okay, here's
where we are today, this is what's going on, this
is our model, this is our you know, our average,
and then run a model to see we're considering this
piece of technology. Will this make an appreciable difference? Will
I get a return on this investment? Will will this
help our our warehouse operations? And then you know, comparing
(21:11):
you know, maybe this versus that, this this particular you
know AGV versus you know this shrink wrapper or whatever,
you know, any type of technology that would come into
a warehouse, you could model that scenario to see if
it's going to have the ROI that at least that
that supplier is promising given the very detailed way and
(21:33):
processes in your own facility exactly. You know.
Speaker 3 (21:37):
And part of the gap in the industry right now
is that only two percent of automation projects hit their ROI.
And as we keep continuing to push for AI and
continue to push for automation, actually having a better understanding
of what it's going to do for me based on
my unique operation. So one of the big I guess
(22:00):
overstatements in automation these days is that, oh, our machine
will do twelve hundred units an hour, So I put
that in and I buy a machine, but I only
do six hundred units a day, so I bought it
in and my machine works one hour a day and
does nothing for the rest of the twenty four hours,
so I don't get anywhere near the utilization. Or it
(22:22):
does twelve hundred units an hour, that's great, but I
do thirty units an hour for till five o'clock at night,
where I put in three thousand units, and now I
have a scalability issue that my automation can't actually do
the work when they need it to be done. So
these are things that you can model inside of our tool.
Speaker 2 (22:44):
Well, and not only that, but you know, whatever technology
you're looking at for automation, you know, is that twelve
hundred units an hour? Is that overkill? Is this too
large a purchase for what I need? Or you know, conversely,
is that not going to be sufficient? Do I need
you know, multiple things? Do I need to look at
a larger a larger scale solution, automation solution, because this
(23:09):
one thing by itself is not going to deliver on
the return that I need on that investment. It's it's
just not enough. It's not enough of an appreciable difference
or improvement that that makes sense. Let's look at something else.
So I think this is this is giving some great
insights what measurable impacts. So we're talking about ROI. What
(23:33):
have some of your clients achieved with this tool that
you can talk about because I know sometimes when it
comes to our clients, they want to keep their secrets
close to their chest, but they all want that competitive advantage.
Speaker 3 (23:49):
Well, nobody really wants anybody to know, you know, where
they were. I was just at a one of the
largest supply chain companies on the planet doing one of
their sites and one of their clients, and in five
days we reduced their labor component by fifty people. We've
(24:11):
done other projects where you know, the client was making
the changes while we were still in the building and
they basically generated a quarter million dollars of savings in
two days. So you know, there's things where people just
are they're not doing there's a gap in their business.
Our typical ROI for clients is somewhere between we'll call
(24:35):
it thirty and forty times the return monthly because we're
measuring labor monthly and we're actually charging the client just
a small monthly fee. The rois are extremely large. So,
(24:57):
as I said, it's an untapped market and if you
can make a solution that's rapid implementation, and as you said,
I can measure accurately today. You know, the biggest problem
right now is most companies actually have no idea what
their own performance is, so they're not managing it. And
if you use the old adage, if you don't measure it,
(25:17):
you can't manage it. The first time we do a site,
we turn on the light bulb. They are already making changes,
and our software can say, oh, you know, you should
probably only have four people in receiving, but you have nine.
So right away they figure out what the problem is
in receiving, or do I have a problem in picking,
(25:39):
Like why do I have six pickers and I have
twenty two checkers?
Speaker 2 (25:43):
All right, So, given that there is a labor shortage
and we find some of these inefficiencies, you're saying that
right away immediately out of the box. As soon as
those inputs are in there, and as soon as organizations
are seeing where things are not ideal in different departments,
(26:04):
they're able to reallocate those resources to where they're actually
needed within the facility. You know, I mean, obviously, we
don't want to get rid of labor that is quality labor,
you know, employees that show up every day on time.
You don't want to lose those valuable assets to your organization.
Speaker 3 (26:26):
But the.
Speaker 2 (26:28):
Redistribution of the effort within the warehouse can be much
better on day one.
Speaker 3 (26:35):
Absolutely. In the industry, we've referred to that as labor balancing.
And what you see is you go into businesses, whether
you're a three pl or your a multi business unit operation,
they typically silo the labor you work for this client,
you work in this business unit, and so they operate
as silos and they don't say, oh, well, collectively we
(26:56):
need one hundred people, but because each business unit has
sort of siloed, they end up having one hundred and
twenty two in the building. Okay, And then we want
to cross train people. Don't just hire somebody to do receiving.
Teach them how to pick or whatever. You know, if
you have somebody on one type of equipment, maybe get
them to learn how to use a man up or
(27:17):
an order picker or something so that if there's no
task associated with that equipment, I just moved them to
a different vehicle. So that labor balancing is critical in
being functionally effective on your labor.
Speaker 2 (27:31):
Well, and back to the other topic that we were
we were on a few minutes ago, where it's talking about,
you know, the work environment and employee engagement and satisfaction.
When you have someone who is only trained on one
thing that gets very hard on physically on their body
to do that repetitive task over and over and over
and never have any change. So the cross training really
(27:55):
gives the organization the opportunity. Number One to have a
more flexible labor but number two, to really look out
for the employee's well being mentally and physically that they have,
you know, different tasks that they can do throughout the day.
Obviously they might need a break, you know, from from
doing picking all the time. Now we're going to put
(28:15):
you over here on you know, palatizing or something like that.
There's there's different ways to keep your team engaged by saying, look,
we're you're not just a robot here. You're not only
doing this one thing. That's not where your value is.
Your value is in the breadth of different things that
you can do for this warehouse or within this this warehouse,
(28:37):
within this facility. That makes you a more valuable employee,
and we want you to be that because that means
you're more valuable to us as well.
Speaker 3 (28:45):
And one of the points in there that we haven't
done a lot at this point of qualifying, and you've
pointed it out, soft tissue injuries by repetitive task in
the warehouse because I only do one thing is actually
a very large concern, especially e commerce, where it's you know,
unless you're using AMR or something the traditional ware else
(29:06):
in North America, is still picking small items and the
people are standing. They're not using equipment, so they're pushing
shopping carts or they're pushing tote carts and things like that.
And these these soft tissue injuries are one of the
greatest causes for employee absence and employee turnover because they
(29:27):
just they're either they can't take it or the monotony
of it drives them crazy. So cross training is a
big thing. And at the same time, you know, one
of the ways to engage people is receiving is a
pretty easy physical task, but it's also where you want
your people that are actually the most meticulous people. Right.
So you know, I always use the analogy, you know,
(29:51):
and I'll use a football analogy. But you've got the
best center on the planet with Jason kelce best center
on the planet. Okay, it's great at you know, doing
one thing, but nobody ever said, you know, we're going
to promote Jason Kelcey to be our quarterback, right because
he's good at one thing, but he's not good in
(30:11):
another thing. And in warehouses, we tend to promote people
because they've been good at one thing when we don't
look at what their skill set is we just promote
them because the natural progression is that the people that
work in the business the longest end up in receiving
because it's the easiest job.
Speaker 2 (30:28):
Okay, well, and you know one other thing I was
thinking about when you were talking about that the soft
tissue injuries is not only does this give them the
opportunity to you know, cross train and do something else,
but it could have an impact on you know, whatever
health benefits and medical benefits that the company is providing,
(30:49):
if they're able to create a more balanced and healthy
environment for their employees to work in through that cross training.
And I know we've gotten a little bit off topic,
but I.
Speaker 3 (31:00):
You know, no, but it's actually it's all it's all
on topic. And I guess the one piece when we
talk about reduction in labor or unnecessary labor, let's put
it that not reduction unnecessary labor is for a lot
of companies. They could then turn this around to ESG
grants environmental sustainability grants because you have less equipment, require
(31:21):
a less travel, less carbon footprint, and all of these
things that are now on the government grants list. Allow
companies then to start focusing on what we're taking these
measures to actually reduce our carbon footprint by getting the
right size of people, so not having a parking lot
that's full of cars, it's eight hundred people. Right, So
(31:41):
this should be part and parcel of when you're looking
at the entire plan of your operation. And obviously ESG
grants are a big topic these days, and rather than
just putting solar panels on your roof, look at things
outside of the box.
Speaker 2 (31:57):
All right, So the ROLI isn't only in the labor
there's so many other areas that can be improved that
will contribute to the return on the investment for this
part particular tool.
Speaker 3 (32:10):
Yeah, we're not greed. We'll just talk about the stuff
that's the easy, little hanging fruit. But the rois typically
for our clients range anywhere from forty to one hundred
to one on a monthly basis.
Speaker 2 (32:22):
Okay, Well, and I know that PROMAT is coming up,
so that you said you were just traveling recently. We've
got some more travel in our future in the upcoming months.
And PROMAT is the largest material handling show in North America.
For those of our listeners who are not familiar with it,
I've talked about it a few times. I know, obviously
(32:43):
MEBACH is going to be there and labor AI will
be there as well in the Embassy data at booth.
So what can attendees or what can our listeners if
they're on the fence about going to PROMAT, what do
they have to look forward to with labor AI at PROMAT.
What what is exciting this year.
Speaker 3 (33:05):
Well obviously introducing the enterprise version and more importantly understanding
and seeing what AI will recommend for you and what
critical operational planning is actually affordable affordable operational planning is
available these days versus just getting you know, dashboards that
(33:25):
for a lot of people are very confusing and having
something that's simple, you know, and tying it back in,
you know. And one of the things that we also
want to outline that labor AIS is really a performance
measurement tool. You still require companies like me back to
actually come up with these solutions that will help you,
(33:46):
you know, make your operation more efficient. Productivity can be
measured as throughput of the building and we measure the
productivity of the people. So there are two different measures
of productivity. Your people can be running it one hundred percent,
but you know, a consulting company me back could bring
you in process changes that will actually increase your throughput
(34:08):
of the business by one hundred percent. Right, But what
we want to do is we focus on the people,
but bringing in automation. What automation's right for you. Those
are things where we actually then look back to companies
like a Meeback who will be partnering with labor Ai,
and you'll be the ones that will drive the benefit
(34:29):
for the clients because we're only getting a certain part.
What are we doing today? How can we manage better?
But how do I get you know, how do I
get to the next eighteen months or where are we
going to be in twenty four months? We don't do that,
So I think that there's a good synergy and whether
you're going to promat for the first time, it's kind
(34:49):
of like supply chain Disneyland. So anything that's in the industry,
you'll be able to see. And one of the beauties
of going to the Meeback booth or coming to the
labor Ai booth is that we'll be able to tell
you if it's just from the top end, maybe it
benefits you, maybe it doesn't. By running through that, if
you're on the fence get off the fence, get out
(35:10):
and see y'all. Get out the promat anything else. It's
a learning experience, and if you don't know about it,
you'll never be able.
Speaker 2 (35:19):
To implement it, all right, So real quick, you know what,
I'm going to make sure just to put the booths
because I can't remember our booth number off the top
of my head, and I doubt that anyone you know
would remember it from the podcast anyway, So we'll put
the booth numbers in there so that they can come
visit us at Promat next month. Looking forward to seeing
(35:41):
you in person and getting to say hello, because you know,
here here in my little podcast studio it's a it's
a little lonely. I don't get the interaction. So I'm
really looking forward to seeing you and all of our
listeners and potential new listeners at Promat. It's a great event.
I know we got completely off topic. They're talking about
(36:02):
the show. Well, that brings us to the end of
this episode. There are some great insights. Thank you so
much for bringing them into the warehouse process optimization and
labor the impact that it has on these facilities. Thank
you so much, Lee for joining us today and telling
(36:23):
us about your amazing new tool.
Speaker 3 (36:24):
Thank you, Ellen.
Speaker 2 (36:27):
I'm sure there are a bunch of listeners out there
who could find this benefit beneficial to their operation, and
I will make sure that they have access to embassy
data from our description and a link directly to you
if they have any questions. If any of our listeners
out there have a suggestion for another topic or would
(36:47):
like to be a guest on our show, likelye we'd
love to hear from you. You can contact me at
podcast at meboch dot com at any time. As always,
thank you for listening to Speaking of Supply Chain. If
you've enjoyed our show, please rate and review us on
whatever podcast platform you prefer. We're on Apple Podcasts, Amazon Music,
and Spotify. Be sure to tune in next time.
Speaker 1 (37:13):
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.
(37:35):
To learn more, visit meeboch dot com. You've been listening
to Speaking of Supply Chain, a meboch podcast. Keep connected
with us by subscribing to the show in your favorite
podcast player. If you like what you've heard, please rate
the show that helps us to keep delivering the latest
in supply chain information. Thanks for listening. Until next time.
You're listening to Speaking of Supply Chain, a meeboch podcast.
This is a show for logistics professionals looking to learn
more about the latest innovations in supply chain. Each episode
will feature a conversation on topics such as mitigating supply
chain disruption and reducing risk, current automation trends, sustainability initiatives,
and more. Let's dive right in.
Speaker 2 (00:31):
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. In today's
rapidly evolving warehouse and distribution landscape, operations grapple with significant changes,
including labor shortages and escalating operational costs. Recent statistics reveal
(00:52):
that labor expenses can constitute up to sixty five percent
of a warehouse's total budget. To address these pressing issues,
labor management solutions like labor ai offer advanced tools designed
to optimize labor efficiency and productivity. In this episode, we're
going to explore how this tool can help warehouses navigate
the complexities of modern operations. And to join me for
(01:15):
the discussion is laborai founder Lee Rector. Welcome Lee, great
to have you.
Speaker 3 (01:21):
Thank you Ellen.
Speaker 2 (01:22):
All right, So let's get to our topic today, which
is your tool labor AI. So can you share the
origin story? How did this all come about?
Speaker 3 (01:31):
Well, I'd been in the industry for well over thirty
years and over the last we'll call it fifteen years.
Part of the problem in the industry from a technical
point of view, is that we were building we'll call
it management systems, whether it's WS or LMS, labor to
manage labor, and the cost of implementing those systems was
(01:55):
actually outstripping the benefit, and.
Speaker 4 (01:58):
So essentially the services were four to five to one
for the cost of the software, which made it punitive
for any company to actually try and install it, and
then to try and get an ROI on.
Speaker 3 (02:10):
That was very difficult, and only one percent of the
companies could actually afford that risk. Going back to twenty fifteen,
I had already envisioned a simpler system by building the
engineering sort of all the services already into the software. So,
(02:30):
rather than pay for a system that enabled an engineer
to put in values, what if we were to pre
populate a solution with engineered values. So we started that model.
I started, took a hiatus on that through acquisitions and
companies growing and things like that, and in twenty twenty
two I decided that I would revisit that and build
(02:53):
it as a new way of handling labor. And our
model was to build it, make it affordable and applicable
to essentially any company on the planet, whether you are
a small mother and poshop or you know you're one
of the largest companies on the planet. Make it scalable.
And the only way to do that was to make
engineering essentially at a base rate that it didn't matter.
(03:18):
So if you take a look at our standards, we
actually have almost one thousand standards in our database. Now.
The standard for stepping on the fork is stepping off
the fork, which is as low as you can get
in terms of a task. But at one point six seconds,
whether I'm in the US, I'm in Asia, or I'm
in Europe. You know, if I'm asure then maybe they're
(03:41):
a little bit shorter, it's one point six to two seconds,
and in Germany, because they're a little bit taller, it's
one point five to eight. At one point six seconds,
that change that differential is so nominal that we've taken
out sort of all the swings and averages from side
to side around the globe. And so by building a
(04:01):
database that is at that level of elemental side and
we build things up, we are now able to translate
that to whether you're a company that's got a full
blown LMS or you are just you know, you're running
almost like a garage type operation. You've got three people
in the back, and we can then calculate the tasks
on that. So the idea was to make a universal solution,
(04:25):
but then make it scalable so that it's attractive enough
to the top one hundred or two hundred companies on
the planet that it would replace their their thought process
of building a full LMS. So we've now got to
the point where labor Ai is enterprise as well as standalone.
(04:45):
And for a company, you know, a large global operation,
they could get up and running once the API is
built in five days so and get calibrated results. We've
just recently done a blind study with another company that
has an LMS system. They pay well over six figures
(05:07):
for that operation, and within a day and a half
we calibrated our solution to their solution. Their standards are
set at eighty five percent, and our software came back
and calculated at eighty three point two. So having something
that's accurate and quick to get up and running is
far more applicable in today's market than something that's being
(05:29):
precise that takes you months and months and months of
services to get up and running to get to the
same answer.
Speaker 1 (05:36):
Right.
Speaker 2 (05:37):
So that reminds me of a saying that one of
my mentors used to say, good enough today is better
than perfect tomorrow.
Speaker 3 (05:45):
Yeah, and Hunderd touch cheaper.
Speaker 2 (05:48):
It sounds yeah, it sounds like your solution is quick
to implement, which I can see essentially a great benefit
to that. But I know that there are you know,
individuals out there who are going to question, if it's
that easy, what are we missing with that increasing complexity
(06:11):
with those operations? You know, when you scale up, we're
experiencing labor shortages right now, you know, across across the industry,
all over the place. So how can it assist these
warehouse managers forecasting those labor needs if it's so easy
and quick to do it? Like, how accurate are we
are we able to get?
Speaker 3 (06:32):
How accurate? I always use the term it's how long
it's a piece of string because there's really no answer
to how accurate. But if we're within two percent of actual,
you can make a management decision on that. And how
easy is it? Well, typically our model you would run
the software, You would run a model today for tomorrow's
(06:54):
work profile, so we would tell you how many hours
of labor you have where those people need to work.
So you know, if we look at typical, where else
I've got people. In receiving, I've got people and put away, picking,
replan so and then all that we'll call it the
subcategories of that. I'm picking units, versus cases, versus pallettes
and things like that. We would then come back and
(07:15):
say you need one hundred and sixty four hours worth
of labor tomorrow, and in that you need twenty four
hours in receiving, you need thirty two hours and thirty
three point seven hours in picking and such, so that
you could build your labor plan for tomorrow now, because
we're using AI in the software as well. The AI
(07:36):
the more models you run, So if you run this
daily over the next six months, Let's say we set
our target standard at seventy five percent, so a modest level,
and then what would happen is our software would learn
as you approach to be continuably at seventy five percent,
so in other words, I'm not using more or less.
Then the software then would actually micro size raise your
(08:00):
your levels. Like flying an airplane. You make small moves
so that at the end of the year, your standard
that you're actually working toward is considerably higher than where
it was. Maybe it's three percent, maybe it's thirteen percent.
But what happens is by making small changes to the
way you do things and when you're ordering people, you
(08:20):
then can forecast your day's business and then intra day,
which is a very big thing, is if I said
I needed to do one hundred units today and at
eleven o'clock in the morning, I've already done sixty units,
I don't need to have those people. Conversely, if I
get the one o'clock in the afternoon and I've only
(08:42):
done forty percent, I'm actually working at a lower standard.
I've had something that's caused me the software and then say, well,
you're probably looking at fourteen hours of overtime? How am
I going to handle that? So we're not fixing the
labor problem, but what we're doing is that critical operational
planning of not having waste right, or if I said, oh,
(09:04):
by the way, you're going to need one hundred and
thirty four hours of overtime today because somebody dropped in
a massive order for you, it's in your best interest
not to pay overtime and to go in and hire
thirty tempts to handle that and to mitigate your cost.
So these are the things that would call critical operational
planning that you can do inside of our tool. All right.
Speaker 2 (09:29):
So recognizing that, you know, one of the one of
the critical aspects of labor managing labor managing people is
employee engagement satisfaction in order to reduce that turnover. So
how does labor AI contribute to, you know, creating that
work environment that supports and motivates the warehouse you.
Speaker 3 (09:53):
Know, part and parcel of this will refer to as
the gamification inside the building. Use an analogy, if you
give me two minutes on this. If you're driving down
in Chicago to Nashville and I say to you, how
long is that gonna take? You're gonna say to me, well,
you know, it usually takes four and a half hours,
And I say, okay, great. But if you're gonna leave
(10:14):
now and you're gonna go to Nashville. What's the first
thing you do. You go to some mapping application, either
Google or Wave, and it says it's gonna take you
because there's traffic on sixty five. You're probably gonna go,
you know, I eighty and around the corner or whatever
and go sixty seven whatever the numbers are right, and
(10:36):
it says, oh, if you're gonna take this, you choose
I'm gonna go sixty five, and it says it's gonna
take you four hours and twenty one minutes. Okay, so
that's the standard. Now you get in your car and
you're going four four hours and twenty one minutes. Do
you drive with the thought process that I'm gonna go
four hours and twenty minutes or do you save yourself? No,
(10:57):
I'm gonna do that in four hours. I'm gonna be
that state. And that's the gamification. Now, if I do
that same thing inside the warehouse and intra day, I'm
showing people that we think that we need to do
all this work by five o'clock today, and we can
do this many orders, and you gamify that and people
then start to see that we are getting better, then
(11:20):
you get engagement, and you also have things where the
company then can do shift type incentives. If we do
this work today, we'll have a barbecue on Friday. And
because it's no longer subjective, my warehouse manager says, we
either did or we didn't, the staff can work as
a team. We got to speed up today because you know,
(11:41):
we got to get all this stuff done, and or
you know what, there's a hockey game on tonight. I
want to get home. I don't want to have to
stay late. I want to do things better. And so
the gamification is whether I work inside of a lot
of businesses. We have different business units. I have E Commerce,
I have B to be, I have commercial or whatever.
(12:01):
You can then compare performance from business unit to business
unit using different metrics but the same ideal performance rate.
So what it does is it allows the organization and
the people on the floor to actually see their performance
in a measurable model versus all those guys got the
easy orders, that's the easy part of the business. We're
(12:23):
never going to approach them. Or if I'm comparing site
to site, you know, all those guys they've got a
little tiny building, they're always going to be faster and
stop looking at comparing activity as a function of productivity. Okay,
so those are the things that will in turn get
(12:44):
better engagement. And if I get more people, I don't
have the requirement for temporary people. I'll be more likely
to hire the right people and not have to swell by.
You know, I'll use you know, some of the big
we'll call it consumers of labor. These days, they order
labor in the morning like it's and all you can
(13:05):
eat buffet. They don't know if they need it, but
they bring it all on. And what they do is
they're driving up the price of labor. They're driving up
the scarcity of labor, even though they don't use it,
and that's one of our major problems in the market
these days. The other problem is we don't build warehouses
(13:26):
where people live, so they have to get there. And
because we're building warehouses further and further out, then we
have a transport issue, right, so that makes labor even
more scarce. So getting the right size labor the day
before is going to be far more efficient than actually
trying to order you know, all you can eat buffet
(13:47):
and hope that I've got the right people right, we've
heard this our whole lives hope is not a plan.
Speaker 2 (13:55):
That's true, absolutely true, And I love that you you
likened that to the challenge accepted of the time on
when you're navigating somewhere, because I know I do that
when I map out somewhere and it gives me, you know,
my destination time, it's like, all right, I think I
can beat it by two minutes, you know, even if
(14:16):
it's a short drive and I think, oh, well, you know,
if I hit all the lights right, or I make
this other turn or take, I can get there faster.
I think that's a great way to really engage your
team in trying to you know, continuous improvement, make sure
that things are getting better all the time, but not
only improving, just informing and letting the employees and the warehouse,
(14:40):
you know, whether they're temporary or whether they're they're full employees,
have some stake in the game and really feel like
the work that they're doing is important and it's valuable
and it's making a difference toward the company's bottom line.
Because nothing is more demoralizing than feeling like the work
you're doing is just time spent doing something and it's
(15:04):
not important. That's a terrible feeling that my contribution isn't
worth anything to the bottom line.
Speaker 3 (15:11):
It's not a dichotomy, but it's a misconception in the market.
Most people, and you've just pointed out, don't aspire to
be average. They want to beat the average, right, But
most operations, all they deal in is trying to be average.
Most WMS systems out there set the target as the
(15:31):
average of what we've done. Right. So one of my
larger customers, like a global supply chain group, the chairman
of that company says, we've spent millions of dollars over
the last five years figuring out everything we've done, and
he says, you have how much money we've spent on
what we should have done? Zero? So what happens is
(15:55):
we have people in the buildings that want to aspire
to be better than average, and yet all all we
do from a corporation is try to be average. Right.
So that's part of the gamification side. Right. If I'm
doing fifty four cases an hour, is that good? Is
that thirty percent of the standard? Is it ninety? Most
(16:16):
companies don't know, so they're saying, well, we measure ourselves
we did thirty four cases an hour this week, we
did thirty five hours. Next week, we're ten percent better. Great,
but then product mix changes and we go down to
twenty seven cases an hour, and then all of a sudden, well,
we're not performing at the right standard because the average
is as you know, there's such a huge swing and
(16:36):
average that that doesn't do anything, and that really demoralizes
the employee because the manager's looking at averages and depending
on your luck of the draw, you may get a
crappy day or a crappy assignment and you're being compared
to average. Yeah, but your order wasn't an average. Those
are the things that bother people.
Speaker 2 (16:57):
So that brings up an interesting point. So considering you know,
the the challenges of integrating something like this. You were
just talking about the customer who spent millions of dollars,
you know, trying to find these answers to bring in
the technology that will help them have this level of
visibility given all the effort that's put into it. How
(17:17):
does labor Ai you know, number one, address some of
that resistance that we know happens with any type of implementation.
But you know that the level of customization that's available
to address that issue of having averages that aren't necessarily accurate.
Speaker 3 (17:35):
So because we calibrate a standard, it's unique for every
single operation. So part of the process of implementation is
tell me the size of the building you've got, what
type of equipment are you running, how do your orders
come out? You know, what is your process. These are
all things that are like tickboxes in our application, and
(17:55):
then it chooses which standards are apply to that particular task.
So we take out all of this customization. So typically
our interface is at the inventory level and at the
order profile level, the order itself level, so we're not
talking about a massive integration. So we're still not talking
(18:16):
about an execution level solution, so we're talking about from
a planning solution. So we get a dump of the
activity file and then we run that through our standards
and within thirty seconds you've got your standards for the
timeframe that you're looking at. If you want to change things,
you can rerun the standard. So let's say you're no
(18:38):
longer shipping toothpaste this week, you're shipping cases of laundry detergent.
Now a case is not a case. You get two
hundred and sixteen cases a toothpaste on a pallet, you
get thirty six of Lindry detergent. So now my work
content's considerably higher. I could change my standards to accommodate
that work standard for that period versus looking at a line.
(19:03):
Oh what's a line again? You know when companies compare lines,
it's how long is a piece of string? Right? I
got the line that had one unit two or one
line two units, and I got the other one that was, oh,
it's twenty seven pallets. So my work content is two
and a half hours on one and thirty seconds on
the other. So we want to take those anomalies out.
Speaker 2 (19:25):
So dialing in to get super specific. But also you know,
having all of those variables as part of the system,
that you're able to have that agility to switch it
up when something has changed that you want to measure
accurately against what's actually happening. It's apples to apples instead
(19:48):
of apples to oranges.
Speaker 3 (19:51):
Yeh one, And that's exactly you know the point. You know,
we still talk about we want to be agile. The
only way the solution makes it versal is that if
it's simple to use, simple to implement, and agile to change.
So you're not calling me to redo your samples or
your standards. You just rerun a model. Right, So you
(20:13):
hit a button. If you want AI to make our recommendation,
you hit the AI button. What does AI recommend based
on our current operation? Oh, maybe you should look at
reducing your distance between skews by three inches. That will
then increase your profitability by you know, seven percent. So
(20:34):
these are the small things. We're not telling you to
go out and buy automation, but if you buy automation,
you could actually model that inside the software. How will
it affect us?
Speaker 2 (20:46):
So that's that's an interesting point because that could be
something where people use the tool to say, okay, here's
where we are today, this is what's going on, this
is our model, this is our you know, our average,
and then run a model to see we're considering this
piece of technology. Will this make an appreciable difference? Will
I get a return on this investment? Will will this
help our our warehouse operations? And then you know, comparing
(21:11):
you know, maybe this versus that, this this particular you
know AGV versus you know this shrink wrapper or whatever,
you know, any type of technology that would come into
a warehouse, you could model that scenario to see if
it's going to have the ROI that at least that
that supplier is promising given the very detailed way and
(21:33):
processes in your own facility exactly. You know.
Speaker 3 (21:37):
And part of the gap in the industry right now
is that only two percent of automation projects hit their ROI.
And as we keep continuing to push for AI and
continue to push for automation, actually having a better understanding
of what it's going to do for me based on
my unique operation. So one of the big I guess
(22:00):
overstatements in automation these days is that, oh, our machine
will do twelve hundred units an hour, So I put
that in and I buy a machine, but I only
do six hundred units a day, so I bought it
in and my machine works one hour a day and
does nothing for the rest of the twenty four hours,
so I don't get anywhere near the utilization. Or it
(22:22):
does twelve hundred units an hour, that's great, but I
do thirty units an hour for till five o'clock at night,
where I put in three thousand units, and now I
have a scalability issue that my automation can't actually do
the work when they need it to be done. So
these are things that you can model inside of our tool.
Speaker 2 (22:44):
Well, and not only that, but you know, whatever technology
you're looking at for automation, you know, is that twelve
hundred units an hour? Is that overkill? Is this too
large a purchase for what I need? Or you know, conversely,
is that not going to be sufficient? Do I need
you know, multiple things? Do I need to look at
a larger a larger scale solution, automation solution, because this
(23:09):
one thing by itself is not going to deliver on
the return that I need on that investment. It's it's
just not enough. It's not enough of an appreciable difference
or improvement that that makes sense. Let's look at something else.
So I think this is this is giving some great
insights what measurable impacts. So we're talking about ROI. What
(23:33):
have some of your clients achieved with this tool that
you can talk about because I know sometimes when it
comes to our clients, they want to keep their secrets
close to their chest, but they all want that competitive advantage.
Speaker 3 (23:49):
Well, nobody really wants anybody to know, you know, where
they were. I was just at a one of the
largest supply chain companies on the planet doing one of
their sites and one of their clients, and in five
days we reduced their labor component by fifty people. We've
(24:11):
done other projects where you know, the client was making
the changes while we were still in the building and
they basically generated a quarter million dollars of savings in
two days. So you know, there's things where people just
are they're not doing there's a gap in their business.
Our typical ROI for clients is somewhere between we'll call
(24:35):
it thirty and forty times the return monthly because we're
measuring labor monthly and we're actually charging the client just
a small monthly fee. The rois are extremely large. So,
(24:57):
as I said, it's an untapped market and if you
can make a solution that's rapid implementation, and as you said,
I can measure accurately today. You know, the biggest problem
right now is most companies actually have no idea what
their own performance is, so they're not managing it. And
if you use the old adage, if you don't measure it,
(25:17):
you can't manage it. The first time we do a site,
we turn on the light bulb. They are already making changes,
and our software can say, oh, you know, you should
probably only have four people in receiving, but you have nine.
So right away they figure out what the problem is
in receiving, or do I have a problem in picking,
(25:39):
Like why do I have six pickers and I have
twenty two checkers?
Speaker 2 (25:43):
All right, So, given that there is a labor shortage
and we find some of these inefficiencies, you're saying that
right away immediately out of the box. As soon as
those inputs are in there, and as soon as organizations
are seeing where things are not ideal in different departments,
(26:04):
they're able to reallocate those resources to where they're actually
needed within the facility. You know, I mean, obviously, we
don't want to get rid of labor that is quality labor,
you know, employees that show up every day on time.
You don't want to lose those valuable assets to your organization.
Speaker 3 (26:26):
But the.
Speaker 2 (26:28):
Redistribution of the effort within the warehouse can be much
better on day one.
Speaker 3 (26:35):
Absolutely. In the industry, we've referred to that as labor balancing.
And what you see is you go into businesses, whether
you're a three pl or your a multi business unit operation,
they typically silo the labor you work for this client,
you work in this business unit, and so they operate
as silos and they don't say, oh, well, collectively we
(26:56):
need one hundred people, but because each business unit has
sort of siloed, they end up having one hundred and
twenty two in the building. Okay, And then we want
to cross train people. Don't just hire somebody to do receiving.
Teach them how to pick or whatever. You know, if
you have somebody on one type of equipment, maybe get
them to learn how to use a man up or
(27:17):
an order picker or something so that if there's no
task associated with that equipment, I just moved them to
a different vehicle. So that labor balancing is critical in
being functionally effective on your labor.
Speaker 2 (27:31):
Well, and back to the other topic that we were
we were on a few minutes ago, where it's talking about,
you know, the work environment and employee engagement and satisfaction.
When you have someone who is only trained on one
thing that gets very hard on physically on their body
to do that repetitive task over and over and over
and never have any change. So the cross training really
(27:55):
gives the organization the opportunity. Number One to have a
more flexible labor but number two, to really look out
for the employee's well being mentally and physically that they have,
you know, different tasks that they can do throughout the day.
Obviously they might need a break, you know, from from
doing picking all the time. Now we're going to put
(28:15):
you over here on you know, palatizing or something like that.
There's there's different ways to keep your team engaged by saying, look,
we're you're not just a robot here. You're not only
doing this one thing. That's not where your value is.
Your value is in the breadth of different things that
you can do for this warehouse or within this this warehouse,
(28:37):
within this facility. That makes you a more valuable employee,
and we want you to be that because that means
you're more valuable to us as well.
Speaker 3 (28:45):
And one of the points in there that we haven't
done a lot at this point of qualifying, and you've
pointed it out, soft tissue injuries by repetitive task in
the warehouse because I only do one thing is actually
a very large concern, especially e commerce, where it's you know,
unless you're using AMR or something the traditional ware else
(29:06):
in North America, is still picking small items and the
people are standing. They're not using equipment, so they're pushing
shopping carts or they're pushing tote carts and things like that.
And these these soft tissue injuries are one of the
greatest causes for employee absence and employee turnover because they
(29:27):
just they're either they can't take it or the monotony
of it drives them crazy. So cross training is a
big thing. And at the same time, you know, one
of the ways to engage people is receiving is a
pretty easy physical task, but it's also where you want
your people that are actually the most meticulous people. Right.
So you know, I always use the analogy, you know,
(29:51):
and I'll use a football analogy. But you've got the
best center on the planet with Jason kelce best center
on the planet. Okay, it's great at you know, doing
one thing, but nobody ever said, you know, we're going
to promote Jason Kelcey to be our quarterback, right because
he's good at one thing, but he's not good in
(30:11):
another thing. And in warehouses, we tend to promote people
because they've been good at one thing when we don't
look at what their skill set is we just promote
them because the natural progression is that the people that
work in the business the longest end up in receiving
because it's the easiest job.
Speaker 2 (30:28):
Okay, well, and you know one other thing I was
thinking about when you were talking about that the soft
tissue injuries is not only does this give them the
opportunity to you know, cross train and do something else,
but it could have an impact on you know, whatever
health benefits and medical benefits that the company is providing,
(30:49):
if they're able to create a more balanced and healthy
environment for their employees to work in through that cross training.
And I know we've gotten a little bit off topic,
but I.
Speaker 3 (31:00):
You know, no, but it's actually it's all it's all
on topic. And I guess the one piece when we
talk about reduction in labor or unnecessary labor, let's put
it that not reduction unnecessary labor is for a lot
of companies. They could then turn this around to ESG
grants environmental sustainability grants because you have less equipment, require
(31:21):
a less travel, less carbon footprint, and all of these
things that are now on the government grants list. Allow
companies then to start focusing on what we're taking these
measures to actually reduce our carbon footprint by getting the
right size of people, so not having a parking lot
that's full of cars, it's eight hundred people. Right, So
(31:41):
this should be part and parcel of when you're looking
at the entire plan of your operation. And obviously ESG
grants are a big topic these days, and rather than
just putting solar panels on your roof, look at things
outside of the box.
Speaker 2 (31:57):
All right, So the ROLI isn't only in the labor
there's so many other areas that can be improved that
will contribute to the return on the investment for this
part particular tool.
Speaker 3 (32:10):
Yeah, we're not greed. We'll just talk about the stuff
that's the easy, little hanging fruit. But the rois typically
for our clients range anywhere from forty to one hundred
to one on a monthly basis.
Speaker 2 (32:22):
Okay, Well, and I know that PROMAT is coming up,
so that you said you were just traveling recently. We've
got some more travel in our future in the upcoming months.
And PROMAT is the largest material handling show in North America.
For those of our listeners who are not familiar with it,
I've talked about it a few times. I know, obviously
(32:43):
MEBACH is going to be there and labor AI will
be there as well in the Embassy data at booth.
So what can attendees or what can our listeners if
they're on the fence about going to PROMAT, what do
they have to look forward to with labor AI at PROMAT.
What what is exciting this year.
Speaker 3 (33:05):
Well obviously introducing the enterprise version and more importantly understanding
and seeing what AI will recommend for you and what
critical operational planning is actually affordable affordable operational planning is
available these days versus just getting you know, dashboards that
(33:25):
for a lot of people are very confusing and having
something that's simple, you know, and tying it back in,
you know. And one of the things that we also
want to outline that labor AIS is really a performance
measurement tool. You still require companies like me back to
actually come up with these solutions that will help you,
(33:46):
you know, make your operation more efficient. Productivity can be
measured as throughput of the building and we measure the
productivity of the people. So there are two different measures
of productivity. Your people can be running it one hundred percent,
but you know, a consulting company me back could bring
you in process changes that will actually increase your throughput
(34:08):
of the business by one hundred percent. Right, But what
we want to do is we focus on the people,
but bringing in automation. What automation's right for you. Those
are things where we actually then look back to companies
like a Meeback who will be partnering with labor Ai,
and you'll be the ones that will drive the benefit
(34:29):
for the clients because we're only getting a certain part.
What are we doing today? How can we manage better?
But how do I get you know, how do I
get to the next eighteen months or where are we
going to be in twenty four months? We don't do that,
So I think that there's a good synergy and whether
you're going to promat for the first time, it's kind
(34:49):
of like supply chain Disneyland. So anything that's in the industry,
you'll be able to see. And one of the beauties
of going to the Meeback booth or coming to the
labor Ai booth is that we'll be able to tell
you if it's just from the top end, maybe it
benefits you, maybe it doesn't. By running through that, if
you're on the fence get off the fence, get out
(35:10):
and see y'all. Get out the promat anything else. It's
a learning experience, and if you don't know about it,
you'll never be able.
Speaker 2 (35:19):
To implement it, all right, So real quick, you know what,
I'm going to make sure just to put the booths
because I can't remember our booth number off the top
of my head, and I doubt that anyone you know
would remember it from the podcast anyway, So we'll put
the booth numbers in there so that they can come
visit us at Promat next month. Looking forward to seeing
(35:41):
you in person and getting to say hello, because you know,
here here in my little podcast studio it's a it's
a little lonely. I don't get the interaction. So I'm
really looking forward to seeing you and all of our
listeners and potential new listeners at Promat. It's a great event.
I know we got completely off topic. They're talking about
(36:02):
the show. Well, that brings us to the end of
this episode. There are some great insights. Thank you so
much for bringing them into the warehouse process optimization and
labor the impact that it has on these facilities. Thank
you so much, Lee for joining us today and telling
(36:23):
us about your amazing new tool.
Speaker 3 (36:24):
Thank you, Ellen.
Speaker 2 (36:27):
I'm sure there are a bunch of listeners out there
who could find this benefit beneficial to their operation, and
I will make sure that they have access to embassy
data from our description and a link directly to you
if they have any questions. If any of our listeners
out there have a suggestion for another topic or would
(36:47):
like to be a guest on our show, likelye we'd
love to hear from you. You can contact me at
podcast at meboch dot com at any time. As always,
thank you for listening to Speaking of Supply Chain. If
you've enjoyed our show, please rate and review us on
whatever podcast platform you prefer. We're on Apple Podcasts, Amazon Music,
and Spotify. Be sure to tune in next time.
Speaker 1 (37:13):
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.
(37:35):
To learn more, visit meeboch dot com. You've been listening
to Speaking of Supply Chain, a meboch podcast. Keep connected
with us by subscribing to the show in your favorite
podcast player. If you like what you've heard, please rate
the show that helps us to keep delivering the latest
in supply chain information. Thanks for listening. Until next time.
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