March 26, 2025 • 40 mins
In this episode of Manufacturing Mavericks, we’re excited to welcome Rebekah Collogan, a seasoned operations leader with years of experience in optimizing production, scaling teams, and driving continuous improvement on the shop floor. Rebekah shares her journey into manufacturing, her challenges in streamlining operations, and her strategies for boosting efficiency and eliminating bottlenecks. She embodies the idea that manufacturing success isn’t just about working harder—it’s about working smarter.
We are honored to have Rebekah as our first female guest on the podcast, and we hope she will be the first of many women in manufacturing to share their stories on the show. She offers a unique and invaluable perspective on leadership and problem-solving, and she is enthusiastic about the power of real-time data in making strategic decisions. She explores the significance of visibility on the shop floor, the role of automation in minimizing downtime, and how modern manufacturers can stay competitive without introducing unnecessary complexity.
This episode is filled with actionable insights on scaling your shop, enhancing productivity, and embracing the future of manufacturing.
SHOW NOTES
- (1:19) Rebekah's background in manufacturing and journey into leadership.
- (3:54) Early Challenges & Lessons in Scaling Manufacturing
- (10:30) Providing Solutions and Making Customer’s Lives Easier
- (15:45) Identifying & Eliminating Bottlenecks
- (22:00) Leading Change & Building a Culture of Continuous Improvement
- (26:19) Operational Excellence & The Need For Accurate Data
- (35:00) Advice to a Young Rebekah
The Datanomix Difference: What Makes It Work?
It’s not just what you see. It’s how fast you see it, how clearly it shows up, and how easily your team acts on it.
- Simple machine and data connectivity, handled by us
- Data that makes sense on day one
- No Operator Input™ means zero burden on your team for data entry
- Product Tracks built for actual shop floor behavior
- Stay connected to your shop floor with the Datanomix Mobile App
- Kick-Ass Coaching that walks the shop floor with your team and keeps you winning
- Best-in-class partnerships and integrations
- The Datanomix Community built on real relationships and real wins
- Mountains of success stories with manufacturers just like you
- Every customer starts with our Pilot Program
Learn more at www.datanomix.io
Mark as Played
Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
(00:03):
Welcome to Manufacturing Mavericks, a podcast where we showcase and celebrate
exceptional people from across precision manufacturing who are boldly
embracing new ways to improve their processes, grow their bottom lines,
and ensure American manufacturing will thrive for generations to come.
(00:26):
Welcome to this episode of Manufacturing Mavericks.
I’m your host, Greg McHale, founder and CEO of Datanomix, and I am
thrilled to be joined today by Rebekah Collogan from Phoenix Mecano.
How are you today, Rebekah?
I am well, Thank you.
I really appreciate having you on, Rebekah.
I know we’ve gotten to know each other over the last couple years,
and I know you are deeply involved in many different aspects of
(00:50):
manufacturing, operations, business data, machine data, so I’m really
looking forward to seeing what topics we get to dive into today.
Yeah, sounds good.
Me, too.
Awesome.
So, Rebekah, just to break the ice on Manufacturing Mavericks, the
way that we typically get started is, I love hearing about the origin
story of how everyone gets into their own manufacturing journey.
(01:14):
So, how about we start with how and when
did the manufacturing torch get lit for you?
I was thinking about this, and I usually say in
college, but I really think it started when I was a kid.
I grew up on a farm in Kansas, and my dad had a wood shop, and
(01:34):
so every night he’d go out to the wood shop—he was a CPA by day,
still is, and a farmer, you know, weekends and evenings, and
then would also make the furniture for our house for my mom.
And so, I’d go out to the wood shop with him, and, you
know, I’d hold the board on the other side of the planer.
I love the smell of wood chips.
(01:57):
And, you know, I was one of four kids, and I think I
was the only one that really just, rain, you know, snow,
didn’t matter, I was out there in the shop with my dad.
And I just loved seeing these things come
together and being part of that process.
And then, when I was in college, after I finished my degree, or in the
(02:20):
last year, I did a degree in industrial engineering, and in my last year,
I did an internship at a manufacturing plant, just to have some experience.
I hadn’t planned to stay in manufacturing.
And then I caught the bug.
I couldn’t shake it.
I loved seeing change, and transformation, and changing people’s,
(02:42):
you know, jobs and how they were able to do their work, as
well as the product changing as it went through the process.
It’s just, you know, you get the bug and you can’t shake it.
So yeah, for me, it was probably a combination of those two.
Do you still have any of the furniture from dad, still in the family [laugh]
? [laugh] . You know, I have a few things.
(03:04):
My dad is—he—precision is just not his strength, [laugh]
and so, it was made out of love, but not for longevity.
I’ll say that [laugh]
. Got it so, really, more for the coffee table book than for the coffee itself?
Yeah, yeah, exactly.
Exactly [laugh] . I think my mom still has the furniture.
(03:26):
She wouldn’t give it up, so I don’t have any of it.
And I mean, as you were describing the smell of wood
chips, who doesn’t like the smell of freshly cut wood?
It’s so good.
So college, industrial engineering.
You start getting drawn to process optimization, problem
(03:47):
solving, getting out there into internships and whatnot.
What were some of your first jobs and career positions?
I was a manufacturing engineer at a plant that made
skid steers, and—which, if you don’t know what that is,
it’s essentially—a Bobcat is a version of a skid steers.
It wasn’t that brand, but—and one of my first projects, after a couple years
(04:10):
in there, was a clean-sheet design of the skid steers, managing that project.
And I was responsible for the manufacturing launch side.
It was a project I had no business, honestly, being
responsible for at that stage [laugh] in my career.
But, you know, when you’re given opportunities, you take
them, and so I took it, and it was a massive project.
(04:34):
There’s another individual that managed the—it was an all new weld line with
robotics, and a press break, and everything done by an outside integrator.
And then my responsibility was the project management, and assembly line
setup, process, validating the design, making sure everything’s manufacturable.
(04:56):
And I learned a lot during that process about failure, what not to do, and
all the wisdom I have now is from doing the wrong things all along the way.
And so, that was my first, I’ll say, big project and position that
just had a great trajectory, I’ll say, for the rest of my career.
(05:17):
Good experience.
So, I mean a skid steer, end to end from clean-sheet?
That’s literally the scope of the project?
Take the existing design, throw it away.
And the concept was an all-new design in 18 months.
We weren’t successful in 18 months, which… thank goodness.
You know, we took the time for testing and validation after that.
(05:40):
But the idea was parallel design, testing, and manufacturing.
And that is… it was aggressive.
How much of the manufacturing of that was
in-source versus outsource for that project?
The base, the frame was welded and cab was welded inside
the tub of the chassis was bent, the large plate was bent.
(06:04):
And there wasn’t at this point—so I mentioned previously, I’m doing
research on near-shoring, and manufacturing, and the impact of the
pandemic, and part of the reason why I’m doing research into that is
because when I started my career in the early-2000s I was working at this
manufacturing plant that had outsourced a bunch of their manufacturing.
(06:29):
The plant that I was working at had previously done, you know, hydraulic
tube bending, and had lasers, and did all the plate work, cutting
plate work there, and had built cylinders, even hydraulic cylinders.
And by the time I arrived, all of that work had been outsourced to
competency centers or locations in other countries or other facilities.
(06:54):
And really, manufacturing was going horizontal at that point.
And we felt the impact of that during this product launch, where you were
trying to make small changes to components that were made in other countries
or in other states or facilities, and the effort it required to get those
(07:15):
changes implemented and then new product into the door was significant.
And so, a lot of the product was made outside, but certainly
all the welding of the chassis and cab and arm were done inside,
and then it ran through a paint line, and then full assembly.
But it was definitely different than the previous generations.
(07:36):
With something that complex and in-sourced components, outsourced
components, things wildly out of your control as part of
that process, how do you even begin to manage all of that?
That sounds like a lot of potential chaos.
It is chaos, you know?
Documentation of, okay, this tube in this
(07:57):
location, it worked in the model, you know?
This is the—I’m married—and I can say this—I’m married to a mechanical engineer.
You know, he’s on the design side; I’m on the manufacturing side.
“Well, it worked in the model.
Why doesn’t it work in reality?” And so… ugh.
So, you know, change the tube, a few degrees bend, or you can route that
tube in the model fine, but once the engine’s installed, there’s no way we’re
(08:20):
going to get the tube underneath there, and you have to put a split in here.
And so, it’s just trying to document all of these changes
while testing is happening in parallel to product design in
parallel to, you know, we’re designing tools for the weld
system at the same time that the product’s being validated.
(08:41):
And so, all—just—it was chaos, but it was—at the same time it was kind of fun.
I can tell by just hearing how you’re talking about it
that you definitely felt rewarded in driving the complexity
to ground and ultimately in producing the product, right?
Yeah.
It was a challenge, and a challenge that I didn’t think was possible,
(09:03):
but we were successful in the end, in launching the product into reality.
That’s manufacturing, right?
[laugh]
. That’s it [laugh] . I mean, you said you caught the bug.
You weren’t kidding.
You have to have the bug to stay on top of projects like
that, on crazy timelines, and keep pushing it through.
(09:24):
And what I was going to say is, I actually learned what a skid steer was
when my kids were babies, and I would read the little truck books to them.
So.
We have a filtering process.
If they don’t call the equipment the right name, then that book is out.
We’re out.
If you call it a Bobcat, get out of here [laugh]
. [laugh] . Yeah, that’s like saying Kleenex, right [laugh]
(09:45):
? Right.
Awesome.
And so, now you’re part of—you’re vice president
of operations at Phoenix Mecano, North America.
Why don’t you tell us a little bit about Phoenix Mecano?
So, we are a solutions developer essentially.
We help end customers.
Our customers are in oil and gas, they’re in the
(10:07):
energy sector, they are in the medical sector.
We help develop solutions for electronics and electrical applications
where anything that needs, any sort of electrical equipment that’s
out in an environment that needs to be protected in some way.
And so, we help provide that protection by
developing enclosure solutions for them.
(10:28):
And that can be as simple as just providing the
housing, and they do the value-added work to it.
But here, our focus is really to help our customers have good solutions for
their product, in that we offer machining to make it easier for them, we’ll
do paint, powder coats, we’ll customize our catalog of standard enclosures
(10:50):
made by our company globally for whatever application the customers need.
And so, I have a team of design, manufacturing quality engineers
that will support, you know, problem solving for our customers.
We also have another product line that is for more on the automation
solution side of the business, and so we’ll develop workstations, or we’ll
(11:12):
integrate linears or lifts into—we’ll do the platforms and staircases.
We’ll design those for customers to integrate
into their equipment machine design.
And so really, we’re kind of at the front end of the supply chain,
where customers are looking to put their products inside—their products.
(11:33):
What we say is that we try to simplify the process
so that the customer can focus on the big picture.
Our solutions are simple.
We try to make it easy for the customers to do business with us.
Got it.
So, is the right way to think about Phoenix Meccano as effectively
an OEM with a pretty broad catalog, but that also needs to support it
(11:56):
sounds like some fairly unique customization in the purchasing process?
Our catalog is quite broad, and applications are also quite broad.
So, we have engineers that need to be able to understand, we
work in the oil and gas sector, and so they need to be able
to understand ATEX and IECEx regulations, and those impacts,
(12:18):
and how our products are used safely in those industries.
And then we also need to understand how to integrate a lift into a
multi-axis system, and be able to support the customers and the controls, and
understand how our lifting system will impact their overall control system.
(12:41):
And so, it’s just, it’s—and then we also have customers
that just need a hole in a box for a prototype part.
And 10 to 15% of our production orders can be
fewer than ten pieces, one or two, three pieces.
And then we’ll have customers that will
order regularly, but we have a wide variety.
We trade with 2 to 2500 customers annually.
(13:04):
Wow.
So, I know you’ve been with the company about a handful of years now,
Rebekah, and obviously, have made many technology investments along the way.
What did the process look like to support that kind of
business when you first joined, and what does it look like now?
Yeah.
I think you’re referencing our machine investment, right?
(13:28):
In the last couple years, we’ve invested in all new machining centers in
our production area to be able to respond to changing workforce needs.
We’re in an area of the country that is, you know, we’re
along the I-95 corridor, which is great for our customer base.
It’s a very—up and down I-95 there’s a lot
(13:51):
of electronics and electrical integrators.
But our specific location is far more medical and biomedical research.
We’re just outside DC, and so there are fewer,
just, heavy industrial manufacturers here.
So, the workforce in this area is far more—just
not the skill set that we need for machining.
(14:11):
So, we invested.
We had 13 different machining centers previously, of all different
vintages, from the late-’60s, all the way to the mid-2000s.
And we, just in two years, got rid of all of them, replaced them all,
and went with one machining center, one type of machine, and standardized
(14:37):
that to be able to offer standard controls to our operators so that the
interface is the same when one operator goes from one machine to another.
Then they know how to operate all the machines.
That’s really what drove it for us, is to be able to reduce our setups,
standardize our setups, standardize operator training, and be able to,
(15:03):
in some ways, standardize our programs so that, you know, offsets are a
little different between the machines, but to have standards established.
We have so much variability in our product line that to have
the variability in the product line, and the machines, and
the controls, and the setup approach, it was just too complex.
(15:23):
I mean, if you’ve worked in manufacturing for any length of time, you
know that there’s just variability everywhere, from all directions.
So, our effort was to try to remove some of that variability
and standardize it so that our operators could be more
successful, so that our setup times could be reduced.
And we see the effect of it.
(15:44):
It’s been a great investment.
And at the same time, we invested in Datanomix.
And we gave that feedback to the operator so that they could better
understand how their decisions, their day-to-day decisions, and how they
interface with the machines, impacts the productivity through the line.
And so, yeah, that’s one of the biggest investments
(16:05):
that we’ve made, and we’ve seen great return on it.
So, 13 different machining types down to one is a heck of
a consolidation [laugh] . That sounds pretty disruptive.
Yeah.
We have six machines today, and are getting more output from the six
machines than we were, from 30 to 40% utilization on thirteen machines.
(16:27):
So.
That’s fantastic.
Reducing the complexity for the operators.
And then based on the catalog of parts that you offer, and then,
you know, it sounds like there’s a lot of potentially little
tweaks, I’m guessing, a pretty significant number of SKUs as
well that gets serviced across this now one machine platform?
(16:47):
If we have 2500 customers, we usually have around 2500—or
more—part numbers, probably 3 or 4000 part numbers annually.
So, yeah.
Every customer has their own and then
several customers have multiple part numbers.
And it could be a profile, a piece of extruded profile running
through the machine, or it could be a plastic housing, or we have
(17:10):
fiberglass enclosures for special applications, like [EX] . Just
all kinds of different materials (17:14):
stainless steel, plastic.
So, it could be any of those, in any shape.
So, you talked also about, sort of, consolidating,
optimizing the setup workflows as part of that change, too.
I mean, with all that variation that you just talked about, number of SKUs,
(17:35):
materials, slight variances, effectively, in the features of what gets
produced, what did that process look like to get those setups tightened up?
We had some standard fixtures for our housings, but we built
on that by standardizing how the parts are in every machine.
(17:57):
And so, the machine that we bought allows us to have a table as well
as a five-axis, and be able to set up all the parts in the same way.
Most of our parts are just in the shape of a six-sided box, and so we’re able
to standardize how the parts are in the machine using the standard fixture.
(18:20):
But then beyond that, we’ve really started to
build standard fixtures for the weird-shaped parts.
Not all housings are six-sided boxes, so we located
where the boxes were, and set the vices on the table,
and the fifth axis in the same place on every machine.
(18:42):
And really—it’s hard to describe it in words—built the extension
for the box, you know, customized them for the machine.
So, every machine has its own version of the setup tools to be able to
put the box in the same location so that the programs can be standard
when we only have to change the offsets in each program by machine.
(19:04):
I know from you know the results that you were talking about previously.
If you go from thirteen machines to six, increase the output
and also service that much additional variance on a reduced
footprint, that there had to be some pretty significant engineering
optimization, machine optimization, to be able to pull that off.
(19:25):
So, really awesome to hear about the layers of complexity that used to be
there, and then how to get it down to something that, I mean, particularly
when you talk about the workforce aspect of it, right, I mean, talk about
levers of automation that you’ve really introduced into this process.
You didn’t just go to a robot; you actually unwound
(19:49):
what sounds like half-a-dozen or more layers of the problem
to get something that’s much easier to make repeatable.
I would say, I’m not satisfied with where we are.
I still think, with all the [laugh] with all the
improvement we made, there’s still more, you know?
I—
There’s that bug you caught.
Oh, man [laugh]
(20:10):
. [laugh]
. There’s just so many more improvements that you could
walk out to our shop and still see more opportunities.
And I… yeah, the improvements that we made were great,
and the shop runs much better than it did previously.
And also we shouldn’t stop.
We should continue.
(20:33):
And I still think there’s opportunity to improve
how we hold the parts, how many we can fit in there.
You mentioned robotics and automation, and we’re starting with that.
Just more and more opportunities.
I think we can’t slow down and stop.
Love the attitude.
In that regard, what investments are you looking at in the next 12 to 24 months?
(20:54):
Where does the next layer of optimization come from?
There’s automation at the machine.
There’s integration of robots and working with the operators, our existing
operators, to support them better, and doing some of the repetitive
tasks that they don’t need to do, that they can help in other ways.
So, we’re starting with those, but I think
(21:14):
a big piece for us is systems integration.
Yes, we need to work on making the parts faster and better, but
for every individual in production, you know, there are two or
three more outside of production that are supporting that function.
And so, for us, it’s the whole system, the entire process, and how—I
(21:38):
mentioned it earlier—but how easy it is to do business with us, how easy
it is to get a quote, to get a part started and out through production.
The time on the machine is just such a small fraction of
the total time and effort that all of us put into making
the parts, and our investments are really in the future.
(21:59):
More on the systems integration side, getting our ERP more closely
tied in with all the other systems—it already is; I mean, we live
and die by our ERP system—but also with our visual work instructions,
with our sales and operational planning, and with our forecasting,
(22:19):
and using more technology to understand where the market is
going, and reading our inventory levels, all of those things.
How the customer receives a quote, how that how we run that new product, every
new item, through our system, and whether we can automate pieces of those steps.
That’s where we’re investing.
(22:40):
Yes, in manufacturing, but in the whole process of manufacturing.
Sure.
I mean, I think you call upon a fantastic point, which is obviously the time
in the machines is one of the big things that draws all of our attention.
And by the way, I don’t always do this live on episodes, but
I had to go look at some of your continuous improvement data.
(23:02):
I have to give you a ton of credit.
Your utilization is up 19%, 19.7% year-over-year.
That’s the rate of change.
So, you guys have picked up almost 20% in the last 12 months,
and your downtime is actually down 24% just in the last
six months, which is some pretty incredible improvements.
(23:23):
I’ll let my team know, but they can’t rest.
It’s not time to sit down [laugh]
. Those are good numbers, but they’re not good enough [laugh]
. That’d be—we’ve not arrived.
Right?
Always happy.
Never satisfied.
Yeah.
Yeah, exactly.
Good job, guys.
Where’s the next 20% [laugh] ? No, but you—so that’s—I mean, that’s
the in-machine stuff, right, so a lot of good things happening there.
(23:44):
But I think you calling attention to the outside of the machine
and the human interaction—looking for information, having the
right information, knowing what to do—how do you try to get
better insight and better tracking on some of those dimensions?
We do track failures.
We track engineering failures, we track production failures, obviously, we
(24:09):
track internal failures, and we really work to drive some of the root cause.
I’m really—I have a stack of 8Ds on my desk.
My team would say I’m a little too particular, but I really
think that if we put the effort into finding what truly happened
at the failure, then it’s easier for us in the long run.
(24:30):
And so, we try to make those improvements through identifying what went
wrong, and then also just looking at, we have dashboards that—we use
Power BI quite a bit with our ERP data, and some of our CRM data—and I
measure how long it takes for us to get a quote to a customer, and then
we set targets, and identify projects to make improvements to that time.
(24:55):
Because a lot of people offer housings and solutions, and I
like to believe that one of the advantages is is the quality of
our product, and our ability to respond to what customers need.
But I also know that effort is always a factor.
You know, when we go buy something, if it’s really difficult to
(25:15):
work with that company, then I’m probably not going back to them.
And so, I know that our customers have the same perception.
So, I think measuring the things that matter to our customers, and identifying
projects to improve those and just continually chipping away at it.
I’m not looking to make significant improvements to the
(25:40):
overall operation in one fell swoop; I’m looking to make
measured, small improvements day after day, year over year.
That is so well stated, Rebekah.
I know we were joking a little before we got on the
air about sometimes manufacturers get lost in the
weeds of tracking fractions of hours on their people.
(26:00):
So, I mean, you’re talking about really instrumenting parts of
the process that, I’ll say, I don’t know that it’s necessarily
obvious or easy to a lot of people in the industry to know how to
instrument and measure the types of things that you’re talking about.
I mean, how do you do that?
We live and die by our ERP system.
(26:21):
And I have, in the last couple years, I added a manufacturing
systems integration engineer who’s now a lead in the area.
And so, we had a reset of our whole system a few years ago, and
after, we were forced to do that, we as a company, we just couldn’t
stop with all of our systems integration and process improvements.
(26:46):
And so, once we started doing that, and we—you know, if your house burns
down and you rebuild it, you’re not going to rebuild it the same way—so once
we started rebuilding our system, we rebuilt it in a way that we wanted to.
And the team here is very collaborative and interested in making
things better for everyone, everyone internally and externally.
(27:08):
It’s a great environment, and I think that part of it is the systems engineer
that is building these dashboards using the data from our ERP system and
linking it together, I mean, Power BI has been a great tool for us to
learn and be able to quantify a lot of those things, and then obviously
(27:32):
also Datanomix with the dashboard that we have in production to, just
data everywhere, but useful data, and we try to pull it and simplify it.
I think the challenge of Power BI and honestly, at
times, Datanomix, is there’s so much information.
What [laugh] what of it is important to us?
And so, that’s really our challenges is not how do we get the data, but
(27:55):
which pieces are we going to drive and which ones are important to us?
And we have a lot of discussion about it, and sometimes we change
and we pivot after we start measuring it for a period of time.
But that’s our challenge.
If I understand correctly, it sounds like, you know, in terms of getting
insight into some of these other dimensions of what I’ll call analytics, like,
(28:18):
time for quote to customer and these other metrics around different pieces of
the process that are outside of the core manufacturing itself, it sounds to
me like rather than trying to say like, “Hey, we have to burden everybody out
there at every step with tracking, tracking, tracking and creating data,” you
invested in a skill set that had the ability to basically look in and across
(28:44):
your data systems, in this integrations engineer function, and basically, when
you rebuilt, started with the idea of we’re going to connect every data point.
We’re going to measure everything from point A to point B to point C, based
on the data we have, and we’ve got to make sure that just the default way
that we operate creates the data that we need to do these kinds of analysis.
(29:09):
Does that about sum it up?
You said it much better and more concisely than I did.
Absolutely.
It’s my job to summarize [laugh] and make sure that I understand.
I wasn’t sure that I had it right because that’s a super unique angle, right?
A lot of people think, “Oh, man, for me to get that
data, I got to go get 25 people doing this,” right?
“I got to go get ten people doing that.” And you sort of
(29:32):
came at it completely the other way, which is like, “No,
I just need that to be in my data, and it’s already there.
And if I can get someone who can connect those things”—which is
what probably didn’t happen naturally on its own, which is why you
know that function is something that you ended up investing in.
You know, if you could get someone to connect those
(29:52):
dots, then you’ve got the information that you need.
It’s not like it wasn’t there.
It’s just it needed to be strung together.
Yeah, and it has to be data that you can trust, too.
I have talked to other manufacturers who say their ERP is about 80%
accurate, or 60%, and I just—if your build material is not correct,
(30:16):
then the challenges that you have, you know, this isn’t even a topic.
Systems Integration isn’t even a topic.
I really think that having good data to begin with, and
then, in my opinion, connecting those dots is way easier
if you can trust what you’re getting to begin with.
Sure.
And I mean, I think I know you and I, Rebekah, have
(30:37):
talked in the past about, I think you have the highest
trust and most accurate ERP data of anyone I’ve ever met.
The gap between 80% and a 100% is a hundred miles [laugh]
. Yeah.
Even if the engineers, you know, “Do we really have to update it?” Yes.
A hundred percent.
I think you’ve covered all the different dimensions of this so nicely.
(31:00):
I mean, this has been a master’s class for me in operational excellence,
in, you know, I mean, literally, starting with something that you worked
on so complex at the very beginning of your career, and really, I mean,
the only way to survive and succeed at a project of that magnitude
(31:22):
and scope is probably to have the good information well organized.
You know, I said it.
I said all my failures are my pearls of wisdom today [laugh]
. Yeah, yeah.
So, I mean, that dovetails very nicely into then, in your current capacity,
dividing the number of machines by more than half, and increasing throughput
(31:45):
significantly, but also standardization, repeatability, capability, capacity.
I mean, all those dimensions are outputs
of all the simplification that was done.
And then, again, I’ve seen some of the things that you and your team do
with Power BI and the analytics you have on different dimensions of the
process, and I know it’s quite amazing stuff, and so it was awesome to hear,
(32:11):
really, how you got there and what went into that, and I think really for
the audience, to hear how investments in data-oriented skills can really
make a big difference in what the business knows and what it knows how to do.
And obviously there’s your core technologies that you choose, the
(32:32):
vendors that you partner with, but also, just saying, it’s a core
competence that we know what our data means every single day on
everything that matters to how our customers become satisfied.
Yeah, absolutely.
So, Rebekah, I deeply appreciate that master’s class.
I’ll brush up on my 8Ds [laugh] for the next time that we speak.
(32:54):
I want to know what really happened.
You got to dig, Greg.
You got to dig [laugh] . I’m good at that.
Sometimes it digging myself, but that’s a different problem.
So Rebekah, what we like to do to end the episode is ask a little bit
of an existential question, which is, if you could go back in time and
talk to the version of yourself that was there when the manufacturing
(33:16):
torch just got lit, which in your case is so nice to picture a young
Rebekah, in the wood shop, with dad, making crooked-ish furniture
[laugh] —it’s just a design element, right?
It doesn’t need to be precise—and you could tap around
the shoulder and whisper in her ear and tell her, give
(33:37):
her some great piece of advice, what would you say?
You know, I think I would tell myself not to hesitate.
I think that I wasn’t sure about whether or not this was for me.
And I think I would say no, no, no, just keep going.
Don’t hesitate.
You don’t have to look right or left.
Keep going.
Just do it.
Do it.
(33:58):
Yeah.
Just do it.
Love it.
Take on those complexities head on, and you end up being—
Yeah, maybe not everybody’s doing what you’re doing, but that’s okay.
Keep going.
Right.
You don’t end up as the highest confidence person with
100% accurate ERP data by waiting, that’s for sure [laugh]
. [laugh] . It’s not just me.
Everybody here contributes to that.
Incredible.
(34:18):
Well, thank you so much, Rebekah.
It has been a joy to speak with you, to continue to learn from you, and
I’m confident that the listeners will enjoy this master’s class as well.
So, thank you very much.
Thank you.
I appreciate the time.
Thank you for listening to Manufacturing Mavericks.
If you’d like to learn more, listen to past episodes, or nominate
(34:39):
a future Maverick to be on our show, visit mfgmavericks.com,
and don’t forget to subscribe to and rate this podcast on
iTunes, Spotify, Google Play, or your favorite podcast app.
Welcome to Manufacturing Mavericks, a podcast where we showcase and celebrate
exceptional people from across precision manufacturing who are boldly
embracing new ways to improve their processes, grow their bottom lines,
and ensure American manufacturing will thrive for generations to come.
(00:26):
Welcome to this episode of Manufacturing Mavericks.
I’m your host, Greg McHale, founder and CEO of Datanomix, and I am
thrilled to be joined today by Rebekah Collogan from Phoenix Mecano.
How are you today, Rebekah?
I am well, Thank you.
I really appreciate having you on, Rebekah.
I know we’ve gotten to know each other over the last couple years,
and I know you are deeply involved in many different aspects of
(00:50):
manufacturing, operations, business data, machine data, so I’m really
looking forward to seeing what topics we get to dive into today.
Yeah, sounds good.
Me, too.
Awesome.
So, Rebekah, just to break the ice on Manufacturing Mavericks, the
way that we typically get started is, I love hearing about the origin
story of how everyone gets into their own manufacturing journey.
(01:14):
So, how about we start with how and when
did the manufacturing torch get lit for you?
I was thinking about this, and I usually say in
college, but I really think it started when I was a kid.
I grew up on a farm in Kansas, and my dad had a wood shop, and
(01:34):
so every night he’d go out to the wood shop—he was a CPA by day,
still is, and a farmer, you know, weekends and evenings, and
then would also make the furniture for our house for my mom.
And so, I’d go out to the wood shop with him, and, you
know, I’d hold the board on the other side of the planer.
I love the smell of wood chips.
(01:57):
And, you know, I was one of four kids, and I think I
was the only one that really just, rain, you know, snow,
didn’t matter, I was out there in the shop with my dad.
And I just loved seeing these things come
together and being part of that process.
And then, when I was in college, after I finished my degree, or in the
(02:20):
last year, I did a degree in industrial engineering, and in my last year,
I did an internship at a manufacturing plant, just to have some experience.
I hadn’t planned to stay in manufacturing.
And then I caught the bug.
I couldn’t shake it.
I loved seeing change, and transformation, and changing people’s,
(02:42):
you know, jobs and how they were able to do their work, as
well as the product changing as it went through the process.
It’s just, you know, you get the bug and you can’t shake it.
So yeah, for me, it was probably a combination of those two.
Do you still have any of the furniture from dad, still in the family [laugh]
? [laugh] . You know, I have a few things.
(03:04):
My dad is—he—precision is just not his strength, [laugh]
and so, it was made out of love, but not for longevity.
I’ll say that [laugh]
. Got it so, really, more for the coffee table book than for the coffee itself?
Yeah, yeah, exactly.
Exactly [laugh] . I think my mom still has the furniture.
(03:26):
She wouldn’t give it up, so I don’t have any of it.
And I mean, as you were describing the smell of wood
chips, who doesn’t like the smell of freshly cut wood?
It’s so good.
So college, industrial engineering.
You start getting drawn to process optimization, problem
(03:47):
solving, getting out there into internships and whatnot.
What were some of your first jobs and career positions?
I was a manufacturing engineer at a plant that made
skid steers, and—which, if you don’t know what that is,
it’s essentially—a Bobcat is a version of a skid steers.
It wasn’t that brand, but—and one of my first projects, after a couple years
(04:10):
in there, was a clean-sheet design of the skid steers, managing that project.
And I was responsible for the manufacturing launch side.
It was a project I had no business, honestly, being
responsible for at that stage [laugh] in my career.
But, you know, when you’re given opportunities, you take
them, and so I took it, and it was a massive project.
(04:34):
There’s another individual that managed the—it was an all new weld line with
robotics, and a press break, and everything done by an outside integrator.
And then my responsibility was the project management, and assembly line
setup, process, validating the design, making sure everything’s manufacturable.
(04:56):
And I learned a lot during that process about failure, what not to do, and
all the wisdom I have now is from doing the wrong things all along the way.
And so, that was my first, I’ll say, big project and position that
just had a great trajectory, I’ll say, for the rest of my career.
(05:17):
Good experience.
So, I mean a skid steer, end to end from clean-sheet?
That’s literally the scope of the project?
Take the existing design, throw it away.
And the concept was an all-new design in 18 months.
We weren’t successful in 18 months, which… thank goodness.
You know, we took the time for testing and validation after that.
(05:40):
But the idea was parallel design, testing, and manufacturing.
And that is… it was aggressive.
How much of the manufacturing of that was
in-source versus outsource for that project?
The base, the frame was welded and cab was welded inside
the tub of the chassis was bent, the large plate was bent.
(06:04):
And there wasn’t at this point—so I mentioned previously, I’m doing
research on near-shoring, and manufacturing, and the impact of the
pandemic, and part of the reason why I’m doing research into that is
because when I started my career in the early-2000s I was working at this
manufacturing plant that had outsourced a bunch of their manufacturing.
(06:29):
The plant that I was working at had previously done, you know, hydraulic
tube bending, and had lasers, and did all the plate work, cutting
plate work there, and had built cylinders, even hydraulic cylinders.
And by the time I arrived, all of that work had been outsourced to
competency centers or locations in other countries or other facilities.
(06:54):
And really, manufacturing was going horizontal at that point.
And we felt the impact of that during this product launch, where you were
trying to make small changes to components that were made in other countries
or in other states or facilities, and the effort it required to get those
(07:15):
changes implemented and then new product into the door was significant.
And so, a lot of the product was made outside, but certainly
all the welding of the chassis and cab and arm were done inside,
and then it ran through a paint line, and then full assembly.
But it was definitely different than the previous generations.
(07:36):
With something that complex and in-sourced components, outsourced
components, things wildly out of your control as part of
that process, how do you even begin to manage all of that?
That sounds like a lot of potential chaos.
It is chaos, you know?
Documentation of, okay, this tube in this
(07:57):
location, it worked in the model, you know?
This is the—I’m married—and I can say this—I’m married to a mechanical engineer.
You know, he’s on the design side; I’m on the manufacturing side.
“Well, it worked in the model.
Why doesn’t it work in reality?” And so… ugh.
So, you know, change the tube, a few degrees bend, or you can route that
tube in the model fine, but once the engine’s installed, there’s no way we’re
(08:20):
going to get the tube underneath there, and you have to put a split in here.
And so, it’s just trying to document all of these changes
while testing is happening in parallel to product design in
parallel to, you know, we’re designing tools for the weld
system at the same time that the product’s being validated.
(08:41):
And so, all—just—it was chaos, but it was—at the same time it was kind of fun.
I can tell by just hearing how you’re talking about it
that you definitely felt rewarded in driving the complexity
to ground and ultimately in producing the product, right?
Yeah.
It was a challenge, and a challenge that I didn’t think was possible,
(09:03):
but we were successful in the end, in launching the product into reality.
That’s manufacturing, right?
[laugh]
. That’s it [laugh] . I mean, you said you caught the bug.
You weren’t kidding.
You have to have the bug to stay on top of projects like
that, on crazy timelines, and keep pushing it through.
(09:24):
And what I was going to say is, I actually learned what a skid steer was
when my kids were babies, and I would read the little truck books to them.
So.
We have a filtering process.
If they don’t call the equipment the right name, then that book is out.
We’re out.
If you call it a Bobcat, get out of here [laugh]
. [laugh] . Yeah, that’s like saying Kleenex, right [laugh]
(09:45):
? Right.
Awesome.
And so, now you’re part of—you’re vice president
of operations at Phoenix Mecano, North America.
Why don’t you tell us a little bit about Phoenix Mecano?
So, we are a solutions developer essentially.
We help end customers.
Our customers are in oil and gas, they’re in the
(10:07):
energy sector, they are in the medical sector.
We help develop solutions for electronics and electrical applications
where anything that needs, any sort of electrical equipment that’s
out in an environment that needs to be protected in some way.
And so, we help provide that protection by
developing enclosure solutions for them.
(10:28):
And that can be as simple as just providing the
housing, and they do the value-added work to it.
But here, our focus is really to help our customers have good solutions for
their product, in that we offer machining to make it easier for them, we’ll
do paint, powder coats, we’ll customize our catalog of standard enclosures
(10:50):
made by our company globally for whatever application the customers need.
And so, I have a team of design, manufacturing quality engineers
that will support, you know, problem solving for our customers.
We also have another product line that is for more on the automation
solution side of the business, and so we’ll develop workstations, or we’ll
(11:12):
integrate linears or lifts into—we’ll do the platforms and staircases.
We’ll design those for customers to integrate
into their equipment machine design.
And so really, we’re kind of at the front end of the supply chain,
where customers are looking to put their products inside—their products.
(11:33):
What we say is that we try to simplify the process
so that the customer can focus on the big picture.
Our solutions are simple.
We try to make it easy for the customers to do business with us.
Got it.
So, is the right way to think about Phoenix Meccano as effectively
an OEM with a pretty broad catalog, but that also needs to support it
(11:56):
sounds like some fairly unique customization in the purchasing process?
Our catalog is quite broad, and applications are also quite broad.
So, we have engineers that need to be able to understand, we
work in the oil and gas sector, and so they need to be able
to understand ATEX and IECEx regulations, and those impacts,
(12:18):
and how our products are used safely in those industries.
And then we also need to understand how to integrate a lift into a
multi-axis system, and be able to support the customers and the controls, and
understand how our lifting system will impact their overall control system.
(12:41):
And so, it’s just, it’s—and then we also have customers
that just need a hole in a box for a prototype part.
And 10 to 15% of our production orders can be
fewer than ten pieces, one or two, three pieces.
And then we’ll have customers that will
order regularly, but we have a wide variety.
We trade with 2 to 2500 customers annually.
(13:04):
Wow.
So, I know you’ve been with the company about a handful of years now,
Rebekah, and obviously, have made many technology investments along the way.
What did the process look like to support that kind of
business when you first joined, and what does it look like now?
Yeah.
I think you’re referencing our machine investment, right?
(13:28):
In the last couple years, we’ve invested in all new machining centers in
our production area to be able to respond to changing workforce needs.
We’re in an area of the country that is, you know, we’re
along the I-95 corridor, which is great for our customer base.
It’s a very—up and down I-95 there’s a lot
(13:51):
of electronics and electrical integrators.
But our specific location is far more medical and biomedical research.
We’re just outside DC, and so there are fewer,
just, heavy industrial manufacturers here.
So, the workforce in this area is far more—just
not the skill set that we need for machining.
(14:11):
So, we invested.
We had 13 different machining centers previously, of all different
vintages, from the late-’60s, all the way to the mid-2000s.
And we, just in two years, got rid of all of them, replaced them all,
and went with one machining center, one type of machine, and standardized
(14:37):
that to be able to offer standard controls to our operators so that the
interface is the same when one operator goes from one machine to another.
Then they know how to operate all the machines.
That’s really what drove it for us, is to be able to reduce our setups,
standardize our setups, standardize operator training, and be able to,
(15:03):
in some ways, standardize our programs so that, you know, offsets are a
little different between the machines, but to have standards established.
We have so much variability in our product line that to have
the variability in the product line, and the machines, and
the controls, and the setup approach, it was just too complex.
(15:23):
I mean, if you’ve worked in manufacturing for any length of time, you
know that there’s just variability everywhere, from all directions.
So, our effort was to try to remove some of that variability
and standardize it so that our operators could be more
successful, so that our setup times could be reduced.
And we see the effect of it.
(15:44):
It’s been a great investment.
And at the same time, we invested in Datanomix.
And we gave that feedback to the operator so that they could better
understand how their decisions, their day-to-day decisions, and how they
interface with the machines, impacts the productivity through the line.
And so, yeah, that’s one of the biggest investments
(16:05):
that we’ve made, and we’ve seen great return on it.
So, 13 different machining types down to one is a heck of
a consolidation [laugh] . That sounds pretty disruptive.
Yeah.
We have six machines today, and are getting more output from the six
machines than we were, from 30 to 40% utilization on thirteen machines.
(16:27):
So.
That’s fantastic.
Reducing the complexity for the operators.
And then based on the catalog of parts that you offer, and then,
you know, it sounds like there’s a lot of potentially little
tweaks, I’m guessing, a pretty significant number of SKUs as
well that gets serviced across this now one machine platform?
(16:47):
If we have 2500 customers, we usually have around 2500—or
more—part numbers, probably 3 or 4000 part numbers annually.
So, yeah.
Every customer has their own and then
several customers have multiple part numbers.
And it could be a profile, a piece of extruded profile running
through the machine, or it could be a plastic housing, or we have
(17:10):
fiberglass enclosures for special applications, like [EX] . Just
all kinds of different materials (17:14):
stainless steel, plastic.
So, it could be any of those, in any shape.
So, you talked also about, sort of, consolidating,
optimizing the setup workflows as part of that change, too.
I mean, with all that variation that you just talked about, number of SKUs,
(17:35):
materials, slight variances, effectively, in the features of what gets
produced, what did that process look like to get those setups tightened up?
We had some standard fixtures for our housings, but we built
on that by standardizing how the parts are in every machine.
(17:57):
And so, the machine that we bought allows us to have a table as well
as a five-axis, and be able to set up all the parts in the same way.
Most of our parts are just in the shape of a six-sided box, and so we’re able
to standardize how the parts are in the machine using the standard fixture.
(18:20):
But then beyond that, we’ve really started to
build standard fixtures for the weird-shaped parts.
Not all housings are six-sided boxes, so we located
where the boxes were, and set the vices on the table,
and the fifth axis in the same place on every machine.
(18:42):
And really—it’s hard to describe it in words—built the extension
for the box, you know, customized them for the machine.
So, every machine has its own version of the setup tools to be able to
put the box in the same location so that the programs can be standard
when we only have to change the offsets in each program by machine.
(19:04):
I know from you know the results that you were talking about previously.
If you go from thirteen machines to six, increase the output
and also service that much additional variance on a reduced
footprint, that there had to be some pretty significant engineering
optimization, machine optimization, to be able to pull that off.
(19:25):
So, really awesome to hear about the layers of complexity that used to be
there, and then how to get it down to something that, I mean, particularly
when you talk about the workforce aspect of it, right, I mean, talk about
levers of automation that you’ve really introduced into this process.
You didn’t just go to a robot; you actually unwound
(19:49):
what sounds like half-a-dozen or more layers of the problem
to get something that’s much easier to make repeatable.
I would say, I’m not satisfied with where we are.
I still think, with all the [laugh] with all the
improvement we made, there’s still more, you know?
I—
There’s that bug you caught.
Oh, man [laugh]
(20:10):
. [laugh]
. There’s just so many more improvements that you could
walk out to our shop and still see more opportunities.
And I… yeah, the improvements that we made were great,
and the shop runs much better than it did previously.
And also we shouldn’t stop.
We should continue.
(20:33):
And I still think there’s opportunity to improve
how we hold the parts, how many we can fit in there.
You mentioned robotics and automation, and we’re starting with that.
Just more and more opportunities.
I think we can’t slow down and stop.
Love the attitude.
In that regard, what investments are you looking at in the next 12 to 24 months?
(20:54):
Where does the next layer of optimization come from?
There’s automation at the machine.
There’s integration of robots and working with the operators, our existing
operators, to support them better, and doing some of the repetitive
tasks that they don’t need to do, that they can help in other ways.
So, we’re starting with those, but I think
(21:14):
a big piece for us is systems integration.
Yes, we need to work on making the parts faster and better, but
for every individual in production, you know, there are two or
three more outside of production that are supporting that function.
And so, for us, it’s the whole system, the entire process, and how—I
(21:38):
mentioned it earlier—but how easy it is to do business with us, how easy
it is to get a quote, to get a part started and out through production.
The time on the machine is just such a small fraction of
the total time and effort that all of us put into making
the parts, and our investments are really in the future.
(21:59):
More on the systems integration side, getting our ERP more closely
tied in with all the other systems—it already is; I mean, we live
and die by our ERP system—but also with our visual work instructions,
with our sales and operational planning, and with our forecasting,
(22:19):
and using more technology to understand where the market is
going, and reading our inventory levels, all of those things.
How the customer receives a quote, how that how we run that new product, every
new item, through our system, and whether we can automate pieces of those steps.
That’s where we’re investing.
(22:40):
Yes, in manufacturing, but in the whole process of manufacturing.
Sure.
I mean, I think you call upon a fantastic point, which is obviously the time
in the machines is one of the big things that draws all of our attention.
And by the way, I don’t always do this live on episodes, but
I had to go look at some of your continuous improvement data.
(23:02):
I have to give you a ton of credit.
Your utilization is up 19%, 19.7% year-over-year.
That’s the rate of change.
So, you guys have picked up almost 20% in the last 12 months,
and your downtime is actually down 24% just in the last
six months, which is some pretty incredible improvements.
(23:23):
I’ll let my team know, but they can’t rest.
It’s not time to sit down [laugh]
. Those are good numbers, but they’re not good enough [laugh]
. That’d be—we’ve not arrived.
Right?
Always happy.
Never satisfied.
Yeah.
Yeah, exactly.
Good job, guys.
Where’s the next 20% [laugh] ? No, but you—so that’s—I mean, that’s
the in-machine stuff, right, so a lot of good things happening there.
(23:44):
But I think you calling attention to the outside of the machine
and the human interaction—looking for information, having the
right information, knowing what to do—how do you try to get
better insight and better tracking on some of those dimensions?
We do track failures.
We track engineering failures, we track production failures, obviously, we
(24:09):
track internal failures, and we really work to drive some of the root cause.
I’m really—I have a stack of 8Ds on my desk.
My team would say I’m a little too particular, but I really
think that if we put the effort into finding what truly happened
at the failure, then it’s easier for us in the long run.
(24:30):
And so, we try to make those improvements through identifying what went
wrong, and then also just looking at, we have dashboards that—we use
Power BI quite a bit with our ERP data, and some of our CRM data—and I
measure how long it takes for us to get a quote to a customer, and then
we set targets, and identify projects to make improvements to that time.
(24:55):
Because a lot of people offer housings and solutions, and I
like to believe that one of the advantages is is the quality of
our product, and our ability to respond to what customers need.
But I also know that effort is always a factor.
You know, when we go buy something, if it’s really difficult to
(25:15):
work with that company, then I’m probably not going back to them.
And so, I know that our customers have the same perception.
So, I think measuring the things that matter to our customers, and identifying
projects to improve those and just continually chipping away at it.
I’m not looking to make significant improvements to the
(25:40):
overall operation in one fell swoop; I’m looking to make
measured, small improvements day after day, year over year.
That is so well stated, Rebekah.
I know we were joking a little before we got on the
air about sometimes manufacturers get lost in the
weeds of tracking fractions of hours on their people.
(26:00):
So, I mean, you’re talking about really instrumenting parts of
the process that, I’ll say, I don’t know that it’s necessarily
obvious or easy to a lot of people in the industry to know how to
instrument and measure the types of things that you’re talking about.
I mean, how do you do that?
We live and die by our ERP system.
(26:21):
And I have, in the last couple years, I added a manufacturing
systems integration engineer who’s now a lead in the area.
And so, we had a reset of our whole system a few years ago, and
after, we were forced to do that, we as a company, we just couldn’t
stop with all of our systems integration and process improvements.
(26:46):
And so, once we started doing that, and we—you know, if your house burns
down and you rebuild it, you’re not going to rebuild it the same way—so once
we started rebuilding our system, we rebuilt it in a way that we wanted to.
And the team here is very collaborative and interested in making
things better for everyone, everyone internally and externally.
(27:08):
It’s a great environment, and I think that part of it is the systems engineer
that is building these dashboards using the data from our ERP system and
linking it together, I mean, Power BI has been a great tool for us to
learn and be able to quantify a lot of those things, and then obviously
(27:32):
also Datanomix with the dashboard that we have in production to, just
data everywhere, but useful data, and we try to pull it and simplify it.
I think the challenge of Power BI and honestly, at
times, Datanomix, is there’s so much information.
What [laugh] what of it is important to us?
And so, that’s really our challenges is not how do we get the data, but
(27:55):
which pieces are we going to drive and which ones are important to us?
And we have a lot of discussion about it, and sometimes we change
and we pivot after we start measuring it for a period of time.
But that’s our challenge.
If I understand correctly, it sounds like, you know, in terms of getting
insight into some of these other dimensions of what I’ll call analytics, like,
(28:18):
time for quote to customer and these other metrics around different pieces of
the process that are outside of the core manufacturing itself, it sounds to
me like rather than trying to say like, “Hey, we have to burden everybody out
there at every step with tracking, tracking, tracking and creating data,” you
invested in a skill set that had the ability to basically look in and across
(28:44):
your data systems, in this integrations engineer function, and basically, when
you rebuilt, started with the idea of we’re going to connect every data point.
We’re going to measure everything from point A to point B to point C, based
on the data we have, and we’ve got to make sure that just the default way
that we operate creates the data that we need to do these kinds of analysis.
(29:09):
Does that about sum it up?
You said it much better and more concisely than I did.
Absolutely.
It’s my job to summarize [laugh] and make sure that I understand.
I wasn’t sure that I had it right because that’s a super unique angle, right?
A lot of people think, “Oh, man, for me to get that
data, I got to go get 25 people doing this,” right?
“I got to go get ten people doing that.” And you sort of
(29:32):
came at it completely the other way, which is like, “No,
I just need that to be in my data, and it’s already there.
And if I can get someone who can connect those things”—which is
what probably didn’t happen naturally on its own, which is why you
know that function is something that you ended up investing in.
You know, if you could get someone to connect those
(29:52):
dots, then you’ve got the information that you need.
It’s not like it wasn’t there.
It’s just it needed to be strung together.
Yeah, and it has to be data that you can trust, too.
I have talked to other manufacturers who say their ERP is about 80%
accurate, or 60%, and I just—if your build material is not correct,
(30:16):
then the challenges that you have, you know, this isn’t even a topic.
Systems Integration isn’t even a topic.
I really think that having good data to begin with, and
then, in my opinion, connecting those dots is way easier
if you can trust what you’re getting to begin with.
Sure.
And I mean, I think I know you and I, Rebekah, have
(30:37):
talked in the past about, I think you have the highest
trust and most accurate ERP data of anyone I’ve ever met.
The gap between 80% and a 100% is a hundred miles [laugh]
. Yeah.
Even if the engineers, you know, “Do we really have to update it?” Yes.
A hundred percent.
I think you’ve covered all the different dimensions of this so nicely.
(31:00):
I mean, this has been a master’s class for me in operational excellence,
in, you know, I mean, literally, starting with something that you worked
on so complex at the very beginning of your career, and really, I mean,
the only way to survive and succeed at a project of that magnitude
(31:22):
and scope is probably to have the good information well organized.
You know, I said it.
I said all my failures are my pearls of wisdom today [laugh]
. Yeah, yeah.
So, I mean, that dovetails very nicely into then, in your current capacity,
dividing the number of machines by more than half, and increasing throughput
(31:45):
significantly, but also standardization, repeatability, capability, capacity.
I mean, all those dimensions are outputs
of all the simplification that was done.
And then, again, I’ve seen some of the things that you and your team do
with Power BI and the analytics you have on different dimensions of the
process, and I know it’s quite amazing stuff, and so it was awesome to hear,
(32:11):
really, how you got there and what went into that, and I think really for
the audience, to hear how investments in data-oriented skills can really
make a big difference in what the business knows and what it knows how to do.
And obviously there’s your core technologies that you choose, the
(32:32):
vendors that you partner with, but also, just saying, it’s a core
competence that we know what our data means every single day on
everything that matters to how our customers become satisfied.
Yeah, absolutely.
So, Rebekah, I deeply appreciate that master’s class.
I’ll brush up on my 8Ds [laugh] for the next time that we speak.
(32:54):
I want to know what really happened.
You got to dig, Greg.
You got to dig [laugh] . I’m good at that.
Sometimes it digging myself, but that’s a different problem.
So Rebekah, what we like to do to end the episode is ask a little bit
of an existential question, which is, if you could go back in time and
talk to the version of yourself that was there when the manufacturing
(33:16):
torch just got lit, which in your case is so nice to picture a young
Rebekah, in the wood shop, with dad, making crooked-ish furniture
[laugh] —it’s just a design element, right?
It doesn’t need to be precise—and you could tap around
the shoulder and whisper in her ear and tell her, give
(33:37):
her some great piece of advice, what would you say?
You know, I think I would tell myself not to hesitate.
I think that I wasn’t sure about whether or not this was for me.
And I think I would say no, no, no, just keep going.
Don’t hesitate.
You don’t have to look right or left.
Keep going.
Just do it.
Do it.
(33:58):
Yeah.
Just do it.
Love it.
Take on those complexities head on, and you end up being—
Yeah, maybe not everybody’s doing what you’re doing, but that’s okay.
Keep going.
Right.
You don’t end up as the highest confidence person with
100% accurate ERP data by waiting, that’s for sure [laugh]
. [laugh] . It’s not just me.
Everybody here contributes to that.
Incredible.
(34:18):
Well, thank you so much, Rebekah.
It has been a joy to speak with you, to continue to learn from you, and
I’m confident that the listeners will enjoy this master’s class as well.
So, thank you very much.
Thank you.
I appreciate the time.
Thank you for listening to Manufacturing Mavericks.
If you’d like to learn more, listen to past episodes, or nominate
(34:39):
a future Maverick to be on our show, visit mfgmavericks.com,
and don’t forget to subscribe to and rate this podcast on
iTunes, Spotify, Google Play, or your favorite podcast app.
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