Welcome back to Count Me In,

IMA's podcast about all things affecting
the accounting and finance world.

Kicking things off for you again is
your host Adam Larson and I'm excited to

introduce our featured guest for
today's episode, Greg Hoggard.

Greg is CFO and VP of finance and
IT at Rembrandt Foods where he is

responsible for all aspects of finance
and accounting, IT, and grain purchasing.

Greg co-authored an article in Strategic
Finance to show how computerized vision

and AI adoption can create
significant value for an organization.

In this episode,

he discusses the value of aligning IT and
OT and getting everyone in the company

speaking the same language.

Keep listening to hear how
the intersection of AI and
technology with finance

leads to improved strategic performance.

So Greg, in your opinion,
what is the value of,

or really maybe even the need
for information technology or IT

and operational technology or OT
to report into the same group.

So for those that may not
work in manufacturing,

operational technology, so
operations and technology,

which are usually called a controls group,
for those who work in manufacturing,

they're the ones who deal with

the hardware that collects the data.

They have a little bit of a
different talent than the IT group.

They're data. They're not
data miners necessarily,

but they they're the ones who
collect the data from the machines.

They're a little bit of electrician, a
little bit of, I hate saying maintenance.

They're not maintenance, but
they understand all those things.

They understand how the operation is
supposed to work and where the data's

coming from.

Think of those screens that you see in
those old buttons and the really big,

old manufacturing steel mill
or just old manufacturing.

When you see it on a television show,
something, all of those buttons,

all those controls, that's all done
by operational technology groups.

Whereas on the IT side,

they really are the ones that control
the data and the servers and the data

bases, they make that data consumable

for end users, and govern that data so

that it's trustworthy and reliable.
I think what we're seeing,

these days in the last
few years, especially,

is that this whole internet of
things push where everything

is communicating with everything,

those lines are starting to cross
much more often than they used

to,

between like the operational technology
groups and the information technology

groups and what I've seen
especially here at Rembrandt is

that when those two teams work together,

it becomes an unstoppable force.

when those two teams are working
separately and disparately,

it can become a little
confusing and unmanageable.

So the need to have them report together,

I think really it just drives change a
lot faster and it gets you to the right

answer much quicker.

So it sounds like, I would assume
you mentioned this, you know,

really being in manufacturing,
but majority of businesses,

they most likely require some
additional change, right?

Some new alignment to really
implement this kind of strategy.

So as far as change management goes,

what are some of the key considerations
for making this shift with the

technology that we're talking
about? The reporting. And really,

I think it comes down to getting everybody
from maybe two different sides to

speak the same language. So, you know,

what kind of steps would
you recommend for that?

So on the change management side, it's

crucial that everybody understands their
KPIs and their OKRs. So objectives,

key results, and key
performance indicators,

really the goals that they're
operating under, or goal posts,

I guess you could say, I like to be
a little more open-ended sometimes,

but I think if you don't have that,

then good luck with change.

You gotta have that good starting point
where everybody's working towards the

same goal.

I think that clear communication
cross-functionally and just within

the singular departments is necessary.

And then I think that once
you establish those clear

metrics, you have, well at first
you have to have something that,

that can be measured, right? I mean,

you can't have a metric
that's not measurable.

So having something that's
accurate and objective to

measure that goal is
needed. And then really,

I think these data visualizations,

are much better tools to report
feedback and then to measure against

these goals than traditional
scorecards and numbers.

Most people don't like to look at numbers,

I don't know if I should
quote a book here,

but I'm actually reading a book right
now called Making Numbers Count, which,

is a really good book by Chip Heath.

And he's talking a lot about how most
people really aren't wired to speak the

numbers language. And I
think as a accountants, we
are wired to speak that way.

At least we've learned that language and
we wanna share the numbers down to the

10th decimal point to show that,

to prove that we did the work and
to prove that this is a real number,

but other people get lost.

so these visualizations that
we're showing now with these tools

like Tableau and power BI,

those speak so much louder than
the numbers that we wanna share.

So I think this leads to accountability
and acceptance of change.

I don't think you can have change without
that accountability and that doesn't

come without that clear feedback.

And then the objective measurements of
clear objectives and key results and

KPIs.

You know, with these metrics
and, you know, the communication,

everything you just mentioned with
change management. I think, you know,

the underlying theme here for both sides,
you know, both languages, if you will,

is the technology itself.

I think that's causing the need
for change and where people need to

potentially, you know, learn a bit
more or improve a bit more in their,

you know, speaking capabilities,
I guess you could say.

So if we could just kind of shift from
the human side of things and focus on the

technological side of things and
those resources for a moment,

when adapting or implementing
technology and, you know,

increasing the need for
this change management,

what should our listeners really be most
aware of when it comes to technology

implementation?

So I'm reminded of an
experience during this,

that I've been on with the company
Rembrandt that I work for now and the team

that we're working with.
We, so I'll take a little

bit dive into the story that was
published as well at this point,

but we have these analog
counters. It's just an example.

And these analog counters are
just old technology. I mean,

analog counting has been around since
what the probably thirties, forties,

maybe even before that,

but anytime anything passes
under this analog counter,

it's counted as an egg in our case.

So we have these counters on
every single row of every single

column of every single
barn in our facility.

And these counters, like I said,
count anything that passes under that,

their measure, their scope
there and what we found,

at the very beginning of this journey,

we wanted to know if the
measurement was accurate.

So we performed an audit of the counters

and we lined a hundred eggs up behind
each of these counters before the

start of the day.

And then we turned them on and then
after the a hundred eggs were counted on

each counter, we turned off the system
and then went and looked at the results.

Some of the counters
were off by 2% positive.

Some of them were off by 2% negative.
Some of them were off by 20% positive.

Some of them were off by 20%
negative in the worst case,

some of them were off by 40%,
both ways, but in the end,

those errors offset each other.
And so our operations group said,

oh, well, we're within 2% overall.
So we're good with these counters.

Now, statistically speaking,

you have to use absolute values and
that would tell you that those counters

are pretty much worthless, but that was
one of the hardest things to talk about,

with our group,

because they had been operating
for so long using that technology.

And they had to trust it because
that's what was available.

I think now with improving technology
and really AI at our fingertips,

you don't have to rely on analog or on gut

instinct or there's data there to be had.

And I think really AI was
definitely the path for us,

but it's probably the path for a
lot of other people with complex

and really high quantity,
high volume of data. Really.

I think that harnessing all of that data
or things that maybe we haven't thought

really are data that's really
what I believe will make

companies more profitable over time.

We're really all just data and supply
chain companies. If you think about it,

if when it boils down to it, you
don't really have to work the hardest.

You don't really have to
make the best product,

although those things are very important.

If you wanna be a really sustainable
ongoing concern type company,

but really if you can make
your product at the right time

efficiently and get it to your customers
at the right time when they want it,

then really that's the value.

I believe that many companies and
most companies need to really tap into

and really data is the way to do that.

Now I know our conversation today really
initiated around an article that you

wrote recently. And I know you,

you said you kind of dove into some of
the background of your company and some

of your experiences,

but I do wanna address the
article and the significant value

of AI adoption.

So are you able to give us a little bit
more of a history behind some of the

initiatives that you implemented and,

what the article is all about
before we wrap up this conversation?

Yeah,

I would love to, actually,

this has been really the high
point of my time at Rembrandt so

far. I'll take it back to
the very beginning. in 2019,

I wasn't working for
Rembrandt at the time.

I was working for a company
called Oregon Freeze Dry in Oregon

in Albany, Oregon. And,

I had signed up and attended the San Diego

IMA conference. And that's where I
met, a gentleman named Daniel Smith,

who I've been working with on this
project, really, really great guy.

If you ever get a chance to
work with him, you should do it.

But he sat next to me during a lunch. I
believe it was a lunch and learn or a,

just,

we had a table full of full
of experts and accountants and

IMA members where we were just
talking through different issues.

And I was seated next to him. And
we started talking about Python.

We started talking about technology.

There were a lot of other
CMAs from the Philippines at

our table as well, really
good conversation convinced
most of us at that table.

Daniel did to go to his
class, that he was teaching,

the seminar that he had
on Python and just making

Python understandable for people like
me, who aren't really programmers,

aren't really data scientists.

And really just got me
thinking about technology.

I switched companies and really came
across these problems that I just talked

about with the counters.

We had a myriad of other problems as well,

but it really occurred to me. I watched,

I followed Daniel on LinkedIn
and he was demystifying facial

recognition and AI. And, I thought
to myself at that point, well,

if AI can recognize faces
people, individuals,

and all the different details that it
takes to really identify who somebody is,

then it should be able to identify an
egg and eggs generally look the same

they're different sizes. And
generally the same shape.

And so went down that path,
contacted Daniel through LinkedIn,

and that's where it started.

I think it took us about two months to
really get a proof of concept and a quote

together to say, this is what
it will take to do the project.

And then we started really down
that path of building a software

system and developing that software
to count these eggs and just

the scale of these eggs. And I think
I mentioned this in the article,

but we've got 6 million birds on our site.

And each of those birds,

they lay eggs a little over
six times a week on average.

So it's about 92% of the time,
of days they're laying eggs.

So it's about 500, or sorry,
about five, five point,

I'll just say 5.5 million eggs a
day, and the are all different sizes.

So we have a big problem
of trying to understand

how efficient we are at
converting the feed into egg,

and then breaking those eggs into liquid,

and then trying to identify
where the loss is happening.

So it seemed like a very
complex problem that AI could

solve. And what we found
is that it works now,

the hardest part has been, convincing
people that this isn't Skynet.

I think that, in the Ag side of the
world, there's a lot of technology,

a lot of AI, actually,

if you read up on the Ag side of just Ag

industry, AI is really taking hold.
I haven't seen it yet in the egg

industry, but,

or I hope we're pioneering
here and I hope it catches on,

but it's, it's definitely
an industry that,

that requires something like
this. There's just so much volume.

When you think about feeding a
nation and feeding the world,

the Ag industry really is in
need of really in depth complex

models, which I think AI
is really the answer to,

to that problem of feeding
the world efficiently.

Now, just real quick
for our listeners. This

was an article that was published in the
February issue of the Strategic Finance

magazine. And,

we have a link to this article in
the show notes here of the episode.

And, you know, I just
reiterating what you said.

The article does a great job outlining
the different phases and the different

considerations that you went through.

So as far as technology
implementation and, you know,

a real case on how it works and the
benefits of it, you know, I think,

it's a great example. And, you know,

we're certainly appreciative
of you writing the article
for IMA and sharing your

story here with us on the
podcast, right before we wrap up,

I just want kind of close out the
conversation. At the end of the day,

when it comes to technology and change
management, you've had, you know,

different experiences now and
success with it. You know,

some of the other considerations
with technology that you,

you briefly mentioned as far as the
data and the governance, and, you know,

really making sure that, you
have your own policies in place.

I'm just curious if there are any
major lessons learned that really stand

out, or maybe even, you
know, more importantly,

some key takeaways that our
listeners can, you know,

think about in their own technology
implementation projects, as far, you know,

things to remember.

I really think the first thing
that comes to my, for me here is,

is so the president of my company
now, he preaches people process tools,

his name's Paul Hardy, great
president. I think for me,

what I'll take it back a second.
So I love to build I'm a,

I love to create things and
we had so many things to fix.

When I first go out here at Rembrandt

I always ask for three years of financial
statements before I accept a job

offer. I think that's a good,

a good habit to form for all
of the accountants out there.

You should know what you're getting into.

And I knew I was getting into
something that needed to be fixed.

And that's kind of what that's my
M.O. I really like to, to build.

I like to fix, I like to,

I think I understand manufacturing enough
that I figured out the algorithm of

how to make manufacturing work,
but there were so many things,

there were so many things to change,
so many things to, to improve.

I think I, I was thinking
about this the other day,

and there were 15 major projects that
I undertook in less than two years.

And we were successful in all of them
when it came to implementing technology,

when it came to building
process and building the tools,

what I wasn't as successful
at, and only looking back,

this was about in February last year,

I realized that I had done all of these
things and I left everybody else in the

dust. I left the people out of it,
and that was devastating for me.

It was devastating for me to, to
know that I did all of this work.

I built all of these tools. I
built all of the, the process,

but in the end of the day, if
the people don't come with you,

then you're just left
with a tool on the shelf.

And that's what if I had to go back
and change it? I would, I think,

taking on less projects and spending
more time communicating with the people,

helping them understand
why it's so important,

maybe modeling and showing why
it's so important before going

off and, and just getting it done.

I think that would've been helpful for
me and something that I will definitely

take with me going
forward. That being said,

we did a lot of projects,

and I think we're getting
the people caught up with us.

So it's not like we wasted all of
our time on the 15 different things.

But yeah, I just have to
say people, perceptions.

Those are the most important
part of change management,

because a tool won't do the work for you,

a process won't of the
work for you in the end,

there are people that are the
most important thing that we have,

the most important resources that we
have. And if we leave them in the dust,

then what good is the tool
that we've put in place.

So that's the lesson I've
learned in the last two years.

This has been Count Me In,

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of thought leaders from

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