In this episode of the Future Proof HR podcast, Jim Kanichirayil speaks with Melissa Ganchev of Universal Nutrition about how AI modeling helped challenge one of the most common assumptions in a growing business: that scaling always means hiring more people.

Universal Nutrition had reached a new growth milestone and was planning for more demand across its manufacturing operations. The initial plan pointed toward a significant increase in headcount. But instead of moving straight into hiring mode, the team paused, used AI modeling to evaluate production lines, equipment efficiency, shift structure, and resource allocation, and found a smarter path forward.

Melissa shares how the company moved from an estimated need for around 30 new hires to hiring roughly eight manufacturing associates while still supporting growth. She explains how AI helped leaders rethink where work should happen, which lines were most efficient, and how to avoid over-hiring in ways that could later lead to painful reductions.

The conversation also makes a clear case for people investment alongside AI adoption. Melissa talks about leadership development, career pathing, employee training, better benefits, cross-training, real-time dashboards, and a culture that has helped Universal Nutrition maintain unusually low attrition in a manufacturing environment. The lesson is not that AI replaces the people strategy. It is when the organization has already invested in the people who will make the work better.

Topics Discussed:

• Why Universal Nutrition challenged the assumption that growth required major headcount expansion
• How AI modeling helped the team rethink production lines, shifts, and staffing plans
• Why the company started its AI pilot in one of its strongest revenue-producing buildings
• How headcount planning changed from an estimated 30 new hires to roughly eight manufacturing associates
• Why employee investment matters before and during operational transformation
• How leadership development, lunch and learns, and career pathing support scaling efforts
• How real-time dashboards can help employees troubleshoot production issues faster
• Why long-tenured manufacturing teams create resilience through cross-training and shared knowledge
• How communication, focus groups, surveys, town halls, and floor-level leadership support retention
• Why AI should be treated as another data point that sharpens critical thinking, not as a replacement for judgment

If you are an HR or People Ops leader thinking through AI adoption, workforce planning, or growth without reactive hiring, this episode offers a practical look at how data, operations, and people investment can work together.

Additional Resources:

• Cleary's AI-powered HR Chatbot
• Future Proof HR Community
• Connect with Melissa Ganchev on LinkedIn

We're done with hiring for many our manufacturing associates.

2

We don't need to hire anymore.

3

I think we've hired eight in total.

4

Our attrition rate has historically

been under 2%, which is just.

5

Incredible.

6

This pilot has been able

to challenge that for us.

7

It's been able to take us

in another direction to say.

8

Maybe we don't, maybe we

haven't looked at that.

9

Jim Kanichirayil: When you hear

manufacturing sector, when you think

10

nutritional supplements, when you

think family-owned company that's

11

been around since the 1970s and is

looking to aggressively grow going

12

forward, you don't immediately

associate that sort of organization

13

with being one that is gonna be heavily

leading into AI to drive that growth.

14

And even if you were to think that

company would be investing in AI, you

15

would think that the direction they

would go would be an efficiency play.

16

It would be, "Let's see how many

people we can have do more with less.

17

Let's see how efficient that we can be.

18

Let's see where we can cut costs and

use AI as a way to stop to identify

19

those areas and also fill in the gaps

where AI could replace actual people."

20

That's what you would think when

you hear manufacturing, nutrition

21

supplies, family-owned company looking

22

If you thought that,

you'd be completely wrong.

23

The story that we're gonna talk about

today is about an organization that's

24

in all of those spaces and leveraged

AI to maximize not only their people

25

footprint, but also hire smartly in a

way that doesn't have them regularly

26

rolling people off and laying people

off as their capacity changes.

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The story that we're gonna be talking

about today involves how you leverage

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AI to model how you take each of your

plants to the next level, and you maximize

29

every employee within the organization,

and you set up the infrastructure and

30

the support system for them to get

to the next level in their career.

31

And that's the story that

we're gonna talk about today.

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Joining Us today is Melissa Ganchev.

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She's guided by strategy, data,

and deep experience, and she

34

believes that alignment between

performance and core values is key

35

to ensuring that any organization

and its people grow and thrive.

36

She's a project manager and people

strategist at heart, and she excels at

37

driving long-term initiatives such as core

value systems and compensation philosophy

38

from ideation through implementation.

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She's a fan of using data as a

launching point, and she often

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gathers feedback in focus groups,

workshops, and engagement surveys.

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Melissa brings over 20 years of experience

across healthcare, e-commerce, financial

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services, and other industries, from

startup environments to Fortune 500

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companies, and She's a certified coach

with a master's certificate in human

44

resources management from Villanova.

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She's a recipient of 2022's Best HR

Team from the Best Companies Group and

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she's a finalist for OnCon's 2026 Top

100 Human Resources Professionals Award.

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Melissa, welcome to the show.

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Melissa Ganchev: Thank you.

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Thank you for having me.

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Jim Kanichirayil: Yeah, I'm looking

forward to this conversation.

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I think this is gonna be interesting

for me at least, because we're gonna

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tell a scaling story and it's gonna

be a story that doesn't involve

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throwing bodies at a scaling solution.

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So that's that's gonna be an interesting

take because most times when we're

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having scaling conversations,

people are always thinking let's

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just let's just hire more people.

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So it'll be interesting to get

into the mechanics of how you

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scaled without having to throw a

whole bunch of hiring against it.

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But before we do that, I think it's

important for us to set the stage.

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So why don't you share with us a little

bit of the details of the company

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and exactly what you're trying to

accomplish from a scaling perspective.

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Melissa Ganchev: Absolutely.

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So I'm with Universal Nutrition and

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They were actually the founder of the Pill

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it's always been a company that's been

rooted in supplements for bodybuilding.

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But over the years we've really focused on

more of the strength training community.

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So how do we optimize nutrition?

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Just to really enhance like

performance for anybody that

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wants to get into fitness.

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In 2024, we were around 85 million

in sales, and last year we hit a big

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milestone of a hundred million in sales.

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And so we're growing, which is great

because that's the landscape that

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we're in right now with so many, so

many other one hit wonder companies

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around there, but we've really been

grounded so much in, in science and just

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practices over the year that that we're

here to stay and we're here to grow.

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When we have decided really on, on growing

and trying to continue to grow we've only

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had between 185 and 200 people really

steady for the last five years or so.

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But everybody's been pretty much at

capacity and we're like how do we keep

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growing if we don't throw bodies at it?

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So our initial assumption was,

let's hire more people and

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let's bring in more people.

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But with AI we've challenged

that, which is exciting.

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Jim Kanichirayil: I wanna

hit rewind a little bit.

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Company's been around since the seventies.

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It hit a major milestone recently.

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Where it hit a hundred million in

revenue and you want to continue the,

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that growth, what's behind the scenes

that is driving that sort of growth?

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Because that seems like a different

sort of mindset when you've spent,

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50 years growing at a steady pace,

and then you hit a hundred million

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and you want to accelerate faster.

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That seems like a pretty drastic change.

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So what's driving that?

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Melissa Ganchev: Absolutely.

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So the first like 35, 40 years,

we were really risk adverse,

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just growth, super slow, steady.

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doesn't matter what's going on

in the economy, we're gonna take

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a lot of risk aversion makes.

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And we were able to establish ourself

with just a really strong foundation.

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That's how it became really

like this best in class.

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Company around like pill packs

and protein and creatine.

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Over the last six years or so, we

went from being really more of like

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a family run company to a little

bit more of a tra traditionally

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run company, taking more risks,

investing more back into the company.

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Purchasing new equipment doing

modifications on driving more efficiency.

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really reinvesting back into our

employees and into the company that's

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given us like this launch to be able to

be more profitable and scale quicker.

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Jim Kanichirayil: So when I hear about

the transition from being a traditionally

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run company to something that's closer

to more of a current corporate structure,

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I think about the people that are in

the organization that might have some

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tenure and they might be sitting there

thinking this is not what I signed up for.

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This is like completely different

than what we're used to.

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We're going against our culture.

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Did you encounter that?

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And if you did.

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What did the messaging and actions look

like to bring those people along who

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maybe weren't into the new direction

that the company has decided to take?

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Melissa Ganchev: It was all

about grounding in our roots.

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We wanted to take our employees

like on this change journey with us.

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The.

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That, that we are still a

family owned organization.

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We still run like a

family owned organization.

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We still have relationships with them.

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They still come on site, say hi they're

still there from an advisory perspective

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and a funding perspective as well too.

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So that hasn't changed.

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The people really haven't changed either.

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We have people working in sales

marketing on the manufacturing

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floor that have been here for 40

years or 30 years, or 20 years.

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And so the, that makeup and

foundation hasn't changed.

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What has changed is the excitement around.

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It's Hey, we've never invested

back into our company.

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We've never bought the newest

equipment where we never bought the.

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Coolest new things and

now we're trying it.

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And so it's helping these employees

come along that change journey with us.

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Helping them also have a voice too.

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'cause they're doing the work.

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They're, they have the

opportunity to share with us what.

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Can change and what

could make things better.

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And so we're giving them outlets

as well too, to show their voice.

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We just want them to come along

with us and they're excited about

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it 'cause they're seeing the impact.

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Having town hall meetings and sharing

the insights and seeing the growth

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trajectories and seeing what the

difference is now it, it could have been

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done a lot of different ways, but I'm

excited that it was done the way that was.

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And people are excited about

where the company's going.

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Jim Kanichirayil: Got it.

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One of the things that you mentioned that

caught my attention was this increase

148

in investment in, into the company.

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Some of the things that you mentioned

seem like capital investments.

150

Tell us, tell me a little bit more about

some of the people level investments

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that have happened as you are shifting

directions into this new way forward.

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Melissa Ganchev: Absolutely.

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One thing off the top of

my head is better benefits.

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For a really long period of time,

we've never had any increases

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into our employee benefits.

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Another thing that we've done

is making sure that we have

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more benefits or better benefits

better, like more robust offerings.

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Increasing in employee self-service,

making sure that people have access

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to what they need to have access

to, and information that they have.

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That they need as well too.

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also grown our HR team.

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Four years ago, HR didn't exist here.

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And so now we're able to be, a

resource to our teams and drive more

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like people strategy as well too.

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Helping people become more

efficient, investing into

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training, investing into education.

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Lots of different opportunities

for people to have career growth

168

within the organization as well.

169

Jim Kanichirayil: So one of the things

that we're gonna spend time talking

170

about in this conversation is about

becoming more efficient as you scale.

171

So you don't necessarily have

to hire a whole bunch of people.

172

But before we dig into the mechanics

of that, I think it's important to

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talk a little bit more about how.

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The learning and development and

training pieces of the employee

175

lifecycle fit into that scaling journey.

176

So tell me a little bit more about how

career planning, succession planning,

177

training, learning and development,

all of those things have taken shape

178

as part of that scaling journey.

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Melissa Ganchev: Absolutely.

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It really starts at the top.

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Last year we did our first pilot

program for our executive team on

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a leadership development program.

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Vetted it out and we're rolling it out

to our supervisors and managers and other

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leaders in the organization as well too.

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Better people, managers are

able to see, really the team

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and the growth that they have.

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Being able to invest back

into them knowing that they

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can lead others even better.

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then it's also that the employee

level, we're launching out employee

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Lunch and learns this year.

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Just for general skillsets, doing

everything from like Excel training

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to how to do conflict resolution.

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All these critical skills will be able to

enable our employees to continue to grow.

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Another thing that we're doing is

looking at career pathing where

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we never really have had different

levels through the organization.

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We're gonna be creating these career

paths to say, Hey, if you're an admin in

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sales, here's additional opportunities

that you can continue to grow in.

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Operations.

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Oh, you showed an example on how

you can make the equipment run

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more efficient or more effective.

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Great.

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Have you ever talked about

maybe operational excellence

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or continuous improvement?

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Brought in an opex and

continuous improvement later.

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Last year we brought in an a brand new

VP of ops that's been helping us with

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these scaling efforts as well too.

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So by giving our employees a lot of

access to data and insights, we're

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able to now train them on reading those

information and doing troubleshooting.

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This has been enabling them to take

more than just that first step of, I'm a

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manufacturing associate and this is only

my role to now looking at it as a way of.

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I see that there's an issue here.

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How can I troubleshoot

that and how can I fix it?

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And so it's empowering them as well to be

more than just a box and a [inaudible].

214

Jim Kanichirayil: Got it.

215

So when I hear what you're describing,

this the thing that I'm curious about is

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when you think about all of this stuff

from an employee lifecycle perspective

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was this all part of the rollout strategy

as you worked on more aggressive growth,

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or did it just come up as needs items

that you identified as you, you're

219

already on the journey for growth.

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Melissa Ganchev: A little bit of both.

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There's always the opportunity

to, challenge as you're growing.

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But when I joined the organization I was

like, wow, we don't have career pathing.

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We haven't really had a built in

leadership development program or lunch

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and learns or soft skills development.

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so that was part of my, focus from

a people strategy perspective,

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knowing that really goes hand in

hand with scaling an organization.

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You have to invest back into the people to

be able to really expect more out of it.

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You can't give a plant no water,

no sunlight and expect it to grow.

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The same thing with our employees.

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You can't not invest back

into them and expect them to

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take on more or to learn more.

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Jim Kanichirayil: Got it.

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Okay, there's a lot of things going on

that's helping to lay the foundation for

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this more aggressive growth strategy.

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And you mentioned this early on

in the conversation, that there's

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an AI component in it as well.

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When I think of nutrition supplements.

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That sort of space.

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I don't immediately think ai.

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So tell me a little bit more about how

AI fits into this growth strategy and

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growth initiative for the organization.

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Especially when considering that this is

not generally a sector that's known for

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being innovative from an AI perspective.

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Melissa Ganchev: Absolutely.

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We're just getting started with ai.

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And it's exciting to see some

of the initial impacts that

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are coming down the pike.

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We have one building that

runs really efficient.

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So if you were to look at the

manufacturing term of OEE overall

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equipment effectiveness as a KPI,

this building is always best in class.

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And it's actually the building

where we see the biggest piece

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of our revenue come outta and.

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When we were looking at headcount

planning, so we, we finished our strategic

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planning and then I got to meet with

each of the leaders, say great, now we

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have our business plan for next year.

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What headcount do we need to make sure

that we're able to meet those needs?

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Originally we were looking at we

need four, let's say 15 new people

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for our manufacturing teams.

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Was like, okay, that makes sense.

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There's, there's a lot going on and.

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This group of cohort people are

gonna go to Y, and Z buildings.

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What we didn't initially look

at is how are, how efficient

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are the buildings being run, how

efficiently are the lines being run?

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just knew that we were investing

and we were bringing in two new

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production lines into that building,

so we needed more headcount for it.

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Our VP of ops came in and was like,

let's pause this for a second.

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Let's do some modeling.

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Let's bring in some AI modeling and

really see what's, is the most efficient

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way of looking at things and what's the

most effective use of the people that

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we have and the equipment that we have.

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Knowing that different equipment

lines run at different OEE

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and what we ended up finding.

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Is that one of our slowest lines where

we were gonna add some additional

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people to it to try and increase it.

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It actually wouldn't help when looking

at the AI modeling, we ended up finding

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that we should do a second shift on

our more efficient lines, that would.

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Really exponentially be able to

bring us more product output not

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more people or needed to run those.

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It was more of a shift of resources

from one room of operating

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into a new room of operating.

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Jim Kanichirayil: There's a handful

of really interesting things coming

282

out of what you just described.

283

I think the first thing that

I'm reacting to is you've, I you

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applied this at the most productive.

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Segment in the business.

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And when I think about that, and when

I think about doing it, running an

287

experiment on anything, I might go the

other direction and say let's apply

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it to something that's underperforming

or something that's low risk.

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Because if it goes sideways and

something that's low risk, there's

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less of a business impact to it.

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So walk me through the decision

process that happened that identified.

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Your biggest revenue generator as

being the subject of an experiment.

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That sounds scary in my mind.

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So what were the in internal

conversations about that?

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What was the reasoning behind that?

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Melissa Ganchev: A lot of it

comes down to our culture here.

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We like to take.

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We like to take ownership.

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We like to try new things.

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One of our company values is

show grit to disagree and commit.

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So it's go in there, try it

out and let's see what happens.

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So that's a little bit more about

like the aura of where we come from,

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but when we were looking at why

this building, we already saw how

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efficient and how effective we were.

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What could it hurt?

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What could the if a model showed

us something different, it could

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increase us even just a hair more.

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The ROI on the end would be significantly

better knowing that this is, again,

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where 60% of our revenue ends up coming

from when, when we saw some of the

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other buildings and some of the other

options that we could implement this

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modeling into, we knew that there was

a whole lot more low hanging fruit.

312

there were a lot of things that we could

do immediately that we didn't necessarily

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need a model to tell us that this would

increase our efficiency or effectiveness.

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We've invested so much back into

this one building where if the

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model showed us how to be better.

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Then it would be a win.

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'cause we feel like we had already

done everything to push the

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envelope, to push it to its max.

319

And so that's where the track came from.

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We didn't really feel that there was

as much of a risk going here because

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we felt that we've already exhausted

all of our opportunity on optimization.

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the AI model was able to show us

like, Hey, here's some tweaks.

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Try it this way.

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By eliminating one of our production

lines that were the slowest production

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line and moving it into a different

production room, we're actually gonna

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be able to see increased efficiency from

that line, that we had never seen before.

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We really saw it as a win-win.

328

Our OE is not gonna change ag

again that much just because

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it is already so optimized.

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The result of moving things around and

not necessarily having to bring in.

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15 headcount, maybe only bringing in

six or seven across the entire year is

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going to not only save us resources,

it's going to make sure that we're also

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being mindful of everybody's roles.

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You look out there in the marketplace

and people will hire people really

335

quick without thinking of those

long-term impacts, and then they have

336

to do some sort of reduction in force.

337

This gave us the opportunity to.

338

To pause and to really be mindful on only

bringing in the people that we truly need.

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Jim Kanichirayil: So when you

describe that I draw this into

340

a capacity planning conversation

and you've optimized for capacity.

341

But where I get stuck is this, like

philosophically when I'm leading

342

teams and generally when I'm trying

to figure out, okay, who on my team

343

needs what sort of development to get

to the next level I'm always thinking

344

about, okay, how do I put this person

in position to play that are strengths?

345

I take a look at what

they're good at and look at.

346

Improve focusing on those things and then

just manage those areas of improvement.

347

And I don't put a lot of

energy into those things.

348

So I see the parallel in

what you're describing there.

349

When you

350

Thomas Kunjappu: This has been

a fantastic conversation so far.

351

If you haven't already done so,

make sure to join our community.

352

We are building a network of the

most forward-thinking, HR and

353

people, operational professionals

who are defining the future.

354

I will personally be sharing

news and ideas around how we

355

can all thrive in the age of AI.

356

You can find it at go cleary.com/cleary

357

community.

358

Now back to the show.

359

Jim Kanichirayil: describe what you

just described, it sounds like a

360

classic sort of capacity planning

problem that you're trying to tackle.

361

And one thing that I'm thinking about

is you've identified, the highest

362

performing line that you're running

this experiment on, and you reallocated.

363

Resources in a different way

to add another shift, which

364

would've given you more bang for

the buck in terms of results.

365

But when I think about this in the when

I step back and think about this in

366

the abstract, I look at it this way.

367

If you have something that's

already performing really well,

368

if you get a 1% increase on

something that's performing well.

369

The total output isn't gonna

be as massive, theoretically as

370

something that is underperforming,

and you take it from like the 25th

371

percentile to the 75th percentile.

372

How big of a gap existed between your

top performing building, which is where

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you ran this experiment in everyone else?

374

Was it.

375

So clear when the model came out that,

yeah, this is where we need to focus on.

376

Because when I think about it, the

additional benefit of improving your

377

worst performers to even average, that

can be significant within an organization.

378

Melissa Ganchev: yeah, it's actually

let us do more pilots and more

379

things in our other buildings.

380

So since.

381

We had last chatted, we are

looking to run out a new dashboard

382

in one of our other buildings.

383

And this new dashboard is gonna

show real time, live metrics to our

384

employees so that they're able to

troubleshoot on like in real time.

385

Instead of having to see like

later in the day oh, where could

386

we have been more efficient or

more effective, or make changes?

387

So that's one thing that we're

doing in another building.

388

And then in another building, we're doing

some massive like CapEx improvements.

389

We're bringing in new blenders

like significant investments

390

in just new equipment.

391

Larger scale equipment.

392

So you think we're, universal nutrition.

393

We have a lot of proteins,

a lot of creatines.

394

We actually make those

blends all in-house here.

395

so we've brought in blenders that are now.

396

Like five times the size

of our original ones.

397

So blending our own protein

blends, blending our own tablet

398

blends before they go into.

399

Us actually making our own tablets

is going to drive the efficiency and

400

effectiveness into these buildings.

401

So it was more of, we have a

bunch of ideas that we want

402

to see what works the best.

403

And so the modeling seemed

to be the best option for.

404

This one building where we

do like our pill pack lines.

405

The dashboard seemed to be like a

really great option for us to do

406

in our powder fill lines and really

those CapEx improvements while scaling

407

our equipment for the, just for the

customers and the amount of orders

408

that we have coming in now so we can

have bigger batches and produce more.

409

That seemed like the best

in the other options.

410

So it was more of a.

411

Here are all of our buildings, here are

all the ways that we want to improve.

412

Let's make sure we match

which ones are the most.

413

Just will give us the best results.

414

Jim Kanichirayil: Got it.

415

So when you think about some of

the assumptions that you were

416

working off of before you started

running these experiments, what did

417

that look like from an employee?

418

Growth perspective, how many employees did

you, were you thinking you needed to add?

419

And now that you've wrapped up one pilot

and are launching multiple other pilots in

420

your other buildings, how has that hiring

assumption changed as you're getting more

421

information from these efficiencies that

you're putting into the various buildings?

422

Melissa Ganchev: That's a great question.

423

I look back at my days when I was at

like a Wayfair and in logistics, it's

424

a, an industrial engineering equation.

425

It's okay, a hundred trailers are

coming in and a hundred pallets

426

are on each of those trailers.

427

And we know one employee

can unload a hundred.

428

in 16 hours.

429

And you know that's your head count.

430

You need one head count for two

full eight hour shifts per the

431

equation or like per trailer.

432

And so at a total, you know that

you need a hundred people to

433

meet those a hundred trailers.

434

And so it was like this

really set equation.

435

you look at us here, we had a similar

equation on saying okay, sales is at this.

436

We're expected to do this many units.

437

This is how many production

runs we're gonna have to have.

438

that was the look that we had back

in September when we were done with

439

business planning for the year.

440

And over the entire organization, we're

like, wow, we're probably gonna need

441

to bring in 30 more people this year.

442

And that's a significant growth for a

company that's been hovering around 185

443

people for, more than five years now.

444

And by doing these evaluations,

these assessments, these different

445

models leveraging the, these AI

models, like what would it look

446

like if we moved these lines?

447

What would it look like if we changed

these shifts and running it through

448

these models to get like more, just

better data back for us so that

449

we're able to make those decisions.

450

We're done with hiring for many

our manufacturing associates.

451

We don't need to hire anymore.

452

I think we've hired eight in total.

453

And we're good.

454

We're excited about the

people that we brought in.

455

A lot of them came through

an employee referral program.

456

We have great employees here.

457

you've been on our manufacturing

line for less than five years.

458

You heard me right there.

459

Five years, you're new.

460

Because again, so many of them have

been here for 20, 30 and 40 years, and

461

so we, we don't want to have tremendous

amount of new people if we don't need to.

462

And that will allow us to grow now.

463

With substantiated assistance

in the areas that we need it.

464

Maybe it's one person here, one person

there, instead of bringing in like a whole

465

new production line worth of employees.

466

Jim Kanichirayil: So that makes sense.

467

I think one of the things that

caught my attention in your answer

468

is the level of tenure that you

have within the organization.

469

I find that unusual for sort of

a manufacturing type environment.

470

And the thing that I, so there's two

elements that's tied to that one.

471

What's been the formula for

retention within the organization?

472

And then the other side of the

equation is as you get busier.

473

You have less people

to share the workload.

474

So I start thinking about like burnout

risk or unplanned attrition where

475

people actually lead the organization.

476

How are you maintaining that 10 year

culture that you've already had?

477

And what are the things that

you're doing to prevent burnout

478

and planning for that unexpected?

479

Departure that you didn't anticipate.

480

It se seems like a very lean operation and

that creates some risk in and of itself.

481

Melissa Ganchev: Yeah.

482

Our attrition rate has historically

been under 2%, which is just.

483

Incredible.

484

And especially when you're taking

into consideration, and that's the

485

manufacturing team as well too.

486

A lot of it comes down to the

culture that we have here.

487

Everybody's really personal.

488

We have great relationships with our team.

489

Our leadership is walking

the floor all the time.

490

We have focus groups.

491

We have our employee surveys, we have

action plans, we have town halls.

492

I think it's a lot of the

communication because it really

493

feels like a tight knit community.

494

There are a lot of friends and

family that work together, that these

495

production lines become best friends.

496

And so you have groups of people that

have been together for so long have

497

really known how to work with each other.

498

It's also making sure that we're support,

supporting the culture, that we have here.

499

A lot of our teams to head

out for lunch together.

500

It's great.

501

They, there's that comradery.

502

It's just something that's really

unique here around the culture that

503

has been ingrained since day one.

504

I'm sure it has a lot to do

with the family that started

505

the company is just everybody.

506

A true person, and there's

a lot of respect for that.

507

It's, you're not a number.

508

We know people by names.

509

We know people by their

families as well too.

510

And that's just ingrained and woven within

the culture here at the organization.

511

We have built into the schedule a really

reasonable production schedule where,

512

most of the time our employees are,

there's, for our manufacturing associates,

513

they're here from 7:00 AM to 3:30.

514

have a, different paid breaks

throughout the day as well too.

515

There's we've built in plenty of

time for cleaning of the equipment.

516

I'm not so worried about

burnout with our employees.

517

Last year we really didn't have any

overtime and employees continued to ask.

518

For overtime.

519

They've liked the overtime.

520

We've had it in years past.

521

This year when we were looking

at the production schedule it was

522

actually built out without overtime.

523

If we see that there's going to be any

increase in production rates, we know

524

that's a trigger that our employees have

asked for to be able to work for it.

525

we do also have a really robust

paid time off plan, so we are able

526

to adjust schedules around that.

527

We have flexibility with the employees

that because they've been here for so

528

long, they've worked on so many of our

different lines that it, it creates

529

a lot of like redundancy or support.

530

So let's say one of our lines was a

little bit slower, they ended up early.

531

Another line is really busy.

532

We have the opportunity to because people

are cross-trained to be able to move e

533

employees from one line to another line.

534

So we have that built in support

system between all of our

535

employees that we have here.

536

Jim Kanichirayil: Got it.

537

One of the other things that you

mentioned is that when you first

538

ran the pilot at your top producing.

539

Plant yielded some pretty good results.

540

And now you've expanded

that across other plants.

541

Are you running the same sort of

pilot at those other plants or

542

are there other areas of focus or

are there other things that you're

543

focused on at those other pilots?

544

Melissa Ganchev: We know in some

of the other pilots that there are

545

different areas of optimization.

546

Like some of our other lines are not as

optimized as they were in our original

547

building where we did the pilot.

548

however, it is a similar

experiment that we're running.

549

A lot of it is on like the efficiency

of the equipment, the efficiency of the

550

employees the time that it is to complete

a, to complete an order coming through.

551

What's different about this one is

that we're actually using dashboards.

552

So we're using real time data for

employees to see was something

553

that was not done in the first one.

554

And so what we're looking here is how

can we educate our employees to do

555

real life problem solving on the fly.

556

If they're looking at this dashboard

and they're starting to see oh,

557

this is yellow, or this area

is red, what can we do to help?

558

To help mitigate that in real time instead

of looking back and being like, wow, that

559

run was slower than we did the other day.

560

What was it?

561

So it's enabling a lot of that

root cause analysis right then and

562

there to rolling out the dashboard.

563

We're doing these trainings with all of

our employees in this line, which we're

564

excited about giving them more, more.

565

Skills, more opportunity to really

have that take ownership mentality.

566

And it would really just better

them and as an employee and becoming

567

like the subject matter expert into

the lines that they're working in.

568

Jim Kanichirayil: So now as you look

back on the first pilot that you've

569

run and you're looking at some of

these other things that are launching,

570

what were the key lessons that you

learned from the first pilot that you

571

applied to future pilots and expansion

of the expansion of your AI strategy

572

across all the different plants.

573

Melissa Ganchev: Yeah, I think it's

just pause on your assumptions.

574

Like it, I think it was so easy when we

were looking at our business plan for

575

this next year to say oh, here it is.

576

We're growing at this amount.

577

Obviously we need more people.

578

How many more people do you need?

579

And you go into this comfort zone of

obviously scaling means more people.

580

This pilot has been able

to challenge that for us.

581

It's been able to take us

in another direction to say.

582

Maybe we don't, maybe we

haven't looked at that.

583

Maybe we haven't thought

through that one piece.

584

And it's allowed us as leaders of the

organization to also pause and not

585

just rely on general assumptions or

previously learned processes in scaling.

586

It's just another data point.

587

I think that's one thing that's really

insightful about AI is it can help

588

challenge the critical thinking so

that we are able to look at things

589

maybe from a new lens or a new angle.

590

If I don't think we had ever expected

to say, maybe we need to pause line five

591

and do a, do another shift on line four.

592

Because four is more.

593

More efficient.

594

And we would've thought oh, maybe we

just need more people on line five, and

595

that would make it more efficient, so

it's all about just challenging some

596

of the norms and some of the comfort

zones and not worrying about stepping

597

out and trying these new things.

598

Jim Kanichirayil: Great stuff.

599

If people want to continue the

conversation, what's the best way

600

for them to get in touch with you?

601

Melissa Ganchev: I've

love to link in with them.

602

I'm on LinkedIn.

603

Jim Kanichirayil: Awesome stuff.

604

I appreciate you hanging out with

us and sharing with us what you've

605

been doing in terms of your AI

initiatives within the organization.

606

I think your story is pretty unique 'cause

we haven't had really too many guests from

607

a manufacturing perspective on the show.

608

And we certainly we haven't had

any conversations regarding how

609

AI could be applied to identify

efficiency points at the plant level.

610

So that's a pretty

interesting conversation.

611

That I think our audience is

gonna find really valuable.

612

With that being said, I think the things

that stood out to me in this conversation

613

outside of what you did from the plant

level stuff, is the foundational work that

614

was done before this stuff was launched.

615

And the thing that I want to

point out is that whenever.

616

You factor in a shift in

organizational direction.

617

Anything that's focused more on increased

efficiency, increased growth at a

618

faster pace than you're used to, that

can lead to its own level of headaches

619

or concern at the employee level.

620

And what I liked about.

621

The approach in terms of how

you handled it is that you

622

demonstrated in a willingness to

invest at the people level first.

623

So you took a look at succession planning,

the employee lifecycle investments

624

into training and development, and

creating more opportunities for those

625

for those existing employees as a

pathway to increasing growth as well.

626

Typically, when we hear

stories about we're gonna.

627

Sell more product, we're

gonna become more efficient.

628

We're gonna embed a ai.

629

All of those conversations involve

a significant amount of the way that

630

we're gonna realize these things is by

running leaner and cutting away from

631

what actually benefits employees and.

632

Your story is a little bit, is

significantly different from the common

633

narrative that we see across businesses.

634

And what's interesting about this is that

the results show up in your environment.

635

Typically when we think about

manufacturing environments,

636

we're thinking the lifecycle

of a typical man manufacturing

637

employee is less than 12 months.

638

And most manufacturing employee

environments have turnover.

639

That's 40% or more.

640

That's industry standard.

641

And here you're doing these things.

642

You're focused on growth, you're

investing in your organization, and you

643

have a retention or you have turnover.

644

That's roughly 2%.

645

That's.

646

That's a model that is an

outlier within the space.

647

And the reason why I'm calling all these

things out is that when organizations

648

are looking at implementing AI as a

way to achieve some organizational

649

objective, that objective.

650

Can be strengthened by investing

in the employee group first and

651

demonstrating your commitment

to building a strong foundation

652

that's employee led versus AI led.

653

Melissa Ganchev: Yeah.

654

Jim Kanichirayil: and I think that's

an important lesson for everybody

655

to pay attention to as they work

on their own AI initiatives.

656

So I appreciate you hanging out with

us and sharing with us your story.

657

For those of you who have been

listening to this conversation and

658

liked the discussion, make sure

you leave us a five star review.

659

And subscribe to the show and then

tune in next time where we'll have

660

another leader hanging out with us

and sharing with us the stories of how

661

they're using AI to future proof HR