Sam: Things like brake fluid and chemical manufacturing
may not seem like gee-whiz artificial intelligence,
but we all may be benefiting from AI already
and just not know it.
Today, were talking with Chris Couch,
senior vice president and chief technology officer
of Cooper Standard,
about how were all benefiting indirectly
from artificial intelligence every day.
Welcome to Me, Myself, and AI,
a podcast on artificial intelligence in business.
Each episode, we introduce you to someone
innovating with AI.
I'm Sam Ransbotham,
professor of information systems at Boston College.
I'm also the guest editor
for the AI and Business Strategy Big Ideas program
at MIT Sloan Management Review.
Shervin: And I'm Shervin Khodabandeh,
senior partner with BCG,
and I colead BCG's AI practice in North America.
Together MIT, SMR, and BCG
have been researching AI for five years,
interviewing hundreds of practitioners
and surveying thousands of companies
on what it takes to build
and to deploy and scale AI capabilities
across the organization.
And really transform the way organizations operate.
Sam: Today were talking with Chris Couch.
Chris is the SVP and the chief technology officer
for Cooper Standard.
Chris, thanks for taking the time to talk with us.
Welcome.
Chris: You bet. Thank you very much.
Sam: Why don't we start by
learning a little bit about your role at Cooper Standard.
What do you do now?
Chris: I am the CTO of Cooper Standard.
We're a tier-one global automotive supplier.
I'm also the founder and CEO of an AI startup
called Liveline Technologies
that has come out of some work that we did
as R&D within Cooper Standard.
We provide components in the spaces of
vehicle sealing and enclosures,
as well as fluid handling,
whether it's brake fluid or coolant,
all the fluid systems in the vehicle.
We also invest in material science technologies
that we believe can have an impact
beyond automotive.
Many of our products may be not visible
to the average consumer.
In fact, some of our products,
hopefully nobody has to worry about them
when were moving fuel around your vehicle,
but they're critically important to the driving experience
and having a safe and reliable vehicle.
For example,
we developed a brand-new
category of polymer
that we have called Fortrex.
Fortrex provides a much
better seal
around the doors and windows in your vehicle.
Why is that important?
It's important especially
as we move into an electrified-vehicle world.
As engine and transmission noises
decrease, because there's no more gasoline engine.
Other sources of noise become more prevalent,
and the largest one of those
is the noise coming in
due to wind around your doors and windows.
By providing an enhanced sealing package for those,
we believe we've got the right products
to service an electrifying world.
Sam: How is artificial intelligence involved in that
development of the polymer?
Chris: We spent a lot of time and money
coming up with advanced polymer formulations.
A lot of it historically has been trial and error.
That's what industrial chemists often do.
We used AI to develop a system
that advises our chemists on
the next set of recipes to try
as they iterate toward a final solution.
We've found dramatic reductions,
in many cases, with that approach.
Dramatic means reducing
those R&D loops 70% or 80%.
Sam: Got it,
but before we talk more about Cooper Standard's
success with AI,
can you tell us a bit more about
your own background and career path?
Chris: Well, I think
the best way to describe myself
is a lifelong manufacturing addict,
first of all.
As a kid, I took apart everything in the house
probably got hit with wall current more than once
Explains a lot about me today, I suppose.
I was the kid that built their first car out of a kit.
I was a hard-core mechanical engineer
with a focus on manufacturing and controls in school.
My side projects include things
like building autonomous drones that
fly at high altitude.
I'm just a manufacturing nerd.
That has really served me well in my career.
I spent the first third of my working life
in a Japanese company.
I worked for Toyota
and went and joined them in Japan.
Spent a dozen years with them
designing and building and
ultimately being involved in plant operations.
I spent the next third of my career
running a P&L
for an automotive supplier
on the business side,
mostly based out of Asia,
so I have a business bent as well,
which may color a lot of what I say today.
Then, the last third or so of my career has been
in CTO gigs.
I'm in my second one here.
I'm again at an automotive supplier,
but we get our fingers into all kinds of interesting
tech domains, just given what's happening
in the world today,
whether it's material science or AI.
So here I am.
Didn't really expect to be doing
my second job here,
if you would have asked me two years ago,
but it's certainly been
a lot of fun,
and were excited about delivering some impact
with these technologies.
Shervin: Chris, tell us about the open innovation
at Cooper Standard.
What is that all about?
Chris: You know,
as we looked around at our tech portfolio
a couple years ago when I joined the company.
I was, first of all, overwhelmed
by the different domains that we really
had to compete in.
I mentioned materials science earlier,
but there's different aspects
of manufacturing technology and product design.
The whole topic of analytics and AI
right, that were going to talk about today.
And was very convinced
that there was no way that we could do it all ourselves.
Cooper Standard isn't a small company.
We're just shy of three billion in revenue,
but were not the biggest.
And so open innovation
was really an attempt to reach out
and build a pipeline
to draw ideas and technology,
and maybe even talent,
from the outside world.
And so through that, we engage with
universities, with consortia
around the world.
We engage heavily with startup companies
and use that as a source of ideas.
In fact, our first
proper AI project, if you will, really came through that
open-innovation pipeline.
We partnered up with a brand-new startup
that was called Uncountable,
out of the Bay Area,
and they helped us develop a system
that would serve effectively as an adviser
for our chemists that make new formulations
for materials that we use all the time.
That wound up being a great accelerator for our R&D process,
cutting iterations out of those
design-and-test loops, if you will.
That was one of those big, aha! moments, right?
That there is a huge
potential to
accelerate ourselves in many domains.
We can't do it all ourselves,
and so how do we really build that external pipeline?
We now call it CS Open Innovation,
but that was the impetus.
Shervin: Sounds like a very, sort of
unique way of bringing
folks with different backgrounds
and different talents
and getting them all work together.
What did you find
was the secret sauce of making that happen?
Chris: I think
whether it's AI, whether it's materials science,
whether it's other domains,
my answer is the same:
It really is all about the ability to focus.
The reason that we, as many other companies,
have put in place
innovation pipelines and processes
and stage-gate processes
that govern innovation is because of the focus.
How do we quickly narrow down
where were going to allocate our precious
R&D dollars,
and how do we govern
those correctly?
So we think like a startup.
We're doing the minimal
investment to sort of answer the next,
most important question
and either wind up killing things quickly
or take them to fruition.
Shervin: And a fair amount of fail fast, and test
and learn,
and sort of go big behind things that are working
and shut down things that aren't, right?
Did I hear that correctly?
Chris: Exactly,
and that's not unique.
I think that
there's nothing special about AI-based projects, right?
We sort of think in the same way
and very quickly try to
motivate those with
a clear-eyed view of ROI.
Frankly, one of the things
that I think we've seen over the years
when it comes to analytics, AI.
Especially coupled with manufacturing and Industry 4.0,
ROI has sometimes been hard to come by.
A lot of creative ideas,
a lot of interesting things to do with data,
but the question is,
how does it translate to the bottom line?
And if that
story can't be told, even as a hypothesis
that were going to prove through the
innovation project, then it's
hard to justify working on it.
Sam: It seems like the opposite,
though, is that,
just to push back a little bit,
if you get too focused on ROI,
where are you going to get something
weird and big and unusual?
Chris: Absolutely.
Sam: How are you balancing that
tension between focusing on ROI and also
trying not to miss out on,
or trying not to be too incremental?
Chris: I think the stage-gate mentality is useful here.
I think in the early state,
we look at a lot of crazy stuff.
We have crazy ideas that come in through open innovation.
We have crazy ideas from our own
teams, and that's fantastic.
We don't hesitate to look at them
and maybe even
spend a little pocket money to chase them down
to some degree.
The question then is,
what are we going to invest in to try to productize?
That's really the next gate, if you will.
So absolutely,
the exploration is important.
We certainly do some of that.
I hesitate to say it almost,
but it's having some space to play
with ideas and technologies,
but then, when it's time to go productize,
right, you have to be clear-eyed on what
you're going to get out of it.
Sam: That seems like something that might differ for
an AI approach.
I mean, you said, Well,
AI's no different, just a second ago,
but it seems like, I guess I wonder if there is something
different about
these new technologies that may require
a little more freedom up front
to do something weird
than perhaps some others.
Chris: I think that's fair.
In our experience,
I think one of the differences with AI is that
you probably have less
familiarity with the tools and the applications among
the general technical population.
Right, so if you're talking to design engineers,
or talking to manufacturing process engineers,
they may have read some things
and maybe seen an interesting demo somewhere
but may not be so versed
in the nuts and bolts of how it works,
much less the nuts and bolts
of what would it take to scale that
at an enterprise level.
Because getting models running in a Jupyter Notebook
off of a
CSV file on your hard drive
is a whole different story from
production on a global scale.
Sam: Exactly.
Chris: And so I think just that lack
of exposure to the technologies makes it a bit different.
If were talking about
traditional robotics, or
maybe simpler types of IOT concepts,
plenty of engineers have a good clue
and maybe have used some things in their career,
but much less so when it comes to AI.
That is a difference, I would agree.
The good news is,
I am very convinced that
one of the wonderful things about AI
is that it is
cheap to pilot.
I was just sort of
making up a silly example about Jupyter Notebooks and
CSV files,
but that's a great way to explore some concepts,
and the cost of that
is very close to zero,
other than acquiring the knowledge
to do it.
And even then, I think that we've proven
over and over in our internal teams
that even the knowledge acquisition
is reasonably priced, if you will.
Shervin: Chris, I want to build on that point you said,
that AI is
relatively inexpensive to pilot,
and I agree with that because
we see, of course,
a proliferation of
proofs of concept and
different teams trying different approaches,
different ideas.
It also seems there is a fact
that AI is also quite hard to scale.
Chris: Right.
Shervin: And so why not sort of get your
reactions to,
something's easy to pilot,
quite hard to scale.
the real meaningful ROI will come
after you scale it.
So how do you make that transition?
And how do you sort of make
things that are really easy to pilot
and get excitement around
but then harder down the line to actually
embed into business processes and ways of working?
How do you envision that transition working?
Chris: Right. Yeah, it's a great question,
and it's definitely not
easy and maybe not for the faint of heart, right?
Because sometimes it does
take a leap of faith
in the ability to scale, ultimately.
The best I can say from our experience with Liveline,
We did some very early prototyping,
we thought we understood
the data science aspect,
but that was only the beginning.
That was nearly two years ago.
Only in the past months have we begun
to go to a global scale-out.
The only insight I have there is,
as you prototype, as you pilot,
you've just got to try to be as judicious
as you can
about selecting use cases that are realistic
and that everybody can get their
heads around and connect the dots to the ROI
at the end of the day.
Sam: How are you getting people,
you know once you've got these solutions in place,
what about the adoption within the organization?
How are you getting people to work on teams that
used to have human partners
and now have machine partners?
Chris: With Liveline, the basic concept of Liveline
is to automate the creation of automation
for complex manufacturing environments.
We're using machine learning techniques to design
the control policies
that we deploy
onto the lines to control machine parameters
in real time.
We think this is very useful for
attacking a diverse range of processes that have been
too complex or too costly to automate.
otherwise, and our early successes
have been in continuous-flow
manufacturing processes,
chemical conversion, polymer extrusion,
and we think there's a broad applicability to this
to areas like oil and gas, wire and cable, etc.
One of my fears when we
first got into plants to do live production trials
is that the plant personnel might view this as
sort of a threat, right were automating.
that has some negative connotations
sometimes in terms of impacts on people's jobs
and so forth.
But there's a couple of things I think really
gained us some traction,
and the reception has been quite warm.
In fact, the plants are pulling very hard now
to roll this out.
My attitude is to really democratize the information
and what's happening with the tool.
For example, we spent
quite some effort to make sure that
operators in the plant environment
had screens where they can
see data streams in real time,
that they couldn't before.
Sometimes they were data streams that we had created
for the sake of the machine learning.
We give them visibility into it.
We give them visibility, if they want,
into the decisions that the system is making.
We also give them the ability to turn it off,
haha, the big red button, right
if they're not comfortable with what the
HAL 9000's doing on their production line.
Also, we give them the ability to bias it.
If they feel,
based on their experience,
that the system is making parts
that are a little,
let's just say too thin or too thick,
they can bias it down a little bit.
I think that sort of exposure
and opening up the black box,
at least in the plant environment,
is very critical to
people buying in and believing in what's going on.
One of our learnings at Liveline with that
was the enhanced feedback that we get.
We have received several very
influential and useful ideas
from people that were really doing nothing more
than looking at data streams and watching
the system making decisions.
They asked good questions back to us
and gave us good insights.
Suggested
new types of data that
we could be tagging that might be useful
once they really began to get a little intuition
about what we were trying to do with the data science.
I think that sort of democratization,
if you will, of the system and
opening up and exposing the guts as it
does its thing has been, at least in this case,
one of the success factors.
Shervin: That's a great example. It covers,
Sam: Exactly. Haha
Shervin: Sam, I feel like it covers
a lot of what we've talked
about in our report,
in terms of different modes of human-AI interaction.
No black box.
Allowing the human to override or bias,
but also - I was going to ask you,
Chris, you hit the point before
I got a chance to ask you,
which is the feedback loop.
I guess my follow-on question is,
how has that feedback loop been working
in terms of
maybe skeptics
having become more sort of AI-friendly,
or more trust having been formed
between humans and AI?
Any anecdotes you can comment on that?
Chris: Absolutely.
I'll give you a great anecdote from
one of our plants in the southern U.S.
In fact, this was the plant where we did our final
pilot for Liveline
before we made the decision as a company to go
do a global rollout.
We first had the line running in
what we call automatic mode.
Gosh, I think it was about Q3 of last year.
One of the criteria for the pilot
was that we would do some A-versus-B runs.
Concept's very simple.
For these four hours, were going to run
with the system engaged in automatic mode.
For these four hours, were going to turn it off,
and you all can run the plant like you always do.
Then, over a series of days and weeks,
we'll add up the statistics about
scrap rates and quality
and unplanned line stops,
and we will quantify exactly what the value was.
We came to the first review point
a few weeks into that,
and as I sat with the team,
they sort of
pulled up chairs and looked at their shoes
and said, Hey, we have an issue.
We don't have the B data with the system off.
I said, Why is that?
They said, Because once the plant turned on,
they refused to turn it off again.
They don't want to run with the system disengaged anymore
because the impact was so significant to them
and helped them to operate the lines better
that they don't want to run with it off anymore.
That was very consistent with the type of
reaction we saw in other
pilots in Canada and our tech center in Michigan.
Shervin: That is great.
Chris: Yeah, that sort of feedback is very reassuring,
but again, I think that
from the get-go, having a philosophy of really
just opening up and showing people what's going on,
letting them look at data,
be participants in
problem-solving and tuning and enhancement,
really sets the stage for that emotional
connection and commitment to the project.
Sam: Those seem like some very different
ways of getting feedback to a system,
and then the other one you mentioned
was the idea of
suggesting new tags or new data to come back in.
Chris: Right.
Sam: I can see, for example,
adjusting the bias being a real-time
sort of feedback, and clearly,
pressing the red button would happen immediately,
I hope. That's the point of a red button.
How do the processes work for some of these
non-immediate feedback?
What do you do with the suggestions
for new data and tags?
Is there a process around those?
Shervin: This is, by the way,
sorry to interrupt your response,
this is Sam's and to some extent,
my chemical engineering background coming out.
Chris: And you can think of,
at least for Cooper Standard,
the majority of our lines are chemical processing lines.
We're taking
different types of compounds,
and were extruding them and,
in the case of thermosets,
putting them through oven stages,
200 meters of processes,
and a lot of it is chemistry, as it goes.
Yeah, so you guys are in your sweet spot.
Sam: What's the process for new data tags?
How do you formalize that process
in something that's less real time?
Chris: I'll give you a real example.
We were doing a pilot maybe a year ago,
and we had a process engineer who's not
a machine learning expert,
was watching the system run,
was looking at the data,
was looking at the analysis
that the machine learning has generated
and how predictable the outcomes
from the line were.
At that stage,
we weren't getting the results that we wanted,
and we were seeing variation in output in the real world
that we weren't picking up and predicting
in the silicone world.
As he was watching the line
he said, Look, I have a theory.
I have a theory that there's something going on
with one of the raw materials were feeding the line.
My theory is that material is more susceptible
to the history of temperature and humidity that
it's experienced as it was shipped to the plant.
Why don't we throw some data-logging
devices on those palettes
as we ship it around the country
and be able to look at that
time and temperature history
and integrate that into the analytics and
see if it would help us be more predictive?
Lo and behold, that was actually helpful.
That's a real-life example of a non-AI expert
interacting with the system
and using their human judgment to suggest
ways to improve it,
even though they can't write AI code.
Once we had exposed enough
of them to what's going on that they were
able to get some human intuition about
what's happening here,
then they were able to participate in the process.
That's a very powerful thing.
Shervin: Chris, I want to ask you about talent.
You've been talking about a lot of innovation,
a lot of cool ideas.
Different groups, internal and external,
coming together to really
experiment new things,
try new things,
make a really game-changing impact.
What do you think it takes to get the right talent,
motivate them,
keep them excited,
and sort of get that virtuous cycle of
excitement and energy and innovation going?
Chris: That's a great question.
I think the answer may be a little different depending on
what sort of technical talent you're talking about.
The way that we would think about a
manufacturing process engineer or controls engineer
may be a bit different how we think about
folks with different
skills in the world of AI,
and sometimes the talent is
in different places in the country.
I'm not sure there's a one-size-fits-all answer.
I think,
in general, when
we find people that we would like to
bring into the company,
I think if we can show them
that the sustained commitment to innovation
and doing cool stuff
is real,
that helps a lot.
I think being able to prove to people
that you're willing to
stay the course in what you're investing in
is part of the story.
Then the second thing that I think is important
is just the culture.
Having people believe that,
in addition to investments
in resource availability,
were just serious about being innovative.
We're just serious
about doing things better.
We're serious about winning through technology,
from the boardroom to the shop floor.
If that culture is real, people know it,
and if it's not real and you're faking it,
I think people know it.
You can't earn that in a quarter;
you've got to earn it over a couple or several years.
I like to think that we've done
a pretty good job with that, but that's really key
in my mind.
Sam: Chris, many thanks for taking
the time to talk with us today.
You brought out some quite interesting points.
Thanks for taking the time.
Shervin: Yeah, Chris, thank you so much.
Chris: You're more than welcome.
Hopefully, you can tell I'm excited about
AI. I'm excited about what it can do
in manufacturing as well as other industries.
I think it's going to be a fun future,
I'm looking forward to help build it.
(upbeat melody)
Sam: Shervin, Chris covered quite a few key points.
What struck you as particularly noteworthy?
Shervin: I thought that was really, really insightful.
Obviously, they've done a ton with AI
and a lot of innovation
and cool ideas, and they've put
many of those into production.
I felt a lot of the key sort of
steps in getting value from AI
that we've been talking about
was echoed in what he talked about.
The notion of
experimentation and test and learn.
The culture and importance of allowing folks to try ideas
and fail fast and then moving on.
The notion of focusing on
a few things to scale,
focusing on a lot to sort of test and prototype,
but a few to scale and invest behind.
I thought that was really interesting.
Sam: I thought Chris also had a nice blend
of both excitement and patience.
I mean, clearly excited
about some of the things they're doing,
but at the same time,
some of the initiatives were taking
two years or so to come to fruition.
That has to be hard to balance.
Being excited about something
and then also waiting two years for it to come out.
I thought that was a nice blend.
Shervin: Yeah, and also, to that point,
the importance of focus, right?
I mean, once you've picked it
and you've decided that this is the right thing to do,
and you're sort of seeing it
progressing toward that,
realizing that it's not time to give up now,
you just have to mobilize and double down on it.
The one thing that really struck me a lot was
the importance of culture
and how he said,
from boardroom
all the way to middle management,
they have to believe
that were behind it,
and were investing, and this is not just a fad.
That has to be sort of
permeating across the entire organization
to keep talent really excited and interested.
Sam: And it went down even into the
people who were using the systems.
I thought that was a beautiful example of
people who may have been so busy
trying to just get their job done
that they couldn't
step back and think a little bit.
He gave a great example of how
that freedom of having the machine do some of the work
let the human do things that humans are good at.
He covered almost all of the steps in our prior report
about the different ways of people
working with machines.
We didn't prompt him for that.
Shervin: The other thing I really liked
was his view on talent.
I asked him, what does it take to recruit and
cultivate and retain good talent?
He said, it's not a one-size-fits-all.
That recognition that
not all talent is
of the same cloth,
and different people, different skill sets,
have different
sensibilities, and they're
looking for different things,
but the common theme of
people that go there want a continuous
focus and commitment to innovation,
and they want to see that,
and maybe that's the common thing.
Then, data scientists and technologists
and chemists and engineers might have different
career paths and career aspirations,
but they all share in that common
striving for innovation.
Sam: I don't think he mentioned it,
but Chris is a Techstars mentor,
and I'm sure that some of that background
also influences the way he thinks about
different people and different ideas
and how that talent can come together.
Shervin: Yup, that's right.
He didn't mention it, but that's true.
(upbeat melody)
Sam: Thanks for joining us today.
Next time, we'll talk with Huiming Qu
about how The Home Depot continues to build
its AI capabilities.
Please join us.
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