Cultural Transformation How Manufacturers Are Changing to Embrace AI

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- How do you, when you're
talking with customers,
advise them or work with
them on their AI journey?
- One, you have to have
a long-term plan and
a big picture, without that
you will just be random
in terms of the things that you deal with.
Instill a data culture, and
go away from, "We always did
"it this way, we were always successful."
I have a gut feeling
to, let me look at data,
let me look at data, and
that is very, very key
for the new digital enterprise
and particularly how you want
to leverage artificial
intelligence as a new capability.
- Our team, before we
went through this digital
transformation in a logistic
space, were operational.
It was more chasing
freight, so we would be
on a phone call, on a Friday
night at eleven o'clock trying
to find out where that shipment
is for that VIP customer.
Now, what we've done is,
our people are much more
digitally aware, so we've
become data analysts.
And that's changed the
makeup of the organization
and how we think and utilize the data.
It's now also interesting in...
We do, every week we'll
sit down as an entire group
and we'll review the
metrics and we'll review
this combination of data to look at
how did we do the past week, how are we
doing currently, and are we on track
for our deliverables this coming week.
We went from subjective
and emotion in the way
that we talk to each
other, now we just talk
to each other through the form of data.
(upbeat music)
- Talk a little about the
cultural transformation
that is necessary for Tetra Pak.
You're designing very advanced automation,
how do you bring your workforce along,
your managers, your factory floor workers?
- You know, if you take
the people who work
closest to customers, the engineers
that have to go and fix
problems and these things,
they follow very quickly because we also
make them a bit more intelligent
because with mobile devices
and so, we give them information.
So you know they, of course,
become more intelligent
in the eyes of the customer,
so most of them really like it.
We try to train our staff
as much as possible.
We try to demystify the
hype around digital,
so people talk about
artificial intelligence,
99% of our staff would
not know what is that.
Yeah, I can read about AI,
but what is it, practically?
So we organize sessions
where they are allowed
to do it themselves a little bit, and then
they can say, "Well, now I've done it."
And then, you know, it
becomes much more natural
for them to talk about.
And then we also organize
these type of sessions
where they can come up with ideas.
What would be a good application?
What would be a good
use case for customers?
So that's what we do
with our current staff.
- We're respecting that
people are scared sometimes,
that they're afraid, and
we'll just keep on talking
and opening up the door,
being fully transparent,
until people fully
understand what they're doing
and we're respecting their concerns.
We're doing a lot of small changes,
and a lot of small steps.
We don't make big jumps.
We like challenges,
and we need to manage
them, so change management
and challenge management
is basically the same.
(upbeat music)
- When we've talked to
companies for this book
we've heard a lot about
the digital transformation,
but we've also heard a lot about
the cultural transformation
that's necessary.
How is that going at MTorres, how are you
bringing along your plant floor workforce?
- The workers will have to adapt
to this new scenario with these new tools.
The challenge for me is
the organizational factor.
With this trend we are
moving from an organization
of executing tasks towards
an organization where
teams collaborate and
look for new opportunities
of efficient process, etc.
50% of the company are engineers,
so they will adapt,
because it's their work
to look for new technologies
and to integrate them.
We also have some assembly workforce,
but they are very used
to integrate, design,
to assembly, integrate
the design and assembly
in the same process.
Of course we will have
to transform ourselves.
- It used to be, we would somehow have
very interesting conversations
about the definition of on time.
Now that we take that data
and we can show it to them
in output visual form,
it takes away the sting
when you have to relay that something is
not happening as you
projected it to being.
What is also interesting is, the entire
supply chain community has
oriented themselves into data.
And so we're all trying
to quickly figure out
how can we leverage data
for the greater good?
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