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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.
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- 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.
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- 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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