Good afternoon, everyone. I'm
going to put up a quote. So there was a US
Navy Admiral, Admiral Rickover,
who said, learn from the
mistakes of others, you'll never
live long enough to learn them
all yourself. And that's a
challenge. When we look at
digital transformation, there's
lots of companies trying to
introduce technology, lots of
different potential
applications, and lots of
mistakes to be made. And so what
we want to do today is look at
some of those challenges that
companies are faced. So both
from the perspective and
primarily from consumers, those
end users of the technology, but
also companies that are selling
technology. So it's a
fundamental shift in mindset.
How do you go around selling
industrial IoT solutions to your
customers as well? And so at
IHS, we look at the industrial
IoT space I'm specifically
focused on the process and
discrete industries. So
everything from oil and gas
through to electronics
manufacturing are one of the
elements of our research. is to
look at how are people doing
these projects? And reality is,
is one of the key challenges is
start with technology. Sometimes
we, we speak with companies and
maybe the CEO says, we want to
do digital transformation. And
he, he throws that over the wall
to his workers and they say,
Okay, great. What do we do with
this? One of the key challenges
is be specific know what the
application is, you want to
introduce technology to don't
lead with the technology, don't
get carried away with the shiny
new toy, but understand how it
will impact your business. And
within the manufacturing space,
there are certain key headaches
that CEOs and manufacturing
companies are facing, you know,
how do we improve productivity?
How do we improve our
reliability? How do we deal with
this challenge of brain drain
that retiring of the aging
workforce and the skills and the
knowledge that they have? How do
we be a more sustainable company
and the reason So when you look
at industrial IoT, you know, a
lot of the things that we hear
in the early days or this point
where we are, it's around asset
health condition monitoring. But
in reality, we see benefits
across the entire lifecycle of a
project, or a product even. And
so this is all the way from
introducing new products through
to deployment, I mentioned the
unplanned downtime. Now this is
where we see the majority of
applications being introduced
today, majority of the projects,
you can see on average,
companies achieve a 20% benefit,
reducing their downtime. Now,
this is an average benefit that
we've calculated through
tracking industrial IoT
projects. put that into
perspective in the automotive
industry. Every minute of
downtime on the line is
equivalent to 20 to $30,000. So
very quickly, cost of downtime
adds up. But don't mention, it
applies to all different areas
of a business. There was a
company many of You know Harley
Davidson, who A number of years
ago really struggling in terms
of meeting customer demand. I
mentioned that issue of
flexibility. Increasingly in the
the consumer space today,
there's this demand for more
product, more personalized
design and product. And Harley
Davidson made a significant
investment. They were struggling
in terms of meeting customer
demand. And they made this
investment to really connect
their factory connect their
enterprise, and they were able
to reduce the time it took to
introduce a new product from 18
weeks, 18 months, sorry, down to
two weeks, fundamentally
changing that flexibility and
meeting specific customer
requirements. Some of the other
challenges obviously,
productivity is always
important, making benefits there
being sustainable, and
increasingly significant focus
for companies. Can you reduce
energy consumption, improving
the utilization of equipment,
it's estimated that machine
utilization rates Somewhere
around 40%. That means on
average, you've got these large
capital pieces of equipment that
are just sitting unused 5040 to
40% of the time, you saw more
than half the time, they're
sitting on the factory floor,
not being used through getting
visibility, you can optimize
those manufacturing processes to
utilize your capital investment.
But it's not just in improving
your manufacturing processes.
companies are looking at how
industrial IoT can also improve
revenue streams. If we look at
where the market is today,
equipment growth, typically
you're looking at two to 4%,
maybe growth. The service
opportunity is far larger than
that. Combined with that, one of
the big challenges today is we
have an aging workforce, many of
the experienced engineers are
retiring. Finding ways to help
customers deal with that
challenge is a significant
opportunity for businesses
helping their customers deal
with that transition.
There's a lot of new business
models coming into the market.
We were speaking to a leading
mechanical components company.
And they were telling us look,
when competing with countries
from the east, you know, we've,
we've always been unable to
compete on price, but we've been
able to compete on quality but
that difference that delta is
disappearing, and in some, some
examples, product quality is no
longer a differentiating factor.
We need new ways of building
relationships with our customer
base. So looking at how services
can help customers to to close
close that relationship
together. We've already touched
upon earlier Martin touched upon
business models a little bit.
There's a lot of business models
out there in the market. Some of
these are, are still being
worked out. One of the really
exciting ones I think is that
outcome based model. This goes
from moving from selling a
product so big pieces of capital
investment to choice Based on
the performance of the product,
so one of the more famous ones
is that of jet engines, you've
got companies like Rolls Royce,
GE, who have moved from a model
of selling to their customer jet
engines to selling as a service
or what they call it is power by
the hour, where they will
collect information on the
health of the asset, they will
take ownership of the
maintenance of the replacement.
And they will charge for the
number of hours that the plane
is in the air. So complete shift
in mindset in terms of business.
There's a similar example in the
water purification industry,
where instead of selling
equipment, they started
challenge, charging by number of
gallons of water that had been
purified. So that completely
changes the business model in
that help companies by moving
away from a capex model to an
OPEC so spreading those costs.
But for a vendor that also comes
with a challenge. It's a
fundamental mindset shift moving
from selling product to selling
us So that influence marketing
sales team accounting really has
an influence on the governance
of organizations as well. But
also you can extend your
business. So motor manufacturers
now are starting to sell motors
with additional sensing onboard
additional connectivity where
they're monitoring the health of
the motor. But not only that,
they then introducing additional
services to support with energy
management as well, to ensure
the motor is being used at an
optimal level. So these are some
of the great benefits of IoT.
And I think Arnaud touched upon
there's lots of different terms
we say for IoT, and analysts, we
do tend to confuse the issue
sometimes. So I wanted to be
clear what we mean, when we talk
about industrial IoT. Really, we
look at four pillars. So the
first and the fundamental point
is connectivity. If assets don't
have connectivity, if you don't
have the ability to derive data
from your assets That's the
start or fail of any IoT
project, a lot of interesting
things happening with
connectivity, a lot of noise,
certainly around the industrial
space at the moment around
things like how 5g is going to
play into that as well. But when
you've got that you've got that
connectivity. What do you do
with data? And this is really
where we see a lot of companies
getting to that point. Okay, we
want to do it. Now. What? Okay,
we're going to collect a lot of
data, how do we store that?
Where's that gonna sit? Is that
going to be at the edge? Is that
going to be local? Or are we
going to use a third party
cloud? But then when we've got
all this data, we're talking
about varieties of different
data sources. And we have just a
big lake of data. Well, how do
we move from data into
information and insight? How do
we transition to being able to
understand what this data is
telling us? And ultimately all
of this is done with a purpose.
You know, this is done to
whether it's creating new
applications, creating new
business models. It's the
ability to either significantly
improve or do something that
wasn't being done before. And so
this is what we're talking
about. When we talk about
industrial IoT, you go to any
trade show in Europe, if you go
to a Hannover, or SPS is coming
up, I don't think there's a
stand that doesn't have IoT or
industry 4.01. On the stand,
everybody's talking about it.
And there's somewhat of a FOMO
this fear of missing out, is
everybody else doing it? And am
I not? Well, in reality, the
market is still we are still
relatively in a in a nascent
phase. So we look here and you
can see majority of companies
are either not considering IoT,
we're still very much in that
evaluation stage. Really, we've
seen the narrative shift over
the last three to four years
from Should we do IIoT? What the
heck is IoT to? Okay, we think
we should. Now what, what do we
do? How do we go and take these
next steps?
We looked at this and we What we
wanted to do is identify where
the industry is in terms of its
roadmap of deploying these
projects. So we asked company,
you know, are they at that proof
of concept phase that they move
to deployment. And while they're
doing this, has this been
successful? What we found is
that when trialing the proof of
concepts, there was around 50%
of companies that saw benefit or
saw value from the project and
other just below 50%. That
didn't. That's fine. You know,
you expect on that proof of
concept, phase project should
fail. You're being agile, you're
trialing new technology, seeing
what works and what doesn't.
What's significantly more
concerning is when you move to
full deployment, and here we see
that even where companies have
moved to deployment, they've
gone through that piloting
phase. They've deployed the
technology, they've made some
significant investments. In a
still a very high proportion of
the cases, they're not seeing
the value that was expected.
We're going to touch on some of
the reasons before behind that I
want to share some of the
examples of companies that have
deployed and what they've
learned. One of the challenges
we do see, though, is that of
expectation. Certainly, some
projects do deliver value very
quickly. But in reality, there's
a lot that can take, you know,
many projects, it's a two to
three year payback before you
start seeing value. But we found
that, in a survey completed a
quarter of all companies
expected payback within the
first six months, over half
expected payback within a year.
So just right setting
expectations. And this is a
challenge both for consumers for
technology and for vendors,
making sure there's a right
expectation of how long these
projects take to start
generating value. But there's
variety. So, we looked at this
by the industry as a whole, but
we also looked at different
industry sectors. As this gives
an example to to the two
industries that we saw, as most
Advanced for those of the
automotive industry, but
especially the oil and gas
industry. So the orange line
here just shows the industry
average compared to the oil and
gas industry. And you can see
here proportion, the oil and gas
industry has more than double
the number of companies already
in deployment. So they're doing
this they're more mature along
the roadmap, even where they're
deploying these projects.
There's only a 50% success rate
in terms of conversion. And when
you look at the proof of
concept, almost a third of
projects are failing. Sorry,
one, only about a third of
projects are failing. So few
reasons for this. Certainly I
think the oil and gas industry
and the automotive industry have
been heavily targeted by many
vendors. What we see in some
cases is these large companies
have actually had to close the
doors on PLCs. I've got so many
projects that they're trialing
that they simply had to close
the door. And in many cases,
even the vendor having to
subsidize the projects and So a
vendor is having to pay for the
project to be run so many
projects and just trying to get
to a point of focus can be a
challenge for some of these
companies. So I mentioned the
oil and gas industry. This was
looking at the roadmaps, how far
along the road are people. But
what we also want to understand
is, where are people today? In
terms of their readiness,
there's lots of noise, like I
say, right, everybody wants to
do IoT see surveys that say, X
percent of CEOs are looking at a
digital transformation. There's
a big difference between
willingness and ability. Lots of
companies want to do this. Do
they have some of the
fundamental things in place that
will enable them to deploy?
As we look at we have a
benchmarking service that looks
at the readiness of industry,
but different industries, but
different regions, company
sizes, how ready are companies
to run projects, and this is
based on six criteria. So we
look at things like the network,
what network They have in place.
Have they got the ability to
connect the factory floor to the
enterprise level to provide that
visibility? Do they have
specific cybersecurity elements
in place? What are they doing in
terms of the data? That's both
in terms of how do they store
data? How are they analyzing
data? But it goes beyond
technology? What are they doing
with their people? What are the
skill sets in place? How do they
get departments to work together
that issue of the IoT and the OT
convergence? And even as I
mentioned before the governance
as well? How do you change a
company's mindset to consuming a
service rather than a technology
or how do you sell a service
rather than a product itself? We
looked at different industry
sectors, we found that oil and
gas industry was the most ready
this means it had the criteria
or the things in place necessary
for a successful IoT project.
Certainly since 2015, we've seen
accelerate In the industry
around really becoming more
digital. We saw the 2015
commodity price collapse, there
was a focus on how do we be more
cost effective? How do we
improve extraction rates? The
power generation industry was a
close second. Again, this is an
industry that has been doing IoT
three things like condition
monitoring, long before IoT was
a thing. You know, they've been
monitoring the health of their
assets. So that was a another
industry that was quite quite
advanced. One result that did
surprise us was the mining
industry, because certainly you
think many people will think of
an example at Rio Tinto when you
think of the autonomous mines
where you've got operator
sitting 100, couple hundred
miles away from the mine,
operating the mine. Really, I
think this highlights an
interesting point that, you
know, industries aren't discrete
in that sense. There's a lot of
variation across an industry and
whilst you would have examples
like the automated processes Rio
Tinto. If you look at cobalt
mining in the Congo, that's
still being done by a spade. So
in some industries is still
aspirational goals to hit
industry 3.0, let alone industry
4.0. And so that's true across a
multitude of industries.
Certainly the automotive
industry is another one where we
see some very advanced
facilities, but also other
facilities are still trying to
really figure out that whole
connectivity element. So a range
across different industry
sectors. Now, I said I was going
to address some of the
challenges, but I wanted to
throw out to the audience and
the challenges that people are
seeing. So we've got a poll
questions if you can bring up
the question the app on your
poll. And so the question we
want to ask is, what are the
main challenges that you see in
introducing industrial IoT? So
if you're a consumer, you know,
what are the pain points you
have in introducing it? If
you're a vendor, you know,
what's that common problem that
your customers Keep coming up
with a reality is, there's many
others. There's a lot of
different challenges people are
facing. But these are some of
the most common. So what are the
what are the most significant
challenges that you're facing
today?
with a different question. Okay.
Let's look at the other
question.
I won't give answers to the
other question, I'm afraid.
Maybe just an example. So one of
them we didn't have this. This
was certainly frequently
mentioned, but it's one that in
previous service, we've that
hasn't come as highly as
cybersecurity. And that's been
mentioned earlier. You know,
this concern of what's going to
happen if I put my data on the
cloud, what about if I connect
are people going to hack into my
facilities? The reality is as a
big problem, we found that 50%
of manufacturing companies have
had a solution. cybersecurity
breach in the last three years.
So it's not to say cybersecurity
isn't a big problem. It is. But
a lot of the times it's not down
to technology, it's people you
know, you get the person
sticking in the USB stick or
connecting their wireless kettle
on the facility. It's people not
patching devices are updating
passwords. So I think we
appointed looking at the
results.
Okay.
Seeing a fairly equal split
between legacy equipment and
data analytics, I know swipe
between employees skills, that's
better that ties in with my
results. Okay, so what exactly
almost equals split legacy
equipment and employee skills
followed by challenges of that
management of data, and then
analytics. And these we see is
very common problems we saw as
part of our results if we could
jump back We found these as the
the four most significant, the
single most important challenge
faced by companies. We asked
companies to rank you know, top
four, what's your biggest
challenge? But also what are the
most common challenges you face
legacy equipment, the single
biggest challenge whether that's
machinery, control systems, you
know, in the some of the process
industries, there are control
systems, DCS that have been
sitting there for 3040 years.
And they don't want to update
them. They don't want to
interrupt the process. And these
are products that never been
designed to be connected to the
outside world. The challenges
around coming collecting
analyzing data, we're going to
touch upon that. And then the
single most common project
problems challenge that was
faced by companies is the people
problem. It goes beyond
technology. How do we solve that
issue of the people providing
the skill sets making them trust
the data? I guess one of the
things we look at is equipment.
A lot of time looking at
equipment. What's happening with
that equipment from an
automation perspective? Is it
being connected? How's it being
connected? And just to explain
this, because I know there's a
lot going on, we'll take the
example of Lv or low voltage
motor motor drives in the top
corner, what they're showing is
that over 80% of drives that are
sold, have connectivity,
functionality on them, they're
enabled equipment. But often
those less than two thirds are
actually being connected. And
that's both field bus and
Ethernet, if you're familiar
with the networking, that's not
even devices that are IP
addressable of that number,
probably half of that is IP
addressable. So looking at that,
we're talking about a legacy
install base of equipment that
hasn't been decided designed
with IoT connectivity in mind.
There's still a challenge of
bringing that technology we
certainly see coming to far more
products you look at the motors
market companies are excited.
Simply bringing that technology,
that issue of connectivity to
motors, even to things like
switchgear, we talk about things
like circuit breakers and switch
gear, they're even starting to
see connectivity on devices. But
getting people to use that
technology is still a big
challenge. People are still very
wary of connecting everything
up. So there's a large legacy of
equipment just sitting out on
the field, not able to be
connected. So how do we upgrade
that? How do we incentivize both
an upgrade of facilities so they
support the technology, but also
how do we support workers start
connecting and seeing the
benefits from connectivity? Then
we start talking about volume of
data and we were speaking to one
leading power generation company
in the US, and they were talking
about how they'd gone from a
manual data collection process
where they collected information
on their assets twice a year and
they moved to an auto auto
Meeting process where they were
collecting data every five
seconds. I just want to give an
analogy. This is my local
library. This is the Abington
library, to employees 11,000
books and not a lot else.
Compare that to the Library of
Congress. We're talking about
over 3000 employees 100 and 60
million cataloged items. And
when I say items, that's not
just books, that's musical
scripts, that's film, that's
posters, all kinds of different
types of items that they store.
That transition from collecting
data every twice a year to every
five seconds is equivalent to
going from my local library to
10 Library of Congress's as a
challenge, how does an
organization move from not
collecting a lot of data to all
of sudden this huge transition
to collecting just a whole load
of data? And how do we then
we're not just talking about one
type of data, we're talking
about a Greater variants of data
as well. How do we now get
meaning from that data? As a lot
of companies that are still
struggling? You know, we hear
the term data lake, sometimes
it's a bit more like a data
swamp. There's lots of data, but
there's just not a lot of
ability to infer what's going
on.
And so, really, when we look at
that part, part of this
situation around storage of data
comes down to should we use the
cloud? I think the survey that
Martin did earlier, there's a
very small proportion of people
that were maybe considering the
cloud. We see there's still a
lot of wariness around the cloud
in the industrial space, you
know, we, we still hear, you
know, over my dead body or data
ever leave my factory floor, you
know, we're not sharing data,
we're not connecting, we're not
going to do it. People are very
hesitant. Now, that's not always
necessarily, I would say an
accurate mind. Oftentimes in
industrial environments, there
isn't necessarily cyber security
in place. industrial companies
still aren't really making
investments needed to make sure
they're cyber secure. And I was
speaking with Michael, a while
ago and I gave an analogy, you
know, if I was to give Michel
Martin maybe a brief case in
front of everyone with a million
dollars, would Martin go up to
his hotel room and put it under
his bed? No, he take it straight
to the bank, because these are
the experts in storing his asset
and giving him access to his
asset. And the same is true when
we think about data. Now, there
are companies that cloud service
providers, these are the experts
in providing security to the
data. And the attitude that data
is safer locally, isn't always
an accurate mindset. That's not
to negate the challenges are
around cybersecurity, but it's
to understand putting it away
somewhere else where it's safe
is may well be a better
opportunity. But then the
question comes, do we put
everything on the cloud? Should
we just use the cloud for
everything? Well, we certainly
see an optimal approach is one
where it's a hybrid solution.
We're talking about a
combination of using the cloud
as an element, but also using it
analytics on the edge as well.
And what we see is really, the
cloud being used for a lot of
the training of models. When we
look at AI, we're talking about
training models that are going
to be then deployed locally at
the edge, because there's
different elements and different
needs. When you start thinking
about latency. Many
manufacturing facilities have
low latency requirements, they
they need that millisecond or
below response rate. Cloud is
not going to be able to provide
that you can't send all your
data to the cloud forever.
That's a very expensive mindset.
I think, as was mentioned early,
start sending terabytes up onto
the cloud. You're going to be
hit with a pretty big bills soon
enough, but it's Same time, if
you want to scale as you're
looking across multiple
facilities, if you want to start
bringing in learning across
different elements, different,
different divisions that you've
got different factories and
facilities, certainly the cloud
gives you that ability to scale.
But where we saw the biggest
challenge was, as I mentioned,
people, the people problem is
the single biggest challenge.
You can throw money at
technology and solve that
problem. But how do you take
people on the journey and many
of you may have seen the film
Moneyball, and some Moneyball
it's a film about bringing
analytics into baseball and
there was a team, the Oakland
A's. They had the second lowest
salary in the whole of Major
League Baseball.
And yet they bought in analytics
and within I think it was a
year. They were challenging the
new Yankees in the world series
based on analytics, but they had
some challenges and this is a
quote From the movie, where the
director goes to the chief
Scout, and he's talking about
analytics, and the scout says to
him, Look, baseball isn't just
about numbers. It's not science.
If it was, anybody could do
this, but they can't because
they don't know. They don't have
our experience. They don't have
an intuition. And you could take
baseball out and scout out and
you can put engineering and
maintenance manager. And you
know, you have engineers and so
will I just put my hand on the
machine and I feel it, I can
tell if something's not quite
right. How do you deal with
getting people on board with the
technology that you're using,
because as I say, you can put
money at introducing technology
but the people have to follow.
And so we had a discussion with
a company, a different company
in the US that was also in the
power generation sector. And
they deployed these technologies
to two different facilities. And
in one, the plant manager was
really excited. He saw some of
the people tential really got on
board in the other very
skeptical. I think it was coming
towards retirement didn't want
to do anything to shape the boat
and make it difficult. A year
down the line, that project had
mothballed and the other one was
starting to show some value from
the project. So enabling people
on this journey is key. There's
a few things you can do. How do
you train people? How do you get
people involved to do this?
There's some companies that have
the mindset, you know what? It's
not about our people, we're
gonna recruit in a load of data
scientists, and they're gonna
bring meaning to all of our
data, they're gonna solve the
problems for us. If that's the
mindset, I've got some bad news.
So a couple of years ago,
LinkedIn did a survey on data
scientists and found that there
was a 50% shortfall in terms of
supply of data scientists add to
the fact that in the industrial
space, we're not working in the
sexiest of industries. This
isn't A place where everybody's
fighting to get to compared to
when you can look at medical or
the financial industries as
well. That mindset, we're just
going to get data scientists in
to analyze that data. It's a
false promise isn't it's not
going to happen for all
companies. So it's really key to
bring people within the
organization on board. Not just
because you won't get data
scientists, but because your
people are the ones that have
that vertical expertise. They
know what's going on in your
facility, they can look and
understand what correlates to
the actual real world
challenges. So it's really a
combination, but training is
critical. We see a number of
efforts were made by companies
partnering with academic
Institute's to train up data
champions, people within the
organization that didn't have
the necessary skill sets
initially partnering with
universities bringing them up to
a level where they could work
with these technologies. Another
company, they made some big ones
vestments in the UI that user
interface. They said beyond all
the other technology we were
deploying. We really focused
just making it as easy as
possible so that the workers
would actually want to use it.
We wanted to really get them
bought in another company in
Germany, a leading automation
vendor was trialing their own
dog food, as they like to say.
And they introduced these
technologies. But what they did
was they started incentivizing
their workers to come up with
that duplicate applications as
well. They found that 40% of the
profit generated from industrial
IoT solutions came from worker
led initiatives, and actually
released them over a million
euros in benefit in bonuses to
those workers that had came up
with these new ideas. So
bringing the people along
another challenge. So with that,
I just want to round off with
six S's for success. Here are
six things if you're looking to
introduce technology six things
that you should be doing.
Firstly, specify, don't just
throw out a load of sensors
collect the information, then
say, Okay, now what are we going
to do with this? Have a pain
point have a problem that you're
going to try this technology on?
Know what you're trying to do?
Can you measure success? You
need a benchmark. You don't want
to roll out the technology have
an application, a year down the
line, your challenge, Hey, is
this providing value? And your
best answer is? Well, I think
so. You want to be able to have
that ability to mark is this a
successful project. Don't boil
the ocean. Start small. Start
with some proof of concepts with
some pilots and then scale from
there.
Get senior support involved.
This is critical. You're talking
about converging different
teams. The it ot convergence
debate is very common. Here
you're talking about very
different mindsets, different
agendas. Sometimes can pitting
for the same budget as well,
having senior support that can
help overcome some of those
points of inertia where maybe
there's a bit of resistance
heels or being stuck into the
ground, having the senior
support that can help overcome
those issues is very important.
But also one that shared
responsibility. Get your people
working together. So I mentioned
the issue of why t ot
convergence, one company we
worked with, they actually
swapped people across the plant
departments. So they actually
took I think it was their CTO.
And he they put him in the
operational side, so he could
understand the pain points from
the OT guys and vice versa. So
they learned each other's
problems, but getting them to
work together. So it's
complimentary and not competing
is key. And ultimately, like I
say, get your people involved,
the benefit is going to be can
you get your people to trust the
technology, and also not to see
it as a threat? How do you bring
them along on the journey so
they don't see this as something
that potentially Going to
replace their jobs in six months
to a year's time. So I think
these are six lessons that we've
learned. I hope that's useful
and just want to open it up now
if there's any questions.
Hello, thanks for the
presentation is the study that
you mentioned somewhere
available with more information
on the data collected like the
number of organization or
company that were questioned? If
you want to shoot me an email or
hit me on LinkedIn, certainly
share, share the details but you
have lots of details on that.
Okay,
the more questions
all I can say is Alex, thank
you, when it was very
enlightening. Very Inspiring a
lot of really good information.
One of the things you mentioned
resonates really in terms of
getting consensus, getting
people ready, trained, aware,
but line without which nothing
goes support. Exactly. And I
think this is something
essential. Alex, thank you so
much. Thank you. Thank you.
