SPEAKER 1: Good
morning, everyone.
It's a pleasure to be here.
And just wanted to say,
30 years ago last week I
first came to MIT and started
my PhD in control systems,
and I was exceptionally
excited to start
using artificial intelligence to
apply to control system theory.
About two weeks later,
I figured I would never
graduate if I did that.
So I did more
conventional, core type
of feedback control theory.
But just to give you an
idea what the state was,
I had a class in
visual recognition.
And they were talking about--
this is maybe 1990, 1991--
in order to recognize
photographs,
to train the neural net, which
only had three layers back
then, it would take
40,000 photographs.
So in my mind, what
did 40,000 photographs
look like back in 1990?
40,000 Polaroid things, right?
And now I have
40,000 photographs
on my desktop on my computer,
like you all do, right?
And if I could've
thought that you
could get 1 billion
photographs of cats
from Google in less than
15 seconds, 30 years ago,
that was the farthest
thing from my mind.
And so one of the things
we need to realize here
is there's really, I see, three
pillars to the industry 4.0.
It's the technology, it's the
people, and it's the system.
And in that three-legged
stool, we're just
experiencing the cost
to implement technology
and its range has changed by
several orders of magnitude.
People and systems haven't
changed quite that fast.
So it's really about-- and
I heard one of our earlier
speakers saying
the technology is
available to just about
everyone in every company.
No one has a technology here
that anyone else can't get.
The difference is
going to be who's
able to adapt it into
their current systems
and create value out of that.
So just a little about me-- so
yes, I came here 30 years ago.
Four years, nine
months, six days,
and three hours
later, I graduated.
I know a lot about
Laplace transforms.
If you'd like a separate
session after this,
I will give that to you.
My third day at work, I was
working in a chemical facility,
and I saw a bunch of people
running back and forth
to the refinery and the
different units, and I said,
there is no Laplace
transform for that.
And what I really didn't
realize at the time
was my whole career is
going to be watching
small, chaotic pockets
operate in large, high-risk,
high-production systems, and
understand and change those.
And that's how you
drive both productivity,
safety, and then finally,
make it an actual place where
people actually want to work.
Fighting fires is fun for
about three to six weeks.
And then after that,
it wears on people.
Also, after I graduated, I
worked in various industries
for 15 years.
Got the opportunity
to come back to MIT.
I'm in my 13th year as
a senior lecturer here.
I offer a popular course out
of Sloan, Executive Education
on Industry 4.0, twice a year.
And I also get the opportunity
to work with the ILP.
So before I introduce
our esteemed panel,
I want to show--
this is a slide of--
I gave a fantastic--
I gave a good presentation,
and then one of our colleagues,
Kelvy Bird, drew
up my presentation
and made it look fantastic.
And so the reason I show this
slide is everyone says, stop,
I want to take a
photograph of this.
But this is actually
better than I speak.
But I learn from it.
And just the ability to put
complex ideas simply is key--
and put it visually.
And we're going to talk
about a systems view,
and how that's around
transformations.
So if you look at
any major report
from the large consulting
firms, everyone
agrees two things
about industry 4.0.
It's a trillion
dollar opportunity,
and it expands across
all industries.
And the reason why
there's so much value
is, my view is that basically
all major industries are
information-starved in
the way they use it.
But the challenge is, nobody--
adoption is slow.
So before we go into that,
what it's all about is--
the great quote from Dirk
Didascalou, from Amazon,
he said, "The IoT is
really about knowing where
everything is, all the time."
And I'll introduce
my colleague, Asif.
Has anyone ever done 5S in the
factory, sort and straighten?
Isn't that what that
problem is, knowing where
your stuff is when you need it?
So a lot of new
technologies in the world,
but we're really solving system
problems that are hundreds
or thousands of years old.
OK, I was born and
raised in Detroit.
And to me, from Detroit, Boston
was the epicenter of the world.
And we used to watch PBS,
all those Boston shows.
And there was a TV
show called Banacek.
Does anyone remember Banacek?
It's like Columbo.
Banacek fans?
Now the reason I show that
is because does anyone
recognize this view?
It's right there.
So as an 8-year-old
watching Banacek,
I never realized I'd
be looking at this.
So we're going to have
a Banacek moment here.
And what Banacek did, he
solved locked-room problems.
And that's what I do all day.
It's, "it went in,
it didn't come out,
but we can't find it."
That's what I do all day.
And so first it's around
physical parts and things,
but I want you to
generalize it and think
in terms of people's time.
Like for this-- yeah, we
wanted this small feature
added to the software piece, so
we put six programmers on it.
And eight months later,
nothing came out.
So it also applies to time.
And I also have
the great fortune
to teach-- how many of you
have heard of Little's law,
by chance?
So those of you who haven't,
you've got some homework.
But it essentially says--
the formula is L
equals lambda W.
It's the F equals m
a of all operations.
And it basically says this--
if you've got a
production rate which
is lambda, the cycle time--
so how long something
spends in the system--
is related to work in
process, in inventory.
And what I usually find
is something's missing,
especially around in
cycle time, and it
means people are building
up work in process.
And what value does
work in process
have to the customer
who pays us money?
Zero.
And having the
fortunate experience
to work in a lot of failing
companies with Asif,
turning them around,
companies go out of business
because they build
up work in process
and then their cycle
time slows down.
And I think we've already heard
several different applications
of measuring and
improving cycle time right
here because of Industry 4.0.
And what's going
to change is we're
going to have much more
visibility in that.
Last point is, when
I first came to MIT,
the chemical
engineering department
was founded by Arthur
D. Little, so I thought
this was Arthur D. Little.
It's actually John Little
up in the Sloan School.
So I basically
got a teaching job
because I knocked on his door
and asked about his formula.
So seek and ye shall find.
OK, now there's a reluctance
in adopting this technology.
I'm sure you've heard all
this quote before from 1974--
why won't these customers
buy our awesome technology?
Well, it goes back to--
Theodore Levitt's
quote is, people
don't want to buy a
quarter-inch drill,
they want a quarter-inch hole."
And what's interesting
now is when
you're looking at things that
weren't possible 40 years ago,
now you can go on and
buy a quarter-inch hole.
Instead of buying a
car, you can Uber.
Instead of buying a vacation
home, you can go to Airbnb.
And you can actually
find someone
to drill a hole in your
wall instead of you
doing it yourself.
So we are moving
towards the services.
And the main driver is
we can actually measure
that service being delivered.
And that's why it's making these
new business models possible.
Finally, just wrapping up here
before we go to the panel, is--
this is MIT, we need a formula--
we need a formula
for human behavior.
Because ultimately--
how many people
work in companies
with people in them?
[CHUCKLING]
So they're are a big part of it.
And so if we don't
change what they do,
the outcome won't change.
And so what Kurt
Lewin did here was
he made a formula of behaviors
a function of the people
and the system that they're in.
Now most of us as leaders
like to just think of,
it's the people, and if we
fired them and replaced them,
that would change.
But how many have been where you
swapped out people and staffs
and the behavior doesn't change?
That's because we need to
actually change the system.
And if we're really
smart about it
we'll have the people in the
system change it themselves.
And finally, this is
where technology comes in.
Because we're trying to change
outputs with technology.
If we don't do that through
the people and the system,
it will not be adopted.
And just as a short
takeaway that,
when you have these
slides, I'm sure you're
going to want to go
back to this one--
Donella Meadows was
a researcher here
in the system dynamics group.
And as we all know, we work
in billion dollar systems.
We don't work in
greenfield systems.
We work in old
brownfield systems.
You can't change everything.
What you need to know is when
you're looking at a system,
where do we implement
our changes?
Where do we start?
She has a list of
12 leverage points.
And just for today,
we're really going
to focus around time
delays and specifically
around missing negative
feedback loops.
Because I've talked
with the palace earlier.
We all agree that the cost
to implement feedback loops
has fallen through
orders of magnitude.
Let's start implementing them.
This is an ugly picture
you will never forget.
This is an example of
leprosy, modern day.
Does anyone know
what causes leprosy?
A guess?
I'm a professor.
We just sit here and wait
till someone answers.
AUDIENCE: Bacteria.
SPEAKER 1: Yeah, and what
does that bacteria do?
AUDIENCE: It kills
the nerve cells.
SPEAKER 1: It's
killing the nerves.
It's not actually
damaging the flesh.
That's a symptom.
It's actually destroying
the sensory nerve.
So what happens, you
keep doing like this.
So the reason I call
that corporate leprosy is
I work in a lot of
billion dollar assets
where corporations keep
sending messages to the assets,
keep producing, keep
producing, keep producing.
And you can put your classic
industrial accident there.
That's what you're going to see.
But what's changing
now is the cost
to put those
sensory loops in has
dropped orders of magnitude.
And this is going
to change industry.
Finally, we're going to
talk about hidden factories.
I do have to introduce it.
It is a Sloan concept.
Dr. Armand Feigenbaum--
who knows the TQM book?
Total Quality Management?
He wrote that as a student here.
So basically, all of our
students, I say, get to work.
But his biggest
thing he discovered
is seeing what's
not there, which
is seeing this thing
called a hidden factory.
And one of the things
we're going to focus on
is how do we actually see
missing feedback loops,
and how do we see missing
parts of technology that
can basically accelerate our
ability for companies to adopt
the technology and scale it.
So with that, I'd like
to introduce our panel.
So on my left, I
have Muhammad Asif,
who's the manufacturing
industry subject matter
expert for Hitachi.
It's a little hard for
me to say that title,
because I've known
you for 12 years.
And he basically trained
me because I was pretty--
how do I say this?
With my PhD, I wasn't all that
effective in being practical.
So not only he trained me to
work in factory and operations,
he now works with
Hitachi and helps
them understand how do we--
and his view, as I
understand, current systems.
And he did a lot
of work in Nissan
and working up his career.
So he understands
the current system
and how to apply the
new technology to it.
On my right, I have
Mike Phillips, who
already has introduced himself.
And then to my left
is Will Koffell,
who is the head of startup
ecosystems for Google "Clou."
Do you know what happened
that D there, Will?
[CHUCKLING]
WILL KOFFELL: It's distributed.
SPEAKER 1: It's a mystery.
Yes, it's a mystery.
And Will will tell a little
bit more about himself.
And then finally, David, who
is joining us from Everactive,
and we had John
also introduce it.
So what I'd like to do is,
just for our panel, give--
except for you, Mike--
give everyone else 30
seconds to introduce.
Now that I took my 30 seconds--
[ALL CHUCKLING]
--I want to give the
people on the panel
their 30, 45
seconds to introduce
yourself and your focus.
MUHAMMAD ASIF: First of
all, thank you so much.
It's a great
privilege to be here
and in front of such a
great bunch of people.
About 40 years ago, I started
as a manufacturing engineer
at Nissan factory floor.
And I thought, I graduated,
I will get a great job.
But for the next six months,
I actually cleaned the floor
on the factory floor.
And at that time, I realized
that I really haven't learned
anything about engineering.
Because every morning
as you got up,
and you picked up a
machine or a tool,
and you did this
repetitive work trying
to put a bolt on an
engine or a vehicle body,
you realize how
mindless that job is.
And I kind of asked
myself why this was never
taught at school.
And to be very honest,
ever since, I literally
fell in love with the
manufacturing factory floor.
And over the last
40 years, I not only
managed plants,
about 13 years, I
work with Nissan in Australia,
in Japan, in the UK,
and Thailand.
And I learned everything
I could about people
and the application of
technology, and so on.
Then I was in Detroit.
I managed a number
of Tier 1 suppliers.
And then later, for various
reasons, I worked with John.
And I'm privileged
to work on turning
around distressed companies.
And now I thought, at
this stage of life,
it is great that the
technology that we have,
Industry 4.0, if I had
access to this 30 years ago,
things would have been
so different from me.
But we didn't have
that technology then.
So when I look
back what I learned
and the opportunity that
I had, if I had this then,
things would have
been different.
So that makes it so interesting
for me in this job trying
to help guys like yourself
who are bringing technology,
and how do you actually plug
into the hearts and minds
of the factory floor,
because that's what's
going to make things different.
So roughly, that's where I am.
SPEAKER 1: Thank you, Asif.
Will.
WILL KOFFELL: So hi, guys.
Echoing the thanks for
everyone being here.
I love the packed room.
So I did, actually,
also MIT, in the '90s.
I did my computer
science degree there.
And that was a great
timing to get swept up
in the internet revolution,
in the dotcom boom.
And I've been doing
startups ever since.
So I'm a six-time
startup founder,
mostly venture-backed startups,
mostly seed and series A,
super-early stage.
I have not been anywhere
near a factory floor,
although I've spent some
time in data centers.
Software people like to
kind of commandeer everyone
else's as their own.
We to use the word
"architecture"
away from the architects,
and "engineers" away
from the people
who drive trains.
And then we also like to steal
things like kanban and process
in order to describe
not where the parts are
but where your project task is.
So that's the closest I've
gotten to the factory floor.
My most recent startup,
I sold to Google Cloud.
And then I get this
great opportunity
to come and support startups
on behalf of Google Cloud.
So my role now is
this great combination
of experiencing a
super-fascinating company
in Google, and then
also still getting
to be close to startups.
So my program manages
all of the partnerships
with VCs, accelerators,
and the ecosystem,
as well as with
corporates, and then
also provides cloud
credits, and support,
and community to early-stage
startups all over the world.
So I've spent a lot of
time working with startups
and working in many different
parts of the ecosystem.
SPEAKER 1: David.
DAVID WENTZLOFF: Sure.
Yeah, thanks, John, and
thanks for the invite.
Good morning, everyone.
My name's Dave Wentzloff.
And I guess, first and foremost,
I'll start with my connection
to MIT.
I got my PhD here back in 2007
in electrical engineering.
I studied with
Anantha Chandrakasan,
before he was director and
now dean of engineering.
That was a fun time
to be in the group.
And in 2012, I
co-founded Everactive
with another Anantha
alum, Ben Calhoun.
And really, at
Everactive, we are
passionate about
removing the batteries.
That's what we're all
about, removing batteries
from IoT devices.
What goes along with that is
enabling new data streams,
so sensing where others can't.
Also really
passionate about that.
So thinking about not
necessarily replacing a sensor
on a device that's
already there,
but adding the 100 or
1,000 additional sensors
to things that
were previously not
sensed that no
one's really thought
about a rhyme or
reason of why we wanted
to collect data from there.
John mentioned these feedback
loops that are absent.
I love that concept.
We put our devices
out in the field
to essentially close
those feedback loops.
And I really
appreciate what Mike
has done with Sense,
where you go into an area
where previously there
was no technology,
and then you enable
all these new features.
So that's what we're
really passionate about,
I'd say, at Everactive.
Our devices power
from ambient energy,
so either temperature gradients,
heat, light, things like that.
In my spare time,
I'm also a professor
at the University of Michigan.
I teach low-power
integrated circuits there.
My research is in
ultra-low-power radio ICs.
That technology is
what's been spun out
into our batteryless
sensing devices.
That's what enables them.
That's the enabling
technology there.
So that's how we build
batteryless devices that
connect to the cloud.
And also, in my
role at U of M, I'm
the faculty director
of education
in the Center for
Entrepreneurship
in the College of Engineering.
I know that's a long title.
But I help define the curriculum
for undergrad and graduate
programs in the CFE at
U of M. So I'm really
excited to be bringing
my startup expertise back
to students at Michigan.
SPEAKER 1: Thank you.
And what I'd like to
do for the panels,
I'm just going to ask
two or three questions,
and then I'm going to open
it up to the audience.
And so first, one of things
we talked about is, how do--
I try to teach students this--
how do you see what's missing?
And I figured out how do
you see what's missing.
You travel to 30
other factories.
And then when you go to the new
place, you see what's missing.
So I just wanted to start
off with you, David,
do you see specific feedback
loops people are missing,
where your sensors can
work, where before it
was prohibitively costly
or some other barrier?
DAVID WENTZLOFF:
Yeah, great question.
So I'd say the limit to the
number of feedback loops
you can find is only limited
by the number of open doors
that we can walk through.
So when you walk in
with a technology that
is batteryless with a
20-30 year lifetime,
you can lick and stick a
sensor, and sense anything,
and connect to the cloud.
What would you do with that?
Probably everyone in this room
might have five or 10 ideas
where they would put it,
either in their homes,
in their workplaces, or
outside of those areas.
So I think there is--
that's why I use the
term, turning over rocks.
A lot of this is just
talking to people,
talking to potential
customers about what would you
use this technology for.
A lot of our first two products
are a result of that exercise.
SPEAKER 1: So and this is
to the home market, Mike.
When did the Xbox go on, when
did the Xbox go off, and has
the trash been taken out?
Could you do that yet?
MIKE PHILLIPS: Well, I
mean, even the more--
first of all, I really
like this talking about it
from closing the loop--
using sensors to close the
loop on how things work.
I mean, this is very much
how we think about it,
and how we've tried
to go about this.
And things like when
the Xbox turns off,
that has some usefulness
about your family, and so on.
That the things that we
think are the most stark
are things like--
I mean, to me, it's just crazy
that a big chunk of energy use
goes to heating and
cooling your home.
It's a completely open loop.
There's building standards,
there's equipment,
and then it's being
operated by humans.
All of them fail.
The buildings don't get
built the right way.
The equipment doesn't
get installed correctly.
The duct work's wrong and so on.
And then it degrades over time.
And then people operate it--
it's not just the set
point on the thermostat.
They leave the circulating
fan on all the time,
or they set it in
some weird mode.
So without paying attention
to that feedback loop,
these things are operating
far off of how they should be.
We spend all this time talking
about energy efficiency,
about light bulbs and so on.
The big gains is in closing
that feedback loop for sure.
SPEAKER 1: And what
size of that market
do you think it is in
terms of lost energy?
MIKE PHILLIPS: Well,
I mean, in the US,
the residential electricity use
is like $350 billion a year.
And I think half of it
could be saved if you did--
SPEAKER 1: So half of $350
billion is a pretty big number.
And it's all basically
because we don't--
you said it, we
have no visibility
into how much energy we use.
MIKE PHILLIPS: Yep.
SPEAKER 1: OK, that's great.
Will, I know you actually
are in an interesting space
where you're actually
working with Google.
And your IoT offering is
targeted towards startups
and helping them scale.
Could you tell us a
little bit more about that
and what their
particular needs are.
WILL KOFFELL: Yeah, there's
been actually a bunch of talk
here about, OK, we've got this
really interesting technology--
I agree with David, let's
go out and ask customers
how they want to use it.
One of the things that
we caution startups
on all the time, though, is
it's a problem if you go out
and no one knows what to
use your technology for.
And they might not,
because they don't
see the thing that they're
missing in their factory
or in their process.
And actually almost
as bad is the one
where every single customer
has a different way
they want to use it.
Because lack of focus will
absolutely destroy startups.
So at some point there's
a place where you have
to kind of connect those dots.
So one of the things about where
those gaps are that we always
encourage startups
to do is you've
got to figure out what the
actual customer pain point is.
What is it that they're
really trying to solve?
And then you're going to
map your technology solution
to that.
And I think the benefit of the
public cloud today, the benefit
of this idea that
everyone has access
to the breadth of technology
that was never possible,
and pretty much all of
the other technologies
that others are
using is that you
get to then craft a solution.
So one example of this is
we see startups who say,
we're going to work with farmers
out in the fields to manage--
I've seen a bunch of things--
to monitor and manage
the rainfall sensors,
to think about control from
pests and pest control.
And they have all sorts of
different solutions to this.
And a common problem is
like, how can they deploy
this in a cost-effective way?
And there, they're mapping the
technology options to them.
So do they need to deploy
10,000 units out into the field?
And if they do, the cost
profile is really different.
It's like a custom PCB, or
an Arduino, or something
incredibly inexpensive,
versus, we've
got to deploy 10
of these per farm,
where we can actually put
an entire Android Things
device out there, have all
of the power of machine
learning on that device.
But in the end, they
don't design that
without looking towards
the customer pain points.
And then I think there's all
the talk about the hybrid aspect
of where the data
lives in cloud,
and where your compute
lives, which I know
has been discussed a lot today.
That's another big theme
that we see in the cloud
and that we're
guiding startups on.
SPEAKER 1: And certainly I'm
starting to hear a theme which
Asif taught me 12 years ago--
go watch how the work is done.
And I have pictures of him.
He made me sit out in
the factory and watch.
This is where
everything came out.
It sounds to me--
we also have to
understand, as a startup,
is when we go from
demonstration in the laboratory
to prototyping and
pilot with 10 units,
to 100, to a millions, that
has to be well understood
or else it's going to create
some financial problems
for that startup.
So classic exponential
scaling problem.
And what I want--
Asif, so you have
the opportunity
to not only work
in plants, you're
working for Hitachi, who has
an industrial-strength type
platform and vision.
How are you going
to expand that?
Your thoughts.
MUHAMMAD ASIF: Let
me just explain.
I mean, Hitachi is
about 100 years old.
It's a close to $80
billion company.
They've also been involved
with IT for the last 50 years,
and operational technology
almost 100 years.
So the challenge we have now
is how we bring that IT and OT
together.
Because you've
got to understand,
almost every manufacturing
company got a lot of legacy.
One of the gentlemen
said earlier
that we don't have data, they
are not even sensor-ready.
Now when you walk into a
factory, what do you see?
You see a lot of machines.
And you can say, this
machine needs a sensor.
It has got PLC.
It doesn't have that, I
can monitor temperature,
I can monitor
vibration, then I'm
going to tell you that your
machine is not working.
And you ask the
operator, and he says,
I have known it for 10 years.
So what are you going
to tell me different?
That this machine was no good.
So why do I need a sensor for?
So now you got to
ask the question,
what are you really
trying to solve?
Now, in a
manufacturing industry,
the product they
produce is what matters.
Equipment,
technology, everything
is a means to get to that point.
So the actual measurement
of the performance
is based by the quality of
the product, the volume they
get out on time, and
getting it to the customer
at the right time while
meeting their productivity.
So now if I tell you that
this product, every day I
produce 10% on my
product defective,
where would you look at?
Now you look at
this value chain--
and that one gentleman talked
about monitoring a jet engine--
you look at that value
chain of the jet engine,
GE may be making their
engine at their site,
but you take the value chain
all the way down to somebody
with a 20-people factory who
is probably making a screw that
goes into that jet engine.
Now if we talk about
applying technology,
can we ignore the
entire value chain?
Can we ignore what's
happening beyond the GE site?
Because your predictive
maintenance, your failure,
is actually a factor
of all those thing
that we do not know why.
So we are focusing
on fixing a symptom,
but not necessarily
understanding the root cause.
So just imagine if we
could take our mindset away
from just the technology and
trying to understand the root
cause of all these failures--
where things are
we losing value?
John talked about hidden
factory, inventory, quality,
scrap.
If we could deploy all our
knowledge and the technology
and trying to harness all
that, the entire value
chain, the costs to
the entire ecosystem,
not only it's the cost, but
it's also the environment.
It is also the people who
have never had the opportunity
to control a lot of this stuff.
Now you are empowering
them using--
leveraging digitization.
I think that's where
the biggest value is.
So when you go to a factory and
say, I want to sell a sensor,
they're not going to question
you because you guys know
your stuff very well.
And at some point,
even though sensors
will become a commodity, you go
to an automotive industry now,
you sell metal,
they actually weigh.
You want to sell a panel?
I mean, I've got
five pounds of metal,
and I'm going to pay
you a dollar each.
You want to sell
[INAUDIBLE] hose?
I'm going to give
you $0.20 a foot.
They don't even care.
But if you're selling a system
that has got everything in it,
it has got the intellectual
property value,
you have got other value.
So startups and all
these great ideas,
they all need to
come together and not
look for a point solution,
but overall system solution
that allows organization to move
from this level to that level.
And that's the kind of solutions
manufacturers are looking for.
And that's really our
biggest challenge.
SPEAKER 1: And I think
that raises a good point.
There's the starting project,
but most of the value
is actually in connecting
the different companies
throughout the supply chain.
And if we don't start
with that in mind,
we're going to limit ourselves
to our own four factory walls.
You know what I'd
like to do now?
I think this is a great
time to open it up
to some of our audience's
questions, both what you've
seen this morning
and from what you're
seeing in your own
experience in the field.
AUDIENCE: [INAUDIBLE].
SPEAKER 1: That is an
Industry-4.0-approved device.
MUHAMMAD ASIF: [CHUCKLES]
WILL KOFFELL: And
they're super fun.
SPEAKER 1: [CHUCKLES] Oh boy.
AUDIENCE: [INAUDIBLE]
WILL KOFFELL: Well
for people who
have never actually seen this
device before, it's super fun.
It actually has an
accelerometer in it
that turns off
the mic while it's
flying so that when
it's being caught,
it doesn't create mic sounds.
It's very clever.
AUDIENCE: Hi.
So Mike, you talked about kind
of one of the things you regret
and that you didn't predict
was how slow some companies
are in interacting with you,
for example, the utilities
providers.
So a question to all of you
is, what are some of your ideas
for, as you're moving along
with your own venture,
hiding those latencies
and still getting
to market as fast as
possible despite all
of these other companies you're
interacting with being slower
than they should be?
MIKE PHILLIPS: So we
actually are not surprised
that this has happened.
I mean, one
philosophy we've had--
and I think it's actually
a good one for companies
to try to make use of
it-- is too much of tech
is focused on the
tech world, and I
think the really interesting
opportunities are
at the intersection of tech and
other much bigger industries
in the world.
And those, by their
very nature, tend
to move more slowly,
for good reasons.
If you're putting an electrical
panel on a home or a meter
on the side of the house that's
got to last for 20 years,
you've got to be really
deliberate and careful
about how to do it.
So there's just definitely
a mismatch in the timing.
And it's for real
reasons and good reasons,
but it's still a
good way to work.
And the way that
we've dealt with it
is by simultaneously
working with utilities,
working with electrical
infrastructure providers,
builders, simultaneously
doing that and having
a consumer-facing product that
we can sell on Amazon to let
us not have to wait.
But because we're doing it in
the home, we have that ability.
I don't know how that translates
into the factory world,
if there's the ability to do
mismatch timing in that way.
SPEAKER 1: And no one
liked my Xbox joke.
I'm disappointed.
But the reason I raised that is
because, in small factories--
and I've done this--
you can use simple
inferential signals.
And in one example, we we're
losing 45 minutes of production
a day, and the
system was shutting
down for 15 seconds
every, like, 20 minutes.
So that's why they
didn't see it.
We did things in that
small-to-midsize company that
was--
they let you do things you could
not do in a larger company.
However, as Asif says,
they're a tier supplier
to the larger companies.
So the larger companies
then saw what we
did with the smaller company.
So if you can see
how that scales
through that chain, that's
an entryway into working
with larger
companies is starting
with their smaller suppliers.
WILL KOFFELL: Yeah, I think
it's easy for a startup
in particular to get
lured by what I think,
in my experience, is the siren
song of giant companies saying,
that's great, we'll
do stuff with you.
And you can easily
run out of money
while waiting for
your giant partner.
So I always caution
startups against that.
But I think for many of them,
and certainly in this industry,
those are your customers.
So when you ask
the question, well,
what else can we do, I find
that a lot of startups,
because they're so
forward-looking,
and there's so much vision,
and there's so much technology
and disruption,
they're focused really
on how much they can achieve.
And they fall victim to what
I call the origami approach
to their stuff.
So when you guys
have done origami,
you know, you followed
this fold, and then fold 2,
and then you unfold, and then
fold 3, and you go all this.
By the time you get to fold
40, you sort of go like this,
and you expect a crane.
But usually we go
like this, and you
get a wrinkled piece of paper.
SPEAKER 1: [CHUCKLES]
WILL KOFFELL: And
I think the thing
is, you should be working
with large companies
to identify the value
of every single fold.
And do not
underestimate the time
that it's going to take
to do each of those.
And so what you want is wins
all the way along that path.
And you've already
made a mistake
if the value you deliver
to that organization
isn't until fold 40.
Take too long.
SPEAKER 1: And
just want to thank
Will Koffell, author
of The Origami Effect.
[ALL CHUCKLING]
Dave.
DAVID WENTZLOFF: Sure.
Yeah, well, I'll maybe answer
that in a couple of ways.
So from a technology
perspective,
I think there are barriers
that you can knock down.
And those get identified
by our customers,
and as you knock those
down, it gets simpler
to deploy your technology
in their environment.
So I'll give you an example.
Connecting to a factory's Wi-Fi
network, locked down, or LTE?
So there's a technology
solution that
can solve a problem,
which is how do you
deal with a large company's
IT infrastructure, which
you typically don't want to
touch with a 10-foot pole.
And then there's other things
along the lines of deployment--
or pairing and
provisioning, we call it--
but how do you
install these things?
Is it easy?
Is it lick-and-stick?
Or does it need a licensed
contractor to do it?
Does it take an hour, or
does it take a minute?
So I think those things, as
those come down over time,
that period and provisioning
of that installation process
gets easier and easier,
we'll see more adoption.
And just over time, we've seen
this time scale has reduced
with our large
industrial customers
that we're working with--
faster deployments,
or shorter pilots,
shorter times to deployments.
AUDIENCE: Thank you.
AUDIENCE: So I personally got
involved in IoT 20 years ago.
That was the time the
buzzword came out.
And I was involved in a
company called Millennial Net.
At that time, everyone was
focusing on the mesh networking
technology, the ubiquity
[INAUDIBLE] computing.
And 20 years later, especially
this morning's talk, nobody
talked about connectivity.
Because I assume that last-mile
problem has been solved.
But still, IoT is not really as
predicted by IDG 20 years ago.
It's going to take off once we
solve that last-mile problem.
So I understand, today,
we have data processing,
whether we do it
locally or in the cloud,
or security or power
harvesting issues.
So other than that, what are
the factors that are really
holding back the IoT potential?
Because the hockey
stick is not here yet.
DAVID WENTZLOFF: I can start.
SPEAKER 1: Sure.
DAVID WENTZLOFF:
Connectivity-- great, I'm
glad you brought that up.
I love connectivity.
So we build our own
radios in Everactive.
We've built radios
that are proprietary,
we've built ones that
are standard-compliant.
Our gen 2 products
talk up to 300 meters
in dense industrial
environments.
They talk about a
kilometer line of sight.
And they talk to our one
of our gateways today.
But Everactive is
now part of the 5G--
or the 3GPP group to define
the next generation of IoT
standards within 5G.
Ideally we get rid
of the gateway,
we talk directly to
the cell networks.
So we've been able
to demonstrate
that with continuous
communication, low latency,
always on networks, using
essentially our low-power IC
technology.
So I think there are
technology plays to solving
that connectivity problem.
You're dead on.
The connectivity--
wireless communication
is the largest power consumer
in battery-operated devices.
That's one.
You asked about big--
like, what are the
challenges to scaling?
There's this vision of
a trillion-node IoT.
And initially, I
think that was--
we were supposed
to be there by now.
2018 was the early
initial production.
Now it's revised
to like 50 billion
by like 2030 or something,
something just depressing
like that.
Where are we at today?
I think we're at around 20
billion devices deployed today.
It's maybe around there.
So we're nowhere close
to the 1 trillion.
So how do we deploy that 1T
minus 20B rest of devices?
What are the challenges?
And again, I go
back to what I was
saying earlier-- make
it easy, make it cheap,
and make them talk anywhere.
So if these devices are
5G-connected, for example,
and they can tell
you where they are,
and they don't run on batteries.
They're maintenance-free.
They live forever,
they're maintenance-free.
I think those are keys to
getting more of those devices
out there, or at least
getting them out there faster.
MUHAMMAD ASIF: But
I think connectivity
is just one issue on consumer
products and all that.
They are selling trillions of
stuff on sensors, thermostats,
to smart washing
machines, and all that.
But in the
manufacturing industry,
the challenge is really
how do you hook up
with legacy equipment, and how
do bring the value to them?
And by and large,
I think people,
when they go to the
manufacturing companies,
they talk about
their technology.
It's almost like you go to a
doctor and the doctor talks
about the medicine, doesn't
want to talk about your
what's your problem is.
And the IT, who
traditionally has managed--
on layer on top
of the OT side, I
don't think they
truly understand
the needs of the factory floor.
And they don't understand
the process itself.
And I think if groups
of people get together,
cross-functional
teams, and they discuss
what is truly the user's
requirements, what
are you trying to achieve--
so if you go to a facility
and say, I want to
put this technology,
and I'm going to take
10% of the people off,
how many people are interested
in working hard just
to lose their job?
Nobody.
And it was the same story
when lean first came in.
And a lot of you
know about lean.
The idea of lean was
to eliminate people.
Now, I practice lean,
but I have never
fired a person because of lean.
SPEAKER 1: In fact,
even me, by the way.
MUHAMMAD ASIF:
[CHUCKLES] And the idea
is-- the idea is that you
utilize that excess resource
to expand your business, to make
your business more productive.
So the conversation needs
to be very different.
And most importantly, also,
that what are the benefit it
brings to the
people on the floor.
Like I said earlier, that
it was tough, six months,
I worked on the factory floor.
And I realized this is not what
I wanted to do all my life.
But I feel really bad
about those people who
are stuck in this environment.
So as an engineer,
as a technocrat, what
is really our responsibility?
If we cannot improve the lives
of those people on the factory
floor, we don't empower them to
make better decision that they
can enhance their life,
then as a society,
we also lose focus on
the value of technology.
It has got no meaning.
So we cannot just
be profit-driven,
we also have to be driven by
what social value it brings.
So if you have good
technology, good ideas,
if you could connect it that
way, it makes a big difference.
75% of the manufacturing
industry in this country
employs less than 20 people--
75%.
And 89%-- no, I
think that number
is 98% of the industries or
small business in this country.
Now ask yourself the question,
who is looking after them?
And why aren't we
reaching out to them?
I think you mentioned that
if you can have a success
story in one place,
you could replicate
in thousands of places.
And these guys do not
have that expertise.
So you don't have
to start very big,
you just have to
have great success.
They [INAUDIBLE] all the
things that they need.
So I suggest you guys
partner with people
who understand manufacturing.
I'm not necessarily
saying it's me.
And try to understand
what the requirements are.
So in every step of
the process, the people
have to be seeing the value.
So we buy through an IoT device.
If I can stop that,
or I can prevent
producing five bad
parts, it's good for me,
it's good for the company,
it's good for the customer.
If I can turn the--
I mean, a long time ago,
I sent a bunch of people
to Toyota to have a
look at their warehouse.
And the guys came back,
said, it was great,
but the place was very dark
and I didn't like that.
And I said, why would racks full
of parts need a lot of light?
That's the mindset, right?
It's the mindset.
So you guys really
have to understand
what drives the factory.
And it is where our
people are employed.
And this is where
they need that help.
So a great institution like
this, all this innovation,
if they can reach not only
just the household, but all
these small mom
and pop shops who
are part of the entire
manufacturing ecosystem, that
would be a great system.
And sometimes the big
companies can learn from them.
SPEAKER 1: And you know,
just as a profound point
is, a lot of when the
offerings in the company--
tech comes, they're to
save money and cut costs.
And the people on the floor
are really more interested
in sustainability, i.e.
sustaining the whole system.
So it's very interesting,
listen to people on the floor
actually have a
better perspective
than from the vendors.
Great question.
Anyone else?
Or should we have
our next question?
Where's our cube?
[CHUCKLES]
MUHAMMAD ASIF: Let's try
that out if it works.
MIKE PHILLIPS: All
right, good throw.
MUHAMMAD ASIF: Good catch.
AUDIENCE: Is this mic on?
Good.
So I want to go back
to a point was made
earlier about cybersecurity.
One of the startup
guys said cybersecurity
is sort of a barrier to
implementation of IoT.
I wondered if you guys could
comment on your thoughts
on sort of the sorry state of
cybersecurity right now in IoT.
WILL KOFFELL:
Leading the witness.
[CHUCKLING]
AUDIENCE: Well that's--
OK, so, that's my field,
and it's also my opinion.
So feel free to refute it.
MIKE PHILLIPS: I mean,
unfortunately there's
a ton of products out there,
certainly on the consumer side,
where we work, that are just
not taking it seriously.
They're open platforms, you
can hack the platforms easily.
Anyways, so I think number one
is to do the state-of-the-art
right thing.
And some companies are.
I think we're certainly
trying to do the right thing.
I think other
companies are, too.
But I actually
would love to know--
the cyber industry
to help us know
what is the best practices
in all these cases.
Because you end up having
to do "roll your own"
for these platforms in IoT
world to make them work well.
And I think some people get it
right and some people don't.
So I think there's an
opportunity to a better
set of solutions there.
Super important.
WILL KOFFELL: I
think that's right.
And I think we're
seeing that, maybe
not as quickly as we'd like.
But certainly the public clouds,
Google and all the others,
are spending a lot of
time saying not only,
you should be employing
best practices,
never roll your own if you're
not a cybersecurity company,
don't do that.
So the question is how do we
provide those best practices?
I think it might
not always be clear.
And so certainly
the IoT platforms,
I think they have
a long way to go.
They're not a "one
size fits all"
solution, as was
said earlier today.
But you want to be standing
on the shoulders of that.
I think there's a lot of
parts to cybersecurity.
There's a lot of attack points.
And it's hard to talk
about them all in one unit.
But I think one
thing that we do know
is the public clouds have
more staff and more dedicated
attention and more expertise
trying to keep your data secure
in the same way that we
keep our data secure,
that you're actually better
off sort of getting it
onto those networks, or even
onto the telecom networks
when they have people
thinking about that.
I think the less that
the data, the compute,
the fewer parts of the stack
are in your control, the better.
But that does not
obviate the need for you
to follow those best practices
and think about using
other people's libraries,
stay vigilant on the security
updates that are happening.
So there is a best practice.
But I think as soon as
you feel like you're
becoming an expert in that,
I would kind of look again.
DAVID WENTZLOFF: I'll just echo.
I love Will's comment.
I think it's spot-on.
Let the security experts
do what they do best.
Don't try and
reinvent the wheel.
I think security
is table stakes.
We have security for data
in transit, data at rest,
on all our devices from
the node up to the cloud.
But we buy, not build,
anywhere we can.
SPEAKER 1: Thank you.
Another question
in the audience?
AUDIENCE: That's too far.
SPEAKER 1: OK.
WILL KOFFELL: Double hop.
SPEAKER 1: Double hope.
There you go.
[INTERPOSING VOICES]
WILL KOFFELL: [CHUCKLES]
AUDIENCE: OK.
You've talked about
the small businesses
and also how we should listen
to these employees and the shop
floor people, all of that.
A lot of small
businesses are not
really close to Industry 4.0.
Sometimes, and even
in bigger businesses,
in part of the
business, you might not
be at 3.0 to move to 4.0.
You're at 1.0, and sometimes you
don't even do a 1.0 correctly.
How do you go into
those businesses,
accompany them to select where
they should put the effort
into moving into 4.0.
Or do you have a magic wand
where somebody who is doing 1.0
not correctly benefit from 4.0?
SPEAKER 1: So it reminds me,
at one point in my career,
I went and visited a company
called Modern Heat Treating.
And if modern we're
a middle-aged forge,
that was modern.
And the first thing
we want to know is--
I learned it from Asif--
where's our stuff
now, and where's it
going to be in an hour.
So classic 5S organization.
And most companies have
three times as much stuff
they need to do the job.
That's all destabilizing.
You must get all that
out of the system,
and then actually start, half a
day, doing short value streams,
and making sure we
get things done.
And then the technology
that you need
will magically start to here.
Oh, we need this
measurement, we need this.
But the tools developed
for 100 years ago
in manufacturing still hold.
I'd just like to open
that up for the group.
It's a great question.
WILL KOFFELL: Do you guys
think you can skip 1.0 and jump
right to 4.0 is kind of an
implication in that question.
Or is it not even worth trying
to fix the companies that
haven't gotten 1.0 right?
MUHAMMAD ASIF:
Yeah, the question
is that I don't
think anybody's 1.0.
You've got to understand
the limitations they have.
Henry Ford made cars perfectly
well without any IT system.
Even today, you go to
the Kennedy Space Center,
they're assembling the
International Space thing
by hand--
by hand.
So I don't think you can say
that you cannot jump from this
to that.
I think what's
really important is
to understand what are
they're trying to do
and what is the most applicable
thing in that scenario.
So even digitization,
it's not that-- even
industry 3.0 or
2.0, still you can
say PLC is a digital device.
So you cannot say that it
is not a digital device.
It's just the way we didn't
have the analytics and the AI
that we're using currently.
Now does every company
need analytics?
Maybe not.
So we don't need to
overkill everything.
So I think it also needs to
follow a continuous improvement
path.
And I think that's a very
important thing that you
can not think that, overnight--
like the word,
disruptive technology,
is a very bad word
for manufacturing.
They don't want to hear
the word, disruptive.
You know, you can say, this
is a helpful technology,
this is a supporting technology.
SPEAKER 1: [CHUCKLES]
MUHAMMAD ASIF: But please do not
say it's disruptive technology,
because they hate that word.
They don't want any disruption.
WILL KOFFELL: Don't
skip to fold 40.
MUHAMMAD ASIF: Right.
SPEAKER 1: There we go.
MUHAMMAD ASIF: So
do not say that.
You can say that on
a telecommunication.
You move from analog to digital.
Hey, that's fine.
If it's IT-based,
you can say that.
So I think the most important
thing you should understand--
and I think John
said earlier, there's
no point putting
digitization in a bad system.
Because it does not necessarily
enhance the system's
capability.
So you really have to
analyze, optimize the thing
through different other things.
So maybe there's
ERP they're missing.
Maybe they're missing MES.
Maybe they're missing
good materials handling.
And a lot of that does not need
a whole bunch of technology
that we talk about.
But if you put it on top of
that one that's rationalized,
then it really
makes a difference.
Because if you don't have
control over what's happening,
how can you improve?
SPEAKER 1: And with
that-- and just
to sum up your
point in one word,
it's start with a
diagnosis, which is great.
Unfortunately-- there's been
a great panel-- we're already
at the end of our time.
So first I'd like to extend
our appreciation to Mike, Asif,
Will, and to Dave.
And thank you again, and we will
be available for discussions
and questions after our panel.
Thank you.
[APPLAUSE]
MUHAMMAD ASIF:
Thank you so much.
