SMART MANUFACTURING
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Dealmakers in Technology
The Rise of The Machines
June 2019
04
CONTENTS

THE VIEW FROM GP BULLHOUND
Dr. Nikolas Westphal, GP Bullhound
I. Manufacturing the Future

Key Trends and Technologies
EXPERT VIEW

14 Raghav M. Narsalay, Accenture
II. The Power of Data

Data and AI in the New Manufacturing World
EXPERT VIEWS

20 Willem Sundblad, Oden Technologies

26 Brian Mathews, Bright Machines
III. A Fast Growing Ecosystem

Key M&A and Funding Trends
EXPERT VIEWS

38 Eric Bielke, GE Ventures

39 Dr. Hongquan Jiang, Robert Bosch Venture Capital
IV. Global Powerhouses

Geographic Clusters of Smart Industry
EXPERT VIEW

50 Michael Prahl & Denis Tse, Partners, Asia IO Advisors
V. Entrepreneurs and Investors

Key People Shaping the Industry of Tomorrow
EXPERT VIEW

58 Siraj Khaliq & Ben Blume, Atomico
VI. The Vision

Intelligent Manufacturing in the Future
EXPERT VIEWS

68 Robin Dechant, Point Nine Capital

72 Amélie Cordier, Dr. of Computer Science with Specialization in AI
METHODOLOGY
06
16
28
40
52
64
76
5
SMART MANUFACTURING
EXECUTIVE SUMMARY
THE VIEW
From GP Bullhound
4
Full automation of human work has been a constant
dream (and nightmare) of civilisation. The ancient
Greeks already spun myths about Hephaistos’
automatons; the ancient Chinese those of Master
Yan.(1) In 1884, William Morris dreamt of “beautiful
factories”, in which people worked only four hours
per day enabled by machines.(2) Today, rules-based
automation is already a reality, as commercial
robots have been around since the early 1960s
and proliferated across various industries.
As software and “intelligent” technology have
already revolutionised the way we work and live,
they will also fundamentally evolve the way
we produce things.
Imagine a shopfloor where machines configure
themselves in a process guided by algorithms;
equipment that anticipates breakdowns and
repairs itself; workers enabled by Augmented
Reality to train and work in endless scenarios; and
a universal data framework that encompasses
everything from demand planning, real-time
modelling of the production line as well as design
automation, honed into changing market needs.
Any single item in this list will have implications
for existing business models and the future of work.
One is the shift to Manufacturing-as-a-Service, where
OEMs sell a subscription to use a physical product
instead of the product itself. Hyperpersonalisation,
predictive manufacturing and massive productivity
gains will similarly lead to a complete (self-)re-
invention of the Capital Goods economy.
The commercial traction that this sector
generates is immense. Overall, smart
manufacturing companies have received more
than €5.9bn of venture and growth funding in 2018,
up from only about €0.6bn five years earlier. 2018
combined such rounds as the $180m seed round
of Bright Machines, the $160m Series E of Desktop
Metal, or the $2.2bn Series C/D of SenseTime.
Many of the prominent entrepreneurs and investors
in this ecosystem have kindly agreed to contribute
to this report, for which I am very grateful.
In Chapter 1, we look at the cyberphysical
production stack and big-picture industry
trends and developments. Chapter 2 drills down
into the virtualised layers of the production stack,
using four concurrent trends to emphasise the
importance of data as the “new oil”. Chapter 3
and 4 cover the growing transaction activity in
this space and show a spotlight on the pace at
which certain world regions – namely, the US and
China – are charging ahead. Chapter 5 features
some of the key investors and entrepreneurs and
Chapter 6 presents different views on the future
of manufacturing specifically and the future of
human labour in general.
The digitisation of production will create huge
opportunities but also challenges to the societies
that it affects. Ultimately, we believe that freeing
mankind from repetitive tasks will enable us to
concentrate on those qualities that set us apart
from machines and algorithms: being and
acting human.
Dr. Nikolas Westphal
Director
Notes:
(1) Price, Betsy B.: Ancient Economic Thought, Routledge Study in the History of Economics, Vol. XIII
(2) Morris, William: A Factory As It Might Be, London 1884
7
SMART MANUFACTURING
6
CHAPTER X
I.
MANUFACTURING THE FUTURE
Key Trends And Technologies
1
Smart manufacturing is part of the
large, global “Smart Enterprise Wave”
Like the “new enterprise”, smart manufacturing focusses
on agile, non-linear processes which are driven by Big Data
analytics, constant monitoring and real-time collaboration.
The defining feature of these new enterprises is the creation of
platforms and the integration of concurrent technology trends.
1
The smart manufacturing ecosystem spans the
entire breadth and depth of the technology stack
Smart manufacturing encompasses all layers of the technology stack,
from the highly physical to the highly virtual. We have grouped it across
five layers: production, interface, orchestration, design and intelligence.
2
Device proliferation has reached critical mass, making
smart manufacturing affordable and potentially ubiquitous
Device costs between 2007 and 2014 have decreased by more than 95% across verticals.
As a result, device proliferation has reached critical mass, enabling ubiquitous application
of smart manufacturing technologies.
3
Smart manufacturing will enable new business
models and significant economic efficiencies
By enabling continuous delivery and continuous innovation, smart
manufacturing has already started to create the outcome economy,
where goods are delivered as a service. In addition, according to
Accenture, smart manufacturing could unlock between 9% and 48% of
additional value, depending on sector.
4
9
SMART MANUFACTURING
8
CHAPTER 1
THE SMART MANUFACTURING WAVE
Technology Converging Towards
Smart Industry
Notes:
(1) Lonsdale, Joe, Man-Machine Symbiosis and The Smart Enterprise Wave (2) Schaeffer, Eric, Industry X.0
(3) McKinsey / Atluri, Venka et al., The trillion-dollar opportunity for the industrial sector: How to extract full value from technology
Silicon Valley Technology Trends
A lot has been written about the fourth industrial revolution
as the continuation of previous innovation waves in industrial
technology: from the steam engines of the first industrial
revolution, via electric power and information technology
to finally the cyberphysical production systems of today.
Interestingly, however, the fourth industrial revolution
is part of a bigger wave that Joe Lonsdale, the founder
of Palantir, describes as the Smart Enterprise Wave.(1)
While the old enterprise featured well-laid out, linear
processes, the new enterprise focusses on agile, non-linear
processes which are driven by Big Data analytics, constant
monitoring and real-time collaboration. The defining feature
of these new enterprises is the creation of platforms and the
integration of concurrent technology trends.
This is one of the main differences to the previous “Web 2.0”
wave: while Web 2.0 applies linear analysis to problems, the
smart enterprise employs a combination of technologies that
enable an additional layer of analytics and abstraction.
This additional layer is powered by what Eric Schaeffer calls
the “combinatorial effect of technologies”.(2) In essence, this
means that the productivity effects of machine learning, Big
Data, IoT, robotics and cloud services grow exponentially as
these technologies are combined.
The potential for value creation is indeed huge. On a global
scale, McKinsey estimates the shareholder value creation
opportunity from smart manufacturing to be in the $2.0 tn
range.(3) We will see some more granular examples later in
this chapter in the expert view provided by Accenture.
In addition, the upcoming industrial revolution may provide
the opportunity for a complete re-invention of the capital
goods sector. The first manufacturers are now using their
newly found agility to move towards subscription models
(we have shown a case study of Rolls Royce’s “power by
the hour” proposition later on). This will enable continuous
upgrades and the creation of product platforms from
which the entire economy will benefit.
Electronic Tools
Semi-conductor
Enterprise
Telecom
Consumer
Smart Enterprise
>75bn
IoT devices / sensors
installed by 2025
48%
incremental value
creation in electronics
and high tech
387k
industrial robots
sold in 2017
$800bn
IT spend by industrial
OEM 2018-2027
11
SMART MANUFACTURING
10
CHAPTER 1
REVOLUTIONISING THE FACTORY STACK
How Technologies Combine To
Create A Holistic Ecosystem
Source: GP Bullhound
The cyberphysical production stack
The core of smart manufacturing is the
combination of different technologies. In order
to better understand the building blocks behind
this, we have grouped the most relevant
technologies into different layers across the
cyberphysical production stack: from the
highly virtual to the highly physical.
The basis of our stack is physical production,
represented by robotics, 3D printing and
augmentation of human workers (e.g. by cobots
or AR). These are connected by a layer of
interfaces: computer vision, AR platforms and IoT.
Next up is the orchestration layer, consisting of
middleware applications as well as edge computing,
which enables orchestration on device level.
Moving further towards the analytical layers of
the stack, we have grouped design technologies,
such as design tools (e.g. CAD) as well as digital
twin, which are key to model the impact of
design as well as process decisions.
Lastly, the top layer of abstraction in our
framework consists of intelligence tools, in
particular Big Data and AI. These will enable
intelligent control of production itself, but also
the planning behind it.
Companies have a choice whether they prefer
to position themselves horizontally or vertically
across this stack. The major theme across the
sector, is however, the creation of platforms,
be they horizontally or vertically integrated.
REACHING CRITICAL MASS
Investments Accelerating At
Decreasing Device Costs
Sources: 1. Statista (IFR), “Worldwide sales of industrial robots from 2004 to 2017” 2. Statista (IHS), “Internet of Things (IoT) connected devices installed base
worldwide from 2015 to 2025” 3. Statista (Gartner), “3D printers - worldwide unit shipments 2015-2020” 4. BCG, “Engineered products infrastructure machinery
components. Drones go work” 5. WEF, “Digital Transformation of Industries: Digital Enterprise” 6. Statista 7. Morgan Stanley Research, “Tech’s Next Big Wave:
Manufacturing”
One of the key drivers behind the current
investment wave into smart manufacturing is the
increasingly widespread availability of cost-efficient
devices. For example, the average cost of robot
units has decreased from $550,000 to $20,000
between 2007 and 2014; average costs for IoT
sensors are projected to decrease by more
than 70% between 2004 and 2020.
As a consequence, devices are proliferating at
an unprecedented scale. It has been forecast,
for example, that there will be nearly ten times
as many IoT devices as humans populating the
planet by 2025.
This coincides with increased investments
by industrial OEMs in equipment as well as IT
infrastructure at the same time. Since about 2015,
both investment categories have been expanding
as a percentage of total capex at the same time,
indicating a widespread upgrading of facilities by
industrial OEMs.
Furthermore, industrial OEMs are forecast to
contribute about 40% of corporate IT spend over the
next decade, significantly more than in the last ten
years. All of this indicates that the market for smart
manufacturing is progressing towards critical mass.
Intelligence
Design
Orchestration
Interface
Production
Big Data
Artificial intelligence
Design tools
Digital twin
Middleware
Edge computing
Computer vision
& inspection
Augmented reality
Industrial
IoT
Robotics
3D printing
Machine-enabled
worker
Physical
Virtual
LAYER
KEY TECHNOLOGIES
Number of
devices
Cost per
unit(5,6)
387,000 units
sold in 2017 (1)
2007 $550,000
2014 $20,000
> 75bn devices
installed by
2025 (2)
2004 $1.3
2020 $0.4
6.7m units
shipments
by 2020 (3)
2007 $40,000
2014 $100
> 1m units
by 2050 (4)
2007 $100,000
2013 $700
Industrial robots
Sensors/IoT
3D printing
Drones
Industrial and IT investment cycles(7)
Corporate IT spend ($trn)(7)
Q1 2007
Q1 2009
Q1 2011
Q1 2013
Q1 2015
Q1 2017
90
120
95
100
105
110
115
Investment in IT Infrastructure as % Total Capex
Investment in Industrial Equipment as % Total Capex
2008-2017
0.4
0.2
0.6
2018-2027
Base Case
1.0
0.7
1.7
2018-2027
Bull Case
1.1
0.8
1.9
Industrial OEMs
Non-Manufacturing
Industry
13
SMART MANUFACTURING
12
CHAPTER 1
TRANSFORMING HOW
PRODUCTS WILL BE DELIVERED
Creating The Outcome Economy
Sources: 1. Eric Schaefer Industry X.0: Realizing Digital Value in Industrial Sectors 2. Company annual reports and press releases.
From product- to service-orientated manufacturing(1)
Successful XaaS Models Already Deployed:
The Cases Of Rolls-Royce And Kaeser
Note: (1) Long Term Service Agreements
Sources: 1. World Finance, 2016. “Rolls-Royce is driving the progress of the business aviation market”.
2. Rolls-Royce, 2012. “Rolls-Royce celebrates 50th anniversary of Power-by-the-Hour”
As the digitisation of the manufacturing sector
progresses, it enables previously unknown levels of
agility and tractability in the design and running of
industrial processes. The end result of this evolution
could likely be a complete re-invention of the
Capital Goods sector.
In a first step, improved maintenance cycles
and the ability to update underlying control
platforms “over the air” allow manufacturers to
sell their product not as a physical good, but as a
subscription service. This has advantages for both
sides: the manufacturer can rely on predictable,
continuous revenue streams and stronger lock-in,
while the customer can channel investments via
opex and only pays for actual consumption of
the product. Some industry pioneers adopted this
concept some time ago, e.g. Rolls Royce with
its Power By The Hour (PBH) concept.
Once capital goods become further digitally
orchestrateable, this will enable not just selling
these goods as-a-service, but the creation of
entire digital ecosystems and marketplaces
around product platforms, similar to what we
know today in the IT world.
Ultimately, agile and predictive manufacturing
will create something that is known as the “pull
economy”: an end-to-end ecosystem where
production is optimised to demand and resources
and mass customisation will be the standard.
» Invented in 1962, ‘Power-by-the-Hour’(PBH) is a pioneering engine maintenance approach at the
foundation of Corporate Care service by Rolls Royce.
» Originally PBH service implied complete engine and accessory replacement on a fixed-cost-per-flying-
hour basis and further was expanded with additional services.
» The concept creates a synergy effect through alignment of interests: manufacturer receives
a guaranteed revenue stream while operator pays for well performing engines only.
» Kaeser equips its compressors with sensors for environmental and performance data
» This enables predictive analytics and optimized maintenance scheduling, resulting in less down-time
» Kaeser now sells “air-as-a-service” by the cubic meter through compressors it owns and maintains
INTERMEDIATE
NEAR TERM
LONG TERM
LONGER TERM
Operational
efficiency
New products
& services
Outcome-based
economy
Autonomous
pull economy
» Asset utilisation
» Operational cost
reduction
» Improvement of
worker productivity,
safety and working
conditions
» New business models
» Pay-per-Use
» Software-based
services
» Product/Service
hybrids
» Data monetisation
» Pay-per-Outcome
» New connected
Ecosystems
» Platform-enabled
marketplace
» Continuous
demand sensing
» End-to-End
automation
» Resource
optimisation &
waste reduction
PRODUCT
SERVICE
OUTCOME
PULL
CASE STUDY: ROLLS-ROYCE’S “POWER-BY-THE-HOUR” (PBH)
CASE STUDY: AIR AS A SERVICE
‘Power-by-the-Hour’(PBH)
lying at the heart of Corporate
Care ® service by Rolls Royce
Lease
Engine
Access
Authorized
Maintenance
Centres
Engine
Health
Monitoring
PBH
1
Sensor-based
engine
performance
tracking
2 Minimised downtime
through replacement
of operator’s engine
during off-wing
maintenance
3
Superior global
customer support
through a network of
authorised centers
KEY BENEFITS FOR THE BUSINESS
» Predictable maintenance costs
» Reduced capital investment
» Increased residual value
» Risk sharing with manufacturer
LTSA(1) service revenue (£m)
2017
2018
+15%
growth
3,015
3,469
IT/OT
Connectivity
Condition Monitoring
Remote Service
Fault Platform
Recognition
Machine Health
Prediction
Create Maintenance
or Service Order
Schedule
Order
Execute Order
on mobile device
Visual
Support
Analysis across Entire Lifecycle
15
SMART MANUFACTURING
14
CHAPTER 1
For industrial enterprises, digital transformation often
translates into a phrase called Smart Manufacturing. Smart
Manufacturing is not only about digitizing the manufacturing
function. Rather, it is about using digital technologies
to unlock new operating efficiencies during product
conceptualization, design and manufacture and towards
delivering hyper-personalized experiences to customers
across the product lifecycle.
A 2017-Accenture survey of 931 senior business executives
spanning 12 industries and 21 geographies reveals that
almost all executives want to leverage digital technologies
to enhance efficiency of their operations and to drive more
personalized experiences for their customers and workforce.
However, only 13% of business executives feel confident of
achieving this goal. Importantly, 64% believe that failure to
drive experiences and efficiencies with digital technologies
will cause their businesses to struggle for survival in as short a
span of next three years.
Many executives, the research team spoke to, concurred
about not knowing where and how to begin their digital
journeys. “How do we know which technologies should we
invest in to drive experiences and efficiencies? How can we
invest in digital technologies at scale when we don’t know
how investment in these technologies will impact financial
performance of a business?”, is what a senior executive from
a fast-moving consumer goods (FMCG) industry had to say
during one of the interviews.
Accenture’s research(1) provides a starting point.
Using a combination of survey data, published company
financials, and econometric tools, this research shares
estimates of the top and bottom line impact businesses
can achieve by systematically combining digital
technologies to deliver efficiencies and experiences.
(See Figure 1 and Figure 2)
For instance, companies in the industrial-equipment
sector could realize additional cost savings of over 19%
per employee if they combined autonomous robots, AI,
blockchain, big data and 3D printing. Whereas, chemicals
companies can potentially unlock growth of around 25%
in their market capitalization by enhancing their ability
to create new value with technologies cluster consisting,
autonomous vehicles, big data, digital twin, mobile
computing and virtual reality.
According to our research, the five percent of businesses
in our sample, that combined six technologies—mobile
computing, big-data analytics, machine learning,
augmented and virtual reality, autonomous robots and
autonomous vehicles – lowered their overall costs by
14% between 2013 and 2016. Cost savings for those not
combining the six, was a negligible 0.6 percent.
Raghav M. Narsalay
Head of Industry X.0 Research, Accenture
TECHNOLOGY CLUSTERS
The Key To Becoming A
Smart Manufacturer
Sources: 2. “Volvo On Call”, Volvo. Accessed on December 26, 2018 and viewable at: https://www.volvocars.com/us/own/connected-car/volvo-on-call
3. “Big Data at Volvo: Predictive, Machine-Learning-Enabled Analytics Across Petabyte-Scale Datasets”, Forbes (July 18, 2016). Accessed on January 25, 2018
and viewable at: https://www.forbes.com/sites/bernardmarr/2016/07/18/how-the-connected-car-is-forcing-volvo-to-rethink-its-data-strategy/3/#21f0f99a612d
4. “Volvo’s next generation of cars will use Nvidia’s self-driving car platform”, The Verge (October 10, 2018). Accessed on December 26, 2018 and viewable at:
https://www.theverge.com/2018/10/10/17958980/volvo-self-driving-cars-nvidia-drive-agx-xavier 5. “Volvo is using Google Cardboard to get people inside its
new SUV”, The Verge (November 13, 2014). Accessed on January 25, 2018 and viewable at: https://www.theverge.com/2014/11/13/7217397/volvo-is-using-
google-cardboard-to-get-people-inside-its-new-suv 6. “Robotics on the rise in manufacturing facilities,” Charleston Regional Business Journal (September 2016).
Accessed on April 10, 2018 and viewable at: https://charlestonbusiness.com/news/manufacturing/70567/ 7. “Autonomous Driving”, Volvo. For more information,
please visit: https://www.volvocars.com/intl/buy/explore/intellisafe/autonomous-driving
Figure 1:
Incremental savings in costs per employee
Figure 2:
Additional gains in market capitalization
Mobile
computing
Volvo’s On Call mobile app gives drivers all sorts of information and utility. Volvo owners use
the app to see where the car is parked, monitor fuel levels, double-check to see if a window
was left open or a door ajar, and even start the engine remotely.(2)
Big-data
analytics
In collaboration with Teradata, the business-analytics solutions provider, Volvo analyses
all user data collected, to find patterns that can make the driving experience of their
customers safer and more convenient.(3)
Machine
learning
Next, Volvo translates trends in the data they collect into something meaningful for everyday
operations. Take their ongoing work to be a leader in driverless cars. More than 20 cameras,
radars, and laser sensors on board every Volvo vehicle stream real-time data to Nvidia’s
Drive AGX autonomous vehicle computing platform, which helps the car learn to react to
situations on the road.(4)
Augmented, virtual
and mixed reality
In 2014, Volvo partnered with Google to use the tech giant’s Cardboard VR for the launch
of its redesigned XC90 SUV. Paper goggles, paired with an Android/iOS app, now allow
potential customers to test-drive the XC90 from their home.(5)
Autonomous robots
and autonomous
vehicles
Volvo, for its part, has used robots to make cars for decades. Some processes – such as the
welding of metal parts and the measuring, placing, and bolting of doors to its cars – are now
completely automated.(6) Robots are currently playing an important role in the production
of Volvo’s popular S60 sedans. Volvo has developed technologies such as adaptive cruise
control, autobraking-pedestrian-detection systems, and parking assist.(7) Volvo has even
launched a large-scale trial of autonomous-driving technology on actual roads.
3D Printing
Autonomous
Robots
AI
Blockchain
Digital Twin
Big Data
Machine
Learning
AR/VR
Autonomous
Vehicles
Mobile
Computing
Automotive
Industrial
Equipment
Natural
Resources
Aerospace
& Defence
Chemicals
Medical Tech
Electronics
& High Tech
Life Sciences
13.9%
19.6%
15.7%
17.3%
22.9%
45.5%
31.1%
41.7%
Mobile
Computing
3D Printing
Autonomous
Robots
AI
AR/VR
Autonomous
Vehicles
Big Data
Machine
Learning
Digital Twin
Blockchain
Aerospace
& Defence
Chemicals
Medical Tech
Industrial
Equipment
Life Sciences
Automotive
Electronics
& High Tech
Utilities
26.3%
25.6%
14.7%
24.9%
12.0%
9.0%
48.1%
38.5%
Natural
Resources
Energy
Consumer
Goods & Services
16.8%
43.9%
34.5%
Sources: 1. “Combine and Conquer: Unlocking the power of digital”, Accenture (September 2017). Accessed on December 20, 2018 and viewable at:
https://www.accenture.com/_acnmedia/Accenture/Conversion-Assets/DotCom/Documents/Global/PDF/Dualpub_26/Accenture-Industry-XO-whitepaper.pdf
Volvo serves as an excellent example of how companies have already started leveraging the power of technology
clusters to become smart manufacturers.
Surely, how technologies should be clustered or combined will vary across industries and will certainly change over time.
But the value takeout associated with their application, regardless of industry, will continue to be significant, is indisputable.
17
SMART MANUFACTURING
16
II.
THE POWER OF DATA
Data And AI In The
New Manufacturing World
CHAPTER 2
Analytics & Foresight
As data generation in the manufacturing sector increases,
so does the role of analytics and foresight. From initial design
to production and in-life management, smart manufacturing
provides unparalleled tools and insights.
1
Design & Simulation
Modern design and simulation tools allow production processes and
outcomes to be fully understood, simulated and designed in real time
and fed back into the real world on a continuous basis. The Digital Twin
is the core tool in this concept.
2
Intelligent Worker Augmentation
While machines play a crucial role in automation, not all tasks can be taken over by
robots. Augmented Reality (AR) and collaborative robots (Cobots), however, can provide
substantial productivity gains to a workforce augmented by these tools.
3
Software-Defined Manufacturing
Combining all the previous trends stands a concept that promises to
create a “lights out” factory, based on disposable robots and machine
intelligence. In a few years, manufacturers will be able to feed designs
directly from CAD straight to the end of the production line.
4
19
SMART MANUFACTURING
18
CHAPTER X
Source: Morgan Stanley Equity Research, “Engineering the 21st Century Digital Factory”
BUILDING THE INTELLIGENT FACTORY
The Importance Of Data In A Smart
Factory Environment
Annual data creation by industry (Petabytes)
The most important factor in creating the smart
factory is data. The manufacturing shopfloor is
already the most data-rich environment in the
world: collectively, it creates 1.8k petabytes
of data every year, twice as much as the
government sector and by far outstretching
communications and media, banking or retail.
Harnessing this extremely data-rich environment
is one of the key challenges of industrial
transformation. The initial struggle in this process
is often to make the data universally available.
Once this has been solved, however, we see an
endless possibility of applications, of which we
have picked four very promising ones to illustrate
the role of data as the “new oil” in a smart
manufacturing economy.
Analytics & Foresight is one of the highly
transformational trends which may ultimately
conclude with the creation of fully predictive
manufacturing. Design & Simulation is already used
to great effect in highly automated environments.
Closely connected to this is Intelligent Worker
Automation, e.g. by AR devices or cobots, to
increase their productivity.
Finally and lastly, we present a view on Software-
Defined Manufacturing, where physical factories
become as agile and automated as a modern
data centre, driven by AI and edge intelligence.
Manufacturing Government
Comms
& Media
Banking
Retail
Professional
Services
Healthcare
Securities
Investment
Services
1,812
911
776
773
424
397
375
336
1,812
Petabytes
manufacturing
data created
2.6m
Cobots sold
until 2025
$3.8trn
incremental Real Gross
Value Added in
manufacturing
through AI
2
21
SMART MANUFACTURING
20
CHAPTER 2
DATA-DRIVEN INDUSTRIAL AUTOMATION
Harnessing Data To Create Actionable Insights
Martin Lorentzon, co-founder of Spotify says, “The
value of a company is the sum of the problems
you solve.” I think it’s true for all businesses, but
especially true for manufacturers. Manufacturing
has always been competitive in nature, but due in
part to globalization, the competition has intensified
tenfold. Improvement methods such as Lean, Six
Sigma, and Kaizen, that emerged as a result of
the competitive landscape, are now considered
table stakes for everyone, forcing manufacturers
to look to a new frontier to gain the competitive
advantage. They’ve found this new frontier in
digital manufacturing solutions.
There are two ways that a digital system can
deliver value to users. It can help them solve
problems faster than previously possible. This has
immediate value, given how time consuming
the process of solving quality, performance or
downtime issues is in manufacturing.
However the second way a digital solution delivers
value is more long-term and transformative: it
allows users to solve problems they would never be
able to solve previously. Take the environmental
algorithms we have delivered at Oden Technologies
as an example. The factory environment (e.g.,
temperature, humidity, etc.) plays a sizeable role in
material processing. But, in order to understand and
adjust process parameters to account for the impact
of environmental factors, one has to first analyze
an abundance of data. The volume is typically too
great for skilled engineers to handle, and since many
do not have the experience to train models, the task
is nearly impossible. However, digital solutions like
Oden have algorithms to analyze millions of historical
data points and make recommendations on the
optimal settings that will drastically improve quality
and output. These trained models do what even
your most skilled engineer cannot.
Getting to a Smart Digital Factory is a journey.
At Oden, we educate the industry on the four
levels to that journey towards data-driven,
intelligent manufacturing.
Level 1 - Almost Accessible Data. This is where most
factories currently sit. Many different siloed systems
combined through ad hoc, manual data collection.
Extracting value from data is time consuming and
reactive, only performed when a ‘fire’ -
an emergency situation - arises.
These factories are leaving a lot of money on the
table since there is a tremendous amount of cost
reductions and profit in eliminating variability and
picking off the low-hanging fruit, like making process
improvements that increase capacity. Digital
investment in the form of new infrastructure and
integration is required to go from a Level 1 factory
to a Level 2.
Level 2 - Instantly Accessible Data. All production
data sources are integrated into one platform, a
single source of truth for the entire factory. When the
architecture is set up correctly, the right people have
access to data and analysis tools that allows them
to solve problems in very short order.
While ‘fighting fires’ is still a reality, identifying the
root of those issues takes minutes. It still requires
effort from people to engage with the system to
be truly proactive with predictive and
preventative improvements.
One of our customers saw $60k return in the first
6 months on just one production line from simple
analytics. The faster a manufacturer installs the right
architecture the faster they can get to Level 3,
since it’s all built on the same data.
Willem Sundblad
Founder & CEO, Oden Technologies
Level 3 - Data Finding People. In a Level 3 factory
you have machine learning (ML) models detecting
insights and anomalies, surfacing them to the right
people. This is where users can start to be proactive
and truly prevent problems from happening. You
will not need new architecture to go from Level 2
to Level 3, but you do need new tools to build up
a robust data science engine.
The architecture itself is very important, traditional
automation systems are not built for this volume of
data. The data then becomes a depreciating asset,
where the more you have the slower the software
runs and the more costly it is. If you have the right
architecture the data becomes an appreciating
asset: the more you have, the more powerful your
solution will be. Examples of Level 3 insights that we
have delivered are Predictive Quality, Performance
optimization models and the environmental analysis
previously mentioned.
Level 4 - Data Creating Actions. In a Level 4 factory
a machine learning model makes recommendations
for new settings that go directly to the machine to be
executed: an intelligent autonomous production line.
We are currently experimenting with an autonomous
system, but just like self driving cars it will take a while
(and lots of data) before it’s ready for commercial
use. That is why it is essential for manufacturers looking
into digital solutions choose providers that are not
just promising ML and AI out of the box, but set your
factory on a journey towards intelligent industrial
automation with value-added along the way.
23
SMART MANUFACTURING
22
2. DESIGN & SIMULATION
Example of Digital Twin
in Manufacturing(1)
Computer-aided design and simulation is not
a new concept, with the first CAD programmes
available since the late 1950s. With increasing
processing power, however, two trends have
emerged which are pushing the boundaries of
what has been possible before: firstly, the ability
to map increasingly complex models in 3D and,
secondly, the ability to simulate at scale in
real time.
Combining those two trends together yields the
real-time digital twin, which enables OEMs to
model both their manufacturing line as well as
their output and directly simulate outcomes of
different decisions and scenarios.
The above chart shows this concept
schematically: every single element of the
manufacturing line is modelled in a “digital
twin” comprising all specifications and physical
properties. Sensors then feed back data into the
digital twin, where the data is analysed, new
configurations are tested and, once a decision
has been made, fed back to the real-world
factory line.
One showcase for the real-life use of this
technology is Siemens’ electronics manufacturing
facility in Amberg, where production has now
reached a quality level exceeding 99.9989%.(5)
The market potential for this technology is
indeed huge. Market studies estimate the digital
twin market will become larger than simulation
software or CAD by 2023; Gartner estimates that
by 2021, half of all large industrial enterprises will
use the digital twin and those that do will become
10% more effective.(6)
Sources: 1. Deloitte University 2. Markets and Markets, “Simulation Software Market by Component (Software and Services), Application, Vertical (Automobile,
Aerospace & Defense, Electrical & Electronics, Healthcare, and Education & Research), Deployment Mode and Region - Global Forecast to 2022 3. Markets
and Markets, “Digital Twin Market by End User (Aerospace & Defense, Automotive & Transportation, Home & Commercial, Electronics & Electricals/Machine
Manufacturing, Energy & Utilities, Healthcare, Retail & Consumer Goods), and Geography - Forecast to 2023” 4. Statista (BIS Research) 5. Siemens AG, “The
digital enterprise EWA – Electronic Works Amberg”, 2017 6. Gartner, “Prepare for the Impact of Digital Twins”, September 2017
Industrial Design &
Simulation Market
Digital Twin(3)
2017
$6.3bn
$13.5bn
2022E
CAGR
16.5%
2016
$1.8bn
$15.7bn
2023E
CAGR
37.9%
Computer aided design(4)
2016
$5.1bn
$1.9bn
$8.4bn
$2.8bn
2023E
3D design
2D design
Simulation Software (2)
CHAPTER 2
1. ANALYTICS & FORESIGHT
Impact across the product lifecycle
Jeffrey Immelt, former CEO of General Electric
stated in 2014: “If you went to bed last night as
an industrial company, you’re going to wake up
today as a software and analytics company.”
This captures the increasing importance of data
analytics in a world of faster-turning product
cycles and asset subscription models. Equipment
may be saturated with devices, but the ability to
collect, interpret and predict data from across the
entire value chain will be one of the key drivers of
industrial success.
As data becomes ubiquitous, platform models
are gaining more and more relevance in this
area. A horizontal example of this is New York-
based Oden Technologies, which provides an
intelligent process automation platform that spans
the entire shopfloor. Other examples include
companies which focus on providing vertical-
agnostic, but technologically deeply embedded
data platforms, such as Munich-based Empolis,
which uses semantic analysis technique for error
prediction, localisation and fixing or the large
data analytics company Palantir, which provides
a fully configurable data platform to capture
parts, equipment and processes.
Far Eastern start-ups are particularly strong in data
analytics and AI – prominent examples include
recently IPOed SenseTime and Horizon Robotics,
which provide edge computing AI solutions.
The ultimate end result of these innovations
will likely be predictive manufacturing systems,
which are not just able to react on their own
performance data, but also on usage and market
inputs, lifting automation to the next – decision-
making – level.
Source: GP Bullhound
Design
Planning
Production
In-life management
ANALYTICS
...MARKET DATA
...PRODUCTION DATA
...USAGE DATA
Understand
market trends
Optimize UX
“Right first time”
Faster time to
market
Demand
forecasting
Direct integration
of suppliers
Resource and
energy optimization
Optimize asset
utilization
Predictive
maintenance
Flexible value chain
In-service
monitoring and
real-time analysis
Smart upgrades
Predictive
maintenance
Holistic data analytics fabric, encompassing:
25
SMART MANUFACTURING
24
CHAPTER 2
4. SOFTWARE-DEFINED MANUFACTURING
The impact of AI on industry output
(Real Gross Value Added(1) in 2035 in the USA in $trn)
The concluding point in our short selection of
data-related smart manufacturing trends is what
we call Software-Defined Manufacturing. To
some extent, this combines aspects of all three
previously mentioned trends but also adds new
aspects to the combination.
The basic notion of Software-Defined
Manufacturing is to create a production line that
is orchestrated in real time by software, without
any human intervention at all. This will require the
integration of strong data analytics capabilities,
real-time digital twin, smart up- and downstream
capabilities (e.g. smart logistics) as well as simple
but new hardware elements for connectivity,
computation and execution.
The idea of a fully software-driven, “lights out”
factory is only in its early stages, but has
already gained significant traction, especially
in the electronics and semiconductor
manufacturing space.
One of the notable new companies in this space
is Bright Machines, which raised a seed round of
$179m in 2018. Similarly, recently-IPOed Foxconn
Industrial Internet has been promoting this idea
since its inception in 2016.
New manufacturing companies, that are not
saddled with existing infrastructure, such as
Tesla or Lilium have been vigorously pushing
this agenda over the last couple of years. The
economic impact from this could be tremendous.
Accenture e.g. estimates that, by 2035, the
impact of AI on manufacturing profits could be
an uplift of 39% compared to baseline, translating
into an additional GVA of nearly $4 tn.
Source: Accenture and Frontier Economics
8.4
12.2
7.5
9.3
6.2
8.4
4.0
4.9
3.7
4.7
3.4
4.6
2.8
3.3
2.1
2.9
2.3
2.7
1.5
2.0
1.0
1.3
Baseline
AI ‘steady state” scenario
Existing CAD; image and
video data are used
and save the expense of
creating new content
3. INTELLIGENT WORKER AUGMENTATION
Creating more autonomous and connected
machinery is only one lever of efficiencies in smart
manufacturing. Equally promising is to provide the
existing human workforce with tools and data to
master the challenges of further automation.
There are two technologies, which are particularly
relevant in this context: firstly, the real-time
provision of data and instructions to human
workers via AR devices and, secondly, the
adoption of collaborative robots, or “cobots”.
Providing real-time instructions via AR devices
(goggles or handheld devices) is a key tool to
enable workers dealing with the complexities of
an automated environment and to “jump start”
their training. Bosch is one of the companies which
is pursuing this area across several dimensions:
the Common Augmented Reality Platform
(CAP) provides a platform to collaborate with
shopworkers using handheld AR interfaces; at the
same time, Bosch is also invested in various AR as
well as computer vision start-ups (e.g. Wave Optics,
Airy3D, allegro, and Mod.Cam, among others).
Another way of augmenting workers is by
providing them with robotic hardware, i.e. cobots.
Cobots address the issue that regular industrial
machinery is too large and unwieldy to directly
interact with workers. This poses two challenges:
firstly, cobots need the physical capabilities to
interact with and imitate human movements;
secondly, cobots require data and intelligence to
understand how and where to move.
The potential size of this market is huge; annual
cobot sales are forecast to grow more than ten-
fold to nearly 750,000 units over the next five years.
While big strategics, as e.g. Hahn Automation
(which acquired Rethink Robotics) or Kuka are
pushing in this space, full-stack start-ups such as
Franka Emika are set to profit from this trend as are
platforms that allow robots to learn from humans,
as e.g. MicroPsi, 20 Billion or Wandelbots.
Source: 1. Statista 2. GP Bullhound
Cobots: projected sales worldwide
(in 1,000)(1)
AR in production: the Bosch Common
Augmented Reality platform
2018
2019
2020
2021
2022
2023
2024
2025
61
66
126
242
353
508
637
735
Integrates the production
of visual and digital
content directly into the
authoring process
1
2
CAP enables implementation of
complete enterprise AR solutions
Target/actual
comparison &
collision planning
Production &
manufacturing
Plant & system
planning
Education
& training
Maintenance,
service & repair
Marketing, trade
shows & distribution
Technical doc. &
digital operating
instructions
CAP PLATFORM
Manufacturing
Professional
Services
Wholesale
& Retail
Public
Services
Information
& Comm.
Financial
Services
Construction
Logistics
Healthcare
Hospitality
Utilities
27
SMART MANUFACTURING
26
CHAPTER X2
SOFTWARE-DEFINED MANUFACTURING
Creating A Fully Autonomous Factory
Brian Mathews
Chief Technology Officer, Bright MachinesTM
At Bright MachinesTM, we have a vision: to transform
the manufacturing industry by delivering intelligent,
Software-Defined Manufacturing. In this future,
new products are deployed to production lines in
seconds rather than months, production equipment
is fully utilized regardless of product mix or volumes.
Yields are increased with automatic data-driven
configuration changes. Product design changes
can be deployed a dozen times a day without
downtime for retooling. Any product issues reported
by customers are automatically traced back to the
precise factory conditions that created the issue,
and software makes recommendations on how to
address the issue. When demand increases, the
production process running at the primary factory
can be digitally brought on-line at other factories
worldwide within minutes, where software adapts
the product design to site-specific production
equipment automatically.
A similar vision has already been realized in the
cloud computing world. Modern cloud computing
data centers are massive collections of dissimilar
production hardware (networking, storage, CPU,
GPU, power generation, cooling, etc.) from many
different companies all controlled by many different
interface “standards”. While data centers have
existed since the Apollo 11 era, the introduction of
software controllable hardware and sophisticated
automation software enabled modern cloud
computing data centers to house millions of servers.
In traditional (self-managed) IT data centers,
you had to trade-off speed of innovation against
complexity of scale, and reliability. But modern
public-cloud data centers automate everything
with software: configuration management,
integration, deployment, and test. The result has
been tremendous increases in the speed of product
innovation and the scale of global operation, while
simultaneously increasing reliability and reducing
costs. In other words, software automation allowed
software companies to change their product more
often while increasing reliability.
The manufacturing of physical goods, meanwhile,
has yet to realize automation’s full potential in this
way. When it comes to manufacturing electronics,
the front of the line (component placement,
soldering, etc.) is already highly automated, but
at the end of the line there are millions of human
workers doing final assembly and inspection. It often
takes dozens of expensive engineers months of effort
to design, build and fine-tune automation for these
production lines.
Bright Machines is changing that. We are making it
just as easy to build physical products as it is to build
digital ones. With Software-Defined Manufacturing,
we’re revolutionizing physical goods manufacturing,
just as cloud computing has done with the
manufacturing of digital goods. Our software
(Brightware™) and robotic cells (Bright Robotic
Cells) make software-defined automation accessible
by complementing robotics with intelligent
machine vision and a dynamic, agile configuration
management layer. This enables the manufacturing
line to autonomously re-configure as required; the
aim is to “automate the automation” by combining
capabilities from CAD, simulation, machine
learning, computer vision, IoT, and configuration
management with an open data platform.
Software-Defined Manufacturing enables
manufacturers to create their ideal assembly
and inspection Microfactories that automatically
re-configure and re-calibrate to different tasks
and different products via computer vision. When
computer vision bridges the divide between
idealized digital-twin simulations and the imprecise
analog reality of factories, it enables the entire
CAD-to-Product workflow to be automated.
Once this level of automation has been reached,
further “shift left” steps are possible: the engineering
and ultimately, the design of the line itself, could be
automated. This will enable far-reaching, universal
mass customization of manufactured goods.
Today, the first use case we are looking at is
“electronics in a box”, i.e. the final assembly of
electronics devices. The automotive industry
looks particularly promising: electric vehicles and
autonomous driving features are dramatically
increasing the demand for electronics, requiring
a complete re-think of how assembly processes
are automated. Similar cases can be made for
other industries.
Our $179m seed round, together with the more
than 400 manufacturing experts including 100
mechanical, electrical, computer vision and
robotics engineers, will enable us to pursue this
first milestone in the near future. Our robotic
cell hardware is already in use by automotive
and electronics customers; and we are building
an adaptive, intelligent machine-vision and
configuration platform behind it. In the end,
our aim is to create the core platform for a new
manufacturing ecosystem, bringing the agility
of software to the physical world.
29
SMART MANUFACTURING
III.
A FAST GROWING ECOSYSTEM
Key M&A And Funding Trends
28
CHAPTER 3
Large, but lumpy M&A and fast,
massive growth in venture funding
Smart manufacturing has seen more than $30bn M&A volume
over the last four years as well as nearly $6bn annual venture
funding in 2018. Especially the growth in venture funding has
been explosive, with almost no venture funding in 2013 and
since then continuously increasing annual volumes.
1
M&A is driven by large consolidators,
building full-stack platforms
All of the top-15 M&A transactions in the sector in 2013-2018 were large
consolidators expanding their footprint or adding new capabilities.
Throughout this time period, only 17% of all M&A transactions were buy-
outs. M&A in the smart manufacturing space is still largely driven
by strategics.
2
The large wave of current venture funding has created
highly capitalized start-ups across all verticals
All of the top-20 funded start-ups in this sector have received more than $100m total funding
to date, with some of the most prevalent rounds in 2017/2018. Two of them – Sense Time and
Magic Leap – have received more than a billion dollar funding.
3
In addition to M&A, strategic consolidators
are building extensive venture portfolios
Out of the large consolidators, there is none that doesn’t hold a VC
portfolio. The list is led by GE with 75 investments, followed by Siemens,
Intel, Bosch, Alphabet and Cisco, all placing significant bets on new
technologies in the smart manufacturing area.
4
31
SMART MANUFACTURING
30
CHAPTER 3
Sources: Pitchbook, Capital IQ, target companies and investor’s websites.
Notes: (1) Landmark transactions included M&A deals for Here Global in 2015, KUKA in 2016 and Mentor Graphics Corporation in 2017. (2) Data on deals covers
the period from 01/01/2013 to 31/12/2018, excluding 49 deals with undisclosed deal date. (3) Total number of deals screened: ~ 7,000 (4) One reason for the
decline in number of transactions may be a reporting lag of up to 18 months in early stage transactions. See this Dealroom.co blog post: https://blog.dealroom.
co/the-dirty-secret-of-venture-capital-investment-data/
M&A AND FUNDING ACTIVITY
Increasing Levels Of Activity Across
Stages And Categories
Key Funding and M&A Trends
As an important part of our research thesis, we have
looked at transaction activity in the smart manufacturing
sector and compiled a set of 1,578 relevant M&A and VC
funding transactions 2013-2018 from a much broader set
of transaction verticals.(3)
It is notable that M&A volumes in this field are lumpy and
dominated by large platform transactions, while venture
funding activity has been increasing constantly over the
last few years.
Overall, last year saw 32 M&A transactions in smart
manufacturing – down from 49 at the peak in 2016,
but up considerably from 2013 – as well as 233 venture
funding rounds,(4) exceeding 2013 by more than double.
Essentially, the venture funding statistics speak for themselves.
Total funding across all stages and geographies last year
stood at an all-time high of €5.9 bn, indicating the current
dynamism of this sector as well as a progressively increasing
degree of maturity.
M&A transactions by number
and volume (EURm)
Venture investment transactions
by number and volume (EURm)
Disclosed deal size
Disclosed funding
Landmark transactions (1)
Number of Deals (#)
Number of Deals (#)
19
20
25
49
38
32
691
1,382
3,816
19,316
7,247
1,420
556
4,213
3,165
9,920
14,638
2,850
872
2013
2013
2014
2014
2015
2015
2016
2016
2017
2017
2018
2018
110
168
220
287
321
233
€16bn
transaction volume
of top 10
landmark deals
1,377
1,726
1,300+
venture capital
investments
2013-2018
3,747
4,712
5,895
€5.9bn
venture capital
funding in 2018
€33.8bn
M&A transaction
volume 2013-2018
60%
annual growth
in venture funding
2013-2018
33
SMART MANUFACTURING
32
CHAPTER 3
M&A BY TYPE & VERTICAL
Sustained Strategic Investor Interest
In Platform Acquisitions
Sources: Pitchbook, Capital IQ, target companies and investors’ websites.
Notes: (1) Data on deals cover the period from 01/01/2013 to 31/12/2018. (2) Significant transactions include KUKA and Mentor Graphics M&A deals in 2016.
M&A by type and verticals
Selected Landmark Transactions
Sources: Pitchbook, Capital IQ, target companies and investors’ websites.
Note: Data on deals cover the period from 01/01/2013 to 31/12/2018.
Looking at the deal statistics for M&A in this sector,
one characteristic immediately stands out: only 17% of
transactions throughout this time period were buy-outs.
This is particularly remarkable, as both the software and the
industrial sector are prime targets for leveraged buy-outs.
One of the reasons for this could be that, apart from the
large, global OEMs, fully-fledged smart industry platforms
are still “in the making”, as we will see when looking at the
venture ecosystem. Indeed, most transactions in this sector
are driven by large strategics further building out their
platform capabilities.
Most prominently, this encompasses players such as
Midea, Siemens, GE, Cisco, big automotive OEMs, Stratasys,
Dassault and many more. As we will see on the following
pages, these are also highly active in building out
their venture portfolios in order to gain access to new
vertical technologies.
Two recent private equity deals highlight the criteria that
late stage investors apply to investments in the smart
manufacturing area. One of them is Investcorp’s investment
into Ubisense, a horizontal IoT device and software
platform, providing a high degree of product maturity
and strong software component. Another example is
Summit’s investment into OnRobot, which is scaling across
collaborative robotics through a buy-and-build strategy.
Both are investments with the hope to create strong
platforms. We expect LBO activity in this field to significantly
pick up once some of the fast-growing companies have
reached a more mature stage in their lifecycle.
M&A transactions by lifecycle (# deals)
M&A transactions volume by vertical (EURm)
2013
2014
2015
2016
2017
2018
Wearables
& VR/AR
Data &
analytics
Simulation
& design
IIoT Platforms
& Hardware
Robotics
& (additive)
manufacturing
Merger/Acquisition
Buyout/LBO
Landmark transactions
Deal
Date
Amount raised (EURm)
4,570
4,213
2,850
1,267
896
653
549
454
343
312
306
222
218
216
209
Acquirer
country
Target
country
13/07/16
30/03/17
04/12/15
22/03/16
25/01/16
28/12/16
12/12/16
19/06/13
17/11/17
11/06/15
15/12/14
25/04/18
15/07/14
25/10/18
06/02/14
Acquirer
Target
Vertical
Wearables & VR/AR
Data & analytics
IIoT platforms & hardware
Simulation & design
Robotics & (additive) manufacturing
19
20
25
49
38
32
105
4,311
14,080
12,559
4,709
7,850
9,734
4,345
2,850
1,461
3
3
9
6
6
5
14
14
19
40
35
29
1,551
1,267
2,818
35
SMART MANUFACTURING
34
CHAPTER 3
Sources: Pitchbook, Capital IQ, target companies and investor’s websites.
Note: Data on deals cover the period from 01/01/2013 to 31/12/2018.
(1) Horizon Robotics total funding amount includes funding round on 29/02/2019.
FUNDING TRENDS BY STAGE & INVESTOR
A Fast Growing And Increasingly
Mature Universe
Sources: Pitchbook, Capital IQ, target companies and investors’ websites.
Notes: (1) Data on deals cover the period from 01/01/2013 to 31/12/2018. (2) Other group of transactions includes corporate investments, PIPE, Product
Crowdfunding and Grants (3) Transactions include private placement deals and M&As (trade sales and LBOs).
Funding volume by stage 2013-2018 (EURm)
Country
Amount raised (EURm)
Selected Investors
Looking at venture funding in the smart
manufacturing space, our data indicates that
volumes have increased more than ten-fold
since 2013, showing substantial growth across all
funding stages. Especially since 2016, volumes
have significantly accelerated with new start-ups
continuously pushing into this sector and later-
stage companies gaining significant traction.
While funding has been driven especially by some
large players, such as Magic Leap, SenseTime
and Horizon Robotics (whose latest funding round
is actually not part of the data set as it closed
in February 2019), it is notable how many well-
capitalised firms exist in the $100-300m range.
These cover all verticals, from the production layer
up to software and design & simulation. Notable is
also the emergence of full-stack start-ups, such as
Bright Machines, which strive to address the entire
smart manufacturing stack with their platform.
Further detail is provided in Chapter V, where we
discuss key investment considerations for full-stack
as well as vertically focused solutions.
Most Funded Companies 2013 – 2018
2013
249
367
905
98
52
47
30
58
113
104
94
54
171
4
880
657
1027
1778
369
2290
2961
2280
2122
22
21
264
2014
2015
2016
2017
2018
556
1,377
1,722
3,748
4,712
5,895
Early Stage VC
Later Stage VC
PE Growth / Expansion
Seed, Angel & Accelerator
Other
1947
1382
600(1)
354
345
304
300
288
257
194
191
175
175
169
152
139
122
Target
Vertical
Wearables & VR/AR
Data & analytics
IIoT platforms & hardware
Simulation & design
Robotics & (additive) manufacturing
507
301
184
37
SMART MANUFACTURING
36
CHAPTER 3
THE ROLE OF LARGE CONSOLIDATORS
Continuously Expanding Footprint
Via Investments And M&A
Selected top 10 strategic investors by number of transactions
Similar to M&A, venture funding in the smart
manufacturing space is also to some extent driven
by large strategics intending to complete their
platforms by gaining access to additional vertical
and horizontal capabilities. Especially in the
early-stage space, this allows them to evaluate
potentially relevant technologies early on.
The list of investors is led by large OEMs, such
as GE, Siemens, Bosch and Cisco, but also by
information technology and software players
such as Alphabet, Intel and Microsoft. This
demonstrates, how the worlds of manufacturing
and software are becoming increasingly
fused together.
While M&A seems to have a transformational role
(either on geographic or business footprint), venture
investments are being used as a tool to gradually
evolve existing solution portfolios. The consolidation
maps on the right hand side as well as the following
expert interviews all show a differentiated, diverse
picture; what they have in common, however, is how
large strategic players are seeking portfolio evolution
and synergies through venture investments.
Interestingly, while portfolio synergies are one
important aspect, the main decision criterion seems
to nevertheless be financial return. The investors that
we have interviewed see this as the main proxy for
solution success and anticipated product-market fit.
Sources: Pitchbook, Capital IQ, target companies and investor’s websites.
Note: (1) Consolidator maps include transactions for which transaction value data (€) is available.
Number of transactions by vertical
Country
Investor
75
49
43
42
36
34
31
23
17
12
1
9
10
5
11
1
2
23
10
15
16
8
16
10
19
7
7
4
4
4
9
4
9
17
9
13
8
2
5
4
12
11
7
13
12
6
3
2
4
2
9
2
8
3
5
1
Wearables & VR/AR
IIoT platforms & hardware
Simulation & design
Data & analytics
Robotics & (additive) manufacturing
Vertical
Type of transaction
Wearables & VR/AR
Data & analytics
IIoT platforms & hardware
Simulation & design
Robotics & (additive) manufacturing
M&A
Venture Investments
39
SMART MANUFACTURING
38
CHAPTER 3
Over the last five years, we have made significant
investments in the area of IIoT, Smart Manufacturing, AI,
AR and hardware. Recently we also expanded that focus
around blockchain technologies for industrial applications
to power IoT with data integrity and identity for machines.
Having opened an office in Shanghai in 2018 we’ll be
increasingly looking to invest into Chinese innovations and
entrepreneurs in the aforementioned fields.
Within smart manufacturing we see solutions serving AI
powered applications or platforms as a critical component
of the Industry X.0. Efficiently set-up hardware components
play therefore an important role. We see solutions serving AI
powered applications or platforms as a critical component
of the Industry X.0.
At the AI-processor level, we invested into Syntiant, a
provider of deep learning powered ultra-low-energy Neural
Decision Processor Units, alongside Microsoft, Amazon,
Intel and others as well as into Graphcore, an Intelligent
Processor Units optimized for machine learning tasks in
cloud and embedded applications, following this theses.
Further up in the physical cyber production stack we can
see human machine interfaces as well as computer vision
and design software suits gaining significant importance.
Dr. Hongquan Jiang
Investment Partner, Robert Bosch Venture Capital
VENTURE INVESTING
AT ROBERT BOSCH
We have been investing over €3bn in smart manufacturing
and industrial automation over the last few years including
acquisitions across all elements of smart manufacturing.
Our investments have manoeuvred GE, in conjunction with
GE Digital, into the foremost position in the race to digitally
transform manufacturing around the globe.
Close to 70% of our recent investments have been directed
toward IIoT & additive manufacturing companies. Going
forward we will likely double down on the latter, while
taking a closer look on design and simulation solutions.
We strongly believe in the power of software platforms
revolutionizing the manufacturing stack. Once a
stakeholder in the space has established a digital core
based on a software platform, individual building blocks
can be added through strategic co-operations and M&A
to solve key pain points.
A great example is our investment with Goldman Sachs
and SilverLake in Aras Software, an enterprise grade open-
source PLM (Product Lifecycle Management) suite. In
addition to organic expansion, that funding has enabled
Aras to acquire Impresa MRO for in-service assets, and
Comet SPDM for simulation management putting the
company on the path to become the global market
leader in PLM.
Eric Bielke
Director, GE Ventures
VENTURE INVESTING
AT GENERAL ELECTRIC
“We believe in the power of software platforms
to revolutionize the manufacturing stack“
GE’s M&A and investment activity by vertical
Sources: Pitchbook, Capital IQ, target companies and investor’s websites.
Notes: Consolidator map includes transactions for which transaction value data (€) is available and based on all M&A and investment activities of General
Electric and subsidiaries in selected themes between 2013 and 2018. (2) * - Past investment.
“We see solutions serving AI powered applications or
platforms as a critical component of Industry X.0.“
RBVC’s investment activity by vertical
Sources: GP Bullhound research (Pitchbook, Capital IQ, target companies and investor’s websites).
Notes: Consolidator map includes transactions for which transaction value data (€) is available. (2) * - Past investment
Vertical
Type of transaction
Wearables & VR/AR
Data & analytics
IIoT platforms & hardware
Simulation & design
Robotics & (additive) manufacturing
M&A
Venture Investments
Vertical
Type of transaction
Wearables & VR/AR
Data & analytics
IIoT platforms & hardware
Simulation & design
Robotics & (additive) manufacturing
M&A
Venture Investments
Robotics & (additive) manufacturing
Transaction value: >€1,993m; number of transactions: 23
Data & analytics
Transaction value: >€280m; number of transactions: 16
Simulation & design
Transaction value: >€1,585m; number of Transactions: 13
Wearables & VR/AR
Transaction value: >€37m; number of transactions: 3
IIoT platforms & hardware
Transaction value: >€333m; number of transactions: 19
Robotics & (additive) manufacturing
Transaction value: >€9.1m; number of transactions: 2
Data & analytics
Transaction value: >€259m; number of transactions: 8
Simulation & design
Transaction value: >€70m; number of Transactions: 6
Wearables & VR/AR
Transaction value: >€68m; number of transactions: 9
IIoT platforms & hardware
Transaction value: >€423m; number of transactions: 10
*
*
*
41
SMART MANUFACTURING
IV.
GLOBAL POWERHOUSES
Geographic Clusters Of Smart Industry
40
CHAPTER 4
China and Korea have started to catch up
quickly on U.S. and European innovation
Smart manufacturing patent applications out of China and Korea
are growing fast and about to reach European and U.S. levels. This
is driven by large R&D budgets, with e.g. China annually spending
almost $400bn on non-pharmaceutical R&D, compared to the
European $322bn.
1
China 2025 is an outstanding example of
a national smart manufacturing strategy
The China 2025 strategy is driving rapid cyberphysical automation on
a national level. This coincides with large Chinese corporates – such as
Foxconn – pushing for wide-reaching automation and the Chinese IIoT
sector to exceed $52bn by 2019.
2
Especially U.S. and Chinese players engage in the
building of large, global platforms with cross-border M&A
Cross-border M&A is predominantly used by U.S. and Asian consolidators to purchase
European assets. During 2013-2018, e.g., foreign strategics have bought €11.3bn of assets
in Europe vs. European strategics only acquiring €1.3bn abroad.
3
The U.S. and Asia are leading the global
venture financing league tables by far
Out of $17.4bn venture funding 2013-2018, U.S. start-ups have received
$11.4bn and Asian start-ups $3.9bn. European start-ups
have only received $2.1bn during the same time frame, cementing
U.S. and Asian leadership in this sector.
4
43
SMART MANUFACTURING
42
CHAPTER 4
Sources: 1. European Patent Office (EPO), “Patents and the Fourth Industrial Revolution”, December 2017 2. (2) GP Bullhound calculation based on OECD,
EFPIA and Statista data 3. WEF / McKinsey, “Fourth Industrial Revolution: Beacons of Technology and Innovation in Manufacturing”, January 2019
Note: (1) 4IR – Fourth Industrial Revolution
THE GLOBAL RACE FOR INNOVATION
Geographic Trends In R&D
In the following section, we look at smart
manufacturing trends in four major global
manufacturing clusters: China, Europe, Japan
and the United States.
Out of these four, China has by far the largest
manufacturing sector, both in absolute numbers
as well as percentage of GDP (29%, translating into
$3.2trn), followed by the EU ($2.3trn, equivalent to
14% of GDP), US ($2.2trn, 12%) and finally Japan
($1.0trn, but a hefty 21% of GDP).
Historically known as the “workbench of the
world”, China is showing a particularly remarkable
evolution. Smart manufacturing-related patents at
the European Patent Office (EPO) have increased
across geographies. China (as well as South Korea),
however, are now quickly closing in to the more
established players in the smart manufacturing
space, showing exponential growth from very low
levels only a few years ago. This quick catch-up is
mirrored by significant R&D investments: in 2016,
China has spent more on innovation than Europe
and almost as much as the US in absolute terms,
translating into the highest percentage of GDP
among these four world regions.
Strategically, China and the US are investing
especially heavily into the creation of platforms.
While the US generates a lot of platform economies
via its thriving tech ecosystem, China is pushing hard
to create strong platforms of its own via the Made
in China 2025 strategy. The effort is paying off: in a
recent WEF / McKinsey study, five out of 16 global
lighthouses in smart industry were situated in China.(3)
Geographic origin of 4IR inventions
at the European Patent Office(1)
R&D spend in 2016 ($bn)(2)
2000 2002 2004 2006 2008 2010 2012 2014 2016
China
EU
Japan
USA
396
3.5%
2.0%
2.5%
2.2%
322
136
403
Europe
US
Japan
Republic of Korea
% of GDP
China
29%
of Chinese GDP in
manufacturing
$3.8bn
venture funding
in Asia 2013-2018
63%
of global venture
funding 2013-2018
in the US
$396bn
Chinese R&D
spend 2016
€11.3bn
European assets
sold to foreign buyers
2013-2018
1404
1036
892
829
581
45
SMART MANUFACTURING
44
<
<<<<<
CHAPTER 4
TRENDS BY WORLD REGION
Sources: 1. World Bank national accounts data and OECD National Accounts data files. 2. OECD, OECD Employment Outlook. 3. Statista, 2018
Large-Scale Greenfield Automation:
The Case Of Foxconn Industrial Internet
Notes: (1) Revenue split is based on 1H2018 financial results. (2) Fog AI - a smart control system for prediction of the fire probability and
optimization of evacuation plan. E-SOP - UWB based positioning platform based on facial recognition technology and behavioral analysis
enabling efficient workload allocation.
Sources: 1. Ecns.cn “Foxconn unit to focus on R&D” 2. Foxconn Industrial Internet official website
The China 2025 manufacturing strategy is an
interesting case, as it stipulates the policy of an entire
country to push for rapid cyberphysical automation.
A leading example in this context is Shanghai-listed
Foxconn Industrial Internet (FII), as it represents
an entire company transforming from electronics
manufacturer to smart industry OEM.
Electronics manufacturing is already a highly
automated sector, so this evolution makes sense.
Falling short of the original plan of deploying one
million “Foxbots” to replace a corresponding number
of human workers, FII has managed to create the first
listed pure-play smart manufacturing player.
This ties into the already highlighted theme of
concentrated platform building in China. The local
market environment is certainly supportive of this:
the Chinese Industrial Internet-of-Things (IIoT) sector
is forecast to exceed 350bn Yuan ($52bn) in 2019.
FII’s current development focus on machine learning
and software orchestration reflects very well the
current strengths of China’s ecosystem as a whole.
Together with the recent immensely large funding
rounds for horizontal platforms, as e.g. Sensetime
and Horizon Robotics, we should expect more
successful smart manufacturing platforms to
emerge in China.
» Themes: Predictive analytics,
IIoT, autonomous logistics and
advanced materials
» 2025: projected market share in
the global Cobots market - 18%
» Themes: Smart Factories, IoT
and digital design, simulation
and integration
» 2018: market share of global
factory automation market - 30%
» Themes: Predictive analytics,
Smart Factories, Cobots and high
performance computing
» 2025: projected market share in
the global Cobots market - 33%
» Themes: Robotics, Cobots,
IIoT and AI
» 2025: projected market share in
the global Cobots market - 18%
% Manufacturing
of GDP (1)
% Workforce in
Manufacturing (2)
Factory
Automation
Market (3)
Key Trends
Key Highlights (1)
Strategic Objectives
Timeline
Enable digital
transformation
Transform
to advanced
IIoT driven
manufacturer
Transfer
industrial
data to AI
Enhance
working
efficiency
Reduce
manufacturing
costs
Labour costs
11.91% YoY
>60,000
INDUSTRIAL
ROBOTS
DEPLOYED
FY2018(1)
REVENUE: €52.9bn
R&D EXPENSES:
€147.9m
200,000+
Employees
2013
2018
2015
2011
Vision statement
for far-reaching
automation with
“Foxbots”
First cobot
creation
Foxconn announced
to build an “Industrial
Internet Ecosystem”
IPO
“Fog AI” release
& E-SOP- launch
#4 in Shenzhen Top
500 Enterprise list
40,000+
Professionals
3,300+
Patents
FII IN NUMBERS
OPERATIONS
FINANCIALS
58%
42%
Communication
network equipment
Cloud service
equipment
6+
Automated
Unmanned
Factories
11+
Countries
12%
9%
14%
14%
29%
14%
21%
16%
$20bn
$24bn
$28bn
Asia -
Pacific
Japan
China
EU
United
States
$2,235bn
15m
$ 2,309bn
35m
$ 3,245bn
112m
$ 1,039bn
11m
47
SMART MANUFACTURING
46
23 23
CHAPTER 4
CROSS-BORDER M&A ACTIVITY
Trends In Global Consolidation
Sources: Pitchbook, Capital IQ, Company websites and press releases.
Notes: (1) Data on deals cover the period from 01/01/2013 to 31/12/2018 (2) Transactions with the RoW are not included.
One of the most insightful M&A indicators are cross-
border acquisitions, as they provide visibility on
consolidation trends and the emergence of global
platforms and cross-border technology transfers.
In this context, Europe emerges as the main
consolidation target for both US and Asian
players, while some cross-border consolidation
seems to be going on from Europe to the US.
Overall, the large majority of European
transaction value is subsumed by either US or
Asian acquirers (€11.3bn vs. only €1.3bn domestic
European acquisitions). On number of deals, the
trend is not quite as pronounced: 38 European
companies in the space have been acquired
by non-European acquirors vs. 43 domestic
acquisitions. The US ranks second in cross-border
activity with €8.4bn acquired by foreign acquirors
vs. €12.4bn domestic deal value. The large
majority of Asian M&A volumes was outbound,
predominantly to Europe.
Looking at the top landmark transactions during
2013-2017, the largest and most widely publicised
one was the acquisition of German robotics OEM
KuKa by Chinese group Midea. The remaining
large landmark deals are broadly split between
European acquirors investing into US footprint
and technology as well as the other way round.
Overall, a review of cross-border transactions
again confirms the theme of strategic players
concentrating into full-stack platforms.
By deal volume (EURm)
By number of deals
Landmark Cross-Border Deals
Sources: Pitchbook, Capital IQ, Company websites and press releases.
Data & analytics
Simulation & design
Robotics & (additive) manufacturing
Transaction
Size (EURm)
Summary
Date
Sector
Seller: Voith
Acquired: 62.81%
Rationale: Synergies for improvement
of factory automation
13/07/2016
4,570
Electrical
Equipment
Seller: Elliott Management
% Acquired: 100.00%
Rationale: Expansion in the electronic design
automation software segment
31/03/2017
4,213
Automation/
Workflow Software
Seller: Nokia
% Acquired: 100.00%
Rationale: Acceleration of open location
platform development
4/12/2015
2,850
Communication
Software
Seller: AEA Investors, Ontario Teachers’
Pension Plan
% Acquired: 100.00%
Rationale: Become a one-stop-supplier for
intelligent supply chain and automation solutions
1/11/2016
1,940
Logistics
Seller: Bank of America Merrill Lynch
% Acquired:100.00%
Rationale: Growth of digital business
& expansion in the industry software
1/03/2016
896
Multimedia and
Design Software
Seller: 3D Systems, Elliott Management
% Acquired: 76.00%
Rationale: Enhancement of additive
manufacturing business
28/12/2016
645
Industrial Supplies
and Parts
Seller: Founder (Frank Herzog)
% Acquired: 76.15%
Rationale: Enhancement of additive
manufacturing business
12/12/2016
549
Electrical
Equipment
Seller: Esben Østergaard, Søren Jørgensen,
Torben Rasmussen
% Acquired: 100.00%
Rationale: Expansion of the portfolio of
advanced intelligent´ automation products
25/04/2018
222
Electrical
Equipment
United
States
20,800
Europe
12,618
Asia
241
United
States
96
Europe
65
Asia
14
United
States
88
Europe
81
Asia
6
Acquirer
Target
Acquirer
Target
United
States
18,748
Europe
9,402
Asia
5,509
12,383
64
21
3
32
43
6
5
1
325
8,092
6,524
1,299
4,795
230
11
49
SMART MANUFACTURING
48
CHAPTER 4
Sources: Pitchbook, Capital IQ, Company websites and press releases.
GLOBAL FUNDING TRENDS
Funding Trends By World Region
Another important indicator for global trends are
funding rounds and volumes by world region.
Looking at the number of rounds for each
region, it is notable how the number of deals has
increased significantly between 2013 and 2017;
the 2018 numbers are probably not yet entirely
reliable due to a reporting lag on early stage
transactions.
Very interesting in this context is the distribution
of funding rounds vs. funding volumes between
the US, Europe and Asia. Europe has seen a
tremendous growth in funding rounds, reaching
five times as many transactions in 2017 as in
2013 and showing much more activity in terms
of number of transactions than Asia. Looking at
volumes, however, Europe is massively behind the
rest of the world, with more than five times the
investment in the US and almost double in Asia.
This is an indicator for the early stage nature of
the European market as well as fewer follow-on
rounds. As we will show in the following section,
European start-ups tend to be acquired earlier
through M&A and thus being taken from the
market. At the same time, the US and China
are investing heavily in placing big bets.
Venture funding rounds by region 2013-2018
(Number of rounds)
Venture funding volume by region 2013-2018 (EURm)
Total funding volume by region 2013-2018 (EURm)
Sources: Pitchbook, Capital IQ, Company websites and press releases.
115
33
51
12
18
4
16
23
134
164
180
148
20
19
32
95
92
54
11
9
17
83
168
220
287
556
11,442
2,055
3,883
632
1,377
1,726
3,747
4,712
5,895
233
321
110
Americas
Europe
Asia
RoW
5
4
2013
2014
2015
2016
2017
2018
2013
2014
2015
2016
2017
2018
Americas
Europe
Asia
RoW
274
46
74
88
119
4
20
30
527
493
612
502
1,124
1,063
2,944
2,485
1,553
1,751
3,325
314
173
105
181
207
824
343
114
58
Total Number of Deals
Americas
Europe
Asia
RoW
51
SMART MANUFACTURING
50
CHAPTER X4
GLOBAL TECHNOLOGY INVESTMENTS
Connecting The Dots Between East And West
At Asia-IO, we focus on pursuing Smart
Manufacturing private equity opportunities that
arise from the convergence of operational and
information technology across the technology stack:
from components, hardware systems, to software
and services; and industrial companies upgrading
their manufacturing capabilities and reshaping their
business models.
To date, our investments enhance infrastructure
that support intelligent manufacturing deployment;
enable high- reliability smart factory build-out; or solve
the technological and supply chain bottlenecks in the
manufacturing of next-generation products.
The technologies powering Smart Manufacturing
are global and supply chains are interconnected.
With offices in Hong Kong and Seoul, and partners
in Europe and North America, we invest in
cashflow-positive opportunities worldwide.
Up to today, we have led or co-led eight
investments in Europe, Korea, Hong Kong and
North America over a combined US$1.3 billion.
We focus on companies with an enterprise value
between US$50m and US$500m, emphasising the
Asian dimension in the value creation plan of our
portfolio companies.
North Asia’s industrial powerhouses of Greater
China, South Korea and Japan account for more
than 50% of worldwide manufacturing value-
add and consequentially together are by far the
largest market for smart manufacturing solutions
and services. They are also home to many global
champions in areas of semiconductors, robotics,
drones or AI - critical building blocks of industry 4.0.
In developing the Asian “angle” we work with a
number of the region’s largest and most innovative
industrial OEMs, often investing jointly in transactions. This
provides us with a deep understanding of these market
makers’ roadmaps and their strategic priorities and
gives our portfolio companies access to collaboration
opportunities, such as introduction to large potential
new customers, co-development programs and
distribution or manufacturing partnerships.
More generally, we specialise in identifying and
solving value chain bottlenecks, bringing core
technologies to new markets/ customers and bulking
up for scale (frequently through buy-and build) and
multi-market presence.
In the context of mid-sized companies, often owner-led
or carve-outs from larger organisations, these activities
help to elevate them to the next level and making
them ready for capital markets or strategic acquisitions.
Michael Prahl & Denis Tse
Partners, Asia-IO Advisors, Hong Kong
Key investment themes since 2015
Selected key investments
$410m
Enhancing infrastructure supporting
intelligent manufacturing
2
$370m
Enabling smart factory build-out
3
$580m
Solving manufacturing technological
and supply chain bottlenecks of
next-generation products
3
53
SMART MANUFACTURING
52
V.
ENTREPRENEURS
AND INVESTORS
Key People, Start-Ups And Investors
Shaping The Industry Of Tomorrow
CHAPTER 5
The landscape of companies is
skewed towards mature verticals
Out of the companies in our data set, almost a third are active in
IoT, a further quarter in robotics and more than a fifth in data and
analytics. Simulation and design as well as wearables & VR are still
relatively small and early stage.
1
The overall ecosystem is still quite early-stage
with many companies exiting to strategics
In both Europe and the U.S., the large majority of companies are either at
seed or venture stage. A relatively large proportion of companies in the
data set has been acquired through M&A (32% in Europe, 19% in the US).
2
Founders are generally experienced, technical
and tend to have worked with relevant strategics
Founders in smart manufacturing tend to be above 30 years of age
(especially in the US) and the large majority have a technical background.
Many combine academic as well as relevant strategic experience.
3
Investors into smart manufacturing tend to
be specialized and looking for platforms
Out of the top 10 venture investors in smart manufacturing, all of
them either have a specific focus or an explicit investment strategy
in this field. The main investment thesis seems to be platform-focussed
or full stack investments.
4
55
SMART MANUFACTURING
54
CHAPTER 5
Sources: Pitchbook, Capital IQ, company websites, GP Bullhound analysis
Note: Data on deals cover the period from 01/01/2013 to 31/12/2018.
FINDING A FORMULA
For Founders Of And Investors
In Smart Manufacturing
Landscape of companies per vertical
Since 2013, our analysis shows a total of 711
companies who have undergone a financing
or M&A transaction. While this is a very diverse
ecosystem across many different verticals, it is also
tightly intervowen in terms of investors, strategics
and founders.
The number of companies per vertical already
provides some insights as to their relative maturity:
the most populous vertical is IIoT platforms
and hardware, reflecting the relatively long
development runway IIoT already had. Second is
robotics and manufacturing, which is dominated
by robotics start-ups as well as 3D printing, shortly
followed by data and analytics.
Simulation and design (mostly digital twin) as well
as wearables and AR/VR seem to be a bit earlier
stage and currently contain fewer companies.
The big debate in the investment community
currently is whether to focus on full-stack start-
ups only or whether vertical solutions can create
sufficient “platform pull” to create ecosystems
within their specific layer of the cyberphysical
production stack. We will be looking at examples
for both models on the following pages, together
with some of the most prominent investors as well
as founders in the space.
Data & Analytics
154
227
87
63
180
IoT Platform & Hardware
Robotics & Manufacturing
Simulation & Design
Wearables & AR / VR
32%
of European
start-ups acquired
through M&A
227
out of 711 start-ups
in IoT platforms
& hardware
50%
of U.S. start-ups
still early stage
58%
of founders
have technical
background
180
out of 711 start-ups
in robotics
57
SMART MANUFACTURING
56
CHAPTER 5
32%
15%
19%
24%
10%
19%
11%
24%
39%
6%
Sources: Pitchbook, Capital IQ, company websites, GP Bullhound analysis
Note: Data on deals cover the period from 01/01/2013 to 31/12/2018.
FORMING AN INVESTMENT THESIS
Investment Strategies In A Quickly
Evolving Ecosystem
In order to better understand the relative degree
of maturity of the ecosystems in the US and
Europe, we have looked at the current financing
status of the companies in our data base.
What is notable is that in Europe a much larger
proportion of companies has been acquired by
strategics (32% vs. 19% in the US). At the same
time, more companies seem to have had seed
round as their latest financing status (15% vs. 11%),
while early and late-stage VC rounds seem to be
much more prevalent in the US.
This reflects on differences in financing
environments – more VC funding available in
the US – but also potentially on different founding
cultures. While it is relatively normal to engage
in repeated financing rounds in the US, it seems
that European founders prefer to bootstrap their
companies and /or sell them relatively early to
a strategic.
Stage of financial investments
Landscape Of Key Financial Investors
Investments by vertical of selected financial investors (excluding strategics)
Sources: Pitchbook, Capital IQ, target companies and investor’s websites.
Note: Data on deals cover the period from 01/01/2013 to 31/12/2018.
Beyond strategic investors, smart manufacturing
is a very VC-dominated world. While we have
excluded seed and incubation funds as well as
corporate VCs from our analysis, the leading
financial investors in this space nevertheless have
concluded a significant and growing number of
investments.
The list of the top-10 selected venture investors
includes a few names that are either exclusively
focused on the sector (such as eclipse) or on
physical high technology in general (such as
Lux Capital). One key theme for these seems to
be robotics and additive manufacturing, with a
particular focus on “full stack companies”, which
offers a solution covering both software and
hardware aspects.
Another thesis is the vertical platform investment,
which covers specific layers in the cyberphysical
production stack while adding enough value and
providing sufficient lock-in to create sustainable
and thriving ecosystems.
These tend to be on the later-stage side of the
investment cycle. One example is e.g. the IIoT
platform Ubisense, which was recently acquired
by international private equity firm Investcorp. By
providing unique hardware sensors combined with
a software layer, Ubisense has created a solution
that is both highly embedded and integrates
into a variety of other systems. This ability to
integrate into a variety of ecosystems seems to be
another success factor for smart manufacturing
investments.
EU
USA
Early Stage VC
Later Stage VC
Growth/LBO
Seed Round/Angel/Accelerator
Acquired through M&A
Wearables & VR/AR
Data & analytics
IIoT platforms & hardware
Simulation & design
Robotics & (additive) manufacturing
8
8
12
50
15
7
1
6
2
10
1
1
11
15
2
1
8
3
18
6
1
3
5
16
4
4
6
1
4
20
8
1
3
10
18
4
1
7
10
3
4
3
13
24
4
4
15
5
24
59
SMART MANUFACTURING
58
CHAPTER X5
INVESTING INTO THE LEADERS
Of The Emerging Smart Industry Ecosystem
We are at the beginning of an epochal shift in
manufacturing (a $12trn sector globally or 17 percent of
global GDP). With inexpensive sensors, cheap wireless
communications infrastructure, highly scalable cloud-based
data processing and novel machine learning methods, the
building blocks are in place for a new Machine Age. 
Dubbed Industry 4.0, these advances have not gone
unnoticed by traditional large manufacturers. They have
no choice: fierce competition from nimble new challengers
from China mean European and US manufacturers need to
step up just to stay competitive.  
A shift from mass, uniform manufacturing to small batch size,
customized products means  traditional methods become
unsuitably expensive. And customers, whether consumers or 
businesses, demand ever quicker turnaround times. 
By some estimates, Venture Capital investment in internet-
of-things in Industry (“IIoT”) was $769m in the first quarter of
2018, roughly eight times what it was the same quarter five 
years earlier. 
At Atomico, we think of these opportunities in terms of
five key areas which are converging to shape smart
manufacturing: Analytics/Orchestration, Computer Vision,
Robotics, AR/Wearables for control, and AI-Driven Design.
These are all by themselves key changes to traditional
manufacturing. Taken together, they represent no less than
a transformation.  
AI and computer driven agent will, over time, be given
nearly complete agency over making critical decisions on
the factory floor. Quality Control will be driven by machine-
learnt inspection and evaluation processes that are far more
robust that those today that rely on human interpretation.
Industrial robotics are moving from being prohibitively
expensive for mid/smaller applications to being cheap,
adaptable and safe enough to use for smaller tasks,  often
alongside humans. Wearables allow humans to interact with
existing and next generation equipment in a way that gives
them “superpowers” - reducing the reliance on human skill / 
memory, and overlaying valuable information into their field
of view when executing complex tasks. Design for objects,
components, facilities will be driven not just by guesswork and 
human skill to a multi-dimensional, integrated analysis of the
requirements and functional capabilities. 
At Atomico we have already made multiple investments into
this field, including Scandit focused on computer vision for
logistics and supply chains, CloudNC, which is automating
CNC milling, and Oden, which adds an analytics and control
layer for injection moulding factories. But we still believe
we’re only at the beginning.  
Luckily for us as European investors, manufacturing is a core
competency of the continent, and we believe the region
is well poised to create global winners in the Industry 4.0
space. Importantly, these ventures are also highly positive
for the world in the long run. Higher efficiency, better & more
customized end products, reduced waste / environmental
impact, increased safety and variety in human labour all
come together to make a compelling case forthis progress.
This transformation of manufacturing may well play a key
role in helping  humankind not only improve our quality of
life but also tackle the many environmental challenges of
our time.
Siraj Khaliq & Ben Blume
Partner & Principal, Atomico Industry 4.0 Initiative
Selected key
investments
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CHAPTER 5
EXCEPTIONAL TECHNICAL TALENT
Education & Experience Of
Smart Industry Founders
Age At Foundation
24%
34%
42%
62%
15% 23%
68%
21% 11%
30-40
>40
<30
....Most of the founders of smart manufacturing start-ups in Europe and Asia are below 30 compared
to the U.S. with the average age at foundation 38
U.S.
Europe
Asia
Previous experience at strategic player
35%
17%
48%
28%
33%
39%
Academia & out of College
Mixed
Strategic
....Cisco and IBM are top contributors to entrepreneurs landscape in smart manufacturing
U.S.
Europe
Source: GP Bullhound analysis: Founders of top-41 capitalized U.S., 14 European start-ups 6 Asian start-ups.
Educational background
60%
40%
57%
43%
....Most of the founders have educational background in Computer Science and Engineering
U.S.
Europe
Business / Other
Computer Science / Engineering
The final, and arguably most important dimension
in this ecosystem are the founders of smart
manufacturing companies. We have tried to gain
some insights on them by screening a sample of
100 companies from our bigger data set.
Overall, founders’ age distribution seems very
diverse with the US being skewed slightly towards
more experienced founders than Europe and Asia.
In both Europe and the US, the extremely technical
nature of this field is reflected by the vast majority
having studied computer sciences or engineering
versus a relatively small proportion of business or
other graduates.
In addition, previous experience seems to be
an important differentiator: especially in the US,
many founders have collected first experience at
major strategics, while almost a third of European
founders have founded their first start-up out of
university, or as a research institute spin-off.
The list of relevant strategics encompasses OEMs
clearly anchored in the manufacturing world
(such as Siemens, GE and Bosch), but also highly
relevant software names and next-generation
manufacturers, such as Tesla.
While the greater age and corresponding more
extensive strategic experience of US founders
to some extent expresses the different start-up
culture in this market, it also affirms the notion that
Europeans tend to build vertical technological
solutions (often as academic spin-offs), while
Americans seem to focus more decidedly on
platform creation.
On the following pages, we will briefly profile
some of the companies that we believe should
be worthwhile to watch across the smart
manufacturing technology stack.
Key Founders’ Selected - Previous
Work Experience
Age at foundation
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62
CHAPTER 5
SELECTED COMPANY PROFILES
Across The Smart Manufacturing Stack
Sources: Crunchbase, PitchBook, Company Informaiton
Note: (1) Total funding in EUR, unless otherwise specified
- Design
- Orchestration
- Intelligence
- Production
- Interface
HQ: Munich
Year: 2013
Total funding: 22m
Provide full transparency about risk
exposures in 1-n-tier supply chains.
HQ: San Leandro
Year: 1980
Total funding: 101m
Application software for real-time data
infrastructure solutions.
HQ: San Francisco
Year: 2018
Total funding: 194m
Enables flexible factory robots with
intelligent software, production data
and machine learning.
HQ: Munich
Year: 2013
Total funding: 45m
Indoor spatial intelligence digital twin
platform intended to digitize industrial
facilities
HQ: Santa Clara
Year: 2013
Total funding: 121m
Developing end-to-end ecosystem to
support cloud connected smart machines.
HQ: Boston
Year: 2013
Total funding: 80m
Enables device manufacturers, app
developers, and software companies to
leverage the power of the IoT.
HQ: Chicago
Year: 2014
Total funding: 259m
Predictive analytics platform designed to
help people and machines work better,
smarter and faster.
HQ: New York
Year: 2014
Total funding: 14m
Developer of a data acquisition and
analytics platform intended to monitor and
optimize production in real time.
HQ: San Francisco
Year: 2014
Total funding: 41m
Developer of robots designed to have
human-like intelligence.
HQ: Redwood City
Year: 2009
Total funding: 206m
Digital enterprise platform for AI and IoT.
HQ: Santa Clara
Year: 2010
Total funding: 120m
IoT platform-as-a-service (PaaS) for device
management and application enablement.
HQ: New York
Year: 2010
Total funding: 155m
Business analytics for complex data
through preparing, analyzing and
visualizing Big Data.
HQ: Labège
Year: 2009
Total funding: 287m
The world leading provider of connectivity
for IoT devices.
HQ: Montréal
Year: 2016
Total funding: 91m
Element AI is an artificial intelligence
solutions provider.
HQ: Singapore
Year: 2011
Total funding: 154m
Grey Orange produces Hardware &
software products for the warehousing
industry.
HQ: Palo Alto
Year: 2010
Total funding: 129m
Artificial intelligence company developing
a general intelligence for robots.
HQ: San Francisco
Year: 2015
Total funding: 198m
Builds sensor systems to combine wireless
sensors with remote networking & cloud-
based analytics.
HQ: Kaiserslautern
Year: 1986
Total funding: PE-held
Smart information management software
for the entire business process.
HQ: Waltham
Year: 2016
Total funding: 11m
Smart device solutions to enable high
performance collaborative industrial
robotics.
HQ: Santa Clara
Year: 2009
Total funding: 309m
Industry’s next generation data platform
for AI and analytics.
HQ: Zurich
Year: 2009
Total funding: 37m
Software for barcode scanning, text and
objects recognition and real-time insights
through AR.
HQ: Munich
Year: 2014
Total funding: 33m
Integration of smart sensor systems
and artificial intelligence to maximize
asset performance.
HQ: Paris
Year: 2010
Total funding: 105m
Smart Energy Management, Machine-to-
Machine(M2M) and IoT services.
HQ: Hong Kong
Year: 2014
Total funding: 1.38b
Artificial intelligence company that focuses
on innovative computer vision and deep
learning technologies.
HQ: Shenzhen
Year: 2013
Total funding: 169m
Intelligent technologies for every human,
everywhere through 3D image sensors and
smart cameras.
HQ: Mountain View
Year: 1999
Total funding: 82m
Developer of a self-driving supply chain
technology for businesses designed for
self-driving enterprise.
HQ: Redwood City
Year: 2013
Total funding: 354m
Intersection of hardware, software
& molecular science to enable 3D
manufacturing.
HQ: Munich
Year: 2011
Total funding: 67m
Developer of an intelligent Big Data
technology designed to analyze and
visualize every process in a company.
HQ: Sunnyvale
Year: 2012
Total funding: 279m
LiDAR sensors and software to capture and
process real-time 3D mapping data.
HQ: San Diego
Year: 2010
Total funding: 99m
Next generation AI based self-driving
technology designed to automate
commercial equipment.
HQ: Sommerville
Year: 2011
Total funding: 87m
Developing powerful and accessible
3D printing systems designed for printing
intricate figures.
HQ: Berlin
Year: 2014
Total funding: 8m
High-end machine learning solutions for
robotics and process control.
HQ: Burlington
Year: 2015
Total funding: 395m
3D metal printing in design &
manufacturing.
HQ: Berlin
Year: 2015
Total funding: N/A
Image search engine that is used as a
software-as-a-service.
HQ: Los Angeles
Year: 2010
Total funding: 119m
Wearable devices and software to
empower the workforce.
HQ: San Francisco
Year: 2017
Total funding: $19m
AI- and RPA-based Intelligent process
automation platform.
HQ: Cambridge
Year: 2002
Total funding: N/A
Developer of real-time location systems
that provide enterprise business automation
services.
HQ: Beijing
Year: 2012
Total funding: 249m
Cloud computing platform that provides
IaaS-based flexible cloud services.
HQ: Munich
Year: 2016
Total funding: N/A
Franka Emika develops and designs cutting-
edge, high-performance industrial robots.
HQ: New York
Year: 2011
Total funding: 45m
Industrial IoT company that brings predictive
maintenance to new markets.
HQ: Munich
Year: 2017
Total funding: 0.025m
Producer of smartwatches for industrial use
with manufacturing apps connecting to an
IoT backend.
HQ: Somerville
Year: 2014
Total funding: 28m
Developer of a manufacturing application
development platform for IoT enabled tools
and applications.
HQ: Odense
Year: 2015
Total funding: N/A
Developer of a gripper system platform
designed to handle industrial robots.
HQ: San Francisco
Year: 2011
Total funding: 14m
Provider of augmented reality training
solutions.
HQ: Stuttgart
Year: 2017
Total funding: N/A
Online B2B marketplace for industrial sheet
metal processing.
65
SMART MANUFACTURING
VI.
THE VISION
Intelligent Manufacturing
In The Future
64
CHAPTER 6
Most smart manufacturing technologies will still
require 5-10 years until mainstream adoption
According to Gartner, most smart manufacturing technologies will still
require 5-10 years until full mainstream adoption. This includes technologies
where we see the highest value potential, especially IIoT, 3D printing,
predictive analytics, digital twin and machine learning.
1
There will be three main archetypes
of smart manufacturing deployments
Depending on use case and scalability, smart manufacturing
deployments will likely fall into three archetypes: large scale, smart
automated plants; highly adaptable customer-centric plants; and
small-scale, mobile facilities “in a box”.
2
A large proportion of activities in advanced
economies can be automated
Looking at the German economy as an example, 54% of working hours fall
into “easily automatable activities”. This will have significant implications
for up-skilling of existing employees and future qualification requirements.
3
“Being human” will be ever more important
in an environment run by algorithms
As activities are being increasingly automated, “EQ” will become
increasingly more important than IQ: while IQ can be replicated by
algorithms, human qualities will remain an important differentiator.
4
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SMART MANUFACTURING
66
CHAPTER 6
Source: Gartner (2018), “Manufacturing Technology Innovation Hype-cycle”, available at https://www.manufacturing-operations-management.com/
manufacturing/2018/06/manufacturing-technology-and-it-trends-update-spring-2018.html
OUTLOOK
A Glimpse Into The Future
The digital transformation trend that many
manufacturers started a few years ago continues
stronger than ever. Given the complexity of the
systems involved, one of the key questions will be
which technologies will reach maturity and when.
The Gartner hype cycle for manufacturing
technology gives a good indication. According to
Gartner, the more service-orientated technologies
as well as digitisation of existing systems are on the
right, pushing towards maturity. On the left, the
more cutting-edge technologies, such as predictive
analytics, smart robotics and AR / VR, still need to
evolve through the hype cycle.
This indicates that a gradual evolution is under way;
nevertheless, most technologies are placed in the
two to ten years window to mainstream adoption,
indicating significant changes to the way how
we work and produce over the next decade.
The Gartner Manufacturing Tech Hype Cycle
Less than 2 years
2 to 5 years
More than 10 years
5 to 10 years
Years to mainstream adoption
Innovation
trigger
Peak of inflated
expectations
Trough of
Disillusionment
Slope of
Enlightenment
Plateau of
Productivity
ExpectationsHighLow54%
of working hours in
easily automatable
activities
3
types of smart
manufacturing
plant archetypes
5-10
years until
mainstream
adoption
Digital Twin
SCM Cloud Services
Mobile Factories
Cognitive Expert
Advisors
Blockchain in
Supply Chain
Cyber Physical
Systems
Workforce Analytics
Digital Business
Smart Robots
Solution-Centric Supply Chains
Machine
Learning
Augmented Reality
Predictive analytics
IT/OT Convergence and Alignment
Manufacturing Segmentation
Supply Chain Convergence
Cloud Computing In Manufacturing Operations
Internet of Things for Manufacturing Operations
3D Printing in Manufacturing Operations
Supply Planning
Corporate Social Responsibilty
Industrial Operational Intelligence
Operatoinal Technology Security
Asset Performance Management
Synchronized BOMs
Track-and-Trace and Serialization
Digital Manufacturing
Manufacturing Network Design
Overall Equipment Effectiveness (OEE)
Lean Production Systems
Supplier Quality
External (Third-Party) Manufacturing
Center of Excellence
Time