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Huge value pool shifts ahead – How rolling
stock manufacturers can lay track for
profitable growth
CONFIDENTIAL AND PROPRIETARY
Any use of this material without specific permission of McKinsey & Company is strictly prohibited
July 5, 2017
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2 McKinsey & Company
Total rolling stock market size generates EUR ~120bn revenue of which OEM’s value pool is ~25%
SOURCE: SCI; UNIFE; company information; McKinsey
Revenues and value pool by player (new business and after-sales, including services)
Europe
and CIS
Asia-
Pacific
Americas 49
16
9
24
~EUR
120 billion
~25%
Operators ≤50%
Suppliers
Geographical split
No. of OEMs
(>EUR 50 million revenue)
OEM employees globally
(excluding employees at
suppliers, rail operators, etc.)
~25%
OEMs
<5%
Others (third party)
>400,000
Arnt
After-sales, including services
EUR 60-70 billion
OEMs: EUR 10-15 billion Suppliers: EUR 15-25 billion
Operators: EUR 25-35 billion
New business
EUR 50-60 billion
OEMs: EUR 15-25 billion Suppliers: EUR 30-40 billion
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3 McKinsey & Company
+4-5
Opportunities
in service
business
Shifting
customer
structure
-3-4
Total OEM
value pool
today
~19
~13
+1-2
Mainte-
nance
efficiency
Market
growth
(real)
+5-7 ~32 -1-2
~19
~19
~ +1
Suppliers’
value chain
presence
Total OEM
value pool
in 2025
~38
Other
factors1
-2-3
Price
erosion
The OEM’s rolling stock value pool is growing by ~EUR 6 bn (+18%) until 2025 driven by significant
opportunities in the service segment
SOURCE: McKinsey
Development in OEM value pool by 2025 by segment, EUR billions (real terms)
After-sales,
including services
~ +45%
New vehicles
+/-0%
1 Includes additional capex need for autonomous rail operations as well as slight price level incline as customers demand more financial securities and improved emission standards due to regulation
+6 growth potential in
OEM value pool by
2025 coming from
after-sales, including
services
other C1/7 C1/7 B2/5 B1/4 A1,A2/1,2 A3/3
Related industry trends
Arnt
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4 McKinsey & Company
9 key trends describe the future of the rolling stock industry
SOURCE: McKinsey
Customer
Technology
Arnt
The rolling stock industry is facing heavy consolidation pressure due to large global
overcapacity
1
The Chinese industry leader is tackling export markets with rolling stock and beyond, resulting in
great price pressure
2
Tier 1 suppliers are capturing an increasing share of the value chain and profits due to limited
competition in core components
3
Market growth is driven by urbanization especially in emerging countries resulting in a value-
driven footprint/localization
4
Investments are increasingly being made by financial investors with a TCO perspective,
demanding more standardized products 5
The financial strength of OEMs is becoming a bigger determinant in winning large projects 6
Digitization and advanced analytics are shifting value-chain control points, creating new
business models
7
Holistic energy efficiency and emission standards are becoming increasingly important with
more opportunity for retrofitting
8
Autonomous rail operations are becoming common, requiring technical prerequisites for new
product families 9
Industry
structure
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5 McKinsey & Company
The rolling stock industry is
facing heavy consolidation
pressure due to large global
overcapacity 1
Arnt
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6 McKinsey & Company
Top OEM‘s market share in rolling stock has significantly increased
SOURCE: SCI, UNIFE, McKinsey
New vehicles business
Arnt
53%
12 OEMs
71%
10 OEMs
CNR + CSR
CRRC 28%
Hitachi + Ansaldo
Hitachi 6%
2015 2010
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7 McKinsey & Company
Dramatic overcapacities across all geographies
SOURCE: McKinsey
Estimated utilization gap in rolling stock factories
Asia Europe
~40%
~60%
North America
~40%
Arnt
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8 McKinsey & Company
Arnt
The Chinese industry leader
is tackling export markets
with rolling stock and
beyond, resulting in great
price pressure
2
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9 McKinsey & Company
CRRC is No. 1 player in many new vehicle segments
SOURCE: SCI, UNIFE, McKinsey
Estimated market share of CRRC in key product segments
Arnt
~50% ~20%
No. 1
for high-speed trains
No. 1
for electric locomotives
~50% ~50%
No. 2
for diesel locomotives
No. 1
for metro cars
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10 McKinsey & Company
Examples of CRRC exports within recent years show sales into more and more countries
SOURCE: Annual report, Press, McKinsey
Arnt
590 locomotives
South Africa
96 metro cars
Thailand
>5,000
freight
wagons
Australia
76
metro
cars
India
240 EMU cars
Rio de Janeiro
135 trains and manufacturing
site Malaysia
20 locomotives,
>250 coaches,
>700 EMU cars,
>80 DMUs
Argentina
100 diesel
locomotives
Cuba
64 metro cars
Los Angeles
~280 metro cars and final
assembly Boston
~850 metro cars and
final assembly
Chicago
Double deck coaches
Montreal
270 passenger coaches
Turkmenistan
~700 metro cars, 160 double-
decker coaches
Iran
50 locomotives
Pakistan
?
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11 McKinsey & Company
Total CRRC exports grew by factor ~10 in last 10 years
SOURCE: Annual reports , McKinsey
EUR millions
351 289 354 387
2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 … 2025
1,954
1,704
3,850
761
1,381 2,240
Arnt
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12 McKinsey & Company
Anselm
Tier 1 suppliers are captur-
ing an increasing share of
the value chain and profit
due to limited competition in
core components
3
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13 McKinsey & Company
Suppliers are gaining importance due increasing share of the value chain
SOURCE: Expert interviews, McKinsey
Share of supplier's value chain activities in rail industry for new vehicles
65%
2025
50%
2015
2000
Anselm
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14 McKinsey & Company
Tier-1 suppliers are taking the lion‘s share of value chain profits
SOURCE: Amadeus, McKinsey
Typical range of EBIT margins
Anselm
0% EBIT
margin
OEM
Tier 1 – system
-10%
Tier 1 – component -5% 10%
10
%
Operator -5% 15%
5% 15% Tier 1 – system
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15 McKinsey & Company
Anselm
Investments are
increasingly done by
financial investors with TCO
perspective demanding
more standardized products
5
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16 McKinsey & Company
Leasing companies have increased their market share dramatically
SOURCE: SCI; GATX; IRG; McKinsey
Market share of freight wagon leasing companies
North America
2015
2005
2025
~50% ~60%
Europe
~10%
2005
2015
2025
~25%
Anselm
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17 McKinsey & Company
The shift towards financial investors as customers has an upside and a downside for OEMs
SOURCE: McKinsey
Anselm
Operators
Growth opportunities
in the after-sales and
service market for
OEMs
Financial investors
Price pressure on OEMs and
suppliers due to lower demand
for tailor-made rail products
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18 McKinsey & Company
Savings identified for standardization of components
Total
90100-10%
10
80100
20
-20%
85100
15
-15%
85100-15%
15 Savings1 Baseline
-15%
Final cost
90100-10%
Savings Final cost
10
Baseline
EXAMPLES
1 Pending assessment of rest of modules (Last estimation ~17%)
Industrial costs per cars in %
Car body
Doors
Interior
finishing
Bogies
Electronic
equipment
Anselm
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19 McKinsey & Company
Anselm
Digitization and advanced
analytics are shifting value-
chain control points,
creating new business
models
7
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20 McKinsey & Company SOURCE: McKinsey
Sensor technologies and big data analytics increasingly available at affordable cost
allowing for new OEM business models
1 Ultra-low-power
SELECTED EXAMPLES Anselm
▪ Sensor technology and real
time data trans-mission are
increasingly available at low
cost
▪ Advanced sensor
technologies being more
and more investigated, e.g.,
ULP1 perpetually powered
sensor systems incl. energy
harvesting devices
▪ Power of big data analytics
has substantially increased
in recent years
▪ Big data algorithm allows for
new OEM business models,
e.g., OEMs taking over
tasks from operators or
rolling stock owners
Infrastructure monitoring
▪ Improved infrastructure
maintenance through
running characteristics
(rail - wheel interface)
Passenger comfort and
security monitoring
▪ Adaptive temperature
control through measuring
number of passengers
▪ Safety and security
monitoring through cameras
and noise surveillance ▪ Reduction of fleet through shorter
stops in maintenance workshops
Rolling stock
monitoring
▪ Optimized
maintenance crew
planning through data
on vehicle status
▪ Optimized railcar
maintenance cycles
through measuring
status of critical parts
(wheels, axles,
bearings, brakes)
Environment
monitoring
▪ Adjusted tree cuttings
through monitoring
environment (growth
of trees)
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21 McKinsey & Company
Industry use case of advanced and graph analytics
SOURCE: Industry use case, McKinsey
Identifying and solving the root causes for delays in mining operations
Situation Approach Finding Impact
The analysis consisted of more than 12
million data points generated by the
operations control systems (OCS),
interlocking system and point machines
over a period of six months. Only the
systems already
installed generated the information, no
additional sensors were applied. For
example, the dwell times for the trains in
each of the track segments were analyzed.
With the help of advanced analytics
methods, key delay reasons for every train
in every
network segment were identified. Then
graph analytics were used to find key
problem spots in the network
The advanced analytics showed that the
delays were caused by a small set of
points and at very specific track areas.
Concrete countermeasures
were proposed to fix the root causes at the
most critical points, e.g., construction
changes on the layout before points or
changes to the point
machines. Furthermore, the operations
team can now optimize the scheduling
based on the analytic
results since they have more detailed
information about the risk profiles of the
main infrastructure components and can
consider this information in
the operations planning
For mining companies where individual
trains carry ore in the value of more than
USD 1 million punctuality is essential.
While small delays in arriving at the port
destination already result in substantial
penalty payments, even bigger delays
trigger revenue losses for the mining
company if trains have
to be canceled and freight ships are not
loaded on time.
In this case the OEM proved to be the
partner of choice for such a kind of
analytics since the deep knowledge on
how to interpret system-generated
data combined with engineering/ domain
knowhow is essential to draw the right
conclusions. Major improvement potential
can be generated in partnerships between
OEMs and operators and their knowledge
and related best practices. Increase in train
punctuality of 10 to 20 percent is
achievable. The annual savings typically
outweigh the investment by a factor of >5
Anselm
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22 McKinsey & Company
Anselm
Autonomous rail operations
are becoming common,
requiring technical
prerequisites for new
product families
9
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23 McKinsey & Company
Wide range of automated metro systems already running around the globe
SOURCE: Companies’ Web sites; McKinsey
Cities with automated metro lines, as of 2015
Anselm
Focus of growth until 2025
Europe
Copenhagen
Lille
Nuremberg
Paris
Rennes
Budapest
Lausanne
Lyon
Brescia
Milan Turin
Toulouse
Rome
Barcelona
North America
Vancouver
Detroit
New York
Las Vegas
Jacksonville
Miami
Latin America
Sao Paulo
Middle East
Dubai
Asia
Uijeongbu
Seoul
Yongin
Daegu
Busan-Gimhae
Tokyo
Yokohama
Nagoya
Osaka Kobe
Shanghai
Taipei Hong
Kong
Manila
Kuala Lumpur
Singapore
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24 McKinsey & Company
The length and popularity of fully automated rail systems is still growing
SOURCE: UITP; McKinsey
Total length and number of cities with fully automated rail systems in the world1
1 Hong Kong’s 3.8 km in Disneyland not included in calculation
5 8
13
17
22
28
800 km
700 km
600 km
500 km
400 km
300 km
200 km
100 km
2000 2005 2010 2015 2025 1995 1990 1985 …
Anselm
37
cities
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25 McKinsey & Company
Arnt
Value pool quantification
and recommendation
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26 McKinsey & Company
Rail OEMs need to make fundamental decisions about their future operating model now in order to
strengthen their competitive position
SOURCE: McKinsey
Leverage partnerships
Strengthen service
business
To capture growth
potential, rail OEMs need
to increasingly focus on
strengthening their
service offering incl. big
data solutions to gain
market shares in service
from rail operators
Prepare for new
customers
Rail OEMs need to tailor
their sales activities to a
more heterogeneous
customer landscape incl.
growing base of financial
investors, leasing
companies and private
rail companies
Focus on cost efficiency
OEMs need to produce
the basic vehicle at lower
cost to ensure price
competitiveness and
remain profitable in light
of intensified
competition, shifting
growth regions and
increasingly cost-
conscious customers
OEMs should evaluate
opportunities for further
consolidation across and
beyond the industry in
order to form scalable
ecosystems or profitable
partnerships
Arnt
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27 McKinsey & Company
