This talk was presented by Georg Doll (McKinsey Digital Munich) at Intland Connect: Annual User Conference 2020 on 22 Oct 2020. To learn more, visit: https://intland.com/intland-connect-annual-user-conference-2020/

1. CONFIDENTIAL AND PROPRIETARY
Any use of this material without specific permission of McKinsey & Company
is strictly prohibited
Georg Doll, Senior Expert, McKinsey Digital Munich
Complex Systems, challenges and where to focus
When things get complex

2. McKinsey & Company 2
Georg Doll
Senior Expert at McKinsey Digital Munich
Georg Doll, Senior Expert at McKinsey Digital in Munich
As Member and Co-Lead of the EMEA Software Service line at McKinsey
Digital, he supports clients along the product development LiveCycle.
With his background of over 20 years’ experience in Automotive and
embedded Software delivery and management of international teams. He
supports clients in Market introduction planning, introduction of systems
engineering, improving in project execution excellence and agile software
development and talent management.
He has served Tier1s, semiconductor vendors and vehicle manufacturers
around the world in Japan, Asia Pacific, EMEA and US.
In 2009 he was instrumental in setting up the GENIVI Alliance, and served as
member of the Strategy Council and the Board for several years.

3. McKinsey & Company 3
Abstract
Software is on the rise. Software is the no. 1 topic in the development of new functions. The software market
is expected to grow from today to 2030 with an average CAGR of ~10%. So what could go wrong?
A closer look at the four major automotive trends shows that they depend on the success of software. OEMs
and Tier1s have recognized the situation and are investing heavily in software.
To the extent that some speak of a "software defined car" or a "computer on wheels".
But where there is light, there is also shadow. Highly automated driving, connectivity, powertrain electrification
and new mobility services introduce additional dependencies between functions.
Dependencies that – as we know continue to increase the system complexity.
The development of software is a constant challenge for projects. What seems trivial at first glance turns out
to be a much greater challenge than many people expect. Falling productivity, increasing communication,
declining quality, constantly rising development costs and project delays are omnipresent.
These challenges can only be tackled with a holistic approach.
Successful organization have mastered the most important dimensions. Development tools, program
management and talent management are just some of this dimensions.

4. McKinsey & Company 4
Software is on the rise..
and it is complex.

5. McKinsey & Company 5
Linus Torwald
Principal developer Linux Kernel
The value of Software is not
in the code, its in the head
of the people who
developed the code
Christoph Grote
SVP Electronics at BMW
Today 95% of innovation
in automotive is software
based
Olla Källenius
Chairman of the Board
Daimler AG and Mercedes-Benz AG
To stay relevant we have to
control the Software in our
vehicles

6. McKinsey & Company 6
63 76 85
50
81
30
44
63
92
129
15625
34
37
50
20
20
13
2020
238
2025 2030
362
469
Components
2,755 3,027 3,800Automotive sales
USD billions
3%
7.0%
Estimated CAGR
2020 - 30
Electronics ECUs/DCUs6%
Integration, verification and validation10%
Sensors7%
Software (functions, OS, middleware)9%
3% Other electronic components
(harnesses, controls, switches, displays)
Power electronics (excl. battery cells)18%

7. McKinsey & Company 7
67.8
1.9
25
103.3
89.5 88.4
2020
25.7
2030
DCU
ECU
91.5
156.2
129.0
ECU/DCU market
USD billions
Estimated CAGR
2020 - 25
21%
68%
3%
0%
6%
2020 - 30
92
156
92
362
469
238
2025 20302020

8. McKinsey & Company 8
4
Autonomous Connectivity
Shared mobility Electrification

9. McKinsey & Company 9Idle stop/start
Airbag deployment
Adaptive front lighting
Adaptive
cruise control
Automatic braking
Electric power steering
Electronic
throttle control
Electronic
valve timing
Head-up
display
Night vision
Engine control
Windshield wiper control
Cylinder
deactivation
Blind spot
detection
Lane
departure
warning
Electronic
stability
control
Active
vibration
control
Remote
keyless
entry
Trans-
mission
control
Seat position
control
Parking
system
Anti-lock braking
Tire pressure
monitoring
Regenerative braking
Hill-hold control
Active suspension
Active exhaust
noise suppression
Security system
Navigation system
Digital turn signals
Electronic
toll collection
Lane correction
Battery
management
Entertainment
system
DSRCCabin
environment
controls
Active
cabin noise
suppression
Voice/data
communica-
tion
Interior
lighting
Auto-dimming
mirror
Event
data
recorder
Driver
alertness
monitoring
Accident
recorder
Instrument
cluster
Parental controls
OBDII
Active
yaw
control

10. McKinsey & Company 10
Eco functions bring the need for new sensors
Highly automated driving increases functional dependencies
Connectivity brings new features to the fleet
ADAS functions increase safety levels of vehicle functions
Connectivity introduces security threads
Road safety requirements drive the need for new sensors
Emission regulations drive need for new power train solutions
Electrification brings new technologies into the car
Eco functions require tighter function integration

11. McKinsey & Company 11
Productivity Complexity
2,25
3,00
0,75
2,75
2,00
1,25
1,00
2,50
1,50
1,75
Relative
Change
Infotainment
Mission critical
SW Industry
Infotainment
Mission critical
SW Industry
SOURCE: McKinsey Numetrics analytics on SW complexity and productivity in automotive
• A widening gap between
complexity and productivity
levels is driving to performance
and schedule challenges for
automotive players
• In order to overcome those
challenges, SW development
organizations need to both:
Manage the increase in
complexity
Increase efficiency to enable
management of complexity
Productivity and Complexity
relative growth over 10 years
2,4x
2x

12. McKinsey & Company 12
5
Unmanaged
interdependencies
between systems
are developed as
point-to-point
interfaces leading to
a high complexity
and variety of
interfaces within
and beyond the
system
Point to point
Interfaces
1 2 6
Portfolio
Product
Lifecycle
74
Multiple
applications
covering same
functionality
redundantly in the
portfolio
Overlapping
functionality
between
components in the
same system
Multiple versions of
the same
application/ system
are “live” at the
same time
Code size, quality,
and documentation
as further sources
of complexity
throughout the
lifecycle
…
Description Operating system,
HW complexity, and
testing environment
with strong
influence on system
complexity
Components within
a system are
developed as
monoliths impeding
accessibility of
single elements for
updates/maintenanc
e and integration
within new
development
Functional
redundancy
Versions
variety
Code and
documenta-
tion quality
Complexity
dimension
Multiple HW
platforms
Closed
systems
Multiple
applications/ sub-
systems within a
SW platform are
competing for
similar resources
(compute, storage,
power)
3
Interfering
sub-systems
Multiple
applications/ sub-
systems within a
SW platform are
competing for
similar resources
(compute, storage,
power)
3
Interfering
sub-systems

13. McKinsey & Company 13
Interfering subsystems Isolated subsystems
Main steps
Sub-systems
Test effort, required tests #
Sub-systems
Test effort, required tests #
Large amount interfering systems
test effort is rising exponentiallyA
Small isolated systems
test effort is rising linearB
Test complexity per
function: The smaller is
the isolated system, the
smaller is the exponential
part of the test complexity
Interfering
sub-systems
3

14. McKinsey & Company 14
Vehicle
Middleware layer/OS
E/E hardware
UI/UX/HMI
Connectivity (back-haul)
Artificial intelligence/Advanced analytics
ActuatorsSensors Power components
Cloud platform
Applications

15. McKinsey & Company 15
 Centralized Computing
 Collaboration of ECUs
 Isolated Functions
 Computer on wheels
 Cross-functional connection
Domain-
centralized
Vehicle
centralized
Distributed
1st
2nd
3rd
4th
5th
Domain
controller
Central gateway
Body/comfort Chassis Powertrain
Infotainment
Central gateway
Sensor Actuator
Cockpit / Displays
Central Compute COM
Zone
Zone Zone
Zone
Today

16. McKinsey & Company 16
How to standardize Software
across my different product lines
and product generations?
How to transform 10.000
hardware-oriented
developers to an agile-minded,
software-driven organization?
My software org is a black box to me. How do I assess and boost the embedded
software dev. productivity of my 5.000 distributed developers and my suppliers?
How can I ensure that my
1 billion USD software
investment is delivered on time
and on budget?
How do I get access to the
best software talent?
How to transform our
management systems to
drive world class
embedded software
performance?
How to organize
software developers
across my divisions
SOURCE: McKinsey

17. McKinsey & Company 17
Top organizations show the potential increase in Software development
performance for average and bottom quartile organizations
SOURCE: Numetrics embedded SW project database
Bottom quartile Average1 Top quartile
65
100
175
2,7x
65
100
224
3,4x
155
100
27
-83%
Productivity
Complexity units per man week1
1 Average indexed to 100
Complexity units per week1
Development throughput
Residual design defects1
Quality

18. McKinsey & Company 18
Developer Velocity Index (DVI)
Technology
Deep structured interviews
100+ industry experts
Working practices
Comprehensive survey
440 large organizations across 12 industries and 9 countries
Organizational enablement
Statistical correlation analysis
Business performance (financial performance, innovation, customer
experience, brand, talent) against the various dimensions of DVI

19. McKinsey & Company 19
DVI is calculated as a weighted average of the scores for the 43 drivers across 3 dimensions
Team characteristics
 Cross-functional teams
 Autonomous scope
 Co-location
 Limited context switching
 Product management
 Product manager/owner capabilities
 Product telemetry
 Rapid prototyping
 Clear product vision and requirement
 Linkage between strategy and team
metrics
Organizational agility
 Agile funding mechanism
 Portfolio management
 Dependency management
Culture
 Collaboration and knowledge
sharing
 Continuous improvement
 Culture of customer obsession
 Psychological safety/fail fast and
learn
 Servant leadership
Talent management
 Recruiting
 Employee value proposition
 Capability building
 Career path
 Performance management
 Team health management
CI/CD practices
 Repeatable builds, continuous
integration, delivery, and deployment
Engineering practices
 Code reviews
 Coding guidelines
 Technical debt management
Agile team practices
 Ceremonies
 Definition of done
 WIP management
Open source, inner source
 Open source usage and contribution
 Inner source adoption
Architecture
 Software architecture
 Data architecture
Infrastructure and platform
 Public cloud adoption (IaaS, PaaS)
 Infrastructure as code
Testing
 Test automation
 Test driven development
Security and compliance
 Security practices
 Compliance practices
Tooling
 Tools (planning, development,
DevOps, collaboration)
 Al assistance
 Low code/no code
Technology Working practices Organizational enablement

20. McKinsey & Company 20
1. Calculated using Johnson's Relative Weights: % importance is relative importance of driver on business outcomes. Total sums to 100%. Higher % indicates
stronger impact on business performance
2. Average score for Innovation, Customer Satisfaction, Brand, and Talent
Source: McK Developer Velocity Survey, Expert Interview
Foundational drivers R2 = 0.6 N = 440
1.0%
4.0%
7.0%
2.0%
0%
6.0%
5.0%
3.0%
7.5%
0.5%
6.5%
5.5%
1.5%
2.5%
3.5%
4.5%
Publiccloudadoption(IaaSandPaaS)
Infrastructureascode
Psychologicalsafety/"failfastandlearn"
Testautomation
Testdrivendevelopment
Servantleader
Security
Compliance
Collaborationtools
Techdebtmanagement
Developmenttools
DevOpstools
Collaborationandknowledgesharing
Dependencymgmt
Codereviews
CI/CD
Lowcode/nocode
Producttelemetry
Employeevalueproposition
AIassistance
Codingguidelines
PMCapabilites
Opensource
Recruting
WIPmanagement
Definitionofdone
Ceremonies
Teamhealthmanagement
Autonomousscope
Limitedcontextswitching
Fundingmodel
Productvisionandrequirements
Linkagestrategyandteammetrics
Rapidprototyping
Portfoliomgmt
Continuousimprovement
Innersource
Customerobsession
Crossfunctionalteam
Capabilitybuilding
Co-location
Careerpath
Planningtools
Softwarearchitecture
Dataarchitecture
Performancemanagement
Drivers relative importance1 on overall business performance indicators2 % Importance1
Infra &
platform
TestingArchitec-
ture
ToolingSecurity
& Com-
pliance
CI/
CD
Engineering
practices
Open
source/
inner
source
Agile team
practices
Team
characteristics
Organizational
agility
Culture Talent managementProduct management
Organizational enablementTechnology Working practices
Softwarearchitecture
Compliance

21. McKinsey & Company 21
A Developer tools B Product
management
(PM) capabilities
C Culture D Talent management
1. Calculated using Johnson's Relative Weights: % importance is relative importance of driver on business outcomes. Total sums to 100%. Higher % indicates
stronger impact on business performance
2. Average score for Innovation, Customer Satisfaction, Brand, and Talent
Source: McK Developer Velocity Survey, Expert Interview
Foundational drivers R2 = 0.6 N = 440
1.0%
4.0%
7.0%
2.0%
0%
6.0%
5.0%
3.0%
7.5%
0.5%
6.5%
5.5%
1.5%
2.5%
3.5%
4.5%
Publiccloudadoption(IaaSandPaaS)
Infrastructureascode
Psychologicalsafety/"failfastandlearn"
Testautomation
Testdrivendevelopment
Servantleader
Security
Compliance
Collaborationtools
Techdebtmanagement
Developmenttools
DevOpstools
Collaborationandknowledgesharing
Dependencymgmt
Codereviews
CI/CD
Lowcode/nocode
Producttelemetry
Employeevalueproposition
AIassistance
Codingguidelines
PMCapabilites
Opensource
Recruting
WIPmanagement
Definitionofdone
Ceremonies
Teamhealthmanagement
Autonomousscope
Limitedcontextswitching
Fundingmodel
Productvisionandrequirements
Linkagestrategyandteammetrics
Rapidprototyping
Portfoliomgmt
Continuousimprovement
Innersource
Customerobsession
Crossfunctionalteam
Capabilitybuilding
Co-location
Careerpath
Planningtools
Softwarearchitecture
Dataarchitecture
Performancemanagement
Drivers relative importance1 on overall business performance indicators2 % Importance1
Infra &
platform
TestingArchitec-
ture
ToolingSecurity
& Com-
pliance
CI/
CD
Engineering
practices
Open
source/
inner
source
Agile team
practices
Team
characteristics
Organizational
agility
Culture Talent managementProduct management
Organizational enablementTechnology Working practices
Softwarearchitecture
Compliance

22. McKinsey & Company 22
Linus Torwald
Principal developer Linux Kernel
The value of Software is not in the
code, its in the head of the
people who developed the code
Software is a people business
The key success factors
- Culture
- Talent Management
- Development Tools
- Product Management

23. McKinsey & Company 23
1. Pycholocial safety (fail fast and learn)
2. Collaboration and knowledge sharing
3. Continues improvement
4. Servant leader
Development
Tools
Product
Management
Culture Talent
Management
1. Planning tools
2. Collaboration tools
3. Development tools
4. DevOps tools
1. PM Capabilities
2. Product telemetries
1. Performance management
2. Team health management
3. Capability building
4. Recruting

24. Thank You