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Breaking up the silos - Utilizing data across companies and
domains - Reflections from a German Perspective
Prof. Dr. Peter Liggesmeyer, Fraunhofer IESE
Tokyo October 31st, 2018
© Fraunhofer IESE
2
Intro: FhG/IESE
© Fraunhofer IESE
Agenda
 Part 1: A Fraunhofer perspective
 Fraunhofer and Fraunhofer IESE
 Trends
 Challenges
 Relevant Projects
 Part 2: A personal perspective
 Positioning of Germany
 Most important trends and challenges
 Implications for research, industry and
society
© Fraunhofer IESE
The Fraunhofer-Gesellschaft at a Glance
The Fraunhofer-Gesellschaft undertakes applied research of direct utility
to private and public enterprise and of wide benefit to society.
25,527 staff
More than 70%
is derived from contracts
with industry and from
publicly financed
research projects.
Almost 30%
is contributed by the
German federal and
Länder Governments.
72 institutes and research units
Financevolume
€2.3 billion
2017
ContractResearch
€2.0
billion
Major infrastructure
capital expenditure and
defense research
© Fraunhofer IESE
Our Core Focus – Software-Intensive Systems
 Quality assurance is
crucial
Example: Managing trust
 Software enables
innovation and growth
Example: Industry 4.0
 In the area of software,
networking is increasing,
smart ecosystems are
evolving, and system
complexity is growing
Example: Internet of Things
IT megatrend: Integration
Digital
Services
Autonomous &
Cyber-Physical
Systems
© Fraunhofer IESE
Fraunhofer IESE
The institute for software and systems engineering methods
 Founded in 1996, headquartered
in Kaiserslautern
 Over 155 full-time equivalents (FTEs)
 Our solutions can be scaled flexibly and are
suitable for companies of any size
 Our most important business areas:
 Autonomous & Cyber-Physical
Systems
 Digital Services
 Defense & eGovernment
6
SOFTWARE-ENABLED INNOVATIONS
for
Innovative
Systems
© Fraunhofer IESE
Top Industry Customers in 2017
© Fraunhofer IESE
Successful Long Term Collaborations with Japan for more than 10 Years!
March 22, 2017
Kaiserslautern Tokyo Area
Osaka Area
8
© Fraunhofer IESE
9
Role of
Software/
Dig. Transform.
© Fraunhofer IESE
10
1950 20202010199019701960 20001980
Airbnb
Uber
Spotify
Digital Transformation
Industry 4.0
Platform Economy
Expectations towards Software Capabilities of Software
Software Crisis
1968
DotCom-Boom
2000
Agile Manifesto
iPod
2001
iTunes Store
2003
Software-intensive Business
© Fraunhofer IESE
11
Digitization Digital TransformationDigitalization
Technology changes
Media & Data
with Digital Products
Technology changes
Markets & Industries
with Digital Processes
Technology changes
Economy & Society
with Digital Business Models
© Fraunhofer IESE
12
Smart (Digital)
Ecosystems
New and innovative
Services & Products
Data-Driven
Business
Models
Cross-Domain
Business Models
Digital Business Processes
Offshore-
Development &
Operation
Business Process
Reengineering
Radical
Innovation
Incremental
InnovationEfficiency Optimization & Cost Reduction
NewValueCreation
Efficiency Optimization & Cost Reduction
NewValueCreation
Digitization | Digitalization | Digital Transformation
© Fraunhofer IESE
13
Industry
4.0
Smart
Mobility
Smart
Energy
Smart X
Smart
Rural
Areas
Smart
Health
Smart
Farming
© Fraunhofer IESE
14
Smart
Ecosystem
Challenges
© Fraunhofer IESE
Complexity
Diversity
Uncertainty
Safety +
Security
User
Experience
Autonomy
Big Data
Example Challenges and Needs for Smart Ecosystems
November 13/14, 2017
Model-based
Systems Engineering
Virtual
Engineering
Integrated and
Participative
Systems
Engineering
Processes
Scalable and
Interoperable
Ecosystem
Architectures
Comprehensive
Systems
Requirements
Engineering
Certification at
Runtime
Smart Data
Usage
Data Usage
Control
 Software is an enabler
of new, innovative
services and business
models in all sectors of
industry and society
 Being able to establish
appropriate Software
and Systems
Engineering practices
in the organization is
crucial
 for staying
competitive and
 for developing
innovative products
on time, within
budget, and with a
high level of quality
Variation
Management
Integrated Quality
of Service
Guarantees
Safe
Architectures
Resilience
Overview and General Strategy 15
© Fraunhofer IESE
Challenge1: Shaping & Simulating Ecosystems
November 13/14, 2017 Overview and General Strategy
1. Initial Shaping
of Ecosystems
2. Holistic Modelling of Ecosystems Facets
F · E · R · A · L
3. Continuous Simulation of Ecosystems
16
© Fraunhofer IESE
Challenge 2: Runtime-Adaptivity and Certification at Runtime
- Safety in Smart Ecosystems
November 13/14, 2017 Overview and General Strategy
ISO 26262: ASIL D: 1 deadly accident per 108 hours ~ 5 bill. km at 50 km/h
Tesla: 210 mill. km of autonomous driving; target missed by a factor of 20!
17
© Fraunhofer IESE
Runtime-Adaptivity and Certification at Runtime
- Safety in Smart Ecosystems
 Machines, sensors, services, and people connect and
interact dynamically in the Internet of Things
 Participants might not be known during development
time
 System behavior might change / emerge during
runtime
 This call for considering dependability aspects at
runtime
18
© Fraunhofer IESE
B-Space
Safe Connected-X – Fraunhofer B
2. getID
3. IID
5a. Virtual Integration
7. Settings
Digital Twins
Space Models
5b Virtual Safety Check
19
© Fraunhofer IESE
Challenge 3: Smart Data Usage - Using and Exploiting data across
companies and domains without losing control
November 13/14, 2017 Overview and General Strategy
Joe may share data
with Jack when
Jack’s vehicle enters
the dynamic geo-
fence
The data Joe
collected on
Jay’s field is
automatically
deleted after
deliveryJim
Joe Jack
Jay
Joe cannot copy
Jay’s data and give
it to Jim
Jim is getting
exactly the data he
paid for
Jay can decide
who gets his
moisture data
and what is
done with this
data
Basic field data
is available as
OpenData
from different
sources
20
© Fraunhofer IESE
Data Usage Control: Explicit policies for capturing data usage
 Policies can be seen as parameters for
managing data behavior across domains
 Devices detect and prevent data misuse according
to policies
 Middleware component for deciding and acting
upon policies
 Fraunhofer IESE’s IND²UCE framework
and MyData control technologies enable
Data Usage Control features
2014 Innovation Prize
Winner
21
© Fraunhofer IESE
22
Key Projects
>
International Data Spaces Association (IDSA)
defining
 reference architecture
 interfaces
 contractual framework
 offering sample code
for an open data-ecosystem.
www.internationaldataspaces.org
// 23
// 24www.internationaldataspaces.org
90+
Companies and
Organisations
17+
Countries
25+
Use
Cases
1
Ecosystem
=
// 25www.internationaldataspaces.org
A TRUSTED PEER TO PEER NETWORK
FOR ALL INDUSTRIES TO SHARE DATA
 Software components enable
all stakeholders (defined
roles) to participate in IDS
 The quantity of all (external)
IDS connectors defines the
IDS
 Internal IDS connectors are
used to link data sources in
the company, to transform
and to improve them.
© Fraunhofer
// 26www.internationaldataspaces.org
MILESTONES REACHED
ARCHITECTURE
Release of the reference
architecture model 2.0 on
Hannover Industry Fair
INTERNATIONAL
Members from across
the world connecting with
important initiatives, major
European RTOs, intense
engagement in European
research activities
STANDARD
Foundation of a
working group at DIN
to create a DIN
specification for the IDS
connector
GO LIVE
Parts of the ecosystem
implemented in
Use Cases
www.internationaldataspaces.org
© Fraunhofer IESE
27
Example Project: BaSys 4.0
– An Open Platform for Industrie 4.0
Challenge
 Improved communication as well as increasing convergence of information
and automation technology
 Process planning and transformation in real time
Goals
 Development of a basic system for production plants that realizes the
efficient changeability of the production process  virtual middleware
 Networking and integration of existing technologies in order to enable
realization of Industrie 4.0 applications
Core Results
 Middleware architecture, central components, and interface definitions for
interaction
 Integration of existing Industrie 4.0 technologies, e.g., OPC-UA and MQTT
Background Information:
 Funded by: 12m €, funded by
BMBF
 Duration: 01 July 2016 – 30 June
2019
 Project Cpnsortium:
ABB Ltd., Bosch Rexroth AG, DFKI
GmbH, Eclipse Foundation, Festo AG
& Co. KG, FORTISS GmbH, ITQ GmbH,
KUKA Roboter GmbH, PSI
Automotive & Industry GmbH,
Rheinisch-Westfälische Technische
Hochschule (RWTH) Aachen, Robert
Bosch GmbH, SMS Group GmbH,
SYSGO AG, ZF Friedrichshafen AG
 Coordinator: Fraunhofer IESE
 http://www.basys40.de/
© Fraunhofer IESE
Example Project: BaSys 4.0 – main concepts
Virtual Representation
 Devices, Products, Tasks
 Material, Worker
 Simulation Model
Networking
 Independent of Technology
 Independent of Maker
 Real-Time
Standardized Descriptions
 Properties
 Services
Behavior
 Replacable (at runtime)
 Simulatable
 Device-Independent
28
© Fraunhofer IESE
Example Project: PRO-OPT – Big Data – Production Optimization in Smart
Ecosystems
Challenge
 Using Big Data more efficiently along the entire value chain
Goals
 Effective & intelligent analyses of Big Data in decentralized cooperative
structures in the automotive industry
 Significant reduction in the number of defects and increased efficiency in the
automotive manufacturing supply chain
Core Results
 Holistic modeling approach & associated technologies
 Making it possible to interconnect data across companies and analyze
them systematically
 Guaranteeing qualities such as data protection
Background Information:
 PRO-OPT is part of the
technology program “Smart
Data”
 Funded by: BMWi
 Duration: 01.01.15 – 31.12.17
 Project Consortium: Fraunhofer
IESE, DFKI GmbH, Audi AG,
camLine GmbH, DSA GmbH,
Continental Automotive GmbH,
SmartFactory KL e.V., VDMA
 http://www.pro-opt.org/
29
© Fraunhofer IESE
Example Project:
CARUSO - A Smart Ecosystem From the Automotive
Aftermarket for the Automotive Aftermarket
Background Information:
 CARUSO has many
shareholders from the
independent after-market
 Duration: 2016, 2017, 2018,
…
 www.caruso-dataplace.com
Background of the Project
 Neutral data and service marketplace focused on telematics services for drivers, vehicles, and
vehicle fleets
 CARUSO helps its partners – equipment providers, repair shops, suppliers, logisticians, insurance
companies, parts dealers, and service providers – to realize their business cases on the CARUSO
Platform
 For this purpose, CARUSO offers supporting functionality for networked services and data
management
Challenges
 Establishing an ecosystem for the IAM and of a highly scalable platform
 Selecting a basic technology and setting the CARUSO Platform on top of it
 Defining the business cases, establishing the User Experience, controlling development with external
partners
Contributions of IESE
 Ecosystem shaping, Architecture design, Requirements engineering
 Generation and visualization of possible use cases and customer journeys via storyboards
 Assessment of candidates for the basic platform
 Support of make-or-buy decisions
30
© Fraunhofer IESE
Part 2: Personal
Perspective
© Fraunhofer IESE
Positioning of Germany
 Not a strong positioning in the primary Software-industry (in comparison to US
companies), but very strong in the so-called secondary Software-industry
 Economy dominated by mechanical and plant engineering and other engineering-
oriented industries
 Many successful companies that need to transform the „classic“ domains into the
digital era
 Both, Digital Transformation and Engineering Competence available
 Need to rethink and transform business models
 Tendency to involve traditional companies rather than start-up culture
 Societal perception: excellent education and individuality important factors
© Fraunhofer IESE
Technical Trends und Challenges
 Agile processes vs. certification of safety
 Dynamic reconfiguration of Systems: guaranteeing properties
 Handling of AI-technologies (Deep Learning): reproducibility, understandability /
transparency
 X@Runtime: Enabling of Systems vs. Current state of standards
 Autonomy: how to handle the unexpected, unanticipated?
 Interdisciplinarity: achieving engineering methods that are accepted across different
disciplins (e.g. Informatics and mechanical engineering)
© Fraunhofer IESE
Implications
 For research and industry
 Software Engineering needs to
address system-requirements
 Development processes get more
agile
 Systems get more autonomy
 New Technologies must be integrated
into the Engineering much faster
 Shift from development time to
runtime
 For our society
 Change Management in our transition
into the digital era, for all members
of our society
 Need to respect and emphasize
privacy by design
 Need for a change in our educational
system, at universities, but already in
school: Informatics in General, AI,
Privacy, …
© Fraunhofer IESE
March 22, 2017
© Japanese Garden in Kaiserslautern, 2015
Partner City of Bunkyo-ku, Tokyo
Dōmo arigatōどうもありがとう
35

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Breaking up the silos - Utilizing data across companies and domains - Reflections from a German Perspective

  • 1. Breaking up the silos - Utilizing data across companies and domains - Reflections from a German Perspective Prof. Dr. Peter Liggesmeyer, Fraunhofer IESE Tokyo October 31st, 2018
  • 3. © Fraunhofer IESE Agenda  Part 1: A Fraunhofer perspective  Fraunhofer and Fraunhofer IESE  Trends  Challenges  Relevant Projects  Part 2: A personal perspective  Positioning of Germany  Most important trends and challenges  Implications for research, industry and society
  • 4. © Fraunhofer IESE The Fraunhofer-Gesellschaft at a Glance The Fraunhofer-Gesellschaft undertakes applied research of direct utility to private and public enterprise and of wide benefit to society. 25,527 staff More than 70% is derived from contracts with industry and from publicly financed research projects. Almost 30% is contributed by the German federal and Länder Governments. 72 institutes and research units Financevolume €2.3 billion 2017 ContractResearch €2.0 billion Major infrastructure capital expenditure and defense research
  • 5. © Fraunhofer IESE Our Core Focus – Software-Intensive Systems  Quality assurance is crucial Example: Managing trust  Software enables innovation and growth Example: Industry 4.0  In the area of software, networking is increasing, smart ecosystems are evolving, and system complexity is growing Example: Internet of Things IT megatrend: Integration Digital Services Autonomous & Cyber-Physical Systems
  • 6. © Fraunhofer IESE Fraunhofer IESE The institute for software and systems engineering methods  Founded in 1996, headquartered in Kaiserslautern  Over 155 full-time equivalents (FTEs)  Our solutions can be scaled flexibly and are suitable for companies of any size  Our most important business areas:  Autonomous & Cyber-Physical Systems  Digital Services  Defense & eGovernment 6 SOFTWARE-ENABLED INNOVATIONS for Innovative Systems
  • 7. © Fraunhofer IESE Top Industry Customers in 2017
  • 8. © Fraunhofer IESE Successful Long Term Collaborations with Japan for more than 10 Years! March 22, 2017 Kaiserslautern Tokyo Area Osaka Area 8
  • 9. © Fraunhofer IESE 9 Role of Software/ Dig. Transform.
  • 10. © Fraunhofer IESE 10 1950 20202010199019701960 20001980 Airbnb Uber Spotify Digital Transformation Industry 4.0 Platform Economy Expectations towards Software Capabilities of Software Software Crisis 1968 DotCom-Boom 2000 Agile Manifesto iPod 2001 iTunes Store 2003 Software-intensive Business
  • 11. © Fraunhofer IESE 11 Digitization Digital TransformationDigitalization Technology changes Media & Data with Digital Products Technology changes Markets & Industries with Digital Processes Technology changes Economy & Society with Digital Business Models
  • 12. © Fraunhofer IESE 12 Smart (Digital) Ecosystems New and innovative Services & Products Data-Driven Business Models Cross-Domain Business Models Digital Business Processes Offshore- Development & Operation Business Process Reengineering Radical Innovation Incremental InnovationEfficiency Optimization & Cost Reduction NewValueCreation Efficiency Optimization & Cost Reduction NewValueCreation Digitization | Digitalization | Digital Transformation
  • 13. © Fraunhofer IESE 13 Industry 4.0 Smart Mobility Smart Energy Smart X Smart Rural Areas Smart Health Smart Farming
  • 15. © Fraunhofer IESE Complexity Diversity Uncertainty Safety + Security User Experience Autonomy Big Data Example Challenges and Needs for Smart Ecosystems November 13/14, 2017 Model-based Systems Engineering Virtual Engineering Integrated and Participative Systems Engineering Processes Scalable and Interoperable Ecosystem Architectures Comprehensive Systems Requirements Engineering Certification at Runtime Smart Data Usage Data Usage Control  Software is an enabler of new, innovative services and business models in all sectors of industry and society  Being able to establish appropriate Software and Systems Engineering practices in the organization is crucial  for staying competitive and  for developing innovative products on time, within budget, and with a high level of quality Variation Management Integrated Quality of Service Guarantees Safe Architectures Resilience Overview and General Strategy 15
  • 16. © Fraunhofer IESE Challenge1: Shaping & Simulating Ecosystems November 13/14, 2017 Overview and General Strategy 1. Initial Shaping of Ecosystems 2. Holistic Modelling of Ecosystems Facets F · E · R · A · L 3. Continuous Simulation of Ecosystems 16
  • 17. © Fraunhofer IESE Challenge 2: Runtime-Adaptivity and Certification at Runtime - Safety in Smart Ecosystems November 13/14, 2017 Overview and General Strategy ISO 26262: ASIL D: 1 deadly accident per 108 hours ~ 5 bill. km at 50 km/h Tesla: 210 mill. km of autonomous driving; target missed by a factor of 20! 17
  • 18. © Fraunhofer IESE Runtime-Adaptivity and Certification at Runtime - Safety in Smart Ecosystems  Machines, sensors, services, and people connect and interact dynamically in the Internet of Things  Participants might not be known during development time  System behavior might change / emerge during runtime  This call for considering dependability aspects at runtime 18
  • 19. © Fraunhofer IESE B-Space Safe Connected-X – Fraunhofer B 2. getID 3. IID 5a. Virtual Integration 7. Settings Digital Twins Space Models 5b Virtual Safety Check 19
  • 20. © Fraunhofer IESE Challenge 3: Smart Data Usage - Using and Exploiting data across companies and domains without losing control November 13/14, 2017 Overview and General Strategy Joe may share data with Jack when Jack’s vehicle enters the dynamic geo- fence The data Joe collected on Jay’s field is automatically deleted after deliveryJim Joe Jack Jay Joe cannot copy Jay’s data and give it to Jim Jim is getting exactly the data he paid for Jay can decide who gets his moisture data and what is done with this data Basic field data is available as OpenData from different sources 20
  • 21. © Fraunhofer IESE Data Usage Control: Explicit policies for capturing data usage  Policies can be seen as parameters for managing data behavior across domains  Devices detect and prevent data misuse according to policies  Middleware component for deciding and acting upon policies  Fraunhofer IESE’s IND²UCE framework and MyData control technologies enable Data Usage Control features 2014 Innovation Prize Winner 21
  • 23. International Data Spaces Association (IDSA) defining  reference architecture  interfaces  contractual framework  offering sample code for an open data-ecosystem. www.internationaldataspaces.org // 23
  • 25. // 25www.internationaldataspaces.org A TRUSTED PEER TO PEER NETWORK FOR ALL INDUSTRIES TO SHARE DATA  Software components enable all stakeholders (defined roles) to participate in IDS  The quantity of all (external) IDS connectors defines the IDS  Internal IDS connectors are used to link data sources in the company, to transform and to improve them. © Fraunhofer
  • 26. // 26www.internationaldataspaces.org MILESTONES REACHED ARCHITECTURE Release of the reference architecture model 2.0 on Hannover Industry Fair INTERNATIONAL Members from across the world connecting with important initiatives, major European RTOs, intense engagement in European research activities STANDARD Foundation of a working group at DIN to create a DIN specification for the IDS connector GO LIVE Parts of the ecosystem implemented in Use Cases www.internationaldataspaces.org
  • 27. © Fraunhofer IESE 27 Example Project: BaSys 4.0 – An Open Platform for Industrie 4.0 Challenge  Improved communication as well as increasing convergence of information and automation technology  Process planning and transformation in real time Goals  Development of a basic system for production plants that realizes the efficient changeability of the production process  virtual middleware  Networking and integration of existing technologies in order to enable realization of Industrie 4.0 applications Core Results  Middleware architecture, central components, and interface definitions for interaction  Integration of existing Industrie 4.0 technologies, e.g., OPC-UA and MQTT Background Information:  Funded by: 12m €, funded by BMBF  Duration: 01 July 2016 – 30 June 2019  Project Cpnsortium: ABB Ltd., Bosch Rexroth AG, DFKI GmbH, Eclipse Foundation, Festo AG & Co. KG, FORTISS GmbH, ITQ GmbH, KUKA Roboter GmbH, PSI Automotive & Industry GmbH, Rheinisch-Westfälische Technische Hochschule (RWTH) Aachen, Robert Bosch GmbH, SMS Group GmbH, SYSGO AG, ZF Friedrichshafen AG  Coordinator: Fraunhofer IESE  http://www.basys40.de/
  • 28. © Fraunhofer IESE Example Project: BaSys 4.0 – main concepts Virtual Representation  Devices, Products, Tasks  Material, Worker  Simulation Model Networking  Independent of Technology  Independent of Maker  Real-Time Standardized Descriptions  Properties  Services Behavior  Replacable (at runtime)  Simulatable  Device-Independent 28
  • 29. © Fraunhofer IESE Example Project: PRO-OPT – Big Data – Production Optimization in Smart Ecosystems Challenge  Using Big Data more efficiently along the entire value chain Goals  Effective & intelligent analyses of Big Data in decentralized cooperative structures in the automotive industry  Significant reduction in the number of defects and increased efficiency in the automotive manufacturing supply chain Core Results  Holistic modeling approach & associated technologies  Making it possible to interconnect data across companies and analyze them systematically  Guaranteeing qualities such as data protection Background Information:  PRO-OPT is part of the technology program “Smart Data”  Funded by: BMWi  Duration: 01.01.15 – 31.12.17  Project Consortium: Fraunhofer IESE, DFKI GmbH, Audi AG, camLine GmbH, DSA GmbH, Continental Automotive GmbH, SmartFactory KL e.V., VDMA  http://www.pro-opt.org/ 29
  • 30. © Fraunhofer IESE Example Project: CARUSO - A Smart Ecosystem From the Automotive Aftermarket for the Automotive Aftermarket Background Information:  CARUSO has many shareholders from the independent after-market  Duration: 2016, 2017, 2018, …  www.caruso-dataplace.com Background of the Project  Neutral data and service marketplace focused on telematics services for drivers, vehicles, and vehicle fleets  CARUSO helps its partners – equipment providers, repair shops, suppliers, logisticians, insurance companies, parts dealers, and service providers – to realize their business cases on the CARUSO Platform  For this purpose, CARUSO offers supporting functionality for networked services and data management Challenges  Establishing an ecosystem for the IAM and of a highly scalable platform  Selecting a basic technology and setting the CARUSO Platform on top of it  Defining the business cases, establishing the User Experience, controlling development with external partners Contributions of IESE  Ecosystem shaping, Architecture design, Requirements engineering  Generation and visualization of possible use cases and customer journeys via storyboards  Assessment of candidates for the basic platform  Support of make-or-buy decisions 30
  • 31. © Fraunhofer IESE Part 2: Personal Perspective
  • 32. © Fraunhofer IESE Positioning of Germany  Not a strong positioning in the primary Software-industry (in comparison to US companies), but very strong in the so-called secondary Software-industry  Economy dominated by mechanical and plant engineering and other engineering- oriented industries  Many successful companies that need to transform the „classic“ domains into the digital era  Both, Digital Transformation and Engineering Competence available  Need to rethink and transform business models  Tendency to involve traditional companies rather than start-up culture  Societal perception: excellent education and individuality important factors
  • 33. © Fraunhofer IESE Technical Trends und Challenges  Agile processes vs. certification of safety  Dynamic reconfiguration of Systems: guaranteeing properties  Handling of AI-technologies (Deep Learning): reproducibility, understandability / transparency  X@Runtime: Enabling of Systems vs. Current state of standards  Autonomy: how to handle the unexpected, unanticipated?  Interdisciplinarity: achieving engineering methods that are accepted across different disciplins (e.g. Informatics and mechanical engineering)
  • 34. © Fraunhofer IESE Implications  For research and industry  Software Engineering needs to address system-requirements  Development processes get more agile  Systems get more autonomy  New Technologies must be integrated into the Engineering much faster  Shift from development time to runtime  For our society  Change Management in our transition into the digital era, for all members of our society  Need to respect and emphasize privacy by design  Need for a change in our educational system, at universities, but already in school: Informatics in General, AI, Privacy, …
  • 35. © Fraunhofer IESE March 22, 2017 © Japanese Garden in Kaiserslautern, 2015 Partner City of Bunkyo-ku, Tokyo Dōmo arigatōどうもありがとう 35