Prezentace v rámci konference Smart Life, kterou pořádala Technologická agentura ČR dne 20. 10. 2016 v NTK v Praze.

1. © Fraunhofer
Fraunhofer Industry 4.0 strategy
Ralf Wehrspohn
Fraunhofer Institute
for Microstructure of Materials and Systems IMWS
20.10.2016,
Smart Living Conference

2. © Fraunhofer 2
1 Fraunhofer-Gesellschaft
Challenges in Industrie 4.0 technologies
Health and Environment
Production and Supply of Services
Mobility and Transport
Energy and Resources
Security and Protection
e.g. autonomous vehicles,
decentralized multi-agent
logistics …Communication and Knowledge
e.g. data security and safety,
data rate and latency, deep
learning …
e.g. Cyber Physical Systems,
predictive maintenance,
customized and adaptive production ...
e.g. cyber security,
trusted data exchange,
resilient systems …
e.g. closed-loop production,
energy self-sufficiency,
intelligent grids
e.g. Human-Machine-
Interaction/Cooperation

3. © Fraunhofer 3
Mechanical loom
Late 18th century
Assembly line at Ford
Early 20th century
Smart Factory
Today
CNC control unit
Early 1970s
1st industrial revolution:
• Mechanical production facilities
• Water and steam power
2nd industrial revolution:
• Specialized mass production
• Electric power
3rd industrial revolution:
• Automatisation of the production
• Elektronics and IT
4th industrial revolution:
• Cyber-Physical-Systems
Participation
Flexible processes
Consumption oriented
Cooperation
Adaptive Real-time processes
Order related
Authoritarian leadership
Rigid processes
Forecast oriented
2 Industrial Transformation – Frame Conditions
The 4th industrial revolution
Darstellung basierend auf DFKI Bildquelle: v.l.n.r.:
Deutsches-Museum; Hulton Archive/Getty Images; http://autoworks.com.ua; http://www.automationspraxis.de

4. © Fraunhofer 4
2 Industrial Transformation – Frame Conditions
Drivers of the 4th Industrial (R)evolution
Industry
 New products
 New processes
 New markets
Socio-economic framework
Innovation players
Demographic change
Changing consumption
Market
competition
expectations
changes
R&D and technology
 Acceleration through ICT
 »Intelligent« technology
 Shorter innovation cycles

5. © Fraunhofer 5
2 Industrial Transformation – Frame Conditions
National platform Industrie 4.0
INDUSTRIAL DATA SPACE
Industry: Bernd Leukert (SAP), Reinhard Clemens (Telekom),
Eberhard Veit (Festo), Siegfried Russwurm (SIEMENS)
Research: Reimund Neugebauer
Management
BM Gabriel, BM‘in Wanka
Technical-practical
competency, decision-
makers
Political governance,
society, opinion leaders
Market activities
Strategy Group
(Politics, associations,
unions, research)
Fraunhofer:
Prof. Bauernhansl, IPA
Steering Committee
(Business)
Working Groups
Scientific Advisory Board
Industrial consortia
and initiatives
International
standardization
Committee as service provider

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Software
Hardware
Cyber
Physical Cooperation Automation
IT-GlobalisationSingle Source of Truth
 Human / Human
 Human / Machine
 Machine / Machine
Collaboration-productivity
 Data storage
 Data distribution
 Data analysis
 Adaptive
 Intuitive
 Robustness
ERP Systems
PLM Systems
Engineering Systems
Business Communities
Social Communities
ERP
Local data
storage
Cognitive
system
2 Industrial Transformation – Frame Conditions
Industrie 4.0 – IT merges with manufacturing technology

7. © Fraunhofer 7
2 Industrial Transformation – Enterprise Level
Dimensions of Industrie 4.0: Fraunhofer »Layer Model«
Enterprise Transformation
 Business models
 Management
 Human resources
Neugebauer, Hippmann, Leis, Landherr, Industrie 4.0 – From the
perspective of applied research, CMS CIRP Conference on
Manufacturing Systems, Stuttgart, May 25th 2016

8. © Fraunhofer 8
2 Industrial Transformation – Enterprise Level
Dimensions of Industrie 4.0: Fraunhofer »Layer Model«
Enterprise Transformation
 Business models
 Management
 Human resources
Information and Communication enabling Technologies
 Standardization
 Data rates and low latency communication
 Data security
 etc.
Neugebauer, Hippmann, Leis, Landherr, Industrie 4.0 – From the
perspective of applied research, CMS CIRP Conference on
Manufacturing Systems, Stuttgart, May 25th 2016

9. © Fraunhofer 9
2 Industrial Transformation – Enterprise Level
Dimensions of Industrie 4.0: Fraunhofer »Layer Model«
Enterprise Transformation
 Business models
 Management
 Human resources
Information and Communication enabling Technologies
 Standardization
 Data rates and low latency communication
 Data and cyber security
 etc.
Data-driven Production Technologies for Industrie 4.0
 Cyber-Physical-Systems
 Machine Learning in Production Processes
 Autonomous Systems
 etc.
Neugebauer, Hippmann, Leis, Landherr, Industrie 4.0 – From the
perspective of applied research, CMS CIRP Conference on
Manufacturing Systems, Stuttgart, May 25th 2016

10. © Fraunhofer 10
2 Industrial Transformations – Potentials and challenges
German economic potential of Industrie 4.0
Forecast until 2025:
 Up to 430,000 new jobs, but simultaneous elimination of 490,000
low-skilled jobs*
 GDP growth of about 30 billion EURO **
 Total investment of about 250 billion EURO **
Source: *Studie: IAB, BIBB, GWS (November 2015), **BCG-Studie: Industry 4.0 (April 2015) from Prof. Neugebauer SFU 2015
Industrie
4.0

11. © Fraunhofer 11
2 Industrial Transformations – Potentials and challenges
Size in 2025, $ trillion1
Nine settings where added value is expected
Factories – eg., operations management, predictive maintenance 1.2 - 3.7
Cities – eg., public safety and health, traffic control 0.9 - 1.7
Human – eg., monitoring and managing illness, improving wellness 0.2 - 1.6
Retail – eg., self-checkout, smart customer-relationship 0.4 - 1.2
Logistics – eg., logistics routing, autonomous vehicles, navigation 0.6 - 0.9
Work sites – eg., operations management, equipment maintenance 0.2 - 0.9
Vehicles – eg., condition-based maintenance, reduced insurance 0.2 - 0.7
Homes – eg., energy management, safety and security 0.2 - 0.3
Offices – eg., augmented reality for training 0.1 - 0.2
Total $ 4 trillion - $ 11 trillion
Global economic potential of the Internet of Things
Low
estimate
High
estimate
1Adjusted to 2015 dollars, for sized applications only; includes consumer surplus.
Numbers do not sum to total, because of rounding
Source: McKinsey Global Institute analysis, June 2015

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2 Industrial Transformations – Potentials and challenges
Protecting know-how and competitive advantage
Industrial technologies
Mechanical Engineering
Internet of Things
Machine Learning
Traditional strengths:
Hardware, industrial machines
microelectronics, embedded
systems, sensors, automotive
Traditional strengths:
Software, networks, server,
clouds, Big Data, Artificial
Intelligence, IT-Services

13. © Fraunhofer 13
Player Situation Goals Means
Industrie 4.0 Germany Growing
competition
Leadership in
Cyber-Physical-
Systems
Integrating
ICT into
manufacturing
Industrial
Internet
USA, UK Service-centred
economy
Re-industrialization Adding
manufacturing
to ICT
Full
Automation
East Asia Labour
shortage,
rising labour
costs
Cheaper, faster,
less labour
Using robots for
manufacturing
2 Industrial Transformations – Potentials and challenges
Directions for the future of manufacturing

14. © Fraunhofer 14
»The spread of computers and the Internet will put jobs
in two categories. People who tell computers what to do,
and people who are told by computers what to do«
Marc Andreessen
(*1971 USA) Founder of Netscape Communications and
Developer of Mosaic, one of the first successful international web browser
2 Industrial Transformations – Potentials and challenges
Challenges
 Machines will perform tasks with repetitive character and low complexity
 Hard- and software will perform planning tasks autonomously
 Knowledge workers with medium qualification will be supervised and monitored by
computers »Human Automation«
New types of jobs emerge

15. © Fraunhofer 15
3 Requirements for data-driven production
Automotive
companies
Electronics
and ICT
Services Logistics
Mechanical
engineering
Pharmaceuticals
& Medicine
Service- and product innovation
»Smart Data Services« (alerting, monitoring, data quality etc.)
»Basic Data Services« (information fusion, mapping, aggregation etc.)
Internet-of-Things ∙ Device proxies ∙ Network protocols
Embedded Systems ∙ Sensors ∙ Actuators
INDUSTRIAL DATA SPACE
Standardization Council
Industrie 4.0
 Standards
 Data security
Machine Learning
New markets
Digital Economy – Smart Services
TactileInternet–lowlatency
Broadband expansion - Data RateENABLER
INDUSTRIAL DATA SPACE© – Digital and Data Sovereignty

16. © Fraunhofer 16
3 Requirements for data-driven production – Standardization
Reference-Architecture-
Model Industrie 4.0
(RAMI 4.0)
 Three-tier system
 Joint development by:
Bitkom, VDMA, ZVEI,
Plattform Industrie 4.0
Source: © ZVEI
Digital
Reproduction
Standardization goals I4.0:
 Identification
(location of participants)
 Semantics
(communication)
 Quality of service
(low latency, reliability)
Requirement: Standardization
 compatibility and interoperability

17. © Fraunhofer 17
3 Requirements for data-driven production – Industrial Data Space
Secure data exchange and data sovereignty:
INDUSTRIAL DATA SPACE©
Retail 4.0 Banking
4.0
Insurance
4.0
…Industrie 4.0
Focus:
manufacturing
industry
Smart Services
Data transmission
Networks
Real-time /
low latency
Industrial Data Space
Focus: Data
Data
…
Optimization:
processes,
market
Disruptive
Innovation
Evolution
New
Products
New
Markets

18. Industrial Data Space
Upload / Download / Search
Internet
AppsVocabulary
Industrial Data Space
Broker
Clearing
RegistryIndex
Industrial Data Space
App Store
Internal IDS
Connector
Company A Internal IDS
Connector
Company B
External IDS
Connector
External IDS
Connector
Upload
Third Party
Cloud Provider
Download
Upload / Download
© Fraunhofer

19. © Fraunhofer 19
3 Requirements for data-driven production – Industrial Data Space
Industrial Data Space – Digital sovereignty for Industrie 4.0
Highlights
 January 2016: Registered
Association founded
 Round-table on EU-level
 CeBIT and Hannover Messe
Fraunhofer-Consortium
 12 Institutes
 AISEC, FIT, FKIE, FOKUS, IAIS,
IAO, IESE, IML, IOSB, IPA, ISST, SIT
Members of the Industrial Dataspace Association:
Key data of the
BMBF-Project
 Start: 1.10.2015

20. © Fraunhofer 20
3 Requirements for data-driven production – Low latency communication
Requirement: Low latency for the tactile Internet
Source: VDE-Positionspapier Taktiles Internet
Human reaction times
… as communication network with minimal signal delay
 Realized through e.g.: 5G, WiFi and wired communication networks
 Enabling new applications in
 Industry (as part of the industrial Internet: e.g. robotics)
 Mobility (as part of the transport network. e.g. collaborative trial)
 Assistance and health (Augmented Reality, Interactive learning)
1000ms 100ms 10ms 1ms

21. © Fraunhofer 21
System architecture for the mobile
communication of the future (2019)
via »Rainforest-Approach«
3 Requirements for data-driven production – Low latency communication
Tactile Internet network architecture
Requirements:
 1000 x data throughput
 100 x no. of devices
 10 x battery life
 1 ms latency
Car2Car & Car2X Communication
Industrie 4.0
Mobile high-speed Internet
Use cases 5G:
Decimeter waves
FR: 300 MHz … 3 GHz
Radio communication,
air traffic control, mobile
phone
Centimeter waves
FR: 3 … 30 GHz
Microwave link, satellite
broadcasting, RFD
Millimeter waves
FR: 30 … 300 GHz
»See-through walls«,
ABS-cruise control,
security scanner
HHI, IIS, FOKUS, AISEC
Providing coverage
Hot spot performance
Device to Device

22. © Fraunhofer 22
3 Requirements for data-driven production – Machine Learning
Machine Learning for optimized production processes
Training
Big Data
Architectures
Data
Resources
Deep
Learning
Complex
Knowledge
Reasoning
Industrie 4.0
Healthcare
Logistics
Smart
Enterprise
BIG DATA SMART DATA KNOWLEDGE ADDED VALUE
Raw Data Information Decision Productivity

23. © Fraunhofer 23
 Flexibility, versatility
 Interactivity, communication skills
 Iterativity, memory
 Contextual analysis, adaptability
3 Requirements for data-driven production – Machine Learning
»Machine Learning« leading to »Cognitive Machines«
Machine Learning (ML): »procedures of Artificial Intelligence that enable
machines to learn from (data)examples in order to optimize their (decision)
processes without being explicitly programmed.«
ML as enabler for »Cognitive Machines«
Progress through
»Moore’s Law«:
 processing speed
 data storage
 »clouds«
 »big data
 fast internet
 miniaturization

24. © Fraunhofer 24
3 Requirements for data-driven production
Competitive Service Portfolios are Increasingly Hybrid,
as the Example adidas Shows:
time
hybridity
Physical
product
(running shoe)
»classic service«
(training monitor)
digitalized service
(Social Network-
Integration)
Images: otto.de (2015), techglam.com (2015), soccerreviews.com (2015).

25. © Fraunhofer 25
3 Requirements for data-driven production
Data as a Strategic Resource
time
value contribution
Data as
process
results
Data as
process
enablers
Data as
product
enablers
Data as
products
Sensors
NC-Machines
CAD/CAM
Big Data
New Business
Models

26. © Fraunhofer 26
3 Requirements for data-driven production
Digitization as driver and enabler of innovative business
models
Automotive
 traffic
management 2.0
 Dynamic routing
 »Connected Drive
Services«
service
innovation
Production
 Intelligent
manufacturing
concepts for small
series
 Self-controlled
manufacturing
organizational
innovation
Pharma
 »Real-Life
Evidence«
 More effective and
efficient therapy
 Personalized
medicine
product
innovation
Commerce
 Autonomous
transparency along
the supply chain
 Consumer-centric
supply chain
process
innovation
Asset

27. © Fraunhofer 27
3 Requirements for data-driven production
Economy 4.0 -
New Business Models Based on Different Data Sources
Automotive
Automotive suppliers
Traffic control center
Cities and
municipalities
Production
Automobile
manufacturers
Suppliers
Logistics provider
Pharma
Pharmaceutical
companies
Pharma. research
Healthcare Provider
Physicians
Commerce
Retail
Consumer industry
Logistics provider
Transport vehicle
pools
Diagnostic data,
pathologies
Therapy
information
Location,
Destination
Vehicle data
Traffic data
Transport data
Environmental
data
Product-,
components data
Planning data
Transport status
DataPartiesinvolved
New Business Models

28. © Fraunhofer 28
4 Fraunhofer R&D for Industrie 4.0 – Smart logistics
Goal:
 Paper-free documentation of the travel paths of
an air-freight container
Autonomous air-freight container iCON:
 High-resolution ePaper display for cargo data
 Monitoring and reporting of environmental
parameters
 Positioning by GPS and GSM roaming data
 Worldwide data exchange : 4G LTE network /
UMTS / GSM
 Identification by QR-Code on the display
 Solar energy harvesting:
LiPo energy buffer for 3-6 months darkness
Hardware for the Industrial Data Space –
the intelligent container iCon
Hardware with TraQ from:

29. © Fraunhofer 29
4 Fraunhofer R&D for Industrie 4.0 – Smart logistics
Smart, networked and autonomous swarm-logistics
BinGo: Drone-based Transport System
 Decentralized multi-agent system with advanced »ant colony« algorithm
 Energy-efficient and safe: Mainly rolling and only flying when necessary
 Battery charging while descending on rail-equipped spirals

30. © Fraunhofer 30
4 Fraunhofer R&D for Industrie 4.0 – Human integration
Challenges:
Utilization of Industrie 4.0 applications for competence development and real-life
learning environments
Requirements
 Process understanding, integration and real-time synchronization of processes
throughout the product lifecycle
 Transversal skills development and training (IT, electronics, mechanics etc.)
 Generic competences about organization, communication and cooperation
 High flexibility and decision-making capability
Developing Industrie 4.0 competencies
Learning factories 4.0Project work, simulations Participation ramp-up
Solutions for competence development: Fraunhofer »FUTURE WORK LAB«

31. © Fraunhofer 31
Processes past
100% anonymous boards
Supplier certification o.k.Cutting
Coil storage
Quality
No knowledge
about the
causes for
faulty parts
4 Fraunhofer R&D for Industrie 4.0 – Processes and connectivity
Challenge: Finding the cause for faulty products

32. © Fraunhofer 32
Processes future
Verifiability with INDUSTRIAL DATA SPACE©
Cutting
Coil storage
Quality
i
Optimized
manufacturing
parameters of
cyber-physical
tool for current
board
Integrated material tester
and board marking
Supplier certificate
for every meter
i
i
i
4 Fraunhofer R&D for Industrie 4.0 – Processes and connectivity

33. © Fraunhofer 33
4 Fraunhofer R&D for Industrie 4.0 – Machine Learning
Machine Learning for milling and cutting methods
Efficient resource usage
 cooling
 lubricants
 compressed air, …
Optimized tool paths
 quality / finish
 energy efficiency
 tool wear, …
Industry 4.0 and Big Data to support future intelligent manufacturing
 more relevant data (I4.0) and correlations between them (Big Data)
 rich data base for application of machine learning
 Cloud Computing and Deep Learning for manufacturing data
Machine design
 data-based modelling
 structure / material
 precision / rigidity, …

34. © Fraunhofer 34
4 Fraunhofer R&D for Industrie 4.0
Electric car without driver
Challenges: Secure autonomous driving with comfort and efficiency
Fraunhofer solution: electric car without driver
 E-car parks autonomously
 E-car finds charging stations without the driver
 E-car navigates safely with help of modern sensors and IT
© Fraunhofer IPA

35. © Fraunhofer 35
4 Fraunhofer R&D for Industrie 4.0
Intelligent IT-Assistence for everyday life
Fotos: Bernd Müller, © Fraunhofer IAO
 Iris-Scan
 replaces access card (ATM, hotel, appartment)
 ergonomic adjustment (hotel, workplace, …)
 Interactive, collaborative Virtual Reality Conference
 Smart Energy Control
 Synchronization of private media library with external media systems

36. © Fraunhofer 36
4 Fraunhofer R&D for Industrie 4.0
Fraunhofer: Hacker protection for Smart Homes
Challenges: A growing number of household functions can be controlled via
internet and are therefore exposed to cyber attacks and hacking
Fraunhofer solution:
Software protection between internet and IT of the building
 »Firewall« blocks harming communication flow
 Useable for all kinds of building IT technologies
 No hardware exchange necessary
Source:FraunhoferFKIE

37. © Fraunhofer 37
4 Fraunhofer R&D for Industrie 4.0
Human-Machine-Collaboration
Care-O-Bot4 © Fraunhofer IPA
 Safety
 Highly developed sensors/actors
 Intuitive communication
 Situational awareness
 Adaptability
 Learning ability

38. © Fraunhofer 38
5 Outlook
Challenges and Chances for Implementation of
Economy and Industry 4.0
Innovative
Business
Models
Security
Trusted
Networks
Latency
Data Rates
Tactile Internet,
Intelligent Grids
Compatibility Standards
Branches
ProcessChains
Economy 4.0Industry4.0

39. © Fraunhofer 39
5 Conclusion
Essentials for implementing Industry 4.0
Latency and
Data Rates
Tactile Internet, Intelligent Grids
Security Trusted Networks
Compatibility Standards
Machine
Learning
Process Optimization,
Human collaboration
ICT skills
Human
Resources