Post on 12-Apr-2017
© Fraunhofer
Fraunhofer Industry 4.0 strategy
Ralf Wehrspohn
Fraunhofer Institute
for Microstructure of Materials and Systems IMWS
20.10.2016,
Smart Living Conference
© 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
© 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
© 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
© 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-Globalisation Single 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
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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
© 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
© 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
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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
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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
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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
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»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
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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
Ta
ctil
e In
tern
et
– lo
w l
ate
ncy
Broadband expansion - Data Rate ENABLER
INDUSTRIAL DATA SPACE© – Digital and Data Sovereignty
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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
Dig
ital
Rep
rod
uct
ion
Standardization goals I4.0:
Identification (location of participants)
Semantics (communication)
Quality of service (low latency, reliability)
Requirement: Standardization
compatibility and interoperability
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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
Industrial Data Space
Upload / Download / Search
Internet
Apps Vocabulary
Industrial Data Space Broker
Clearing
Registry Index
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
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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
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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
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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
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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
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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
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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).
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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
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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
Ass
et
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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
Data
Part
ies
invo
lved
New Business Models
© 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:
© 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
© 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 4.0 Project work, simulations Participation ramp-up
Solutions for competence development: Fraunhofer »FUTURE WORK LAB«
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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
© 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
© 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, …
© 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
© 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
© 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
Sou
rce
: Fr
au
nh
ofe
r FK
IE
© 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
© 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
Pro
cess
Ch
ain
s
Economy 4.0 In
du
stry
4.0
© 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