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Industry 4.0 President Lyndon B. Johnson, USA, 1963 - “Automation is not our
enemy. Our enemies are ignorance, indifference, and inertia.
Automation can be the ally of our prosperity if we will just look
ahead, if we will understand what is to come, and if we will set our
course wisely after proper planning for the future.”
Germany 2011, Hannover Trade Fair
Industry 4.0 is that by connecting machines, work pieces and
systems, businesses are creating intelligent networks along the
entire value chain that can control each other autonomously .
2018: AI super powers - China, Silicon Valley and the new world order
“These changes are coming, and we need to tell the truth
and the whole truth. We need to find the jobs that AI
can’t do and train people to do them. We need to
reinvent education. These will be the best of times and
the worst of times. If we act rationally and quickly, we
can bask in what’s best rather than wallow in what’s
worst.”
—Prof. Kai-fu Lee (MIT 2018)
Industrial Evolution
4. Industrial
revolution Based on cyber-physical-
systems
3. Industrial revolution Through the use of electronics
and IT further progression in
autonomous production
2. Industrial revolution Introducing mass production
lines powered by electric
energy
1. Industrial revolution Introducing mechanical
production machines powered
by water and steam
End of the
18th century. Beginning of the
20th century
Beginning of the
70th
Industry 1.0 Industry 3.0 Industry 2.0 Industry 4.0
Le
ve
l o
f c
om
ple
xit
y
Today
Source: DFKI/Bauer IAO
Phases of earlier 3 Industrial Revolutions
1. 1760 to 1840 - Ushered in Mechanical production; railways
and steam engine
2. 1870 to 1940 - Mass production; electricity and assembly line
3. 1960 to 2010 - Computers; semi conductors, main frame
computing, personal devices, internet
Four design principles in Industry 4.0. that support companies in identifying and implementing Industry 4.0 scenarios
•Interoperability: machines, devices, sensors, and people to
connect and communicate with each other via the
Internet of Things (IoT) .
•Information transparency: information systems to create a
virtual copy of the physical world by enriching digital plant models
with sensor data. This requires the aggregation of raw sensor data
to higher-value context information.
• Technical assistance: assistance systems to support humans by
aggregating and visualizing information comprehensively for
making decisions. Ability of cyber physical systems to physically
support humans by conducting a range of tasks that are
unpleasant, too exhausting, or unsafe for their human co-
workers.
• Decentralized decisions: The ability of cyber physical systems
to make decisions on their own and to perform their tasks as
autonomously as possible. Only in the case of exceptions,
interferences, or conflicting goals, are tasks delegated to a
higher level.
Industry 4.0: Germany 2011 Smart Manufacturing Leadership Coalition: USA
A collective term for technologies and concepts of value chain
organization
Builds on the Digital revolution Ubiquitous internet
Smaller & powerful sensors Artificial Intelligence (AI)
Machine Learning Robotics and automation
Human intelligence augmented
18
Automobile industry: Vendor change or vendor cooperation ?
Power revolution Centralized electric power
infrastructure; mass production
by division of labor
1st industrial revolution Mechanical production
systematically using the
power of water and steam
today
Digital revolution Digital computing and
communication technology,
enhancing systems’ intelligence
Information revolution Everybody and everything is
networked – networked
information as a “huge brain”
Characteristics of industrial revolutions:
The vendor change
around 1750 around 1900 around 1970
Ford 021C concept car
2012, designed by Newson
now at Apple (1999)
latest version of Google’s self
driving car (Huffington Post,
28.5.2014)
Nevada issued Google a license:
the world's first driverless car to
drive on public streets (2012)
first sightings of the iCar (?) in
New York and San Francisco
(16.2.2015)
Apple Inc.
Car specialists? – No.
Connectivity & data
specialists.
6 Not restricted to industry: cyber physical systems in all areas
Back to: The earth converted into a huge “brain”… (Tesla 1926)
Integrating complex information from multiple heterogeneous sources opens multiple possibilities of optimization:
e.g. energy consumption, security services, rescue services as well as increasing the quality of life
… and more
Building
automation
Smart grid
Room
automation
Smart
environment
Smart
metering
4
Communication technology bandwidth and computational power
Embedded systems miniaturization
Semantic technologies information integration
Breakthroughs - Everybody and everything is networked
Team
Robotics
Swarm
Robotics
Smart
Grid
Car2Infra-
structure
Smart
Factory
8 2009: Truck robot platoons – distributed intelligence The KONVOI project
2005-2009
automated / partly autonomous
transportation e.g. by electronically
coupling trucks to convoys
several successful tests with trucks:
Chauffeur, KONVOI, SARTRE (EU), Energy-
ITS (Japan), …
expected improvements:
beyond safety, reduction of fuel
consumption and gained road space
!
Adv. driver assistance system for trucks
short distances between vehicles of
approx. 10m at a velocity of 80 km/h
Energy-ITS: 4m ! (2013)
KONVOI:
Car2infrastrcuture components!
Model of multi agent systems
Connectivity…
9 Technological development – … to decentralized lot size 1
Organization forms on demand – individualized by client - initialized by product
! Product agitates as “super-agent”:
Plans production and transportation steps
Requests service from agents
Negotiates with other products for agent-resources
Heterogeneous player modeled as multi agent concept
Models from biology and social sciences
Basis on Autopoiesis & embodiment theory
transport
unit
production
unit
virtual service
provider fabri
cation
outs
ide w
orl
d
© D
anie
l Ew
ert 2
013
10 …to socio-technical assembly systems and cooperative robotics
Robots are no longer locked in work-cells but cooperate
with each other and/or with humans
machine-machine cooperation
hybrid planning for
real-time capability
integrates several robots
and/or human and robot
in assembly task
(„assembly by disassembly“),
split into
„online-offline“
for real-time capabilities
human-machine-machine
interaction in the X-Cell
12 Leading to: Interdisciplinary science and education
New fields
of work
… ?…
Virtual reality
Augmented
reality
Natural language
communication
Human-
Machine
Interaction Autonomou
s systems
Automated
driving
Lightweight
robots
Autonomous
flying
Data Analysics
Business
Computing
Risc analysis
Social Robotics
Antropo-
morphism
Uncanny
valley
Car2X
Smart Logistics
Cloud logistics Swarm
robotics
Autonomouos
intralogistics
Impact
Economy
Business
National & Global
Society
Individual
Impact
Business perspective: Time to reach 100 Million customers
• Telephone 75 Years
• Web 7 Years
• Facebook 4 Years
• Instagram 2 Years
• Pokemon Go 1 Month
How does all this affect you? Products that did not exist in 2006
• iPhone
• iPad
• Kindle
• 4G
• Uber
• Ola
• Airbnb
• Android
Examples of Product evolution: Connected and smart products
Harvard Business Review
Cyber Physical Systems
A cyber-physical system (CPS) is a system of collaborating computational elements controlling physical entities. CPS are physical and engineered systems whose operations are monitored, coordinated, controlled and integrated by a computing and communication core. They allow us to add capabilities to physical systems by merging computing and communication with physical processes.
Today’s Factory
Tomorrow’s Factory
Potential Implications
Robot Assisted production
Predictive Maintenance
Additive manufacturing of complex parts
Machines as a service
Big data drive quality control
Production line simulation
Smart supply network
13
Excellence through Interdisciplinarity
I
Implications for Future Engineering Education
Without interdisciplinarity, there is no innovation.
Development of highly complex , socio-technical systems requires the
collaboration of various academic disciplines.
Future Engineers need the skills to “look beyond their own nose”.
Adaptability to rapid innovation cycles
II The “half-life” of knowledge sector is shortening rapidly.
Students need less detailed specialized content than the ability of life long
learning.
Future Engineers need the skills to adapt to changes quickly.
Survival in Industry 4.0 requires IT skills IT is the main driver of innovation in future industrial contexts
Independent of the specialization, engineers must have the basic knowledge
and understandings of others
Future Engineers need to be able to “speak code”.
III
22.09.2015
S. Jeschke
17
Innovations in 4.0
If innovation cycles become faster, we need more enterprises!
22.09.2015
S. Jeschke
[Jeffrey Baumgartner, http://www.creativejeffrey.com/creative/ipm.php 2009]
19
System- oriented broad
potential
Capital risks
Finding out about market borders
Innovative Ideas Founding
a new existence
Start-Ups
Innovations in 4.0
Innovation comes from fresh minds!
22.09.2015
S. Jeschke
20 The question is – how do we teach them to be like that?!
22.09.2015
S. Jeschke
Classical Entrepreneurs needed
Classical Skills…
?
But is that ENOUGH
to prepare for industry 4.0?
accepting uncertainty
ability of taking risks
innovative
change-oriented
persistent
broad !!
high-speed adaptive
environmental observation
design & individualization
communication-oriented
Human maschine interaction
decision-making
fast and based on knowledge
as well as on instinct
leadership skills, motivating
marketing, financial aspects,
selling, …
Entre- preneurial
Skills
Technical Skills
Manage- ment Skills
21
Communication technology bandwidth and computational power
Semantic technologies information integration
2nd entrepreneurship
revolution 1 man show + basic communi-
cation and information
1st entrepreneurship
revolution 1 man show + raw materials
3rd entrepreneurship
revolution 1 man show + extensive
communication and
information
4th entrepreneurship
revolution 1 man show + a village’s
support in communication and
information
around 4000 BC around 1900
Outsourcing comes of age:
The rise of collaborative partnering around 1970 today
From „1 Man 1 Sign“ to the „Entrepreneur Village“
[Price
wate
rhouse
Coopers
2008, M
acC
orm
ack
et al. 2
007]
23
LEs
SMEs
SMEs and LEs and Freelancer will be brought together for a more robust system
that includes outsourcing, using common logistics, open sources…
Entrepreneurship - the Motor for the Economy
Freelancers as a new form of permanent employment?
Freelancer
innovative
creative
specialized
stable
robust
international
specialized
flexible
linked
New types of employment, New business-models – examples: globalization, personalization, Pay by the hour,
… with strong consequences to the whole complex of “work and life”, stability, predictibility, etc.
22.09.2015
S. Jeschke
27
New Business Thinking
Above the classical basic skills to manage development projects, Future
Entrepreneurs need additional skills in particular in leadership, decision making, …
They need to know how to communicate business ideas to different stakeholders.
Future Engineers need to know, how to collaborate in the “global village”.
IV
Implications for Future Engineering Education
Taking Risks and Dealing with Uncertainty
Uncertainty cannot be managed. Even the best prediction will end up as “only
partially correct”. And… good predictions need time which is lost for other things.
Future Engineers need be to unterrified – and capable to adapt to changes quickly
and through broad competencies.
V
Bursting with Creativity
When speed of innovation cycles increases, creativity becomes the “new gold”.
Students need the ability to critically assess issues and develop sound,
responsible, and creative solutions.
VI
22.09.2015
S. Jeschke
29 … MOOCs around the World: a boom in about 3 years
Rest of World
Japan: Schoo
Malaysia & Indonesia: MOOCs on Entrepreneurship
Australia: openlearning, open2study…
Brasil: veduca…
…
North America
change.mooc.ca
CCK08/09/10/12
LAK 11/12/13
PLENK 2010…
…
Europe
25.08.2015
S. Jeschke
30 Higher Education… the Usual Recipe
Online Distribution of Learning Material
Feedback /Peer Exchange
Exam & Certificates
Group-/Peer-Based Learning Activities
Face to Face Teaching
Lab Experience
31 Higher Education… the „New Way“
Online Distribution of Learning Material
Feedback /Peer Exchange
Exam & Certificates
Group-/Peer-Based Learning Activities
Face to Face Teaching
Lab Experience
25.08.2015
S. Jeschke
32
Okay, MOOCs are nice, BUT… the paragigm shift in education
Adaptive
Technology
now 1990s
PCs
A PC in
every
class!
Cloud and Smart
Phones
2012s
Making
education
widely
available
Making education
smart and
individualized
2000s
The Internet
Log on
and
learn
Accessibility
25.08.2015
S. Jeschke
33 Let’s ask Google
“Big data is the term for a collection of data sets so large and
complex that it becomes difficult to process using on-hand
database management tools or traditional data processing
applications. The challenges include capture, curation, storage,
search, sharing, transfer, analysis and visualization.”
“Every day, we create 2.5 quintillion bytes of
data - so much that 90% of the data in the
world today has been created in the last two
years alone. This data comes from
everywhere: sensors used to gather climate
information, posts to social media sites,
digital pictures and videos, purchase
transaction records, and cell phone GPS
signals to name a few. This data is big data.”
“Big Data refers to technologies and initiatives
that involve data that is too diverse, fast-
changing or massive for conventional
technologies, skills and infrastructure to
address efficiently. Said differently, the
volume, velocity or variety of data is too great.
But today, new technologies make it possible
to realize value from Big Data.”
34 Big Data induce “intelligence”: from Big Data to Smart Data…
Acquistion/
Recording
Extraction/
Cleaning/
Annotation
Integration/
Aggregation/
Representation
Analysis/
Modeling Interpretation
The Big Data analysis pipeline…
! … transfers big data (many…) into smart data (meaningful data)
+
! … accumulates intelligence from information fragments
! … is a pipeline of aggregating (artificial) intelligence.
39
In the tradition of the other industrial revolutions
Non-privileged
All you need is a web
connection
(Higher) Education
becomes affordable
Flexibility
Independence from
real-life teachers
[Kindeswohl Berlin, 2015/ Gradireland, 2013]
Special Needs
Better insights into
habits of slow learners
Combine with specific
learning software
Optimal encroachment
of learning channels
possibility to learn at
home
Towards democratized, diverse and globalized education
Society
Reusability of content
Optimization of
Teaching
Improvement of
future courses
Early warning-system
for knowledge gaps
Individual
Individualization
Prediction of
Performanc
e
Adaption to any
knowledge level
Control over learning
process
possibility to learn at
home
India : Task force on AI and Industry 4.0
WHILE
Challenges
Government policy:
Grand Challenges for India:
Health Care:
Agriculture:
Manufacturing
Education:
Environment
Presentation title
Data Security:
National Security:
Presentation title
Ethics ?
Presentation title