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