INDUSTRIE 4.0 CHANCES AND CHALLENGES FOR...

© Fraunhofer IPA, IFF University Stuttgart

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INDUSTRIE 4.0 CHANCES AND CHALLENGES FOR SME

Prof. Dr.-Ing. Thomas Bauernhansl April 25th, 2016


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The Digital World of Today and Tomorrow Internet of Everything

source: The Internet of Things, MIT Technology Review, Business Report, Siemens

Holistic global integration as base for new business ecosystems

3 billion people used the internet in 2014

17 billion things were connected in 2014 via internet. In 2020 the number it expected to rise up to 28 billion.

internet services are uncounted. example apple store: > 1 million apps were downloaded more than 75 billion times

new economic activities arise:

shared economy

prosumer

Industrie 4.0/industrial internet…

Connected devices (billions)

„Devices“ (machines, sensors etc.)

Tablet computer

PC & laptops

Mobile phone


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Change of Product Architecture The ability to manage complexity effectively becomes a key competitive advantage

sources: Wildemann, H.: Wachstumsorientiertes Kundenbeziehungsmanagement statt König-Kunde-Prinzip; Seemann, T.: Einfach produktiver werden – complexity im Unternehmen senken; Bildquellen: apple.de

degree of integration

s imple

cyber-physical

mechanical

mechatronical complicated

complex

degree of personalization

Minimal complexity, maximum personalization and economies of scale

Customer is part of the personalization process and pays for it

Innovation focus: eco-system, user-friendliness, design

Success factor: openness

standard mass

products

individualized regionalized, personalized


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Elements of the Fourth Industrial Revolution Networking and computing power enables new design and optimization dimensions for added value systems

Internet of Everything (Human beings, services, things)

Analytics (Big Data/ machine learning)

Cloudbased Platforms (Private, Community, Public)

Software Service (Machine Skills, Apps for humans, Platform services)

Digital Shadow (Real time Model of Everything)

Cyber Physical System

Infrastructure (physical, digital)

Physical Systems (act, sense, communication) Human Beings (decide, create, communicate)

Product Life Cycle (valuable = personalized + sustainable)

Interaction

Reflection

Transaction

Interoperation

Prescription

Communication


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Back End Front End Focus Adding Value Focus Positioning

Ecosystem

X Prosumer

Production network

Factory

Structure of Ecosystems Integrated design of Front and Back End

Value adding system


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pilot project 2013/2014 digitization of farming initially through integration of agricultural machines

evaluation of customer data for optimization of the whole farm through service applications

allocation of applications through partners from various branches on Farmnet online platform

data storage on platform as central access point

today: ecosystem with 15 partners from farm management (among others Allianz, GEA, Horsch)

source: Farmnet 365

„Farmnet 365“ − an agricultural machinery initiative Business Ecosystems

Sensors Sensors

LTE LTE

Farm

data evaluation („Big Data“)

path optimization sustainability vs. speed autonomous driving

quality data for selling quantity data for silo management

Data Platform weather, machine

position, crop quality, soil condition


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Digitization of Business Models Everything goes smart and changes industrial branches

CPS Cyber-Physical System, RFID Radio Frequency Identification

of People

Platforms

of Services of People


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Connectiv ity Performance Metcalfe:

“The benefit of a communication system

increases with the square of the number

of participants.”

Moore:

“Computer performance doubles

every 18 months.”

Ecosystems for Smart Business Models

Knowledge Transparency cyber-physical systems

Internet of Things and Services

real time & at run time

everything as a service

The Base: Processing Power and Connectivity Moore and Metcalfe are Proven Right and Define the Scope and Value of an Enterprise

sources of pictures: wikipedia.de, ibm.com, abcnews.com


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Core thesis for value creation systems of the future

Optimal distribution of value creation in ecosystems (prosumer, horizontal integration) leads to low complexity costs und high profit margins.

Optimal distribution of functionalities (services) in the cyber-physical system-architecture (cloud vs. fog, vertical integration) leads to economies of scale and high functional adaptivity along the life cycle.

Mass-based forecasts of futures based on the digital shadow (real time, big data) set the fundament for high process capability of complex systems.

The production of personalized hardware through actionable, generative manufacturing decides over economic efficiency.

Waste-free integration of workers through adaptive and self-learning human-computer interaction interfaces (remote and physical interfaces) ensures comprehensive acceptance in the working system.

Source of pictures: faz.net, google.de


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Optimization of Value Added


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All Objects in a Factory will be Smart iBin − Intelligent bins order their filling autonomously

With an integrated camera combined with its cloud, iBin counts the parts enclosed in it.

Free Access

Open by pushing a button

2stage front flap

push trays for VDA labels

iBin®-Modul, RFID compatible

source: Fraunhofer IML, Prof. Dr. Michael ten Hompel


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source: IPA

Robots will be mobile, flexible and safe Example: SEW Eurodrive – freely navigating DTS (carries the robot for bin picking)

KUKA Agilus

Bin with cut-pieces

Mobile platform − Inductive power transmission

3D-camera system ensenso N20

Magnetic gripper

Cut-pieces

Point cloud


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source: audi-mediaservices.com

All Objects in a Factory will be Mobile as Far as Possible Example: Audi R8 – freely navigating AGV (navigation as a service)


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AppMES, AppERP, eApp,…

Equipment CPS1 CPS2

Manufacturing Service Bus (ESB++)

IS1 IS2 IS3

mOS AS1

S1 S2 S3

AS2

S4 S5 S6

Private or Public Cloud

mOS AppStore

App 1

App 2

Devices

…

App Development Kit

…

Everything as a Service” (XaaS) Use case Virtual Fort Knox – integration platform

Legend:

S Service

AS Aggregated Service

IS Integration Service

CS Cloud Service

CPS Cyber-Physical System

mOS Manufacturing Operating System

SOA, WS

http://www.google.com/url?sa=i&rct=j&q=virtual+fort+knox&source=images&cd=&cad=rja&docid=ASmU0VFBAIjFMM&tbnid=oSHM8wPcPOZT6M:&ved=0CAUQjRw&url=http://www.flexis.com/de/news/flexis-auf-der-hannover-messe.html&ei=5z7kUZqUIYHVPPnCgfgN&bvm=bv.48705608,d.ZWU&psig=AFQjCNHnGGPD_IY8wEV-Twud-mJObWXg_g&ust=1373999196536674


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Automated Detection of Dependencies Between processes and deriving optimization potential

Through

“minimally invasive“ process monitoring via camera without elaborate system integration

feature-based configuration and recognition of conditions in the videos via adaptive evaluation algorithms

Benefits

near real-time process analysis with direct assignment of the cause for loss

detection and quantitative evaluation of potential for process optimization

permanent transparency through forwarding errors and machine condition to operators and planers


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XaaS − Everything as a Service Integrated service-orientation leads to new value-adding structures

Tasks Examples

Value as a Service (VaaS)

personalized end to end services meeting user‘s needs (e.g. mobility, health)

Logistic as a Service (Amazon) Mobility as a Service (Daimler) Assembly as a Service (Foxconn)

Modules as a Service (MaaS)

open hardware and software modules for developing personalized services

Ara modules (Google) Apps (Runtastic) cars (Local Motors)

Platform as a Service (PaaS)

life cycle environment & communication for economic availability of software and hardware modules

App Store (Apple) production platform

(emachineshop) Virtual Fort Knox (FhG) home applications (First built)

Infrastructure as a Service (IaaS)

infrastructure services as base for platforms and for the application of modules

Cloud Infrastructure (IBM) mobile Communication

(Telekom) electric network (ENBW)

Ev

ery

thin

g a

s a

Se

rvic

e (

Xa

aS

)


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What if Bin-picking came out of the Cloud?

Advantage

externalization of skills, services, maintenance

lean robot workcell (“Lean Client“)

centralized collection of data

optimization by statistical learning

best practice solutions accessible

to be displayed at HMI2015

Bin-Picking App

Object localization

Task/Path planning

Work piece CAD-model Planner

Portal

System planning

Part teaching

Operator

Service Bus

Object Features

Motion Data

Actors

Skills, Services

Sensor

Part models


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Business potential of Integrated Industry (Industrie 4.0) Specialists expect an increase in overall performance between 30 to 50 % in value creation

Pilot project at Bosch: Restructuring of complete distribution process based on an in-plant logistics center in an Industrie 4.0 project.

source: IPA/Bauernhansl, Bosch

-10 % milkruns

+10 % produc-

tiv ity

-30 % stock

reduction

Estimation of potential benefits

supplier

Automotive manufacturer


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Barriers for Industrie 4.0 from the Perspective of SMEs „Sound half knowledge“ leads to misjudgement

requirements of IT safety and security, data privacy

high invest/lacking willingness to invest

insufficient competence/availability of skilled personnel

legal uncertainty, copyright

no transparency of economic benefit

necessary processes/work organization

lack of technical or accepted standards

insufficient broadband connectivity

missing research

source: u.a. Das IHK-Unternehmensbarometer zur Digitalisierung „Wirtschaft 4.0: Große Chancen, viel zu tun“, 2014, tecchannel.de/bildzoom/2076641/1/947033/EL_mediaN-1006F/; Bildquelle: manager-magazin.de


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„When the wind of change is blowing, some people build walls , while others

build windmills .“

(Chinese proverb)


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INDUSTRIE 4.0 CHANCES AND CHALLENGES FOR SME

Prof. Dr.-Ing. Thomas Bauernhansl April 25th, 2016

INDUSTRIE 4.0 CHANCES AND CHALLENGES FOR...

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