industrial data management and digitization

© Fraunhofer ·· Seite 1

Prof. Dr. Boris OttoDortmund, March 4, 2015

INDUSTRIAL DATA MANAGEMENT AND DIGITIZATION


CONTENT

»Industrie 4.0«

Industrial Data Space

Fraunhofer Data Innovation Lab


Use Case Supply Chain: Permanent Integration of Material and Information Flows at Maersk

Source: Maersk, Ericsson (2014).

Solution Components

Monitoring of climate conditions in oversea containers

GSM and satellite communication

Benefits

Improved ripeness level of bananas in stores

Improved port operations

Improved fuel consumption and carbon footprint balances

»Banana Supply Chain«


Use Case Inbound Logistics: Automated Check-in with »Geo-Fencing« at Audi

Solution Components

Fixed delivery sequences through time tables

Automated truck sequencing on supplier side

Truck control center acts only on exceptions

Automated goods receipt booking

Source: Audi (2014).

Benefits

Ensuring stable, smoothed and sequenced goods delivery

Reduced check-in cycle times

Recued effort in truck control center

Productivity gains through improved employment of labor

Improved infrastructure use around plant


Use Case Warehousing: The RackRacer consists of 85 percent additive manufacturing components

Solution Components

Autonomous navigation in the shelf

No lift needed

Flexible deployment of rack racers

Benefits

Functional and cost advantages compared to state-of-the-art

Increased flexibility of storage systems

Reduced fixed costs

No bottleneck through lift, thus reduced storage cycle times

Source: Fraunhofer IML (2014).


Use Case Transport Logistics: Serva Ray parks cars automatically

Benefits

Improved utilization of parking space

Up to 100 percent improved capacity use

Stable parking processes

Reduced likelihood of accidents and damages to cars

Solution Components

Parking robots navigate to any location in a parking lot

Modular deployment in any layout

No use of rail systems

Easy integration in existing systems

Automated storage area assignment

Source: Serva, Fraunhofer IML (2014).


Use Case Picking and Packing: Innovative Human-Machine-Interaction

Source: Fraunhofer IML (2014).

Solution Components

»Augmented Reality« technologies such as smart glasses

Integration in warehouse management and ERP systems

Benefits

Reduced number of picking errors

Improved work place ergonomics


Use Case Production Logistics: Smart Factory for Electric Car Production

Solution Components

All objects and items are interconnected

Assembly parts find their way on their own through production

Redundant manufacturing capacity are autonomously distributing work loads among each other

Benefits

No central control systems required

Dynamic system reaction in case of exceptions

High scalability of all production processes

Source: SMART FACE-Projektkonsortium (2014). Supported by


Use Case FMCG Supply Chain: Visibility of Transport Items at all Times Through »Databirds«

Real-time management of load carriers

Cloud-based

Service-based

Standardized (EPCIS)

Intelligent load carriers such as

Retail pallets

Unit Load Devices (ULD)

Postal service bins

Internet-of-Things-based processes

Autonomous

Decentralized

Data service support

Data platform

Analytics

Apps


Use Case Shop Floor Logistics: Integrating »Industrie 4.0« with SAP

Transport Task Management

(SAP HANA APPLICATION)

IoT Device Adapter

(on board)SAP IoT Client

(web-based)

Source: Still; Fraunhofer IML (2014).


Fundamental »Industrie 4.0« Principles

Industrie 4.0

Connectivity

Autonomy

Human-Machine-

Interaction

Virtuality

Modularity

Real-Time Capability


Industrial »Revolutions« in a Nutshell

Source: Cf. DFKI (2011).

First Automatic Loom by Edmund Cartwright

(Source: Deutsches Museum)

Assembly Line at Ford(Source: Hulton Archive/Getty

Images)

First PLC Modicon 084 (Source: openautomation)

CPS-based Automation(Source: VDI)

1st Industrial Revolution 2nd Industrial Revolution 3rd Industrial Revolution 4th Industrial Revolution

Introduction of mechanicwork machines in production processes

Division of labor (Taylorism) in production supported by electrical energy

Introduction of electronics and IT for automating mass production

Introduction of cyber-physical systems for controlling production processes

Late 18th Century Early 20th Century Early 1970s Today


»Industrie 4.0« in the Light of Changing Customer and Market Requirements

Source: Koren (2010), cited in Bauernhansl (2014).


CONTENT

»Industrie 4.0«




EMPLOYEESplan, control, orchestrate

Connected data are the enabler of networked supply chains

Image Sources: Fraunhofer IML, Jettainer, Daimler

BINSgive picking instructions

CONTAINERS are aware of their payload and

their way on their own

TRUCKSdrive autonomously

VEHICLESorganize themselves as a swarm

SHELFSplace replenishment orders

Connected Data


Connected data are the enabler for smart end-user services

Smart home

Context model

World wide web

Personal

calendar

Public transport

services

Traffic light and

sensor data

Transport and

purchase orders

Connected Data

Car sharing

offerings

Mobile

communication data

Vehicle movement

Images: Istockphoto


Image sources: ©www.Fotolia.de, © 2014 Daimler AG, © Volkswagen AG 2014

Smart

Trusted

Secure

INDUSTRIAL DATA SPACE

Data assets are dynamically connected to smart services


Source:http://www.scientific-computing.com/news/news_story.php?news_id=2624http://www.fraunhofer.de/en/press/research-news/2015/february/industrial-data-space.html

Media coverage on the Industrial Data Space has been significant recently

http://www.scientific-computing.com/news/news_story.php?news_id=2624

http://www.fraunhofer.de/en/press/research-news/2015/february/industrial-data-space.html


CONTENT

»Industrie 4.0«




Digital Business Engineering as a Methodology for Sustainable Digital Business Transformation

Digitization

Digital Business Model

StrategicPerspective

ProcessPerspective

SystemsPerspective

E2E Customer Process Design

Ecosystem Design

Digital Product & Service Design

Digital Capabilities Design Data Mapping

Digital Technology Architecture

1

2

3

4 5

6

Legend: E2E - End-to-End.


Digital Business Engineering Component Overview

DBE Phase

Description Goal Involved Roles Techniques

1 CustomerProcess

Understand end-to-end customer process from outside-in

Digital business development Sales and marketing

a. Customer journeysb. Multi-channel

analysisc. Consumer process

modeling

2 Ecosystem Understand actors within customer process and customer interaction points

Digital business development Sales and marketing Product management

a. SWOT analysisb. Network analysis

3 Digital Products and Services

Design digital products and services based on end-to-end understanding of customer process

Digital business development Sales and marketing Product management Business architect

a. Business model canvas

b. Digital artifact design

c. Design thinking

4 DigitalCapabilities

Identify capabilities needed to provide digital products and services

Digital business development Business architect IT architect

a. Capability modeling

5 Datamapping

Identify data assets needed to provide digital products and services

Digital business development Data architect IT architect

a. Data architecture

6 Digital technology architecture

Sketch digital technology architecture

Data architect IT architect

a. Digital tool chain


Data Innovation Lab Services for the »Data Economy«

Business Cloud SolutionsBig Data ServicesIndustrial Internet

Business Cloud Design

Cloud-based Business

Processes

Cloud-based Applications

Data-Driven Business

Processes

Digital Business Process

Innovation

Big Data Technologies

and Analytics

Feasibility Studies

SAP and Cloud

Integration

M2M Integration

Enterprise Data Labs

Competence Centers


Enterprise Labs are a proven format at Fraunhofer

Lab Name Audi Logistics Lab Logistics and Digitization Lab

Ericsson Enterprise Data Lab

SICK Enterprise Lab

Sponsor Head of Brand Logistics

President of the Board Schenker Germany

Head of IT Strategyand Architecture

Head of LogisticsAutomation

FocusTopics

• Big data and cloud

• »Industrie 4.0«• Supply chain

governance and transparency

• CKD logistics

• Customer-centric logistics

• Digital supply chains

• Intelligent assets

• Digital services in the networkedeconomy

• Digital product design

• Digital capabilities

• Image processing• 2D and 3D

sensor fusion

Duration 9/1/2013 - 8/31/2018 1/1/2015-12/31/2017 1/1/2013 -12/31/2017

1/1/2013 -12/31/2015


DB Schenker Enterprise Lab for Logistics andDigitization


Ericsson Enterprise Lab

Digitization

Success in the Networked Society

StrategicPerspective

ProcessPerspective

SystemPerspective

Data Management for Digitization

• Smart data services• Digital capabilities• Digital process models• Data and integration

architectures• Innovative data

management technologies

Networked Economy Devices and Services

• »Industrie 4.0«• 5G applications• Devices and services• Internet of Things and

Services• Business cloud platforms

Innovation Radar

NB: Englisch gemäß Lab-Sprache.


Prof. Dr. Boris OttoDortmund, March 4, 2015

INDUSTRIAL DATA MANAGEMENT AND DIGITIZATION

industrial data management and digitization

Business

fraunhofer iml

digitization fraunhofer

plant fraunhofer seite

use case warehousing

use case supply chain

use case production

use case transport logistics

use case inbound logistics