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© Fraunhofer · Seite 1
Prof. Dr.-Ing. Boris Otto · Munich · February 7th, 2017
TURNING DATA INTO VALUE
LEVERAGING THE OPPORTUNITIES OF INDUSTRIAL DIGITIZATION
© Fraunhofer · Seite 2
AGENDA
 Digitization of the Industrial Enterprise
 Smart Data Management
 Leading Examples
© Fraunhofer · Seite 3
Image sources: Audi (2016).
Legend: AGV - Automated Guided Vehicle; VR – Virtual Reality.
Industrial digitization happens in all value creation processes – as the
example of Audi shows
Autonomous AGVs for Modular Production Human Robot Collaboration Autonomous Tugger Trains
Drone Use in Assembly VR in Engineering Predictive Analytics in the Yard
© Fraunhofer · Seite 4
These developments are a response to fundamental changes manufacturing
companies need to cope with in terms of their production strategy
Production
Volume per
variant
No. of Variants
1850
1913
1955
1980
2000
Ford Model T
VW Beetle
Production
Audi Configurator
Mass
Production
Individualization
»Sharing Economy«
Complexity
Globalization
iPhone
3D Printed Car
Source: Koren (2010), in Bauernhansl (2014); image sources: Wikipedia (2015), Impulse (2015), Audi (2015), O2 (2015), computerbild (2015).
© Fraunhofer · Seite 5
Image sources: ihs-gmbh.de (2016); silicon.de (2016).
Legend: ERP – Enterprise Resource Planning; LAS – Logistics Assistance System; OEM – Original Equipment Manufacturer.
Data is the key resource in digital value creation networks
© Fraunhofer · Seite 6
Required is a central information management instance – what Audi refers
to as the »Tower«
Image sources: Audi (2016).
© Fraunhofer · Seite 7
Image sources: Audi (2016).
The »Tower« is at the core of smart data management
 Conceptual information model of the digital factory
 Source of the »Digital Twins« and »Single Source of the Truth«
 »Data Lake« functionality
 Collection and analysis of manufacturing and supply chain
event data
 Close to real-time process analysis
 Backbone for data analytics and machine learning
© Fraunhofer · Seite 8
AGENDA
 Digitization of the Industrial Enterprise
 Smart Data Management
 Leading Examples
© Fraunhofer · Seite 9
Legend: Information flow; Material flow.
Smart data management is the key capability of the digitized industrial
enterprise
Public
Data
Value Chain Data
Commercial
Services
Industrial
Services
Lot-Size 1
End-to-End Customer
Process
Business Ecosystem
Hybrid Offerings
Smart Data
Management
Interoperability
Human-Machine-
Collaboration
Autonomous Systems
Internet of Things
Customer
Production
Networks
Logistics
Networks
Digitized Value PropositionDataDigitized Value Creation
© Fraunhofer · Seite 10
Industrial data has evolved into a strategic resource with an economic value
Time
Value Contribution
Data as
process
result
Data as
process
enabler
Data as
product
enabler
Data as a
product
© Fraunhofer · Seite 11
Source: Moody & Walsh (1999).
Despite its intangible nature, industrial data has a value which can be
quantified
Number of users
Share of value
100%
Data
Tangible
Goods
Tangible
Goods
Value
Data
Usage Time
Potential value
Data
Data quality
Value
100%
Data
Integration
Value
Data
Volume
Value
Data
© Fraunhofer · Seite 12
Source: Otto (2012); Otto (2015).
Many examples exist demonstrating the applicability of valuation
procedures in the data domain
Company Industry Country Data domain
Valuation
approach
Value per record
Retail US
Customer data
including shopping
profile
Market value 1.6 EUR
Social Network US User data Market value 225 USD
Automation and
drives
DE Master data on parts
Production
costs
500 to 5.000 EUR
Agrochemical CH Material master data Use value 184 CHF
© Fraunhofer · Seite 13
Source: Leveling et al. (2014).
Smart data management is aware of the heterogeneous nature of data
Peripheral data of greater fuzziness, volume,
volatility, heterogeneity…
Peripheral data less controllable, critical,
unambiguous…
Nucleus Data
(Customer data,
product data etc.)
Community Data
(Spatial data, GTIN,
addresses, ISO codes,
EPCIS events etc.)
Open Big Data
(Tweets, social media
streams, sensor data etc.)
Megabytes
Gigabytes
Terabytes
Petabytes
© Fraunhofer · Seite 14
Smart data management rests on a future-proof data service architecture
Industrial Data Sources
ERP  MES  SCADA  Installed Base etc.
Commercial Data Sources
CRM  Loyalty Programs etc.
Social Data Sources
Facebook  Twitter etc.
Cloud-based Data Storage
Data Source Connectors  Data Space Infrastructure  Shared Information Model
Industrial Data Service Architecture
Data Quality Assurance Mapping/Transformation Integration/Aggregation Data Provenance …
Data Analysis Data Mining Visualization Data Delivery …
Industrial Use-Cases
Preventive Maintenance  Digital
Farming  Supply Chain Visibility
Commercial Use-Cases
Smart Home  Mobility  HealthCare
etc.
Internal Use-Cases
Data as a Process Enabler
Context-free Use
Data as a Product
© Fraunhofer · Seite 15
Source: VDI (2015).
Smart data management enables digital twins of the real word
Reference Architecture Model Industry 4.0 Administrative Shell Concept
The Administrative Shell stores all data of a hardware or software component in production scenarios
It makes data and services related to that component available for Industry 4.0 scenarios in a standardized way
© Fraunhofer · Seite 16
A set of design principles guides the transformation to smart data
management
Design Perspective Design Principles Implementation Examples
Strategic principles Productizing of data Data products with clearly defined data elements or
configuration, service levels …
Managing data as an asset Data valuation and pricing, data lifecycle management …
Data co-creating and sharing Collaboration in communities of interest and eco-systems
Organizational principles Governing data in participative ways Transparent responsibilities, digital sovereignty, data owners
in control …
Managing data supply chains and life-cycles end-to-end Data acquisition, pre-processing, processing, distribution, use,
retirement…
Recognizing data quality as probabilistic Dealing with fuzzy and volatile data
with limited traceability
Systems and architecture
principles
Deploying federated architectures Open platforms, linked data…
Decentralizing information security and data sovereignty Data tagging, blockchain technologies…
Sharing data processing resources Cloud platforms, intelligent devices, edge computing
© Fraunhofer · Seite 17
AGENDA
 Digitization of the Industrial Enterprise
 Smart Data Management
 Leading Examples
© Fraunhofer · Seite 18
Source: Otto (2016).
The Industrial Data Space addresses the squaring of the data
sovereignty circle
Interoperability
Data Exchange
»Sharing Economy«
Data-centric Services
Data Ownership
Data Privacy and Security
Data Value
Data sovereignty is the capability of a natural person or corporate entity for exclusive self-determination with
regard to its economic data goods
© Fraunhofer · Seite 19
Data flow
Material flow
Legend: IDS – Industrial Data Space; LSP – Logistics Service Provider; IoT – Internet of Things.
The Industrial Data Space connects various digital platforms and the
internet of things
Public context data
Weather
Factory/Warehouse
LSPElectronic
Marketplace
Traffic
IoT
Cloud
IDS Broker
IDS
IDS
IDS
IDS
IDS
IDS
IDS
IDS
Supply chain planning data
Supply chain event data
Internal process data
© Fraunhofer · Seite 20
Source: Cf. Kagermann (2015).
The Industrial Data Space defines the data architecture between
smart services and the internet of things

Connected physical platforms Smart Products
Technical infrastructure Smart Spaces
 Industrial Data Space
Service platforms Smart Services
Smart Data Services
(Alerting, Monitoring, Data quality etc.)
Basic Data Services
(Fusion, Mapping, Aggregation etc.)
 Use restrictions attached to the data
 Secure data supply chain
 Data Fusion
 Certified software endpoints
 Multiple use scenarios
 Federated governance models
© Fraunhofer · Seite 21
NB: As per December 2016.
The initiative rests on solid and continuously growing industry
commitment organized in the Industrial Data Space Association
© Fraunhofer · Seite 22
Image source: Competence Center Corporate Data Quality (2016).
The »CDQ Framework« is a standard capability model for
managing the data core
© Fraunhofer · Seite 23
Source: CDQ AG; Corporate Data League (2016).
The Corporate Data League is a community approach for managing
business partner data
© Fraunhofer · Seite 24
Prof. Dr.-Ing. Boris Otto
Fraunhofer ISST
Managing Director
Boris.Otto@isst.fraunhofer.de
https://de.linkedin.com/pub/boris-otto/1/1b5/570
https://twitter.com/drborisotto
https://www.xing.com/profile/Boris_Otto
http://www.researchgate.net/profile/Boris_Otto
http://de.slideshare.net/borisotto
Your Contact Person!
© Fraunhofer · Seite 25
Prof. Dr.-Ing. Boris Otto · Munich · February 7th, 2017
TURNING DATA INTO VALUE
LEVERAGING THE OPPORTUNITIES OF INDUSTRIAL DIGITIZATION

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Turning Industrial Data into Value

  • 1. © Fraunhofer · Seite 1 Prof. Dr.-Ing. Boris Otto · Munich · February 7th, 2017 TURNING DATA INTO VALUE LEVERAGING THE OPPORTUNITIES OF INDUSTRIAL DIGITIZATION
  • 2. © Fraunhofer · Seite 2 AGENDA  Digitization of the Industrial Enterprise  Smart Data Management  Leading Examples
  • 3. © Fraunhofer · Seite 3 Image sources: Audi (2016). Legend: AGV - Automated Guided Vehicle; VR – Virtual Reality. Industrial digitization happens in all value creation processes – as the example of Audi shows Autonomous AGVs for Modular Production Human Robot Collaboration Autonomous Tugger Trains Drone Use in Assembly VR in Engineering Predictive Analytics in the Yard
  • 4. © Fraunhofer · Seite 4 These developments are a response to fundamental changes manufacturing companies need to cope with in terms of their production strategy Production Volume per variant No. of Variants 1850 1913 1955 1980 2000 Ford Model T VW Beetle Production Audi Configurator Mass Production Individualization »Sharing Economy« Complexity Globalization iPhone 3D Printed Car Source: Koren (2010), in Bauernhansl (2014); image sources: Wikipedia (2015), Impulse (2015), Audi (2015), O2 (2015), computerbild (2015).
  • 5. © Fraunhofer · Seite 5 Image sources: ihs-gmbh.de (2016); silicon.de (2016). Legend: ERP – Enterprise Resource Planning; LAS – Logistics Assistance System; OEM – Original Equipment Manufacturer. Data is the key resource in digital value creation networks
  • 6. © Fraunhofer · Seite 6 Required is a central information management instance – what Audi refers to as the »Tower« Image sources: Audi (2016).
  • 7. © Fraunhofer · Seite 7 Image sources: Audi (2016). The »Tower« is at the core of smart data management  Conceptual information model of the digital factory  Source of the »Digital Twins« and »Single Source of the Truth«  »Data Lake« functionality  Collection and analysis of manufacturing and supply chain event data  Close to real-time process analysis  Backbone for data analytics and machine learning
  • 8. © Fraunhofer · Seite 8 AGENDA  Digitization of the Industrial Enterprise  Smart Data Management  Leading Examples
  • 9. © Fraunhofer · Seite 9 Legend: Information flow; Material flow. Smart data management is the key capability of the digitized industrial enterprise Public Data Value Chain Data Commercial Services Industrial Services Lot-Size 1 End-to-End Customer Process Business Ecosystem Hybrid Offerings Smart Data Management Interoperability Human-Machine- Collaboration Autonomous Systems Internet of Things Customer Production Networks Logistics Networks Digitized Value PropositionDataDigitized Value Creation
  • 10. © Fraunhofer · Seite 10 Industrial data has evolved into a strategic resource with an economic value Time Value Contribution Data as process result Data as process enabler Data as product enabler Data as a product
  • 11. © Fraunhofer · Seite 11 Source: Moody & Walsh (1999). Despite its intangible nature, industrial data has a value which can be quantified Number of users Share of value 100% Data Tangible Goods Tangible Goods Value Data Usage Time Potential value Data Data quality Value 100% Data Integration Value Data Volume Value Data
  • 12. © Fraunhofer · Seite 12 Source: Otto (2012); Otto (2015). Many examples exist demonstrating the applicability of valuation procedures in the data domain Company Industry Country Data domain Valuation approach Value per record Retail US Customer data including shopping profile Market value 1.6 EUR Social Network US User data Market value 225 USD Automation and drives DE Master data on parts Production costs 500 to 5.000 EUR Agrochemical CH Material master data Use value 184 CHF
  • 13. © Fraunhofer · Seite 13 Source: Leveling et al. (2014). Smart data management is aware of the heterogeneous nature of data Peripheral data of greater fuzziness, volume, volatility, heterogeneity… Peripheral data less controllable, critical, unambiguous… Nucleus Data (Customer data, product data etc.) Community Data (Spatial data, GTIN, addresses, ISO codes, EPCIS events etc.) Open Big Data (Tweets, social media streams, sensor data etc.) Megabytes Gigabytes Terabytes Petabytes
  • 14. © Fraunhofer · Seite 14 Smart data management rests on a future-proof data service architecture Industrial Data Sources ERP  MES  SCADA  Installed Base etc. Commercial Data Sources CRM  Loyalty Programs etc. Social Data Sources Facebook  Twitter etc. Cloud-based Data Storage Data Source Connectors  Data Space Infrastructure  Shared Information Model Industrial Data Service Architecture Data Quality Assurance Mapping/Transformation Integration/Aggregation Data Provenance … Data Analysis Data Mining Visualization Data Delivery … Industrial Use-Cases Preventive Maintenance  Digital Farming  Supply Chain Visibility Commercial Use-Cases Smart Home  Mobility  HealthCare etc. Internal Use-Cases Data as a Process Enabler Context-free Use Data as a Product
  • 15. © Fraunhofer · Seite 15 Source: VDI (2015). Smart data management enables digital twins of the real word Reference Architecture Model Industry 4.0 Administrative Shell Concept The Administrative Shell stores all data of a hardware or software component in production scenarios It makes data and services related to that component available for Industry 4.0 scenarios in a standardized way
  • 16. © Fraunhofer · Seite 16 A set of design principles guides the transformation to smart data management Design Perspective Design Principles Implementation Examples Strategic principles Productizing of data Data products with clearly defined data elements or configuration, service levels … Managing data as an asset Data valuation and pricing, data lifecycle management … Data co-creating and sharing Collaboration in communities of interest and eco-systems Organizational principles Governing data in participative ways Transparent responsibilities, digital sovereignty, data owners in control … Managing data supply chains and life-cycles end-to-end Data acquisition, pre-processing, processing, distribution, use, retirement… Recognizing data quality as probabilistic Dealing with fuzzy and volatile data with limited traceability Systems and architecture principles Deploying federated architectures Open platforms, linked data… Decentralizing information security and data sovereignty Data tagging, blockchain technologies… Sharing data processing resources Cloud platforms, intelligent devices, edge computing
  • 17. © Fraunhofer · Seite 17 AGENDA  Digitization of the Industrial Enterprise  Smart Data Management  Leading Examples
  • 18. © Fraunhofer · Seite 18 Source: Otto (2016). The Industrial Data Space addresses the squaring of the data sovereignty circle Interoperability Data Exchange »Sharing Economy« Data-centric Services Data Ownership Data Privacy and Security Data Value Data sovereignty is the capability of a natural person or corporate entity for exclusive self-determination with regard to its economic data goods
  • 19. © Fraunhofer · Seite 19 Data flow Material flow Legend: IDS – Industrial Data Space; LSP – Logistics Service Provider; IoT – Internet of Things. The Industrial Data Space connects various digital platforms and the internet of things Public context data Weather Factory/Warehouse LSPElectronic Marketplace Traffic IoT Cloud IDS Broker IDS IDS IDS IDS IDS IDS IDS IDS Supply chain planning data Supply chain event data Internal process data
  • 20. © Fraunhofer · Seite 20 Source: Cf. Kagermann (2015). The Industrial Data Space defines the data architecture between smart services and the internet of things  Connected physical platforms Smart Products Technical infrastructure Smart Spaces  Industrial Data Space Service platforms Smart Services Smart Data Services (Alerting, Monitoring, Data quality etc.) Basic Data Services (Fusion, Mapping, Aggregation etc.)  Use restrictions attached to the data  Secure data supply chain  Data Fusion  Certified software endpoints  Multiple use scenarios  Federated governance models
  • 21. © Fraunhofer · Seite 21 NB: As per December 2016. The initiative rests on solid and continuously growing industry commitment organized in the Industrial Data Space Association
  • 22. © Fraunhofer · Seite 22 Image source: Competence Center Corporate Data Quality (2016). The »CDQ Framework« is a standard capability model for managing the data core
  • 23. © Fraunhofer · Seite 23 Source: CDQ AG; Corporate Data League (2016). The Corporate Data League is a community approach for managing business partner data
  • 24. © Fraunhofer · Seite 24 Prof. Dr.-Ing. Boris Otto Fraunhofer ISST Managing Director Boris.Otto@isst.fraunhofer.de https://de.linkedin.com/pub/boris-otto/1/1b5/570 https://twitter.com/drborisotto https://www.xing.com/profile/Boris_Otto http://www.researchgate.net/profile/Boris_Otto http://de.slideshare.net/borisotto Your Contact Person!
  • 25. © Fraunhofer · Seite 25 Prof. Dr.-Ing. Boris Otto · Munich · February 7th, 2017 TURNING DATA INTO VALUE LEVERAGING THE OPPORTUNITIES OF INDUSTRIAL DIGITIZATION