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Turning Industrial Data into Value

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This presentation was held at the 2nd Internet of Manufacturing Conference on February 7, 2017, in Munich, Germany. It addresses the need of a new kind of data management to cope with the requirements digital scenarios pose on the industrial enterprise. Motivated by examples, the talk outlines design principles for smart data management and concludes with two leading examples, namely the Industrial Data Space initiative and the Corporate Data League.

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Turning Industrial Data into Value

  1. 1. © Fraunhofer · Seite 1 Prof. Dr.-Ing. Boris Otto · Munich · February 7th, 2017 TURNING DATA INTO VALUE LEVERAGING THE OPPORTUNITIES OF INDUSTRIAL DIGITIZATION
  2. 2. © Fraunhofer · Seite 2 AGENDA  Digitization of the Industrial Enterprise  Smart Data Management  Leading Examples
  3. 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. 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. 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. 6. © Fraunhofer · Seite 6 Required is a central information management instance – what Audi refers to as the »Tower« Image sources: Audi (2016).
  7. 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. 8. © Fraunhofer · Seite 8 AGENDA  Digitization of the Industrial Enterprise  Smart Data Management  Leading Examples
  9. 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. 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. 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. 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. 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. 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. 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. 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. 17. © Fraunhofer · Seite 17 AGENDA  Digitization of the Industrial Enterprise  Smart Data Management  Leading Examples
  18. 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. 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. 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. 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. 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. 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. 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. 25. © 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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