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Industrial Data Management and Digitization

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This presentation introduces leading edge industrial internet cases and identifies implications for data management.

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Industrial Data Management and Digitization

  1. 1. © Fraunhofer ·· Seite 1 Prof. Dr. Boris Otto Dortmund, March 4, 2015 INDUSTRIAL DATA MANAGEMENT AND DIGITIZATION
  2. 2. © Fraunhofer ·· Seite 2 CONTENT  »Industrie 4.0«  Industrial Data Space  Fraunhofer Data Innovation Lab
  3. 3. © Fraunhofer ·· Seite 3 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«
  4. 4. © Fraunhofer ·· Seite 4 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
  5. 5. © Fraunhofer ·· Seite 5 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).
  6. 6. © Fraunhofer ·· Seite 6 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).
  7. 7. © Fraunhofer ·· Seite 7 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
  8. 8. © Fraunhofer ·· Seite 8 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
  9. 9. © Fraunhofer ·· Seite 9 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
  10. 10. © Fraunhofer ·· Seite 10 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).
  11. 11. © Fraunhofer ·· Seite 11 Fundamental »Industrie 4.0« Principles Industrie 4.0 Connectivity Autonomy Human- Machine- Interaction Virtuality Modularity Real-Time Capability
  12. 12. © Fraunhofer ·· Seite 12 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 mechanic work 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
  13. 13. © Fraunhofer ·· Seite 13 »Industrie 4.0« in the Light of Changing Customer and Market Requirements Source: Koren (2010), cited in Bauernhansl (2014).
  14. 14. © Fraunhofer ·· Seite 14 CONTENT  »Industrie 4.0«  Industrial Data Space  Fraunhofer Data Innovation Lab
  15. 15. © Fraunhofer ·· Seite 15 EMPLOYEES plan, control, orchestrate Connected data are the enabler of networked supply chains Image Sources: Fraunhofer IML, Jettainer, Daimler BINS give picking instructions CONTAINERS are aware of their payload and their way on their own TRUCKS drive autonomously VEHICLES organize themselves as a swarm SHELFS place replenishment orders Connected Data
  16. 16. © Fraunhofer ·· Seite 16 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
  17. 17. © Fraunhofer ·· Seite 17 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
  18. 18. © Fraunhofer ·· Seite 18 Source: 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 Media coverage on the Industrial Data Space has been significant recently
  19. 19. © Fraunhofer ·· Seite 19 CONTENT  »Industrie 4.0«  Industrial Data Space  Fraunhofer Data Innovation Lab
  20. 20. © Fraunhofer ·· Seite 20 Digital Business Engineering as a Methodology for Sustainable Digital Business Transformation Digitization Digital Business Model Strategic Perspective Process Perspective Systems Perspective 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.
  21. 21. © Fraunhofer ·· Seite 21 Digital Business Engineering Component Overview DBE Phase Description Goal Involved Roles Techniques 1 Customer Process Understand end-to-end customer process from outside- in  Digital business development  Sales and marketing a. Customer journeys b. Multi-channel analysis c. Consumer process modeling 2 Ecosystem Understand actors within customer process and customer interaction points  Digital business development  Sales and marketing  Product management a. SWOT analysis b. 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 Digital Capabilities Identify capabilities needed to provide digital products and services  Digital business development  Business architect  IT architect a. Capability modeling 5 Data mapping 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
  22. 22. © Fraunhofer ·· Seite 22 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
  23. 23. © Fraunhofer ·· Seite 23 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 Strategy and Architecture Head of Logistics Automation Focus Topics • 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 networked economy • 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
  24. 24. © Fraunhofer ·· Seite 24 DB Schenker Enterprise Lab for Logistics and Digitization
  25. 25. © Fraunhofer ·· Seite 25 Ericsson Enterprise Lab Digitization Success in the Networked Society Strategic Perspective Process Perspective System Perspective 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.
  26. 26. © Fraunhofer ·· Seite 26 Prof. Dr. Boris Otto Dortmund, March 4, 2015 INDUSTRIAL DATA MANAGEMENT AND DIGITIZATION

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