Shared Digital Twins: Collaboration in Ecosystems
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The document discusses the concept of shared digital twins as a means to enhance collaboration in various ecosystems, particularly in industries like rail and automotive. It outlines the benefits of digital twins for data sharing, predictive maintenance, and operational efficiency, while also addressing challenges such as standardization and data governance. The document emphasizes the importance of creating trusted infrastructures and harmonized data models to facilitate effective usage of digital twins across different sectors.
Shared Digital Twins: Collaboration in Ecosystems
- 1. © Fraunhofer ISST SHARED DIGITAL TWIN: COLLABORATION IN ECOSYSTEMS Prof. Dr.-Ing. Boris Otto Berlin 18 September 2019 public· 1
- 2. © Fraunhofer ISST Agenda Business Rationale and Use Cases Definition and Conceptual Framework State of the Art and Outlook public· 2
- 3. © Fraunhofer ISST Ecosystems – as in the railway industry – are an organizational form to facilitate innovation Source: Knorr-Bremse (2018). public Railway Markets Original Equipment Manufacturers 1st and 2nd Tier SuppliersInfrastructure Providers Energy Suppliers Railway Operators Domain Knowledge Vehicle Knowledge Operational Knowledge Leasing Companies · 3
- 4. © Fraunhofer ISST Various proprietary platforms – no standards Integration and accumulation of knowledge along the value chain Many use cases – no business models Lacking proliferation of platforms and services – no critical mass Use Cases To realize the business benefits in ecosystems, a set of challenges has to be overcome Source: Knorr-Bremse (2018). public Challenges · 4
- 5. © Fraunhofer ISST Using and sharing Digital Twins is a prerequisite for business benefits in many different scenarios Image Source: JDA Software Group, Inc. (2019); DirectIndustry (2019); ABB (2019). public Digital Twin of Supply Chains Demand and Capacity Management Supply Bottleneck Management Production stability Buffer stock Delivery quality Digital Twin of Industrial Assets Predictive Maintenance Condition Monitoring Fast Deployment Productivity TCO OEE Digital Twin of Products Data-Driven Business Models Service-Based Business Models Customer Loyalty Customer Retention Service Profitability · 5
- 6. © Fraunhofer ISST Supply networks in the automotive are complex and prone to disruptions Source: VW, thyssenkrupp. ACT ComponentTier-2 Jászfényszaru Salzgitter Ilsenburg Valvetrain Győr Ingolstadt Wolfsburg Emden Pamplona Setúbal Puebla Mladá Boleslav Kvasiny Uitenhage Martorell Zwickau Osnabrück Nizhny Novgorod Chemnitz Győr Salzgitter Engine Plant Assembly Plant public … · 6
- 7. © Fraunhofer ISST Exchanging and sharing data across the supply network to mitigate risks and to overcome co-ordination challenges public Risks and Challenges Data Demands SoP Delay · 7
- 8. © Fraunhofer ISST Many requirements exists with regard to a Digital Supply Chain Twin Data must be available on demand Data events (access, use etc.) must be logged Access and usage rights must be customizable Data exchange must follow a harmonized data model Data use in backend systems must be prohibited/tracked Data provenance Data must only be shared together with usage constraints Only recent updates of data must be stored – if at all Data sharing follows »quid pro quo« principle Views must be defined with regard to entire digital twin … public· 8
- 9. © Fraunhofer ISST Source: Platform Industrie 4.0, Working Group 1 & 3 (2019). Manufacturer X Condition monitoring of components is a mature Industrie 4.0 use case – generating business benefits along the value chain Integrator V Operator A public· 9
- 10. © Fraunhofer ISST Component Manufacturer X Business Scenario Product component P1-X was built in M1-W, M2-W, operated by A Business Case Condition monitoring for improved productivity Challenges Organizational, technical, legal prerequisites for data access and use Data monetization with regard to data provisioning and use End-to-end condition monitoring requires a shared digital twin that meets data security and usage/access rights requirements BP1 A M1 V M2 W P1 X P2 Y P1 X P2 Y P3 Z P3 Z Operator A public· 10
- 11. © Fraunhofer ISST Agenda Business Rationale and Use Cases Definition and Conceptual Framework State of the Art and Outlook public· 11
- 12. © Fraunhofer ISST Digital Twin A digital twin comprises data about all lifecycle phases of a real-world object Design Support Material Sciences Supply Chain Risk Management Outage Predictions Design and Engineering Material Management Manufacturing Distribution and Logistics Use and Services Manufacturing Asset Management Adaptive Tool Engineering Maintenance Predictive Process Management Efficient Material Management public· 12
- 13. © Fraunhofer ISST A digital twin comprises both type-related and instance data for real-world objects Domain-specific Digital Twin Digital Master Master and Reference Data Digital Shadow Process, Event and Context Data Digital Master Model Fundamental Data Model public Design and Engineering Material Management Manufacturing Distribution and Logistics Use and Services · 13
- 14. © Fraunhofer ISST Data Owner Process Owner Data User Enterprise-wide Business Units Data modelling is the foundation for digital twins Source: Volkswagen (2017). Asset Data Process Data Organizational Data public· 14
- 15. © Fraunhofer ISST A digital twin is a representation of a real-world object Digital Twin Digital representation of a real-world object containing all required information over the entire lifecycle Dimensions Type vs instance Granularity Type of data »Ownership« and usage rights … Definition: Tao et al. (2019); Boschert et al. (2016). Viewgraph source: Column Five (2019). public· 15
- 16. © Fraunhofer ISST A conceptual framework for shared digital twin data integrates three different perspectives public Shared Digital Twin Business Technology Legal Aspects Ecosystem roles Shared information model Data use cases Data governance and data sovereignty Data modelling Data access and usage Data interoperability Data storage Data integration Ownership Compliance to regulations Ethics · 16
- 17. © Fraunhofer ISST Agenda Business Rationale and Use Cases Definition and Conceptual Framework State of the Art and Outlook public· 17
- 18. © Fraunhofer ISST A variety of use cases in different ecosystems adopt the idea of a shared digital twin Source: Skywise – Tardieu, ATOS (2019); DataConnect – John Deere (2019); NEVADA – VDA (2018). public Airline Industry (Skywise) Farming Industry (DataConnect) Mobility Industry (Nevada) Ecosystem Approach Originated from manufacturing scenarios Based on Palantir Data Platform Focus on data exchange Farmer access to data from multiple OEMs Co-opetition mode Interoperability of connected car data Mobility ecosystem Trusted data sharing and exchange SKYWISE Engine Maintainers AIRBUS Equipment Vendors · 18
- 19. © Fraunhofer ISST The »Administration Shell« concept functions as a blueprint for digital twins in manufacturing Source: BMWi (2016). Reference Architecture Model Industry 4.0 Administration Shell Concept The Administration 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. public· 19
- 20. © Fraunhofer ISST The Asset Administration Shell allows for sharing digital twin data Image source: Hoffmeister & Jochem (2018) according to Epple (2016). Source: Platform Industrie 4.0 (2018). IntegratorSupplier Internal public Operator Repository Verteilte Repositories 2 Publish A1 T B1 T Receive Publish ReceiveComposite Type machine Internal A4 T B4 T C1 T C4 T D1 E1 Composite Instance machineD4 E4 F1 (D4,E4) G3 X F4 (D4*,E4*) product type consolidate consolidate consolidate delivery delivery product product 2nd operator master data G4 Composite production line I4.0- platform 18 I4.0- platform Internal delivery product A2 A3 B2 B3 C2 C3 D2 D3 E2 E2 F2 (D4,E4) F3 (D4*,E4*) public· 20
- 21. © Fraunhofer ISST The Asset Administration Shell enables shared digital twins Source: Belyaev & Diedrich (2019). public Identifies and describes assets over networks in an unambiguous way Allows controlled access to asset data Makes data along the entire lifecycle available Can be used for smart and legacy assets · 21
- 22. © Fraunhofer ISST The Asset Administrative Shell is implemented as a prototype on the SAP Cloud Platform Source: SAP, cited in All-Electronics.de (2019). public· 22
- 23. © Fraunhofer ISST The International Data Spaces (IDS) initiative enables ecosystems around the sovereign exchange of data Source: Otto et al. (2017); extended representation of the reference architecture model content. public Runtime EnvironmentRuntime Environment authorize publish app transfer data data flow metadata flow software flow identification useIDSsoftware useIDSsoftware useIDSsoftware identify Data Owner App Provider Vocabulary Provider Clearing House App Store Provider Identity Provider Data Consumer Broker Service Provider Service Provider Software Provider Data Provider Certification mandatory Membership in the IDSA mandatory Certification Authority · 23
- 24. © Fraunhofer ISST The International Data Spaces (IDS) initiative proposes an architecture for the sovereign exchange of data Legend: IDS Connector; Usage Constraints; Non-IDS Communication. public Industrial Data Cloud IoT Cloud Enterprise Cloud Data Marketplace Company 1 Company 2 Company n + 2Company n + 1Company n Open Data Source IDS IDS IDS IDS IDS IDS IDS IDS IDS IDS IDS IDS IDS IDS IDS IDS IDS · 24
- 25. © Fraunhofer ISST The IDS Information Model ensures a shared understanding of fundamental concepts of data ecosystems Source: https://mvn.isst.fraunhofer.de/nexus/#browse/browse:ids-local:de%2Ffraunhofer%2Fiais%2Feis%2Fids%2Finfomodel public· 25
- 26. © Fraunhofer ISST The IDS Information Model describes shared data resources via the so-called C-Hexagon Source: IDS Reference Architecture Model 3.0 (2019). public· 26
- 27. © Fraunhofer ISST Legend: IoT – Internet of Things. Different deployment options for the integration of IDS Connector and Asset Administration Shell are envisaged public· 27
- 28. © Fraunhofer ISST Data Provenance · Fraunhofer IOSBLabel-Based Usage Control (LUCON) · Fraunhofer AISEC D° (Degree) · Fraunhofer ISSTMYDATA Control Technologies · Fraunhofer IESE Information Provisioning Instantiation Policy Provisioning Policy Deployment & Revocation Consultation Decision Storage Execution DECISIONMANAGEMENTENFORCEMENT A B C D Different usage control technologies address access and usage rights for digital twin data public· 28
- 29. © Fraunhofer ISST Source: vocol.iais.fraunhofer.de (2019). VoCol is an collaboration environment to develop shared vocabularies public· 29
- 30. © Fraunhofer ISST The IDS architecture allows for trusted and sovereign data exchange based on a shared digital twin – as shown in the supply bottleneck case above * Release of data through data owner through rule: »ALLOW_RAW_EXPORT«, can be opted out. Data Sovereignty Data with Usage Constraints No Data Sovereignty System 1* Tier 1 Supplier IDS Connector Logic Rights Log Filter REST-API OEM IDS Connector Logic Rights Log Filter REST-API Data Sovereignty Data with Usage Constraints System 1 Tier 1 Supplier IDS Connector Logic Rights Log Filter REST-API OEM IDS Connector Logic Rights Log Filter REST-API … Data from Tier 1 Supplier to OEM Data from OEM to Tier 1 Supplier public· 30
- 31. © Fraunhofer ISST Policies can be set and enforced through IDS implementations public· 31
- 32. © Fraunhofer ISST Legend: Circle-shaped Nodes – Ecosystem Member; C – Connector; B – Broker; I – Identity Provider; H – Clearing House; Edges between Nodes – Data Exchange. 1:1 »Few to Few« n:m C C Bilateral Data Exchange C C C C C B I Closed Community Data Sharing C C C H C C I B Open Dynamic Data Ecosystem II IIII Business ecosystems evolve in stages public· 32
- 33. © Fraunhofer ISST VALUES & FRAMEWORK FOR INNOVATION ENTERPRISE/DIGITAL ECOSYSTEM (using EU standards) SMART ECONONY & SOCIETY SERVICE PLATFORMS DATA SHARING INFRASTRUCTURE CLOUD/EDGE INFRASTRUCTURE NETWORK SMART SERVICES SMART DATA SMART PRODUCTS SMART NETWORK European values Secure and trusted Easy-to-use Federated, neutral Vendor-agnostic Design Principles Urgent demand for a neutral enabler for trusted data sharing and data usage across multiple service platforms across industries! Certification Body Transaction services Data connector services Platform access, antitrust Micro-payment services Quality scoring Encryption services Certification AuthorityClearing House Broker, auditability Inter-operability Serivces Data Governace/ Privacy Essential Trust Services Basic Data Services Dynamic Trust Management Dynamic Attribute Provisioning … Appstore Data Usage Control … … … NB: Architecture stack adapted from Smart Service Welt Working Group (2015). Required is a trusted digital infrastructure for Europe and beyond public· 33
- 34. © Fraunhofer ISST The concept of the digital twin has evolved over time and will further develop public Digital Shadow Digital Twin Autonomous Digital Agent I II III Fragmented data traces of real world objects No adherence to a consistent or even shared information model Low data interoperability Distributed storage of data – efficient information retrieval (querying) hardly possible Consistent representation of real- world object across different lifecycle stages Shared information model Integration of type and instance data Allows simulation (ex ante) and analysis (ex post) Enabled by Artificial Intelligence Acts autonomously Makes recommendation for action Develops automatically Value Proposition · 34
- 35. © Fraunhofer ISST Current research and development activities mainly focus on integrating existing concepts Conceptual integration of Asset Administrative Shell and IDS Information Model Prototype implementations of integrated scenarios Development of a trusted, secure infrastructure for sharing digital twin data Transfer of B2B concepts to B2C scenarios public· 35
- 36. © Fraunhofer ISST SHARED DIGITAL TWIN: COLLABORATION IN ECOSYSTEMS Prof. Dr.-Ing. Boris Otto Berlin 18 September 2019 public· 36