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.

1. © Fraunhofer ISST
SHARED DIGITAL TWIN:
COLLABORATION IN ECOSYSTEMS
Prof. Dr.-Ing. Boris Otto  Berlin  18 September 2019
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Agenda
 Business Rationale and Use Cases
 Definition and Conceptual Framework
 State of the Art and Outlook
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Ecosystems – as in the railway industry – are an organizational form to
facilitate innovation
Source: Knorr-Bremse (2018).
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Railway Markets
Original Equipment
Manufacturers
1st and 2nd Tier SuppliersInfrastructure Providers
Energy Suppliers Railway Operators
Domain
Knowledge
Vehicle
Knowledge
Operational
Knowledge
Leasing Companies
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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).
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Challenges
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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).
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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 
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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
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…
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Exchanging and sharing data across the supply network to mitigate risks and
to overcome co-ordination challenges
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Risks and Challenges Data Demands
SoP Delay
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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
 …
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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
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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
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Agenda
 Business Rationale and Use Cases
 Definition and Conceptual Framework
 State of the Art and Outlook
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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
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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
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Design and
Engineering
Material
Management
Manufacturing
Distribution and
Logistics
Use and Services
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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
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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).
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A conceptual framework for shared digital twin data integrates three
different perspectives
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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
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Agenda
 Business Rationale and Use Cases
 Definition and Conceptual Framework
 State of the Art and Outlook
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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).
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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
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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.
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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
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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
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I4.0-
platform
Internal
delivery
product
A2 A3
B2 B3 C2 C3
D2 D3
E2 E2 F2
(D4,E4)
F3
(D4*,E4*)
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The Asset Administration Shell enables shared digital twins
Source: Belyaev & Diedrich (2019).
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 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
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The Asset Administrative Shell is implemented as a prototype on the SAP
Cloud Platform
Source: SAP, cited in All-Electronics.de (2019).
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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.
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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
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The International Data Spaces (IDS) initiative proposes an architecture for the
sovereign exchange of data
Legend: IDS Connector; Usage Constraints; Non-IDS Communication.
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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
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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
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The IDS Information Model describes shared data resources
via the so-called C-Hexagon
Source: IDS Reference Architecture Model 3.0 (2019).
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Legend: IoT – Internet of Things.
Different deployment options for the integration of IDS Connector and Asset
Administration Shell are envisaged
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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
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Source: vocol.iais.fraunhofer.de (2019).
VoCol is an collaboration environment to develop shared vocabularies
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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
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Policies can be set and enforced through IDS implementations
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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
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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
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The concept of the digital twin has evolved over time and will further
develop
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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
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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
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SHARED DIGITAL TWIN:
COLLABORATION IN ECOSYSTEMS
Prof. Dr.-Ing. Boris Otto  Berlin  18 September 2019
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