This document discusses how digital twins can enable new value propositions in the context of servitization. Ten case studies from an Innosuisse project are analyzed. The cases demonstrate how digital twins supported value creation by improving decision making, providing training simulations, and enabling some automated functions. Digital twins were shown to bridge product lifecycles and support value co-creation between technical and commercial aspects. The analysis finds that digital twins have the potential to advance servitization by enabling new types of advanced services.

1. Value propositions enabled by digital twins in
the context of servitization
The Spring Servitization Conference 2020
Advanced Services for Sustainability and Growth
Aston Business School, 14-16 September 2020
Shaun West, Jürg Meierhofer, Oliver Stoll and Lukas Schweiger

2. West, Meierhofer, Stoll & Lukas Schweiger | Spring Servitization Conference 2020
Introduction
The problem and the purpose of this paper
To identify the digital twins’ contribution to the value proposition
To assess ten cases on a standardised basis
Purpose of this paper
How can the digital twin enable value propositions within the
context of servitization?
Problem
The motivation of this
paper is to investigate how
digital twins can enable the
design of new value
propositions in digitally
enabled servitization

3. West, Meierhofer, Stoll & Lukas Schweiger | Spring Servitization Conference 2020
Background
The definition of digital twins
Literature
The digital twin is a real mapping of all components
in the product life cycle using physical data, virtual
data and interaction data between them.
Tao, Sui, Liu, Qi, Zhang, Song, Guo, Lu & Nee, (2018)
Our definition
The Digital Twin is digital model of a real object or
components of it with the purpose to provide situation
specific advice to different actors in the business
ecosystem.
A service perspective on the Digital Twin

4. West, Meierhofer, Stoll & Lukas Schweiger | Spring Servitization Conference 2020
Background
Digital twins along the lifecycle and bridging technical and commercial layers
Lifecycle analysis
Beginning of Life
Design
Product
design
Process
design
Plant
design
Manufacture
Produc-
tion
Internal
logistics
Middle of Life
Distrib-
ution
External
logistic
Use/
operate
Optimize
Support
Train Maintain Repair
End of Life
Upgrade
MOL2
Retire
Reverse
logistics Recycle Resell
Strong focus on value co-creation over the lifecycle and between the technical and commercial layers
https://upload.wikimedia.org/wikipedia/commons/9/9f/PE
RA_Equipment_organization.jpg

5. West, Meierhofer, Stoll & Lukas Schweiger | Spring Servitization Conference 2020
Methodology
Ten cases from a single Innosuisse project were analysed
The cases are from the “Smart Twins” project supported by Innosuisse with 10 industrial partners
Case
selection
Case purpose
identification
Case Supported business
functions
Service classification Life-
cycle
Environment Capabilities /
Level of delegation
Breakdown support
twin for ships
Inbound logistics,
operations, service
Product life cycle
services, process
support services
MOL Smart products Monitoring
Optimization
Footfall around public
transport
interchanges
Operations
Infrastructure
asset efficiency
services, process
support services
BOL
MOL
System of system Monitoring,
Optimization,
Operations scheduler
digital twin for a
joinery factory
Logistics, operations,
marketing and sales human
resources management.
process support
services
MOL System of system Monitoring
Control
Optimization
Operations smart
factory planning and
materials flow digital
twin
Inbound logistics,
operations, outbound
logistics, marketing and
sales, procurement.
services, process
delegation services
MOL smart connected product,
product system and system
of system
Optimization
Operations support in
facility management
Operations
Infrastructure
process delegation
services
BOL
MOL
System of system Monitoring, Optimization
Wood cutting patent
digital twin
Logistics, operations,
outbound logistics, market
Process delegation
services
MOL Smart product Optimization
Case
descriptions
5 dimensions
Case Value creation measure Digital twin supported value proposition Servitization context
Breakdown support twin
for ships
Reduced cost of disruption due to
unplanned changes to schedules.
The digital twin provided options to help with the
decision-making processes
OEM led, move to provide
advanced services.
Operations smart factory
planning and materials
flow digital twin
Improved resource planning
leading to reduced lead times and
forecastable raw materials.
The digital twin demonstrated how it could provide
options for the planning of the factory based on
multiple constrains.
University based, could be
transferred to both OEMs and
asset owners.
Operations support in
facility management
Reduce cost for services and
improved customer experience.
The digital twin supported “what if” planning
situations based on real operational (reliability,
product operational life) data.
Facility management led, could be
transfer to system integrators.
Tunnel drainage system
advisor
Tunnel availability, travel
disruption.
The digital twin provided predictions of flooding
and with integration into local weather data
provided forecasts of when safety would be
impacted based on actual operational data.
OEM and asset owner lead, could
be re-applied by either party.
Tunnel MRO digital twin Tunnel availability, cost of
maintenance, MRO planning time.
Supported planning a longer-term budgeting by
providing answers to support “what if” questions.
OEM and asset owner lead, could
be re-applied by either party.
Wood cutting patent
digital twin
Reduced use of materials,
reduced planning time.
The digital twin supported value creation by
improving the yield per SQM of wood. The
integration with the stored cutoffs allowed further
improvements to costs.
Asset owner led, could provide as
a service to others.
Cross case
analysis
3 dimensions
Discussion

6. West, Meierhofer, Stoll & Lukas Schweiger | Spring Servitization Conference 2020
Server room temperature management and control digital twin
– the purpose of this twin is to improve overall equipment up-
time and provide further business stability through equipment
risk mitigation. Value accrues from operational support,
temperature stability, and routine reporting.
Results and analysis
Case purpose identification
The ten cases were selected to be all different yet complementary from each other
Breakdown support twin for ships – the twin mimics the current
status, allowing drill-down to individual ships and subsystems within
each ship. The value is from the reduction in disruption when
changes occur, and understanding the down-time associated with
unplanned events.
Footfall around public transport interchanges – the purpose is to
better manage the flows of individuals around a station based on the
current and future station layout and train schedules. The value
comes from improved traveller safety through the re-platforming of
trains and/or investments in new walkways to improve flows.

7. West, Meierhofer, Stoll & Lukas Schweiger | Spring Servitization Conference 2020
Results and analysis
Case descriptions
The case dimensions provided additional insights (and discussions)
Case Supported business
functions
Service classification Life-
cycle
Environment Capabilities /
Level of delegation
Breakdown support
twin for ships
Inbound logistics,
operations, service
Product life cycle
services, process
support services
MOL Smart products Monitoring
Optimization
Footfall around public
transport
interchanges
Operations
Infrastructure
asset efficiency
services, process
support services
BOL
MOL
System of system Monitoring,
Optimization,
Operations scheduler
digital twin for a
joinery factory
Logistics, operations,
marketing and sales human
resources management.
process support
services
MOL System of system Monitoring
Control
Optimization
Operations smart
factory planning and
materials flow digital
twin
Inbound logistics,
operations, outbound
logistics, marketing and
sales, procurement.
services, process
delegation services
MOL smart connected product,
product system and system
of system
Optimization
Operations support in
facility management
Operations
Infrastructure
process delegation
services
BOL
MOL
System of system Monitoring, Optimization
Wood cutting patent
digital twin
Logistics, operations,
outbound logistics, market
Process delegation
services
MOL Smart product Optimization

8. West, Meierhofer, Stoll & Lukas Schweiger | Spring Servitization Conference 2020
Results and analysis
Cross-case analysis describing the digital twin support of value propositions
Example of six of the value propositions supported by the digital twins
Case Value creation measure Digital twin supported value proposition Servitization context
Breakdown support twin
for ships
Reduced cost of disruption due to
unplanned changes to schedules.
The digital twin provided options to help with the
decision-making processes
OEM led, move to provide
advanced services.
Operations smart factory
planning and materials
flow digital twin
Improved resource planning
leading to reduced lead times and
forecastable raw materials.
The digital twin demonstrated how it could provide
options for the planning of the factory based on
multiple constrains.
University based, could be
transferred to both OEMs and
asset owners.
Operations support in
facility management
Reduce cost for services and
improved customer experience.
The digital twin supported “what if” planning
situations based on real operational (reliability,
product operational life) data.
Facility management led, could be
transfer to system integrators.
Tunnel drainage system
advisor
Tunnel availability, travel
disruption.
The digital twin provided predictions of flooding
and with integration into local weather data
provided forecasts of when safety would be
impacted based on actual operational data.
OEM and asset owner lead, could
be re-applied by either party.
Tunnel MRO digital twin Tunnel availability, cost of
maintenance, MRO planning time.
Supported planning a longer-term budgeting by
providing answers to support “what if” questions.
OEM and asset owner lead, could
be re-applied by either party.
Wood cutting patent
digital twin
Reduced use of materials,
reduced planning time.
The digital twin supported value creation by
improving the yield per SQM of wood. The
integration with the stored cutoffs allowed further
improvements to costs.
Asset owner led, could provide as
a service to others.

9. West, Meierhofer, Stoll & Lukas Schweiger | Spring Servitization Conference 2020
Discussion
The digital twins supported value (co-)creation but in different way
Servitization barriers are linked to different service transition stages (Baines et al., 2017)
…as a decision support advisor providing
the options, their implications and possible
consequences to “what-if” questions
…as a cyber-co-worker with a defined
role and delegations of authority
…as a trainer to help stakeholders learn to
react to different situations

10. West, Meierhofer, Stoll & Lukas Schweiger | Spring Servitization Conference 2020
Discussion
Theoretical and practical contributions
Theoretical
- Existing frameworks focus on the technical layers
- The ten cases described in this paper are not fully
compliant with an OEM-lead servitization context
- Shaping the topic of the digital twin through the
lens of service dominant logic supported value
identification
- Understanding of the larger problem space was
necessary to build the individual value propositions
Practical
- The digital twins supported decision making, training
and automation
- The schemes can be seen as blueprints
- The approach needs to be industrialised
Digital twin can be considered an enabler for service value propositions

11. West, Meierhofer, Stoll & Lukas Schweiger | Spring Servitization Conference 2020
Conclusion
Digital twins can support the development of advanced services
Digital twin enabled value propositions can bridge the lifecycle
Value propositions supported management decision making
Value propositions allowed for delegated decision making
The results of the ten cases have
confirmed that digital twins can
enable new value propositions in
the context of servitization
Two of the value propositions supported training

12. West, Meierhofer, Stoll & Lukas Schweiger | Spring Servitization Conference 2020
Recommendations
More research is needed to support firms develop new digitally enabled solutions
Companies should consider adopting a wider ecosystem view
Firms have to learn to embrace multiple points of view
Future research should focus on the value co-creation processes
More work is needed on digitally
enabled PSS and servitization. The
integration of digital solutions into
traditional PSS offers in terms of
servitization is not trivial and has
many unexpected results.
An operational framework is required to support firms

13. West, Meierhofer, Stoll & Lukas Schweiger | Spring Servitization Conference 2020
Thanks for your time!
Questions over virtual coffee…
Industrie 2025, F&E Konferenz zur Industrie 4.0 |Jürg Meierhofer & Dr Shaun West
For more information please visit slideshare.net/ShaunWest
Who we are…
A multidisciplinary group from four universities in two countries
2
Projekt4 2.8.2007 19:17 Uhr Seite 1
Senior Lecturer
Industry 4.0
Coordinator Platform
Executive MBA
Dr. sc. techn. ETH (PhD)
Jürg Meierhofer
8401 Winterthur, Switzerland
Rosenstrasse 3, P.O. Box
www.zhaw.ch/idp
www.zhaw.ch/=meeo
juerg.meierhofer@zhaw.ch
Phone direct: +41 58 934 40 52
School of
Engineering
IDP Institute of Data Analysis
and Process Design
Zurich University
of Applied Sciences
Jürg Meierhofer
Expert Smart Service Engineering
Industrie 2025, F&E Konferenz zur Industrie 4.0 |Jürg Meierhofer & Dr Shaun West
For more information please visit slideshare.net/ShaunWest
Who we are…
A multidisciplinary group from four universities in two countries
2
Projekt4 2.8.2007 19:17 Uhr Seite 1
Senior Lecturer
Industry 4.0
Coordinator Platform
Executive MBA
Dr. sc. techn. ETH (PhD)
Jürg Meierhofer
8401 Winterthur, Switzerland
Rosenstrasse 3, P.O. Box
www.zhaw.ch/idp
www.zhaw.ch/=meeo
juerg.meierhofer@zhaw.ch
Phone direct: +41 58 934 40 52
School of
Engineering
IDP Institute of Data Analysis
and Process Design
Zurich University
of Applied Sciences
Jürg Meierhofer
Expert Smart Service Engineering