Digital Twins at the Heart of Smart Service Systems - An Action Design Research Study

Abstract

Today’s rapidly advancing technology is shaping many aspects of our personal and working lives. In the context of the continuing digitalization of the manufacturing industry Digital Twins (DT) have emerged as a new kind of networked information system, which converges real-world assets with virtual counterparts. Smart service systems (SSS) provide a new perspective on these information systems enabling a connection between the technical concepts of industry 4.0 with service science. We conduct an action design research study in the machine manufacturing industry and use the SSS as a guiding perspective for our DT design. This paper contributes three design principles for DTs in
manufacturing: (1) cyber-physical (re-)configurability, (2) smartness of the product, and (3) IT platform as a boundary object. The design was evaluated with several manufacturing companies
based on a user interface prototype.

Full text

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Digital Twins at the Heart of Smart Service Systems - An Action Digital Twins at the Heart of Smart Service Systems - An Action
Design Research Study Design Research Study
Hendrik Wache
Chemnitz University of Technology
, hendrik.wache@wirtschaft.tu-chemnitz.de
Barbara Dinter
Chemnitz University of Technology
, barbara.dinter@wirtschaft.tu-chemnitz.de
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Wache, Hendrik and Dinter, Barbara, "Digital Twins at the Heart of Smart Service Systems - An Action
Design Research Study" (2021).
ECIS 2021 Research Papers
. 146.
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Twenty-Ninth European Conference on Information Systems (ECIS 2021), [Marrakesh, Morocco|A Virtual AIS
Conference]. 1
DIGITAL TWINS AT THE HEART OF SMART SERVICE
SYSTEMS - AN ACTION DESIGN RESEARCH STUDY
Research Paper
Hendrik Wache, Chemnitz University of Technology, Germany, hendrik.wache@
wirtschaft.tu-chemnitz.de
Barbara Dinter, Chemnitz University of Technology, Germany, barbara.dinter@
wirtschaft.tu-chemnitz.de
Abstract
Today’s rapidly advancing technology is shaping many aspects of our personal and working lives. In
the context of the continuing digitalization of the manufacturing industry Digital Twins (DT) have
emerged as a new kind of networked information system, which converges real-world assets with
virtual counterparts. Smart service systems (SSS) provide a new perspective on these information
systems enabling a connection between the technical concepts of industry 4.0 with service science. We
conduct an action design research study in the machine manufacturing industry and use the SSS as a
guiding perspective for our DT design. This paper contributes three design principles for DTs in
manufacturing: (1) cyber-physical (re-) configurability, (2) smartness of the product, and (3) IT
platform as a boundary object. The design was evaluated with several manufacturing companies
based on a user interface prototype.
Keywords: Digital Twins, Smart Service Systems, Design Principles, Action Design Research.
1 Introduction
Today's working world is largely shaped by the ongoing digitalization of society. Industry 4.0 (I4.0), a
collective term for the use of advanced information technologies to revolutionize the manufacturing
industry by connecting it with the internet of things and services, constitutes a prominent example for
this megatrend (Kagermann, Wahlster, & Helbig, 2013). The realization of the I4.0 vision is driven by
the concept of cyber-physical systems (CPS), a network of systems implementing a convergence of
the real and virtual worlds (Brandt, Feuerriegel, & Neumann, 2017). A special form of CPS are Digital
Twins (DT) (Qi, Zhao, Liao, & Tao, 2018), which describe a system consisting of physical and virtual
components, as well as data, services, and connections between these elements (Grieves, 2014; Tao,
Zhang, & Nee, 2019). Not only things but also value creation partners in networks must be more
closely linked with the newly emerging infrastructure. Ideally, this networking will result in more
intensive information and knowledge flows that support value creation beyond organizational
boundaries. Although novel CPS collect vast amounts of data and create great potential for the various
actors, current research results show that this potential often remains unused (Martin, Kühl,
Bischhoffshausen, & Satzger, 2020). The problem is attributed to the fact that the focus is too limited
to individual organizations and not on the interaction of many actors in so-called service systems (a
configuration of resources). It can be observed that the development and provision of novel services
enabled by this type of technology are still strongly restricted (Martin et al., 2020). A broader scope,
embedding systems like the DT in service systems, is needed to move from discussing pure technical
issues to improved value creation in networks (Beverungen, Breidbach, Poeppelbuss, & Tuunainen,
2019). In this context, recent research on CPS discusses the understanding of CPS as boundary objects

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used by actors to integrate resources to generate mutual benefits (Beverungen, Müller, Matzner,
Mendling, & vom Brocke, 2019). This new perspective on CPS is called Smart Service Systems (SSS)
(Beverungen, Breidbach, et al., 2019; Beverungen, Müller, et al., 2019; Wessel et al., 2019).
Applying the new perspective, the DT in manufacturing (e.g., as a smart machine) assumes the role of
the smart product in the SSS conceptualization, providing services such as predictive maintenance to
various actors in the service system. Looking at recent research on DTs, it is apparent that this new
perspective of SSS is not yet well established for DTs. Recent DT publications discuss mostly
technical factors, such as sensor data security (Chanson, Bogner, Bilgeri, Fleisch, & Wortmann, 2019),
the design of connections between organizational processes and smart devices, or the standardization
of interfaces for data processing (Olivotti, Dreyer, Lebek, & Breitner, 2019). Furthermore, there are
contributions providing a taxonomy of DTs (van der Valk et al., 2020). However, it should be
emphasized that DTs have the primary goal of offering smart services (Dreyer, Olivotti, Lebek, &
Breitner, 2017; Tao et al., 2019), which is not yet sufficiently considered in the research discussion.
Current research agendas in the field of CPS show that new design purposes have to be considered for
smart objects, which can be transferred to the design of DTs (Baiyere, Topi, Venkatesh, Wyatt, &
Donnellan, 2020). We follow this specific call for research to conduct design studies in this context by
using the SSS perspective to design DTs in manufacturing. First design frameworks look at DTs in
conjunction with service systems and thus pave the way for bringing DT design together with the
conceptual continuation towards SSS (Zheng, Lin, Chen, & Xu, 2018). We follow this research
endeavor to address the problem that the technical possibilities of DTs have so far not been
sufficiently used to provide higher-value services in service systems (Martin et al., 2020). In this
paper, we present a case in which four manufacturing companies are experiencing problems with
efficient, digital service development and delivery for DTs. The focus here is on difficulties with the
exchange of information, knowledge and service. The persisting obstacles to cross-actor information
exchange in SSS demand design-oriented studies around systems such as DT to enable collaborative
service development and delivery across organizations in service systems with multiple actors for joint
value creation (Baiyere et al., 2020; Beverungen, Müller, et al., 2019; Martin et al., 2020).
We aim to add to the existing design knowledge on DTs and SSS by considering DT design influenced
by the SSS perspective, which leads us to the following research question: What are design features
and principles for DTs in manufacturing from an SSS perspective? We want to address this exciting
new branch of research by presenting an action design research (ADR) study (Sein, Henfridsson,
Purao, Rossi, & Lindgren, 2011) in which four manufacturing companies want to digitalize the joint
value creation with their partners using DTs. In our ADR study, we consider DTs to be a system that
can be used by several value creation partners to map the life cycle of networked complex
manufacturing machines. The remainder of the paper is structured as follows: Foundations on DTs and
SSS are presented in the next section. After a description of the research approach, the problem
formulation and presentation of the first build-intervention-evaluation (BIE) cycle follow. In the first
BIE cycle, design requirements (DR) and design features (DF) are formulated which are then
implemented in a user interface prototype in order to carry out an organizational intervention and
evaluation. In the subsequent formalization of learning, the results are abstracted to obtain generally
applicable prescriptive knowledge in the form of design principles (DP). The paper ends with a
discussion and subsequent conclusion.
2 Foundations
2.1 Digital Twins
The technological basis for the implementation of I4.0 are so-called CPS, which consist of physical
and virtual components that are networked with each other and allow data exchange (Brandt et al.,
2017). CPS emerge when technical/physical components are merged with information technology
(Brandt et al., 2017). DTs can be interpreted in two ways, either as an enabler for CPS if focused

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purely on the virtual component, or DTs as a subtype of CPS if the virtual and physical components
are both seen as part of DTs (Dietz & Pernul, 2020). As an enabler of CPS DTs can be defined as
follows: A Digital Twin is an integrated simulation of a physical component that uses the best
available models, sensor updates, and data to mirror the life of its corresponding physical twin
(Glaessgen & Stargel, 2012). As a subtype of CPS, DTs consist of three to five components. In 2002,
when the term DT was coined, three essential elements of DTs were assumed: a physical component, a
virtual component, and a connection between them (Grieves, 2014). As time went by and as the
discussion went on, two more components were added: data and services (Tao et al., 2019). It is
important to note, that DTs as a subtype of CPS have a specialization on data integration: “Compared
with CPS, digital twin pays more attention to the construction of the models, including geometric
shape model, rules, behavior and other constraints models, etc.” (Qi et al., 2018, p. 4). In this sense,
DTs represent a connection of the physical world with the virtual world. They contain physical assets,
sensors, and actuators and their virtual representations so that they can collect and process data and
thus provide their users with different services such as simulations (Glaessgen & Stargel, 2012). From
our point of view, it is essential for the design of DTs to consider both the virtual and physical
components, as this is the best way to realize the aspect of mirroring the life cycle of the physical
instance. Thus, we follow the understanding of DTs as a subtype of CPS: a DT is a closed-loop
system, which usually guarantees real-time data transfer, enabling services such as condition
monitoring or predictive maintenance in addition to simulation (Alam & El Saddik, 2017). DTs
connect and integrate data in different formats from various sources and different phases in the
product life cycle, therefore DTs can act as informational entities along the whole life cycle of a
product (Schleich, Wärmefjord, Söderberg, & Wartzack, 2018) and can furthermore act as a central
data hub in this context (Stark, 2009) with the central knowledge base of product, process, and master
data (Landolfi et al., 2018; Umeda et al., 2019). This in sum makes DTs a key concept of the I4.0
vision.
2.2 Smart Service Systems
Apart from the rather technology-focused CPS and DT literature, SSS has emerged as a new
theoretical perspective on CPS in recent years (Beverungen, Breidbach, et al., 2019; Beverungen,
Müller, et al., 2019). The SSS is a complex concept; an early and comprehensive definition is provided
by the National Science Foundation: Intelligent service systems are able to learn and dynamically
make decisions based on available data to enable automated responses to a future situation (NSF -
National Science Foundation, 2014). The system achieves these features through self-organized
behavior, such as self-monitoring or self-correction, which is made possible by appropriate
technologies for example sensing, actuation, and communication (NSF - National Science Foundation,
2014). As can be seen from the definition, an SSS is not a pure IT artifact, but a complex and dynamic
sociotechnical system (Beverungen, Breidbach, et al., 2019), which not only considers the technical
artifact but also the social aspect: the interaction of service providers and service consumers using a
smart product as a boundary object to exchange resources and to synchronize their worldview
(Beverungen, Müller, et al., 2019; Maglio, Vargo, Caswell, & Spohrer, 2009). Research in SSS is
conducted from different scientific perspectives, such as information systems, service science, and
engineering. The main focus lies on the types of smart services that can be offered with smart
technologies, how to innovate with smart services, and on smart service systems as a lens for different
application areas (Wessel et al., 2019). To understand what defines SSS, it is useful to take a look at
the factors that influence an SSS: smartness, service systems, and the enabling underlying technology.
The attribute smart/smartness originally describes the intelligence and ability of a person and is now
often used to describe devices, services or systems, but without going into detail about what this
means exactly (Alter, 2020). A recent article defines five main principles that capture smartness in a
way that makes it applicable to devices, automated systems, and socio-technical systems. Alter (2020,
p. 384) states, that smart things have “at least some automated information processing and at least
some degree of self-control, learning, adaptation, and/or decision-making related to performing
activities or functions that have consequence in the world”.

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The term service from service science means the application of competencies such as knowledge and
skills for one's own benefit or that of another (Lusch & Nambisan, 2015). The value of a service is co-
created in interactions between service providers and service customers and is based on resource
integration. A service system refers to a configuration of resources such as people and technologies
that interact with other service systems to create reciprocal value (Maglio et al., 2009). Thus, many
systems can be considered as service systems, for example, manufacturing networks, manufacturing
companies, departments, cyber-physical workplaces, but also engineers. The mentioned capacities for
information processing are based on the paradigm Internet of Things (IoT), which describes the
presence of things that allow cooperation by interacting with each other to achieve common goals
(Atzori, Iera, & Morabito, 2010). In order to make IoT possible, physical things are equipped with
embedded systems and networking capabilities, so that networking between them can occur, which in
turn allows the creation of more complex systems with logics and behavior (Baiyere et al., 2020).
Consequently, we see the following as the three main concepts of the SSS perspective. Service is
exchanged in an actor-network and thus value is created by integrating resources between multiple
actors (service systems). The central component is a smart object, which acts as a boundary object for
service exchange in this network. IoT serves as an infrastructure, which enables the exchange of
informational resources independent of time and space. Using mechanical engineering as an example,
an SSS could be a local production network in which services are exchanged between actors such as
mechanical engineering companies, software development companies, and machine users. Thus, an
ecosystem is created around a smart machine as a boundary object that generates data via sensors.
3 Research Approach
To answer the research question, the paper at hand follows a design-oriented research approach and
employs the iterative ADR method. ADR combines the interventional action research approach to
solve immediate organizational problems with the design science research approach of developing
prescriptive knowledge aimed at solving an identified class of problems (Sein et al., 2011). First, we
derived our research goal and involved four companies in a theoretical sample (Glaser & Strauss,
1967) to map the value creation in a manufacturing network in which different processing methods
(from additive processing to laser cutting and assembly of complex machines) are used. Taken
together, the partners implement the full life cycle of the design, production, and service of their
machines. In addition to the three academic partners and manufacturing companies, the ADR team
also includes two software development companies. The targeted artifact is a DT software platform to
manage DT instances in manufacturing networks. Our ADR study started in April 2018 with the first
problem formulation workshops and was launched as a three-year government-funded research project
in March 2019. Figure 1 provides an overview of the ongoing ADR study.
Digital Twin
Platform
Practitioners
Redesigned Digital
Twin Platform
Prototype
ResearchersEnd-Users
Problem Formulation Build Intervention Build
Reflection and Learning
Design Principles
for Digital Twins
Platform
Ability to Develop
and Deliver
Digital Twin-
Enabled Service
Systems
Formalization of
Learning
Contributions
Evaluation
Formulate
Research
Question and
General
Problem Prepare
Sampling and
Data Collection
Workshops, Interviews,
Questionnaires to Formulate
Specific Problem
Abstract to Class of
Problems and Validate
with Service System
Literature
Define Design Requirements
and Features and Validate with
Service System Literature
Build User Interface
Prototype of Digital
Twin Platform
Intervention with Prototype
Testing and Subsequent
Interviews
Analyze
Feedback
Abstract Design Principles
and Rationale via Service
System Literature
Build/ Program
Figure 1. Structure of the ADR study (representation according to (Schacht, Morana, &
Maedche, 2015; Sein et al., 2011)).

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The problem formulation and preparation of the first build intervention and evaluation phase (BIE)
(Sein et al., 2011) were based on four qualitative questionnaires (challenges and visions), eight hours
of semi-structured interviews (process and data flows in the value network) (Fontana & Frey, 1994),
six hours of focus group discussions (IT architecture and system landscape of the companies)
(Morgan, 1997), 20 hours of workshops (vision and deployment of a DT solution) and more than 30
hours of direct and indirect observations (Mayring, 2004) (during data collection but also shop floor
viewings). The data collection was carried out with representatives of the manufacturing companies in
different positions as key informants (Marshall, 1996), including chief executive officer (CEO), chief
innovation officers (CIO), sales managers, product managers, project managers, leading software
developers, data scientists, and additional employees. The data was analyzed partly deductively
(coding by Work System Theory elements (Alter, 2013)) but also inductively to identify problems,
requirements, and possible DF. The class of problems, DR, and DF were derived from the collected
information. Thus, we developed our resulting solution class and solution design from the
organizational setting and then reflected it against SSS as kernel theory in order to design our artifact
in a theory-engrained manner (Sein & Rossi, 2018). SSS can be seen as a specialization of the service
systems theory (Spohrer, Maglio, Bailey, & Gruhl, 2007) which, using the theory classification
scheme (Gregor, 2006), can be classified as an explanatory theory which is (together with design
theories) considered as the most suitable type of kernel theory for ADR projects (Sein et al., 2011).
We built a user interface prototype according to our DR and DF and used it for the intervention and
evaluation (intervention with end-users in Figure 1 refers to end-users inside the observed companies).
For the intervention and the preparation of the next BIE cycle, we conducted seven hours of semi-
structured interviews (feedback regarding prototype) as well as six hours of hackathon events
(software connectors and APIs). We used open coding followed by axial coding (Corbin & Strauss,
2015) to find opportunities to improve our design. In line with the ADR method, we generated
prescriptive design knowledge by abstracting our specific implementation to DP, which were again
reflected against our kernel theory. The findings of the first BIE cycle led to a problem redefinition,
which resulted in a second BIE cycle to create a software prototype.
4 Building the Co-TWIN platform
4.1 Problem formulation
For the problem formulation and preparation of the first BIE cycle, a meeting of the ADR team was
arranged in April 2018, where three individual groups with participants of mixed affiliations discussed
their current challenges in the context of product development and product management in a workshop
setting. Three thematic areas were identified: sales configurations of DTs as complex cyber-physical
machines (Company A), smart services such as predictive maintenance for DTs as complex cyber-
physical machines (Company D), and coordination among different actors (e.g. for production and
procurement of spare parts) in the context of DT as complex cyber-physical machines (Companies B
and C). Thus, all thematic areas consider service exchange evolving around DTs as cyber-physical
machines in a network of different actors. These initial insights were enriched by conducting the
above-mentioned data collection. The individual problems of the manufacturers thus led to the
following class of problems: “efficient, digital service development and delivery for DT-enabled SSS
in the manufacturing industry”. SSS was considered as a kernel theory, since smart systems, such as
the DT, act as a central element in a service system around which the service exchange evolves
(Beverungen, Breidbach, et al., 2019; Beverungen, Müller, et al., 2019; Wessel et al., 2019), as in our
case with the DT as a cyber-physical machine in a manufacturing network. From the perspective of
SSS, our defined class of problems can be considered as an issue with efficient resource exchange in
an SSS in a manufacturing context. The reasoning for this class of problems based on the individual
problems of the companies and SSS is explained hereafter.
The manufacturing companies were asked about their concrete challenges and visions for solutions in
their problem areas. Company A described the problem that information and knowledge about

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complex machines and systems are very difficult to convey without media support and digital
representation. The CEO stated in the context of the configuration of complex machines: “The sales
department should be able to piece together and show this [machine] during the sales conversation.
Machine concepts develop in the course of the conversation and it is helpful to be able to see this
development directly. For this purpose, one should be able to select the components and determine
their position within the machine.” This would help in the context of a sales conversation, because
“The customer sees what he gets. It's an unbeatable selling point.” The company thus needs a
configuration system to configure its complex cyber-physical machines to convey information to the
potential customer. The most important resource in this context is knowledge, which is made available
to the user via the configuration system. To extract this sticky information (knowledge) from the
experts and transfer it to the system is not easy (von Hippel, 1994; von Hippel & Katz, 2002), but it is
a basic principle of the service concept resource liquefaction (Lusch & Nambisan, 2015), which
enables efficient service development and delivery.
In the second problem area, Company D focuses on the operation of a complex machine on the
customer's premises and the handling of maintenance and repair work, which is complicated by
modifications to the machines by various stakeholders. Among the challenges the head of the research
and development (R&D) department stated were: “Reducing the susceptibility to errors in machine
modifications” and “Increasing efficiency in handling service cases for the machines”. As a vision,
Company D hoped “that fault diagnosis would be significantly faster and easier, which would greatly
reduce the corresponding costs”. This problem can be addressed by the machine generating data at the
customer's site (e.g. about usage) and using this data to inform the manufacturer and, if necessary, the
customer. These types of services are based on data from the machine and can reduce the problem of
inefficiencies in operation and maintenance due to information asymmetry between the machine user
and producer. The generated data is a resource which allows the development and delivery of higher-
quality service from the service providing actor to the service receiving actor (Lusch & Nambisan,
2015). The value in use of the machine (Vargo, Maglio, & Akaka, 2008) can be increased by lower
downtime and the value in use of the maintenance service is increased since it can be faster and less
complicated. Utilizing the data would improve the value creation in the SSS.
Company B and C focused their problem description on the inefficiencies in the exchange between
different value creation partners. The CIO of Company B using the example of the preparation of
offers said: “Currently, offers are calculated by people. They consider not only the complexity of
production but also the current machine utilization and material prices”. As a solution to the problem
of inefficient exchange, they stated that “reducing media breaks” and “creating digital interfaces
between machine manufacturers and suppliers” would solve the problem. The head of Digital Twin
Office (DTO) of Company C stated, among other things, the problem areas of “dissolving data silos”,
“[lack of] standard interfaces for data exchange with external partners”, and “[lack of] central human-
and machine-readable maintenance of equipment and asset characteristics/information”. As a vision,
they stated, that they wanted to implement an “integrative data maintenance of the information models
of products” via an IT platform. This shows that, within the value chain, diverse information on
complex products must be exchanged and media breaks that require manual processing must be
avoided. An IT platform with an integrated information model to which the partners in the value
network have access would alleviate this existing problem. Here, again, the liquefaction of the
information provides the actors with the resource of knowledge (Lusch & Nambisan, 2015). The idea
to use an IT platform as an information/resource distribution mechanism (Tilson, Lyytinen, &
Sørensen, 2010) to provide access and transparency is an essential prerequisite of joint value creation
(Prahalad & Ramaswamy, 2004) in an SSS (Beverungen, Müller, et al., 2019) and enables service
development and delivery.
Through these individual problems and their connections to value creation in SSS, the ADR team
determined that these three separate problems could be generalized by combining them into a more
abstract class of problems. The three practice-oriented challenges describe problems with (1) the
exchange of knowledge and information as well as resource configuration in sales conversations

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concerning DTs as complex cyber-physical machines due to a lack of digital representations, (2)
inefficiencies in service exchange (e.g. maintenance of DTs as complex cyber-physical machines such
as exchanging a physical component and updating the corresponding virtual model and digital
services) due to poor documentation, and (3) the exchange of information and knowledge between
different actors in the context of DT as complex cyber-physical machines due to data silos and manual
processes. Abstracting these three problems by combining them results in the above-mentioned class
of problems. While still in the problem definition phase, the ADR team was thus able to identify the
appropriate solution class for the separate problems as well as the general class of problems: a
configuration system for DT as complex cyber-physical machines connected to an IT platform with an
integrated information model to develop and deliver data-driven (smart) services to various actors.
Such an integrated platform approach enables the efficient exchange of information and knowledge (S.
Fischer, Lohrenz, Lattemann, & Robra-Bissantz, 2020) and represents an IT platform connecting the
various stakeholders (Tilson et al., 2010; Tiwana, Konsynski, & Bush, 2010; Yoo, Henfridsson,
Lyytinen, & Lyytinen, 2010).
4.2 The first BIE cycle
We used the solution class outlined in the previous section together with the SSS as kernel theory and
the input of the companies as a start for the design and the basic idea of the DT platform with a
configuration system and data-driven services. We implemented these features in the first build phase
in a detailed user interface prototype which represents the alpha version of a web-based management
platform for DTs and evaluated the design during a lightweight intervention in a limited organizational
context in all four companies as suggested by Sein et al. (2011) for an IT-dominant BIE variant. The
intervention allowed for the identification of additional design details as for example which
information should be rendered in a tabular form or where a button is needed to start a search process.
4.2.1 Cyber-physical (re-) configurability
The companies want a configuration system for complex machines to be used in sales conversations.
For example, Company A considered all functions that support machine planning as very relevant in
the questionnaire and in the interviews. This indicates that DT must accompany the life cycle of a
machine and that the associated change in the physical and virtual components must be representable.
According to the SSS perspective, a DT (including the connected physical and digital components
with data and services) represents the smart product, which later acts as a boundary object for the
exchange of resources such as information or services between producer and customer (Beverungen,
Müller, et al., 2019). These smart products are inherently configurable and thus act as a generative
resource (Henfridsson & Bygstad, 2013; Wessel, Baiyere, Ologeanu-Taddei, Cha, & Blegind-Jensen,
2020). The first problem description in combination with the requirements mentioned here and the
SSS perspective leads to DR1: The system should offer the possibility to perform cyber-physical (re-)
configurations (cf. Table 1).
A complex cyber-physical machine consists of a physical and a virtual part. Therefore, a configuration
system for DTs, which can perform the configuration of both the physical and the virtual part, is
required. This can be achieved by using different conceptual views in the configurator (Kristianto,
Helo, & Jiao, 2015). Thus, simple physical components as well as sensors that generate data can be
configured. These sensors can then serve as data sources during the configuration of the virtual part,
whose data can then be processed and displayed. See Figure 2 for a screenshot of the configuration of
a dummy physical component. The context change from the configuration of the physical component
to the configuration of the virtual component was realized with a button in the prototype. At the push
of a button, the placement of the sensors in the virtual configuration was shown with the possibility to
configure the data flows for service generation.
For this purpose, it is necessary that not only the physical component level can be configured
modularly, but also the virtual one. Individual functionalities are encapsulated in small modular
services, which can be configured and combined in different ways to create higher-level functionality

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and added value via a modular service platform (Lusch & Nambisan, 2015). In the context of planning
a plant for a customer, the head of technical customer service of Company D said: “[...] then a design
layout is successively worked out in which it is modeled how the production chain looks, how the
process steps are integrated and how this is divided into different individual machines in a modular
way. We're talking about modules here.” Furthermore, the head of DTO from Company C said in
connection with scalable digital implementation of DT services: “It should be modular. The
architecture has to be right.” In order to satisfy DR1, based on the central idea that smart products in
an SSS must be configurable (Wessel et al., 2019) and the input of the ADR team two DF were
derived, DF1: a configuration system for the physical and virtual assets of the DT (in this case
machines including sensors and actuators) and DF2: service modularity to virtually mirror the
modularity of the physical configuration.
Figure 2. Screenshot of user interface prototype configuration view.
4.2.2 Smartness of the product
In the questionnaires and interviews, the companies stated that they would like various data-based
services from the introduction of a DT to a production machine. On average across all companies, 14
of the 16 items corresponding to functions from planning (e.g. configuration of the system) to
production (e.g. simulation of the production) to maintenance (e.g. preventive maintenance of the
system) were rated as rather or very important. Data-driven, smart services are also an essential
component of SSS (Beverungen, Müller, et al., 2019). When designing this data-driven smartness of a
system, it is crucial that the purpose of the ensemble of physical and virtual objects is considered
(Baiyere et al., 2020). The second problem description in combination with the requirements and the
SSS perspective leads to DR2: The system should offer the possibility to provide goal-oriented smart,
data-driven services. We observed three main focuses for smart data-driven services for the DT
platform: product life cycle, sustainability, and knowledge management. These service areas were
realized in the prototype by the ADR team, which is briefly explained below.
The first focus lies on services in the area of product life cycles: For example, can suitable partners for
the planning phase or suitable components for the production of a new machine be identified?
Company D emphasized functions in the maintenance phase of the machine as very relevant. This was
echoed in the overall rating of the companies, who have stated that simulations, diagnosis, and

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maintenance are particularly important functions when using the DT concept in the operating phase for
systems already delivered to the customer. In our prototype, this was realized by the possibility of
“navigating” through the different life cycle phases of the DT in the product life cycle view. Thus, the
historical data of a machine in operation can be viewed from design to production to operation.
Depending on the life cycle phase, different services are available, which were designed in accordance
with product life cycle management systems (Park, Lee, & Yoo, 2005; Thimm, Lee, & Ma, 2006).
The second area of services has a focus on sustainability. For example, predictive maintenance can be
performed by monitoring machine data, which significantly extends the runtime of a machine and thus
increases the sustainability of the development (Tao et al., 2019). The interviews showed that the
companies are already beginning to adjust their machines to sustainability considerations in terms of
productivity and environmental impact. As the head of R&D of Company D explains: “So what are
the important parameters that I should record to get to know the energy footprint of my machine? […]
For this purpose, we have installed energy sensors with very fine granularity in one of the machines
[...] and measured the power consumption and correlated it with the machine data”. The head of DTO
of Company C has a similar opinion: “Very different data are measured here. From temperatures to
current curves, voltages, speed, torques, all kinds of things”. This aspect is realized in the prototype by
using sustainability dashboards, which present information to the user. This way the user can be made
aware of sustainability aspects, which is an essential element for sustainability-conscious information
systems (Huber, Püschel, & Röglinger, 2019; Loeser, Recker, vom Brocke, Molla, & Zarnekow, 2017;
Melville, 2010; Seidel, Chandra Kruse, Székely, Gau, & Stieger, 2018).
The last focus of the services deals with the management of knowledge. This can be human
knowledge in the form of competencies, on the basis of which the appropriate contact person for an
issue can be found. Likewise, in the case of new machine development, suitable solution approaches
can be recommended or reused. In an interview, the CEO of Company A gave an example of the
additional work that arises when experts don’t sufficiently communicate during the construction of a
machine: “And that’s exactly how our colleagues in the design department assess whether [a design
engineer] has found the best solution. It is quite possible that there is suddenly a design engineer in the
internal design approval department who says: ‘Gee, why did you do it like this and not like that [...]?’
And then [the other design engineer] says: ‘Yeah, you should’ve said that before. It would have been a
good idea.’ And then it's changed again [...]”. The knowledge management functions were realized
with a dedicated knowledge management view in the prototype. In this view, services for the
management of knowledge objects like drawings or documentation, but also services for the
management of human knowledge networks in the context of the DT are provided (Alavi, Maryam, E.
Leidner, 2001; Loebbecke, van Fenema, & Powell, 2016). These services can be technically
represented by ontologies and knowledge graphs (Tao et al., 2019).
The idea of transforming the produced machines into smart products for the customer is supported by
the SSS perspective. Here, smart products act as the center of a service system, offering services to
producers and customers (Beverungen, Breidbach, et al., 2019; Beverungen, Müller, et al., 2019). To
satisfy DR2, a DF was derived based on the idea that smart products increase the performance of the
SSS (Baiyere et al., 2020; Wessel et al., 2019) and the input of the ADR team: DF3: Smart, data-
driven services that should be focused on specific objectives, such as product life cycle management,
knowledge management, and sustainability management, to ensure goal-oriented smartness of the
product. Examples for such services could be suggesting problem solving strategies or controlling the
energy consumption of a machine.
4.2.3 IT platform as a boundary object
The companies placed great emphasis on involving customers and partners in the value creation
process with the help of DTs. An IT platform is an established design paradigm to realize this
integration of different actors in a service system (De Reuver, Sørensen, & Basole, 2018; Yoo et al.,
2010). From the perspective of SSS, this platform plays a central role. While in a dyadic producer-

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consumer relationship a single DT acts as a smart product and thus as a connecting boundary object
(Beverungen, Müller, et al., 2019), we posit that a DT platform can map this connection function for
the numerous actors in a value network. The DT platform is a management infrastructure for
numerous DTs which are used across different actors. For example, the manufacturer has an
administration interface with which he can manage the DTs of his many customers in one place;
customers and partners can only see and manage their own DTs. In the context of collaborations on a
recently launched IT platform for digital services, the head of R&D at Company B emphasizes, “So
that means the issue of platform openness with other manufacturing companies in one-to-N, N-to-N
relationships, that's definitely an issue that is going to be important in the long run.” The third problem
description in combination with the mentioned requirements and the SSS perspective leads to the
DR3: The system should offer possibilities for several actors to synchronize their different world
views.
In order to ensure optimal communication and thus the connection between these many actors, the
ADR team agreed to develop an IT platform. The platform offers an environment that allows the
virtual and physical parts of DTs to be managed across partners. In this way, different partners can
work together to develop and deliver a digital service (Tilson et al., 2010; Tiwana et al., 2010). The
requirement will be implemented via an IT platform, which offers login areas with different
functionalities for producers, their suppliers, and also customers. Thus, all actors can access common
information, whereby some information on the platform is also only provided for individual actors. If
there is a problem with a machine, all participants can log on to the platform and develop a common
understanding. This creates great transparency in the value network and thus reduces information
asymmetries, which leads to a common world view of the platform users in the SSS (Lusch &
Nambisan, 2015). The DT platform implements this mechanism with different features and also
supports the previously derived DF1-3 with a modular layered architecture (Yoo et al., 2010) and
thereby offers generativity in the SSS (Henfridsson & Bygstad, 2013). After examining possible
implementation approaches, the ADR team decided to rely on a microservice architecture (re-using
encapsulated small services to create higher-level functionality) that also directly supports the
aforementioned service modularity. As a result, producers can configure a machine together with
customers in the planning phase and customers can view the production progress during the
production of the machine. In the maintenance phase, the prototype contains buttons for integrating a
remote maintenance solution. This creates a comprehensive view of the DT and of the SSS as a whole.
To satisfy DR3, based on the idea that a DT platform acts as a boundary object in the SSS
(Beverungen, Müller, et al., 2019) and the input of the ADR team, a DF was derived, DF4: IT
Platform with a microservice architecture, through which virtual parts of the DTs can be managed
across partners, the assets can be configured cyber-physically and smart service offerings can be
accessed and managed.
4.2.4 Intervention and Evaluation
As described in the research approach section, the DF were implemented in a user interface prototype,
which was evaluated after an intervention using interviews. The user interface prototype was made
using animations and buttons to simulate the user experience of a web application. The prototype
consists of 23 simulated webpages separated into different logical sections. To conduct the evaluation,
it was made available to the members of the manufacturing companies. See Figure 3 for a conceptual
overview of the prototype components. This section contains an extract of the gathered feedback.
Concerning the configurator (DF1-2), the interviewees confirmed the importance of navigating
through the hierarchical composition of a machine, as well as the existence of templates (pre-
configurations) that form a basis for a configuration. Several interviewees stated that it is important to
be able to name multiple people or roles from different departments as responsible for a machine. In
addition to the demonstrated configurator, it was suggested to implement a mechanism for real-time
collaboration on 3D designs, since drafts of machines need an explanation because of their inherent
complexity, and providing designs without explanatory notes is only sufficient in very few cases.

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Figure 3. Conceptual overview of user interface prototype components.
With regard to the smart services (DF3), the interviewees stated that aspects of sustainability and key
figures should not be implemented as separate views (web pages) in the web platform, but should be
located in the context of the respective object of observation in order to strengthen the perception of
these aspects. Regarding knowledge management, the interviewees confirmed that the support of a
collaboration of the platform users is important and that a shift from a document-oriented world view
to knowledge that is detached from documents should be the goal. The approach of capturing the
competencies of experts and being able to search for and contact them was considered positive. The
sales manager of Company A stated: “The fact that you have to find someone who has knowledge on a
subject often occurs in our company.” He emphasized: “When I have a problem, I ask myself who
knows what about it. Either I know who knows or I ask someone who might know. The bigger the
company, the less it works from person to person.” Concerning the DT platform as a boundary object
for an SSS, the overall feedback was positive. In general, a desire was expressed for the possibility of
being able to configure the individual views (web pages) themselves in a modular way according to
individual needs - so that each company can optimize the platform to its individual requirements. The
user interface prototype was seen as a very positive vision for a DT platform, but aspects were also
identified where companies were unsure whether they could already achieve the necessary technical
integration. In addition to the feedback on DF, some feedback was aimed at backend data management
and integration. Therefore, it is important to focus on these topics in the next BIE cycle.
4.3 Formalization of learning
Table 1 summarizes the relationships between DR, DF, and DP. The DR are realized by the DF, which
in turn are abstracted to DP, which act as prescriptive knowledge regarding DTs from the SSS
perspective to address the identified class of problems. The table represents a formalized result of the
first BIE cycle. A more detailed description of the DP follows below. The formulation adheres to the
anatomy of DP with the structure of aim/user, context, mechanism, and rationale (Gregor, Chandra
Kruse, & Seidel, 2020).
The first DP is called cyber-physical (re-) configurability and is formulated as follows: To allow users
of DTs to design and adapt DTs in regards to the physical asset as well as the virtual asset in an SSS
for the manufacturing context, modularize the DT cyber-physically-(re)-configurable using physical
components backed by service modularity because SSS are dynamic configurations of resources (e.g.
manufacturing machines) capable of self-reconfiguration to ensure survival in the service system in
order to meet system goals (Wessel et al., 2019).
The second DP is called smartness of the product and is formulated as follows: To allow users of DTs
to be supported and enabled for their work e.g. in the areas of product life cycle management,
knowledge management, and sustainability management in an SSS for the manufacturing context,

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offer smart data-driven services, because smartness is an essential component of (smart) service
systems (Alter, 2020; Beverungen, Breidbach, et al., 2019) and creates smart objects out of traditional
objects (Baiyere et al., 2020).
The third DP is called IT platform as a boundary object and is formulated as follows: To allow users
(in a sense of locally distributed actors of different companies) of DTs to synchronize their world
views and help to exchange services in an SSS for the manufacturing context, manage the DT via an
IT platform as a boundary object because SSS uses boundary objects such as smart products to
exchange services (Beverungen, Müller, et al., 2019; Wessel et al., 2019) and to build the center
around which the SSS evolves. IT platforms are a paradigm to engage actors and build the necessary
infrastructure for service exchange (De Reuver et al., 2018; Yoo et al., 2010).
Design requirements
Design features
Design principles (short form)
DR1: Possibility for cyber-physical
reconfiguration
DF1: Configuration System
DF2: Service Modularity
DP1: Cyber-physical (re-)
configurability
DR2: Possibility for goal-oriented
smart data-driven services
DF3: Implemented set of smart data-
driven services
DP2: Smartness of the product
DR3: Possibility for actor
engagement and worldview
synchronization
DF4: IT Platform with a microservice
architecture
DP3: IT platform as a boundary
object
Table 1. Derivation of design principles.
5 Discussion
In our study, we developed a prototype of a DT platform as a boundary object for resource integration
in manufacturing. The impetus for our research is based both in theory through the usage of SSS as a
kernel theory and in practice through the real-world class of problems. We were able to successfully
answer the research question of what DF and DP arise for DTs in manufacturing from an SSS
perspective. Four DF were implemented to meet the requirements in our study. The generalization
from the four DF to the three DP allowed us to address the class of problems, namely problems with
efficient, digital service development and delivery for DT-enabled SSS in the manufacturing industry.
Our first DP cyber-physical (re-) configurability states that modularization of physical and virtual
components allows the design and adaption of DTs in an SSS for the manufacturing context.
Reconfigurability is not only one of the key aspects of SSS in our context, independent of the research
perspective on SSS (evolutionary, viable systems and boundary objects) other studies also find that
reconfiguration is an intrinsic property of SSS (Wessel et al., 2019). Cyber-physical (re-)
configurability is a particular DP that applies to DTs in manufacturing but is not necessarily
transferable to DTs in other areas. For example, production facilities are highly modular and
changeable as physical assets such as machines and their components (Tao et al., 2019), which may be
limited for the physical components of a smart home (M. Fischer et al., 2020).
Our second DP smartness of the product states that offering smart data-driven services supports and
enables users with their tasks in an SSS for the manufacturing context. Although the concept of
smartness is not strongly emphasized in DTs, the concept is an implicit feature of DTs (Tao et al.,
2019) through its services and self-directed behavior. The DT creates a smart product and the DP is so
strongly abstracted that it can apply to DTs beyond the manufacturing area if viewed from an SSS
perspective. The big difference in the characteristics is the implemented set of smart services, which
differs strongly by DT domain (smart maintenance in manufacturing vs individualized sports program
in healthcare) and thus determines the content of the resource exchange, service design, and delivery
over the SSS (Beverungen, Müller, et al., 2019).
Our third DP IT platform as boundary object states that managing the DT via an IT platform as a
boundary object allows actors to synchronize their worldviews and to exchange services in an SSS for

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the manufacturing context. The platform thus acts as a connecting element between the actors in the
value creation network (De Reuver et al., 2018; Lusch & Nambisan, 2015; Yoo et al., 2010). DTs in
manufacturing focus life cycle and knowledge management of complex machines and therefore
require cross-instance management of DTs on a shared platform, which is not the case with smart
products in general. This is also valid for DTs in other contexts, but in our case, a special feature is
that the platform as digital infrastructure is jointly owned by the companies and therefore cannot be
compared to classic marketplace models with a single powerful owner (De Reuver et al., 2018).
As implications for practice, we provide DF and DP for DTs in manufacturing. The abstract topic of
SSS has been broken down into applicable knowledge for practitioners, making it easier to translate
theoretical assumptions into practical implementations in I4.0. As implications for research, we
provide prescriptive knowledge and could thus contribute to the knowledge bases of DTs and SSS.
Multi-partner collaboration approaches for DTs are a new emerging research field but currently
discussed rather technically and pursued with decentralized solutions (Putz, Dietz, Empl, & Pernul,
2021). We have joined this line of research and address a multi-partner collaboration approach for DTs
via a centralized IT platform. As previously shown, the focus in DT discourse lies on technical aspects
(Wache & Dinter, 2020), which is also reflected in existing DP for DTs (Dreyer et al., 2017). Our
combination of DT with the SSS perspective puts an important focus on aspects that have so far not
been considered much in the DT context, such as business models, services, actors and their skills, and
value-in-use. This addresses an existing problem in DT research by applying a new perspective.
Additionally, it is shown how the nested nature of (smart) service systems (Maglio et al., 2009) can be
applied to DT, in that both a concrete DT and, in the next higher instance of the SSS, a DT
management platform can act as a boundary object. The contribution type is an exaptation (Gregor &
Hevner, 2013) since we have transferred the SSS perspective to the DT service design and delivery in
manufacturing. Our research positions the DT platform as a boundary object in the center of a
manufacturing SSS and thus forms the solution class for our problem class. According to Arthur
(2009), the combination of known technology concepts into new designs can be called combinatorial
evolution of technology, thus our contribution can be regarded as an innovation in the context of DT
and SSS. However, our study has some limitations: The results have not yet been validated in another
case setting and the study has only completed a single BIE cycle and is therefore not yet complete.
The second BIE cycle focusing on software development has started, and initial feedback collection
has taken place but is not finished yet. The integrated IT platform is not yet available for long term
testing. Furthermore, as already mentioned, the DPs are not transferable to all kinds of DTs, which
was not the aim of the study as we are limited to the manufacturing context.
6 Conclusion
This paper aims to derive DF and DP for DTs in the manufacturing context based on the perspective of
SSS. To this end, we conduct an ongoing ADR study with four special machine manufacturing
companies and built and evaluated a user interface prototype of a DT platform. We provide
generalized prescriptive knowledge and thereby contribute to the knowledge base of both DT and SSS
literature. The prototype is currently being implemented as a web application using a microservice
architecture in the backend. Further research will include the next BIE cycle with an implemented DT
platform to refine the DF and DP. The new combination of DTs and SSS opens up new avenues of
research that can be investigated. For example, a conceptual examination of DT's affordances through
an SSS lens could contribute to a deeper understanding of DT design.
Although only the first of several BIE cycles in the project has been described in this paper, in
subsequent cycles we can collect additional feedback to further develop the artifact and DP. We
believe that the DP in their current form can already be helpful for research and practice to develop
DT platforms as boundary objects in an SSS context.
Acknowledgement: The research in this paper was supported by a grant from the German Ministry
for Research and Education (BMBF), project name: Co-TWIN, nr: 02P17D146.

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