=Paper=
{{Paper
|id=Vol-2305/paper05
|storemode=property
|title=Platform ecosystems for the industrial internet of things – A software intensive business perspective
|pdfUrl=https://ceur-ws.org/Vol-2305/paper05.pdf
|volume=Vol-2305
|authors=Dimitri Petrik,Georg Herzwurm,Tapani N. Joelsson,Sami Hyrynsalmi,Sabine Molenaar,Arash Hajikhani,Marja Turunen,Matti Mäntymäki,Andrey Saltan,Ahmed Seffah,Zeena Spijkerman,Slinger Jansen,Teppo Yrjönkoski,Felix Schönhofen,Sixten Schockert,Georg Herzwurm,Andrey Saltan,Slinger Jansen,Kari Smolander,Jan Bosch,Helena H. Olsson,Ivica Crnkovic,Jorge Melegati,Xiaofeng Wang,Jürgen Münch,Stefan Trieflinger,Dominic Lang,Rafael Chanin,Dron Khanna,Kai-Kristian Kemell,Wang Xiaofeng,Afonso Sales,Rafael Prikladnicki,Pekka Abrahamsson,Katariina Yrjönkoski,Anu Suominen,Matthias Gutbrod,Jürgen Münch
|dblpUrl=https://dblp.org/rec/conf/sibw/Petrik18
}}
==Platform ecosystems for the industrial internet of things – A software intensive business perspective==
https://ceur-ws.org/Vol-2305/paper05.pdf
SiBW 2018 57
Platform Ecosystems for the Industrial Internet of Things
– a Software Intensive Business Perspective
Dimitri Petrik1 and Georg Herzwurm1
1 University of Stuttgart, Graduate School of Excellence advanced Manufacturing Engineering
(GSaME), Nobelstr. 12, 70569 Stuttgart, Germany
{dimitri.petrik, georg.herzwurm}@gsame.uni-stuttgart.de
Abstract. The global competition requires the machine tool industry to provide
more flexibility and productivity to its manufacturing customers, enabled
through software-intensive services. A platform approach receives an increasing
attention within the machine tool industry, offering a solution to provide such
services. Software platforms, adapted to the needs of the industry and used in
the industrial application are also known as industrial internet of things (iIoT)
platforms. Despite the growing interest among manufacturing companies in
iIoT platforms, they have been limitedly researched from the economic perspec-
tive. Consequently, a further in-depth analysis of platform-based business mod-
els in the area of iIoT is still needed. Firstly, this paper offers new insights on
technical and economical criteria for business models and design of existing
iIoT platforms and transforms them into a taxonomy. These merged criteria
provide a detailed perspective on iIoT platforms and support machine tool com-
panies in their decision process of suitable iIoT platforms. The criteria are based
on the results of 17 qualitative interviews with companies from the machine
tool industry. Secondly, the identified criteria are summed up in a morphologi-
cal box, in order to reduce the selection complexity of an iIoT platform by the
machine tool companies and sharpen the software-intensive business models of
the platform providers.
Keywords: Industrial IoT, IoT Platform, IoT Ecosystem, Business Model
Analysis, Morphological Box.
1 Introduction
The machine tools industry nowadays experiences an increasing competitive pressure
due to the globalization and the individualization in manufacturing, requiring more
efficient manufacturing processes [1]. The German Mechanical Engineering Industry
Association (VDMA) together with McKinsey have surveyed the machine tool com-
panies and identified the customer demand for customized systems and solutions as
the most relevant trend. Another finding was the increasing importance of the after-
sales, ranking it as the third most relevant trend in the market [2]. Digital services are
provided remotely and modularly during the whole life cycle of a machine tool, creat-
ing a steady revenue source in after-sales for a machine tool company [3-4]. The plat-
SiBW 2018 58
form approach enables the provision of digital services for a variety of customers,
fulfilling the flexibility needs and even building new software-intensive business
models. The services are provided through enterprise applications, which are devel-
oped on specific software platforms [5], classified as iIoT platforms. Such a platform
interacts with smart connected machine tools and its components across companies’
borders, processing the data it receives from the machine tool. Based on the processed
data the platform triggers microservices, changing the parameters of the machine tool
through the data feedback loops. Accordingly, platforms play coordinating roles for
connected machine tools, acting as a digital infrastructure [6-7]. Gawer and Cusu-
mano coined the understanding of open technological platforms [8]. iIoT platforms
also act as multi-sided markets [9], as machine tool companies provide applications,
based on the platform, for the machine operating companies in different industries.
The current state shows, that a successful platform initialization in a machine tool
industry remains a high complexity challenge for both: the platform providers and the
machine tool companies, acting as a collaborating customer for a platform provider in
an iIoT ecosystem [10]. The complexity is partially caused by the variety of the spe-
cific functional characteristics offered by each platform provider, by the iIoT platform
evolving the machine tool company into an ecosystem and by the variety of the mar-
ket-available platform solutions [11-13]. In addition, various iIoT platform providers
describe only a fuzzy value proposition, without meeting the specific customer needs
of the machine tool industry, as mentioned by Herzwurm [14]. However, a selection
process for a suitable iIoT platform is a major challenge [15] and highly interdiscipli-
nary, as it is crucial for the product servitization and affects stakeholders from multi-
ple departments throughout the whole company [7, 16]. Despite the recognized poten-
tial of iIoT, machine tool companies experience difficulties to identify which iIoT
platform best suits their own requirements and the current state of market hinders the
formation of a “platform leader” in the machine tool industry. The current state indi-
cates an industrial problem setting, revealing the lack on relevant technical and eco-
nomic criteria for the choice of iIoT platforms from the perspective of a machine tool
company as a collaborative customer. This paper is based upon the assumption, that
the fragmented market for iIoT platforms (offering up to 450 market-ready solutions)
causes problems for the manufacturing companies to choose the right platform. On
the other hand, the practical relevance of the problem is present, as new studies con-
ducted by the VDMA, show an increase of interest in iIoT platforms by machine tool
companies. Although more than 60% of surveyed companies indicated iIoT platforms
as an unknown topic or irrelevant in 2016, for 75% of surveyed companies iIoT plat-
forms are important in 2018 [17].
Considering the current state of research on platforms, Gawer has already bridged
economic and technical perspectives on platforms and offered a platform classifica-
tion. However, this classification is not specific to iIoT platforms. In addition, the
scientific papers about concrete design or business model patterns within the industri-
al application of platforms and platforms specifically used for the machine tools in-
dustry (see Chap 2.1) are still rare. As stated by Kude in the Dagstuhl position state-
ment, the existing literature on the iIoT has mainly focused on the technical imple-
mentation and the platform literature has been mainly too generic [18]. This indicates
SiBW 2018 59
a research gap on relevant business model criteria of iIoT platforms, which if known,
collaborative customers in the iIoT ecosystems could use for interdisciplinary plat-
form selection decisions. Hence, the overall goal is to provide relevant criteria for this
selection process through a more in-depth analysis of design and business models of
market-ready iIoT platforms for the industry of smart connected machine tools.
Hence, this article answers the following two research questions:
RQ1: What are the relevant criteria in the selection process of iIoT platforms by
manufacturing companies for data-driven maintenance services?
RQ2: Which market-ready iIoT platforms fulfill the identified criteria?
The structure of this paper consists of three parts. The second section of the article
presents conceptual foundations and current state of research on iIoT platforms. The
third section addresses both research questions, presenting at first the identified tech-
nical and economic design criteria of iIoT platforms. Criteria are based upon a multi-
ple case study analysis of qualitative data, collected in interviews with machine tool
companies. Afterwards, each elaborated criterion is applied on the market-ready iIoT
platforms, in order to ensure the transferability of the identified criteria to the current
state of the market for iIoT platforms. The final part presents the future research out-
look and limitations.
The main result is a characteristics taxonomy for iIoT platforms, both technical and
economical, integrated in Zwicky’s morphological box. The morphological box could
act as a decision support tool for the cross-department collaboration during the iIoT
platform selection, building the main artefact of the paper. Morphological analysis as
a method has been already used to gain a holistic understanding of business model
concepts within a certain context [19-20]. Researchers and practitioners from the
platform provider perspective could use the taxonomy for a further business model
analysis of iIoT platforms, in order to better understand currently existing or even
build new configurations and develop new business model patterns [21] for iIoT plat-
forms. Practitioners from the machine tool industry could use the results in a selection
process of a suitable iIoT platform. Moreover, the results can support the iIoT plat-
form providing companies in a more precise communication of their platform design
to the collaborative customers or complementors. As a result, this could increase the
transparency on the design and the business models of the offered platforms, therefore
involving additional collaborators in the platform-based iIoT ecosystems and stimu-
lating the network effects [9]. Taking the research context of previously mentioned
business models into account, this paper provides integrable criteria for the business
model dimensions of the St. Gallen Business Model Navigator [21].
2 Industrial Internet of things and prior work
Following paragraph describes the theoretical background in the area of iIoT plat-
forms. IoT integrates information and communications technology (ICT) with objects,
connecting them with wireless and wired technologies and extending them by real-
time analytics. iIoT integrates these technologies in the industrial area of application
SiBW 2018 60
[22]. The relation to the concept of Industry 4.0 is close, which means iIoT can be
understood as the vertical and horizontal connection of people, machines, objects and
ICT systems, which are real-time capable and intelligent, for dynamic management of
complex systems [23]. Hence, connected machine tools act as cyber-physical systems
(CPS) [24] and this transition could greatly increase the productivity and the flexibil-
ity. It is estimated, that it is possible to increase the productivity and the lifespan of
machine tools up to 5%, to lower the maintenance costs between 10 and 40% and
reduce the energy consumption up to 20%, if the machine tools are connected and
monitored [25]. The listed benefits could be achieved through processing and analysis
of machine-generated data. An intelligent machine tool could stream data considering
its condition and its energy consumption, the current process or the quality of the
workpiece and combine them with a cross-domain analytics. Lastly, the processing of
the data appears in scalable iIoT platforms [26]. Moreover, if an iIoT platform pro-
vides open interfaces, the information could be enriched with external information
sources and enable integration of third-party companies, [7, 9] thus enabling ecosys-
tems in the area of iIoT. Compared to the customer branches, iIoT ecosystems are
significantly smaller, have different requirements for platforms [27] and possess more
complex structure of collaborating complementors, compared with traditional soft-
ware ecosystems [28].
This paragraph shows the current state of research on the business models for iIoT
and platforms. Gawer has created a unified view on open digital platforms and classi-
fied supply-chain and industry platforms as open [9]. This classification framework
was only applied in the area of industrial robotics, to extract business model patterns
and its dependency from the right degree of openness [29]. Besides the previously
mentioned IoT stack [7], important work on business models also considered different
revenue patterns in the area of iIoT [30]. Ehret and Wirtz identified a variety of poten-
tials for IoT in the industrial application and concepts of iIoT business models [31].
Previous research has also discussed the appropriate organization structures and the
required capabilities for non-standard partnerships and the make-or-buy decisions for
iIoT platforms for manufacturing companies [32]. Some research also has identified
iIoT related changes in business model elements [33]. Many research papers propose
strategy frameworks, either for an integration in an existing IoT ecosystem [34], or for
a classification of business models in IoT ecosystems including platforms [35]. Im-
portant work also explored of specific IoT platforms. Wortmann and Flüchter
achieved a first classification of iIoT platforms [15]. Agarwal and Brem investigated
the IT-enabled transformation of General Electric to an iIoT platform provider [36].
Sandberg et al have described the platform-based transformation of ABB [37]. Ardo-
lino et al researched the capabilities for a successful service transformation in indus-
trial companies [38].
Previous research on iIoT did not focus on the challenges of selecting the right
platform from the perspective of a collaborative customer or a complementor. Ac-
cordingly, further research on concrete design criteria of iIoT platforms is required,
addressing this challenge is required [15]. This paper fills this gap and extends the
existing research in two directions. Firstly, the proposed taxonomy could extend the
currently existing research on business model patterns for the growing area of iIoT
SiBW 2018 61
platforms. Secondly, the proposed taxonomy provides a focused view on the machine
tool industry in the iIoT and its characteristics of openness, which despite the increas-
ing relevance of platforms, stays little investigated in the broad area of IoT.
3 Evaluating the business model criteria of iIoT platforms
3.1 Methodology
Qualitative research is suitable to analyze business decisions, which in our case was
the decision for a certain iIoT platform. The database for this purpose contained pri-
mary data, which was obtained during qualitative interviews with practitioners. The
interviews were conducted between March and August 2018 using a predefined inter-
view guide and were thus semi-structured. The guide ensured the comparability, sim-
ultaneously offering enough freedom to create new specific or more in-depth ques-
tions, based on the answers. The interviews were compared and analyzed and the
received information was recognized as single subjective dimensions of expert
knowledge, which build a conceptualization and can be used for a theory generation
[39]. As stated previously, data-driven maintenance was chosen as a platform-based
service, to support the understanding of the interviewees, consequently defining the
qualitative case study context. In the pre-selection process, suitable companies from
the machine tool industry were identified based on publicly accessible company
blogs, product presentations and press reports looking for digital services in the field
of data-driven maintenance and related software-intensive services. The core target
group consisted of mechanical engineering companies for various manufacturing
processes in the metalworking, plastics processing and woodworking industries, as
the initiators behind data-driven maintenance services. The interviewed representa-
tives of the companies are specialized on processes such as milling, honing, turning,
laser cutting and welding, injection molding wood construction joinery and others. An
additional clustering of the identified companies includes machine makers, toolmak-
ers, component makers and providers of automation solutions and software solutions
for the automation or machine tools. Despite the heterogeneity of the processes and
the companies, there are certain similarities between the studied companies. All these
companies count as collaborative customers or complementors from the platform-
provider perspective. At first, they all use iIoT platforms to build applications for
software-intensive services as data-driven maintenance or similar. Consequently, the
data-driven maintenance efforts of the studied companies and the applications built by
them increase the overall value of the used iIoT platform and has impact on the iIoT
ecosystem. The data collection process included interviews with machine tool compa-
nies (n=8), component suppliers including toolmakers, end effector manufacturers and
automation solution providers (n=6), as well as manufacturing-related system integra-
tors and consulting companies (n=3). The overall sample size consists of 17 inter-
views. After the evaluation of the 17th interview, the study has reached a theoretical
saturation due to repetitive statements of the interviewees. The interviews were con-
ducted with representatives working in the area or leading the digital service projects
for their company’s products. The second requirement towards the representatives
SiBW 2018 62
was to have at least 5 years of experience in their industry and in the digitization to
ensure the qualification of the interviewees. The potential representatives were
screened towards these two requirements, in order to count as experts on specific
issues from the researcher’s perspective [40]. The following table depicts the full list
of interviewed experts during the data collection process of the study:
Table 1. Information on interviewed experts and their companies
Rounded no.
ID Position of the interviewee Company profile
of employees
Consulting and sys-
1 Head of Product & Services 50
tem integration
2 Product manager After Sales 350 Machine tools
3 Head of Industry 4.0 Campaign 7000 Components supplier
4 Head of Digitization 2000 Machine tools
5 Business Developer 800 Components supplier
6 Head of Maintenance 1300 Special machine tools
7 Managing Partner 10 Consulting
8 Corporate Innovation Management 900 Components supplier
Head of Technical Sales – E-
9 250 Machine tools
conception
10 Technology manager Industry 4.0 2150 Machine tools
11 Head of industrial Data Services 500 Machine tools
Head of Development and Stand-
12 200 Components supplier
ardization Control
13 Head of Product Management 150 Machine tools
14 Head of Product Management 220 Components supplier
15 Lead Architect Industry 4.0 14000 Components supplier
16 Head of Product Management 70 System integration
Product manager Technical
17 11500 Machine tools
Support
Predefined questions of the interview guide focused on the following topics:
Which challenges of current importance do you experience during the implementa-
tion of data-driven maintenance?
To what extent do you collaborate with partners during the implementation of data-
driven maintenance?
Which role do iIoT platforms take for data-driven maintenance?
SiBW 2018 63
The received information contained the project experience of the machine tool in-
dustry on iIoT platforms, including the challenges, the potentials and the value of the
platform usage for data-driven maintenance and similar services. Hence, the data
contains empirical evidence from companies about particular decisions on data-driven
maintenance and iIoT platform selection and implementation, thus underlining inter-
pretive research [41]. The analysis process of the recorded data included the transcrip-
tion and coding processes of the interview recordings. During the coding process the
answers were labeled, based on the interpretive identification of themes. The extrac-
tion of results underlies inductive reasoning [41], as the criteria and the characteristics
are built from individual statements of the interviewed experts.
3.2 Building the taxonomy for iIoT platforms
The comparative analysis of coded transcripts returned five business model criteria
for iIoT platforms. Each criterion can be aligned with the business model dimensions
“How?” and “Value?” defined by Gassmann [21]. The first criterion provides a more
detailed classification of platform openness and complies with the “How?” dimen-
sion. The taxonomy classifies this criterion in three additional characteristics:
Hardware integration openness: While every iIoT platform mentioned by the
interviewees was advertised as open, the least open iIoT platforms did not allow
third-party application development at all. This means the business model of the
iIoT platform provider also included the development of platform-based software.
Openness on the other hand affects only the hardware integration. That means
there are no strict exclusions of certain machine tools or electrical control compo-
nents for process automation. Lastly, with this degree of openness the ecosystem
can arise over the hardware components, as the platform provider develops the
software-intensive services. The iIoT platform tapio, used in the wood working in-
dustry, currently shares this characteristic.
Project-related software integration openness: This degree of openness allows
external third-party development. The iIoT platform providers make the necessary
resources for software development either available for a machine tool company
(for its own IT department) or for an external system integrator on a project basis, a
machine tool company can contract. The main distinctive feature of this certain de-
gree of openness is that specific platform-based applications are developed in pro-
jects, without the orchestration of the integration or the distribution processes of
the application through an app store by the platform provider. This degree of open-
ness shares similar aspects as the supply-chain platform classification, shaped by
Gawer [9]. However, the interviewed practitioners, who used an iIoT platform with
this degree of openness, did not see any necessity in a further standardization in
terms of an app store, due to the high specificity of their software-intensive ser-
vices. Extending the hardware ecosystem, the software developing complementors
can for instance be system integrators, either close to the machine tool company or
to the platform provider company [28]. General Electric for instance shares this
degree of openness for its platform Predix, maintaining a software ecosystem with
SiBW 2018 64
complementors for software development and integration [42], without the provi-
sion of an application store.
App store supported software integration openness: This degree of openness
means sharing of software development resources, consequently enabling external
third-party development for a platform. The ecosystem evolves in terms of both
hardware and software. Main distinctive features are the transparency of the ser-
vice offerings and the standardization of applications driven by the app store.
Though this degree of openness also requires checks and audits of complementors
by the platform provider, the complementors can use the transparency of an app
store for their advantage, for instance to screen it for missing software-intensive
services. In addition, the machine tool companies can search for third-party part-
ners for specific scenarios through the app store more precisely. That is why this
degree of openness can be considered as the most open for a platform-based eco-
system. Siemens and SAP decided to share this degree of openness with their iIoT
platforms Mindsphere and Leonardo, which are connected to enterprise application
stores.
The next two identified characteristics concern the revenue stream of a platform pro-
vider and include the integration options and the revenue stream structure of the busi-
ness model. As various iIoT platform providers also include the application develop-
ment supplementary to the iIoT platform offering, they generate additional revenue
streams, besides the infrastructure usage expenses. However, some platform providers
offer free applications or pilot integration projects. The differences in the integration
conditions belong in two dimensions of the Business Model Navigator: “How?” and
“Value?”. The following list depicts the taxonomy:
Free integration: In this context, it is important to understand the variety of strat-
egies of provided iIoT platforms for the industrial application. There are some ma-
chine tool companies, which were able to introduce their own iIoT platforms and
provide them within their industry. The interviewed representatives stated that the
main goal of their company is to increase their end customer’s loyalty through ad-
ditional value. The value is provided through iIoT platform-based applications for
the machine tools, which are developed and integrated for free. The iIoT platform
tapio for the wooden branch provides such integration conditions.
First integration free: This integration allows the machine tool company to carry
out a pilot use case without a financial risk. The first initial integration with a ma-
chine tool’s control unit and the development of an application are provided for
free to lock-in the complementor on the iIoT platform and get additional revenue
streams through the follow-up IoT projects. The Bosch IoT Cloud offers such an
integration condition for the machine tool companies.
Paid integration: This type of integration is different from the previous one, be-
cause the first application development is already a paid project. According to the
interviewed representatives, Siemens offers this integration option for its iIoT plat-
form Mindsphere.
SiBW 2018 65
Differing integration options also affect the revenue streams of an iIoT platform pro-
vider. The differing revenues belong in the “Value?” dimension of the Business Mod-
el Navigator. The taxonomy consists out of two characteristics, depicted below:
Indirect revenues: The free integration generates additional indirect revenues in
the business model of a platform provider through increased customer loyalty and
access to customer’s specific problems in the production, consequently allow an
improvement of the next generation of machine tools.
Direct revenues: Integration conditions, which require direct payments for plat-
form-based applications, whether from the beginning or from the second project
on, generate direct revenue streams. Such a revenue structure differs significantly
from the typical platform-based business models, which typically generate reve-
nues through app store transactions or usage of infrastructure. These revenue
streams differ from the typical platform-based business models for instance in the
market for mobile OS.
The next two characteristics consider the differences in the service model architec-
tures of the iIoT platforms. Although the iIoT platforms mostly seem as a PaaS mod-
el, an in-depth analysis reveals significant differences. Often, the cloud service model
architecture of a focal iIoT platform is not evident from the perspective of a machine
tool company. Nevertheless, this criterion plays an important role in the decision pro-
cess for the right platform, as it has an impact on future partnerships of the machine
tool company. Consequently, it affects different departments and lastly the whole
platform-based iIoT ecosystem. The cloud hosting model complies with the “How?”
dimension in the Business Model Navigator. The following list presents six most
important out of eight characteristics of this criterion (see Fig. 1):
IaaS + PaaS: This combination is mentioned separately due to its influence on the
ecosystem growth. If the iIoT platform is bound to a predefined infrastructure pro-
vider, the machine tool company lacks the flexibility of provider change. Conse-
quently, the vertical cooperation of the machine tool company with the infrastruc-
ture of choice and the ecosystem growth are restricted. If a machine tool company
chooses for instance the Bluemix service by IBM it also uses IBM’s infrastructure.
PaaS + SaaS: If the iIoT platform restricts third-party development and the plat-
form provider is developing application in addition to the iIoT platform on its own,
such a business model as a result restricts the horizontal cooperation of the ma-
chine tool company for instance with software development companies for future
software-intensive services.
Partly IaaS + PaaS: This type of cloud service model allows the machine tool
company to choose, whether to buy the infrastructure additionally to the platform
from the same provider or not. This optional offer extension could potentially re-
strict the selection of a third-party infrastructure partner and thus influencing the
vertical ecosystem growth. Hewlett Packard Enterprise provides such a type of
cloud service model.
PaaS + partly SaaS: Some iIoT platforms as Mindsphere or Cumulocity allow
third-party development. However, they also offer software development for their
platforms by their own departments, competing with their business model in the
SiBW 2018 66
horizontal cooperation of a manufacturing company. That means applications
could be developed by an external complementor or a platform provider. The plat-
form provider could be more efficient in terms of adoption and integration of the
application, while the complementor could have more knowledge about the specif-
ic process. Mindsphere app store represents this characteristic, as one can find
there some basic applications developed by Siemens.
Partly IaaS + PaaS + partly SaaS: This level of cloud services means that the
iIoT platform can optionally be extended by the own infrastructure and application
development, obtained from the iIoT platform provider. The machine tool compa-
ny can decide about the restrictions, whether it chooses the full cloud computing
stack from one source or not. SAP for instance shares this level of flexibility in the
cloud service model for its iIoT platform Leonardo.
IaaS + PaaS + SaaS: If the whole cloud computing stack is provided by one com-
pany, the iIoT platform business model restricts the horizontal and the vertical co-
operation of a machine tool company. Bosch for instance offers the whole cloud
computing stack, hosting its IoT Cloud on its own infrastructure and providing the
implementation and the application development on their own.
Besides the cloud service model, the ability of iIoT platforms to be installed on-
premise or support on-premise installations is also an important criteria for the ma-
chine tool companies. Connectivity and hosting possibilities were mentioned as an
important criterion by many interviewed companies. This criterion is assigned to the
“How?” dimension of the Business Model Navigator, divided as follow:
Cloud only: This characteristic contains the iIoT platforms which are only hosted
in the cloud. Additional connectivity modules could connect the iIoT platform with
on-premise systems. However, the functionalities of the iIoT platform remain in
the cloud. Most iIoT platforms typically provide this type of installation.
Hybrid installation: This type of iIoT platforms allows an on-premise installation
of modules and functionalities, if certain use case requires this. That means the
iIoT platform is modularly divided between the cloud and the on-premise infra-
structure. This type of installation is also commonly seen, as some functionalities
or applications are installed in the edge and communication with the cloud, where
historical data analysis is possible. Hybrid installations of iIoT platforms are com-
monly seen, if the platform provider offers additional hardware modules with cer-
tain pre-installed proprietary applications.
Possible on-premise installation: This type of iIoT platforms allows to run the
whole iIoT platform on-premise, if it meets the customer’s requirements as an al-
ternative to the cloud. This type of installation was a clear expressed requirement
for some manufacturing companies and iIoT platforms such as edbic or Cumuloci-
ty allow this type of installation.
To sum up, the analysis of iIoT platform business models contains five criteria of iIoT
platforms, extracted from single dimension statements of the interviewed experts.
Each identified criterion is assigned to the dimensions “How?” or “Value?” of the
Business Model Navigator [21]. The dimension “What?” represents the value proposi-
SiBW 2018 67
tion, which is regardless of the identified characteristics does not differ and has the
goal to provide technologies for data-driven maintenance. This unifying dimension
finding makes it possible to bridge different (economic and technical) criteria. Fur-
thermore, each single characteristic of the taxonomy is assigned to at least one mar-
ket-ready iIoT platform, additionally increasing the validity of the identified business
model criteria, as it shows their occurrence on the market. The following structure of
the identified criteria in a morphological box is the second artefact of this paper:
Fig. 1. Business model taxonomy of iIoT platforms
4 Conclusion
4.1 Findings and Limitations
This paper presents a taxonomy of iIoT platform criteria in the machine tool industry,
based on dimensions of the St. Gallen Business Model Navigator and assigned to
market-ready iIoT platform solutions. With the increasing relevance of the platform
approach within the manufacturing industries, the identified criteria could help re-
searchers and practitioners during further investigation of successful platform-based
business models or suitable platform design in the iIoT. The demonstrated classifica-
tion within the degree of openness could support the on-going benchmarking of the
iIoT platforms, and by showing the differences supports the unanswered question
about the right degree of openness and appropriate governance for manufacturing
industries. Besides the classification of the openness degree, its interpretation by the
potential complementors is even more important. The hardware integration openness
may look as the least open alternative for iIoT platforms, but the interpretation by the
complementors could be different. If for instance, the software integration openness
SiBW 2018 68
provided by the app store is somehow restricted by the support of particular protocols
or supports only platform-related proprietary standards and modules as certain pro-
grammable logic controller (PLC) systems are excluded on the hardware level, it may
be the most closed alternative for a machine tool company at a second glance.
The morphological box forms a decision support tool for the important process of
platform selection, which can be extended by additional platforms, not mentioned in
the interviews. As the artefact contains economical and technical criteria, it could
support heterogeneous stakeholders within a company, (for instance different depart-
ments), who are affected by the selection of a platform. In terms of the ISO 16355 the
morphological box could assist the voice of the customer [43], providing a unifying
artefact for affected stakeholders in different departments. Furthermore, manufactur-
ing company at the early stage of entrance in the iIoT ecosystem could profit from the
clearly assigned characteristics to market-ready platforms.
The morphological box features practical implications for platform providers to
clarify their value propositions, because the criteria list represents the view of collabo-
rative customers and complementors. In addition, the platform providers could use the
morphological box to compare their iIoT platform against the competition and identi-
fy future niches for their branch of industry for the extension of their current offering.
Nevertheless, the results are limited, regarding the sample size of the qualitative in-
terview study and the specific case study context (data-driven maintenance for ma-
chine tools) as the case study setting for IoT platforms. These limitations refer to the
lack of generalizability of the findings. Furthermore, the interviews are subjects of
subjective influence of the researcher and his understanding of the iIoT platform, thus
forming the interview questions. As the conducted interviews were semi-structured,
the follow-up questions, triggered by the answers of the practitioners could have led
to an incomplete or wrong understanding of iIoT platforms – after all not every inter-
viewed manufacturing company has already been using an iIoT platform for its soft-
ware-intensive services. Some of the studied companies have developed their own
software without using an iIoT platform and some companies have just managed to
initiate pilot projects in the area of iIoT. Consequently, their knowledge on platforms
could be limited, affecting the quality of the data sample.
4.2 Future research
The limitations of this paper require further research work on extension, generaliza-
tion and evaluation of the taxonomy. The maturity of the platforms used in the cases
has not been considered during this research, although the criteria evolvement during
the platform lifecycle [44] could be a potential research area for a follow-up research.
In addition, future research could also consider the sizes of the manufacturing compa-
nies as the collaborative customer and their impact on the selection of iIoT platforms
and their criteria. A follow-up multiple case study analysis based on the taxonomy
could also be a useful extension of the current result to check the completeness and
the dependencies of the business mode criteria. During the given time, it was not pos-
sible to evaluate the taxonomy. Thus, future research should provide evaluation
mechanisms, based on the performance of the utilized iIoT platforms for the manufac-
SiBW 2018 69
turing companies or on the impact of the identified criteria on the growth performance
of the complete platform-based iIoT ecosystem.
Further research towards the customer’s or complementor’s interpretation of open-
ness in the industrial application context is required. The interviews showed a varying
and non-uniform understanding of platform openness from the practitioner’s perspec-
tive. Moreover, the openness criteria of the taxonomy could also support a deeper
research on optimal organizational capabilities of platforms in the field of iIoT and
their interdependency with the identified criteria.
As the findings of this paper provide a conceptional base for a further research on
iIoT platforms, a follow-up work should consider the platform governance. Especially
an in-depth study of the currently used architecture and management of the applica-
tion programming interfaces (API), software development kits (SDK) [45] and other
boundary resources [46] in the field of iIoT platforms could make progress towards its
impact on building an iIoT-platform based ecosystem.
Finally, the identified criteria could support the software-intensive business re-
search on the development of new revenue streams for platform providers, beyond the
ordinary pay-per-use models and traffic billing and have impact on the development
of new business model patterns for iIoT.
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