THE IMPACT OF THE INDUSTRIAL INTERNET OF THINGS ON

Transcription

International Association for Management of Technology
IAMOT 2016 Conference Proceedings
THE IMPACT OF THE INDUSTRIAL INTERNET OF THINGS ON ESTABLISHED
BUSINESS MODELS
DANIEL KIEL
Friedrich-Alexander University Erlangen-Nürnberg (FAU), Industrial Management,
Lange Gasse 20, 90403 Nürnberg, Germany,
daniel.kiel@fau.de (Corresponding)
CHRISTIAN ARNOLD
christian.arnold@fau.de
MATTHIAS COLLISI
matthias.collisi@fau.de
KAI-INGO VOIGT
kai-ingo.voigt@fau.de
Abstract
The Industrial Internet of Things (IIoT) has gained much attention in practice and research over
the last years. It serves as a novel manufacturing paradigm ensuring flexibility and adaptability
of production systems and value chains in order to maintain the future global competitiveness
of manufacturing enterprises. Its implicated technological change will additionally result in
extensive organizational consequences; existing value chains will change resulting in
opportunities for new business models (BM). Consequently, established BMs have to be
reflected critically.
In general, the IIoT constitutes a relatively young research field. While prior literature
concentrated on technological aspects, economic has a backlog compared to technical research.
Nevertheless, some authors are already dealing with potential influences of the IIoT on BMs
in general, but merely focus on specific aspects and have to be regarded in the respective light
of their field of research. Thus, there is no comprehensive picture about the impact of the IIoT
on established BMs. Hence, this paper addresses this research gap by answering the research
question: In what way does academic literature address the impact of the IIoT on BMs of
established manufacturers?
For this purpose, a systematic literature review is chosen as research method. After a structured
selection process with regard to high quality and subject relevance, we eventually identified 87
articles of journals, collected editions, as well as studies of research institutions published
between 2011 and 2015, which are analyzed in detail. Subsequently, these were synthesized
according to the nine components of the BM Canvas by Osterwalder and Pigneur serving as
the analytical framework of this review.
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Our results show that existing academic literature mainly focuses on IIoT-triggered changes of
companies´ key resources and activities, whereas particularly the entire customer perspective
is mostly disregarded. Furthermore, none of the reviewed scientific works comprehensively
deal with the influences of the IIoT on the entire set of BM components at all. Regarding the
most prominent qualitative BM changes, we disclose that the IIoT enables an increasing
offering of customized, individualized, and smart products and services associated by a
consequent service-orientation. For the purpose of the latter, customers are increasingly
integrated in development and manufacturing processes on a collaborative basis. In addition,
the IIoT-inherent generation of enormous amounts of undirected data, i.e. big data, requires
target-oriented data analysis, which then again necessitates specific experts capable of data
mining and processing activities. For all the IIoT´s intelligent production systems, human
beings associated with their keen senses still represent resources of particular importance.
Since one of our research goals is to derive need for further research, the review enables us to
identify areas to be examined more extensively in future. For instance and among others, we
suggest to investigate the customer perspective in more detail as well as to examine the BM as
an entire system contributing to a more comprehensive understanding of changes in the context
of the IIoT.
In conclusion, our paper contributes to a better understanding of IIoT-triggered BM changes,
which is not only valuable for academia, but also for enterprises facing the integration of the
internet into their value creation processes.
Keywords: Industrie 4.0, Industrial Internet of Things, Business Models, Business Model
Innovation, Industrial Manufacturing, Systematic Literature Review
Introduction
Problem outline
Today, manufacturing companies have to deal with an increasingly globalized world that leads
to an accentuation of competition, especially by new competitors from Asia (Sendler, 2013).
This intensification of competition is further enhanced by both shortened technology and
innovation cycles as well as deregulation resulting in a particularly more dynamic competition.
Besides this substantial impact on the manufacturing environment, the industrial sector has to
cope with a strengthened volatility of markets, which complicates quantity forecasts. Another
challenge for industrial companies is inherent in an increased product and process complexity
(Bauer et al., 2014; Bauernhansl et al., 2014; Hirsch-Kreinsen and Weyer, 2014; Spath et al.,
2013; Wirtz, 2011). Consequently, companies’ competitiveness highly depends on their
capability of flexibly and fast supplying customized products at the cost of a large-scale
production (Bauernhansl, 2014; Dais, 2014). This requires production and logistics systems
that are in particular flexible, efficient, and adaptable (Bauer et al., 2014).
Against this background, several future projects emerged all over the world, e.g. the “HighTech Strategy 2020” in Germany or the “Industrial Internet Consortium” in the USA. These
initiatives aim at developing and implementing the Industrial Internet of Things (IIoT), which
is known as “Industrie 4.0” in the German-speaking world, in order to maintain and strengthen
the global competitiveness of the respective industrial locations (Ramsauer, 2013).
Besides a production-technical change, the IIoT also results in extensive organizational
consequences and opportunities (Bauernhansl, 2014; Botthof, 2015). The implementation of
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IIoT technologies enables the restructuring of entire value chains, which is reflected by
cooperations between companies, product and service offerings, as well as a company’s
relationship to its customers and employees (Kagermann et al., 2013). These shifts in value
creation make it necessary for established manufacturers to critically reflect their current
strategy, systematically and early identify new business opportunities, and consequently
innovate and adapt their business models (BM) in order to stay competitive (Bieger and Krys,
2011; Chesbrough, 2007; Gausemeier and Amshoff, 2014; Jonda, 2007; Sendler, 2013).
According to Wirtz (2011) and Schallmo (2014), a BM represents the characterization of how
a company operates. In this context, a BM describes value creation, the value proposition to
customers, relevant resources, as well as the cost and revenue model.
Essentially, the IIoT characterizes the proceeding digitized connection of industrial
manufacturing. Corresponding to its official definition, the IIoT is constituted by the real-time
capable, intelligent, horizontal, and vertical connection of people, machines, objects, and
information and communication technology (ICT) systems to dynamically manage complex
systems (Bauer et al., 2014). This will ultimately result in a “smart factory”, where production
facilities are entirely digitized, intelligent, connected, and arising problems are solved
independently. Moreover, intelligent products contain all necessary information to
autonomously head for production plants and overview future production stages (Kagermann
et al., 2013; Lichtblau et al., 2015).
In general, the IIoT constitutes a relatively young research field. While prior literature
concentrated on technological foundations, challenges, and opportunities, economic has a
backlog compared to technical research (Brettel et al., 2014; Emmrich et al., 2015; Krückhans
and Meier, 2013). Nevertheless, some authors are already dealing with potential influences of
the IIoT on BMs in general (e.g. Bauernhansl, 2014; Herterich et al., 2015a; Russwurm, 2013;
Spath et al., 2013). Since these contributions all focus on specific aspects and have to be
regarded in the respective light of their field of research, there is no comprehensive picture
about the impact of the IIoT on established BMs. Hence, this paper addresses this research gap
by answering the following research question: In what way does academic literature address
the impact of the IIoT on BMs of established manufacturers? In this context, this paper aims
specifically at:
(i) identifying the most frequently analyzed BM components when it comes to IIoT-triggered
changes,
(ii) analyzing academic literature regarding the comprehensive consideration of BMs when it
comes to IIoT-triggered changes,
(iii) identifying the most prominent qualitative BM changes discussed in academic literature,
(iv) identifying potential research gaps having need of further investigations.
To answer the research question, we conduct a systematic and integrative literature review to
reveal IIoT-triggered consequences on BM parts discussed in academic literature to date.
The remainder of this paper is structured as follows: The next section outlines the relevant
theoretical background by defining both the terms “IIoT” as well as “BM”. Furthermore, the
BM Canvas, which serves as the theoretical framework for the analysis of our findings, is
explained. Next, the research design is outlined before the key findings are presented and
subsequently discussed. Eventually, our paper’s contribution to theory and practice is
disclosed.
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Theoretical foundations
Definition and opportunities of the IIoT
Today, manufacturing enterprises have to cope with increasing competition, highly volatile
markets, rising product and process complexity, rising customer expectations, as well as
shortened technology and innovation cycles. As a result, flexibility and adaptability serve more
and more as success factors. Against this background, several initiatives all over the world aim
at developing and implementing the IIoT in order to maintain and strengthen the global
competitiveness of the respective industrial locations (Ramsauer, 2013).
The IIoT, which is often referred to as the fourth industrial revolution, integrates recent trends
from the ICT area into industrial production systems (Spath et al., 2013). Essentially, the IIoT
characterizes the proceeding digitized connection of industrial manufacturing. Correspondent
to its official definition, the IIoT is constituted by the real-time capable, intelligent, horizontal,
and vertical connection of people, machines, objects, and ICT systems to dynamically manage
complex systems (Bauer et al., 2014).
The invention of the steam engine and associated introduction of mechanical production lines
at the end of the 18th century represent the starting point of the Industrial Age. The second
industrial revolution was triggered by the electrification and Frederik Taylor’s principle of
labor division and assembly-line production, allowing a large-scale production of goods. The
third industrial revolution is constituted by the implementation of electronics, information
technologies (IT), and automation in the 1970s. Today, enterprises stand at the threshold of the
fourth industrial revolution, which is characterized by the IIoT (Bauer et al., 2014; Kagermann
et al., 2013; Schlick et al., 2012).
Nowadays, technological achievements in the information and communication area like
sensors and actors are also applicable to the production environment since they are highperformance and favorable at the same time. In this regard, the IIoT can be seen as a new
manufacturing paradigm triggered by recent ICT developments. In this novel production
environment, intelligent and self-controlling objects result in smart products that are constantly
identifiable, steadily locatable, as well as know their latest condition and alternative paths to
their destination (Ramsauer, 2013; Spath et al., 2013). The core element required for the
implementation of the IIoT are cyber-physical systems (CPS), which enable the connection of
information and software technologies on the one hand with mechanical and electronic parts
on the other hand. Furthermore, CPS allow the mutual communication of these elements by
applying communication technologies like the internet (Kagermann et al., 2013; Ramsauer,
2013), which leads to the connection of the virtual and the real world (Spath et al., 2013).
The vision of an entire penetration of the production environment with this manufacturing
approach can be summarized as a “smart factory”. In this manufacturing plant of the future,
workpieces control themselves through the shop floor to machines using the fastest possible
route and book both these machines as well as all required materials. Furthermore, machines
in a smart factory employ information of workpieces to set-up automatically. After being
produced, smart products are further able to organize their delivery to customers autonomously.
In the case of a predicted error, affected machines just reschedule the production themselves
(Kagermann et al., 2013; Kaufmann, 2015; Lichtblau et al., 2015; Spath et al., 2013). The
factory of the future is in control of complexity and consequently less vulnerable to losses of
production. This leads to increased resource efficiency in terms of material usage, energy
consumption, and human work (Kaufmann, 2015; Rehage et al., 2013; Wildemann, 2014).
Moreover, increasing flexibility (Hinrichsen and Jasperneite, 2013; Kagermann et al., 2013),
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customization (Kagermann et al., 2013; VDMA, 2014), demography-sensitive job design,
improved work-life balance (Hirsch-Kreinsen and Weyer, 2014; Kagermann et al., 2013; Spath
et al., 2013; von Garrel et al., 2014; Wieland and Pfitzner, 2014), optimized decision making
(Ganiyusufoglu, 2013), and highly profitable BMs (Bauernhansl, 2014; Botthof, 2015)
represent further opportunities facilitated by the IIoT.
Definition of BM
The BM construct is research subject for more than 20 years (Ghaziani and Ventresca, 2005).
Nevertheless, academia has not yet been able to agree on one clear and consistent definition
(e.g. Casadesus-Masanell and Ricart, 2010; Enkel and Mezger, 2013; George and Bock, 2011;
Johnson, 2010a; Zott et al., 2011). This is evidenced by the fact that the characteristic of
describing the blueprint of how a company creates and incorporates value is the only
widespread consensus about the nature of a BM (e.g. Gassmann et al., 2011; Johnson, 2010b;
Magretta, 2002; Osterwalder et al., 2005; Teece, 2010; Wirtz et al., 2010; Zott and Amit, 2010).
Beyond this lowest common denominator, several different opinions exist about what a BM is.
Demil and Lecocq (2010), for instance, identified two different perceptions of a BM: a
transformational and a static approach. The first one understands a BM as a tool to initiate
changes within the organization. The latter approach regards a BM as the blueprint of the
relations between a business’ core components and represents the majority. However, this
perception constitutes only a frame, which again contains various definitions and perspectives.
Chesbrough (2010) and Chesbrough and Rosenbloom (2002), for instance, follow a very
technology-focused comprehension. In their concepts, a BM links technical potential with
value creation and therefore makes the technology’s latent value exploitable. Another view is
enrooted in the transaction theory. Here, a BM is constituted of the content, structure, and
governance of value creating transactions. Furthermore, the BM describes the steps required to
execute these transactions (Amit and Zott, 2001). According to the BM understanding of
Hendrix (2005), a BM describes all parties concerned with the value creation as well as the
value proposition, whereby he directs his attention towards the necessary process steps, i.e. a
very process-oriented BM conceptualization. Morris (2009) also follows such a processoriented BM approach, but is rather market-concentrated. He determines a BM mainly as a
company’s relationship to the market and all processes required for establishing these
relationships. Another approach is an architectural one, which is consistent with most
approaches in academic literature (Wirtz, 2011; Zott and Amit, 2010). It defines a BM as the
configuration of tasks an organization has to conduct to create and deliver value to its
customers. Beyond a company’s own activities, this approach also considers the organization’s
partner network, which is necessary to create value. Furthermore, it illustrates how these single
elements are related to each other (Amit and Zott, 2012; Chesbrough, 2007; Osterwalder et al.,
2005; Santos et al., 2009; Sorescu et al., 2011; Teece, 2010; Zott and Amit, 2010).
For further proceeding, it is necessary to agree on one definition. Osterwalder et al. (2005), as
representatives of an architectural approach, compared the most frequently mentioned BM
definitions and deduced nine components constituting a BM. Therefore and since it is widely
spread in academic literature, their BM concept should be applied for the purpose of this study.
Moreover, the illustrative character of the BM Canvas, a graphic presentation of this BM
concept developed by Osterwalder and Pigneur (2010), facilitates its understanding without
over-simplifying the complexity. This is doubtless one reason for its high relevance for the
application by organizations all over the world (Osterwalder and Pigneur, 2010). Consequently,
its utilization also qualifies for the IIoT´s highly practical nature. According to Osterwalder
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and Pigneur’s (2010) definition, “[a] business model describes the rationale of how an
organization creates, delivers, and captures value” and consists of the following nine building
blocks shown in figure 1.
Key
activities
Key
partnerships
Customer
relationships
Value
proposition
Key
resources
Customer
segments
Channels
Cost
structure
Revenue
streams
Figure 1: The BM Canvas
The center is formed by the value proposition, which describes products and services that add
value for the particular customer segments representing the different groups of customers the
company intends to address. The channels characterize how the company reaches its customers
and communicates with them. The description of the customer relationships represents a third
customer-oriented field. The key resources are those resources that are mandatory to perform
the key activities, which describe the activities a company has to perform to create the value
proposition and to run the entire BM. The firm’s network of crucial suppliers and other
important partners is described by the key partnerships. Finally, both the revenue streams and
the cost structure give some indication of the financial aspects (Osterwalder and Pigneur,
2010).
Methodology
In a general sense, this paper aims at reviewing and displaying relevant, existing, and academic
literature in terms of manufacturers´ BM changes triggered by the IIoT. We apply a systematic
and integrative literature review as an appropriate methodological approach for the
achievement of our objectives for two reasons. First, it is systematic, scientific, transparent,
elaborate, and replicable for the identification, evaluation, synthesis, and discussion of existing
works of researchers, academics, and experts (Fink, 2013; Tranfield et al., 2003). Associated,
anybody using the same databases, keywords, and selection criteria should obtain the same set
of results (Leseure et al., 2004). Second, the exemplary collocation of several studies serves
not only the presentation of the state-of-knowledge but also the disclosure of critical and
disregarded aspects or unsolved problems. Eventually, areas where knowledge is still lacking
can be observed and needs for further research can be derived (Fink, 2013). The results and
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novel insights of an integrative research review are able to replace former studies and provide
significant scientific benefits (Cooper, 1989; Cooper, 1998). The methodological approach
used in this paper follows the works of Rashman et al. (2009), Soni and Kodali (2011), and
Winter and Knemeyer (2013). Thus, the subsequent explanations refer to the aspects of time
horizon, database selection, journal selection, article selection, article classification, and
classification analysis.
Time horizon for article selection
For the review and evaluation process, the publication date of the articles is between April 2011
and November 2015. The reason for choosing 2011 as the starting point for the collection of
relevant material is that in 2011 the term “Industrie 4.0” was officially published the first time
by the German government in the context of the world´s most important and popular industry
trade show, the “Hannover Messe”. The date of the official definition of the term by the
Industrial Internet Consortium (USA) was some time later, in March 2014. Consequently, it is
made sure that these terms were not used before. The reason for choosing November 2015 as
the end point is to collect the most recent scientific publications addressing this highly
significant and forward-looking topic.
Database selection
In the paper at hand, we collected data from three different databases: Business Source
Complete (EBSCO), Science Direct, and Google Scholar. Additional potential articles were
identified by applying the snowball method, which aims at collecting literature from previous
publications´ reference lists. In doing so, we avoid to leave out potential relevant articles, which
may not be registered in the databases. This procedure is widely accepted and has been
employed in existing literature reviews (e.g. David and Han, 2004; Franke and zu KnyphausenAufseß, 2014; Soni and Kodali, 2011; Webster and Watson, 2002; Winter and Knemeyer,
2013).
Journal selection
There is wide consensus that papers published by highly cited and ranked journals are usually
of better quality, whose integration again results in an enhanced quality of a literature review
(e.g. Berning and Holtbrügge, 2012; Light and Pillemer, 1984; McKinnon, 2013).
Nevertheless, due to the IIoT´s degree of novelty in research, we decided to not restrict our
review by the selection of journals with a particular high reputation. On the contrary, we
decided to extend it by reliable and relevant collected editions and book chapters. This decision
is in line with Cooper (1989), who stated that relying on only journal articles is appropriate
“when the published research contains several dozen, or in some cases several hundred,
relevant works”. Obviously, this is not applicable to the term IIoT, which has not become
apparent until 2011, neither in practice nor in theory.
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Article selection
In the first step, we both defined databases to be applied as well as relevant keywords
addressing our research project. Since we aimed at the highest possible reliability and relevance
of keywords, they were not only derived from existing literature but also from two
brainstorming and discussion rounds with three independent research colleagues within the
Friedrich-Alexander University Erlangen-Nürnberg. Eventually, the following keyword
sample was revealed: “Industry 4.0”, “next industrial revolution”, “fourth industrial
revolution”, “internet of things and services”, “industrial internet of things”, “cyber-physical
systems”, “business model”, “business model development”, “business model innovation”,
“business concept”, “business logic”, and “business creation”. These were extended by their
respective German synonyms since the IIoT was first defined in Germany, as mentioned before.
To link them, we used the operator “AND”, which is recommended by Theisen (2013).
 Industry 4.0
 Business model
 Next industrial revolution
 Business model development
 Fourth industrial revolution
 Internet of things and services
AND
 Business model innovation
 Business concept
 Industrial internet of things
 Business logic
 Cyber-physical systems
 Business creation
Figure 2: Combinations of search keywords
Subsequently, our search resulted in 146 articles, which contained at least one of the keyword
combinations (see figure 2) and was published between April 2011 and November 2015. After
having removed duplicates from the sample (Rashman et al., 2009), each of the authors read
their abstracts and conclusions independently in order to assess their relevance to our research
question. The authors learned that some of the abstracts and conclusions proved irrelevant or
low-quality. Thus, the sample was narrowed down to remaining 94 articles, which were
subsequently read in their entirety by all authors. The critical requirement, the articles had to
fulfill, was a strong relationship between the IIoT and its economic influences on established
companies´ BMs. Additionally, the articles´ bibliographies were scanned (snowball method)
to avoid leaving out potential relevant articles not registered in the searched databases. These
were also read in their entirety by all authors. The full-text review followed the systematic
approach of Rashman et al. (2009). Finally, the authors met in a discussion session and
evaluated the articles´ relevance and quality. The research goals, definitions of key terms,
methodological rigor, results and conclusions, as well as the relevance to IIoT/BM were of
particular interest. Non-relevant documents were consequently extracted. In doing so, we
received a final list of 87 articles and book chapters, which represented the data corpus of the
subsequent integrative literature review. Thereby, we also ensure the high quality and
comprehensiveness of the paper at hand. Figure 3 gives an overview of the article selection
process in order to ensure a systematic, transparent, and replicable literature review.
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Search in electronic databases:
 EBSCO
 Science Direct
 Google Scholar
Are the following criteria
fulfilled?
Exclusion
 Time period
 Keywords
146 articles
Reading of abstracts and conclusions
Are the articles lacking in
relevance and/or quality?
Are there duplicates?
Exclusion
94 articles
Reading articles in their entirety and
scanning of bibliographies
Are the articles of high quality
and of subject relevance?
Exclusion
87 articles
Figure 3: Overview of article selection process
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Article classification
Afterwards, the final sample of 87 documents was analyzed in depth and the respective authors´
deliberations were classified according to the nine building blocks of the BM Canvas (for their
explanation see section “theoretical foundations”), wherever possible and justifiable. Thus, the
BM Canvas serves as the analytical framework for the synthesis of the existing body of
literature. It gives our analytical review a clear, unambiguous, and comprehensible structure.
Classification analysis
In the final step, all authors of the paper at hand compared, critically reflected, and discussed
the classified articles and book chapters. Given the descriptive nature of this systematic review,
we did not apply statistical methodologies for the classification of the literature sample and the
derivation of future research necessities. The final step of the review process enables us to
achieve four research goals we set in the beginning, i.e.:
(i) identify the most frequently analyzed BM components,
(ii) analyze academic literature regarding the comprehensive consideration of BMs,
(iii) identify the most prominent qualitative BM changes discussed in extant literature, and
(iv) identify potential research gaps and future needs for research
in the context of IIoT-triggered BM adaptations.
Findings
In the following, we depict the review results in terms of the existing literature´s deliberations
with regard to IIoT-triggered BM changes. For this purpose, each of the nine BM building
blocks is explained successively and explicitly with regard to its most important aspects.
Value proposition
According to Brettel et al. (2014), under the influence of the IIoT, the portfolio of products and
services offered to customers mainly aims at individualizing offerings (e.g. batch size 1 and
mass customization) and accelerating time to market. In addition, the increasing integration of
ICT systems enhances quality, flexibility, innovativeness, as well as the development and
offering of smart products and services (Dais, 2014; Kagermann, 2014; Russwurm, 2013). The
latter is e.g. represented by condition monitoring, which serves as the basis for predictive
maintenance. Such data-driven product and service offerings are enabled by smart components
equipped with sensors, processors, connectivity, and cloud interfaces. Associated, a consequent
service-orientation entails opportunities in terms of novel business concepts and income
sources (Porter and Heppelmann, 2014).
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Customer segments
Manufacturing enterprises integrating the IIoT into their value creation face the opportunity to
not merely change existing, but rather create new markets constituted by novel customer
segments, which were not addressed previously (Porter and Heppelmann, 2014). Here, carsharing concepts of several automotive original equipment manufacturers (OEM) serve as a
good example: enhanced customer- and service-orientation, implementation of smart ICT
systems, and shared-usage models approach customers, who were not willing to afford a car in
the past (Davis et al., 2012; Sendler, 2013).
Channels
The IIoT leads to an extended use of social media and online communities. Accompanied by
the opportunities of e-commerce and interactive online markets, companies establish a basis
for approaching customers and delivering value propositions. Furthermore, social networks
provide manufacturing potentials in terms of e.g. replacing emails during escalation
management processes, sourcing data to optimize production processes, and analyzing social
networks (Schließmann, 2014; Schöning and Dorchain, 2014).
Customer relationships
With regard to customer relationships, the reviewed articles emphasize the importance of
collaborative and intensified relations, which are characterized by closer and earlier teamwork
with and integration of customers (Schuh et al., 2014). In this context, exemplary methods and
concepts are social media, online communities, open source, and open innovation. These
produce large amounts of data, which can be used to add value throughout the whole value
chain. Therefore, key partners and customers not only have to be provided with sufficient,
transparent information, but also have to be integrated in corporate structures. To enable
cooperation and co-design with customers, manufacturing enterprises need high collaboration
and integration levels (Ehrenberg-Silies et al., 2014; Schuh et al., 2014).
Key resources
One of the most important resources in the context of the IIoT are value creation networks.
They enable enterprises to act and react in real-time and across corporate boundaries enhancing
flexible value propositions. For this purpose, a standardized, automatized, and intelligent
connectivity of several production and logistics systems, manufacturers, suppliers, service
providers, and customers via cloud-based platforms is essential (Forstner and Dümmler, 2014;
Kleinemeier, 2014). Both these platforms and IT/ICT systems in general represent crucial
resources. According to Herterich et al. (2015a; 2015b) and Ramsauer (2013), companies
depend on IT capabilities since they enable cost efficiency and added values. Due to the
influence of the IIoT and the Internet of Things and Services, established ICT systems have to
be adapted, modernized, and optimized. Associated, the primary objectives are a cross-
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company connectivity, availability, flexibility, man-machine integration, and mastering of big
data. Additionally, the use of ICT systems for the coordination of and interaction with partners
and customers plays an important role. In this context, software integration has to be mentioned
as well. High levels of software integration into traditional production systems and processes
result in significant productivity increases.
Moreover, the integration of cloud technologies into production environments, i.e. cloud
manufacturing, constitutes novel key resources. In the course of this, the functionality of cloud
computing in terms of offering decentralized on-demand services is adapted to production
processes. The most important advantage of cloud manufacturing is billing of services
according to real operating times, so that users save capital expenditures and investments. This
is particularly important for small and medium-sized enterprises, which are willing to benefit
from computer-based analyses and applications, but do not have financial resources available.
Examples are IT services such as “software-as-a-service”, “platform-as-a-service”,
“infrastructure-as-a-service” or even “everything-as-a-service”. Due to its influences on
traditional and established product-oriented manufacturing, cloud manufacturing opens up new
possibilities in terms of service-oriented manufacturing. On the one hand, open source
approaches and digital platforms form the appropriate basis for customer integration. On the
other hand, in the context of smart manufacturing, digital platforms serve technology
integration across functional areas and users as well as the collection of relevant information
and data with regard to processes, fulfillment of tasks, or procurement/supplier quality (Davis
et al., 2012; Marston et al., 2011; Repschläger et al., 2010; Wu et al., 2014; Xu, 2012; Zhang
et al., 2014).
The extensive integration of smart components equipped with sensors yields the collection of
large amounts of data (= big data) as well as their targeted analysis in order to benefit from
innovation and value creation potential. Certainly, data can also be collected from social
networks. In this way, consumer sentiment can be observed and, consequently, communication
with customers improved. Admittedly, large amounts of data do not necessarily provide
valuable information. All the more important is a targeted, reasonable, and professional data
mining and processing (Adolph, 2014; Ehrenberg-Silies et al., 2014).
According to Gaziulusoy and Twomey (2014), Kalva (2015), and Würtz et al. (2015), additive
manufacturing (AM) serving as a technological driver of the IIoT represents a further key
resource in the context of IIoT-affected BMs. In general, AM is known as 3D printing
procedures, which change not only established development processes but also production
activities. Based on a digital model, 3D printing applies materials, e.g. plastic, metal, or
ceramic, layer-by-layer to construct products. Due to research and development advances, the
variety of applicable raw materials continuously increases. Consequently, AM substitutes
traditional mindsets of late customer decoupling points, platform strategies, and passive
customer roles with fulfilling customized demands under the slogan “mass customization”.
Currently, AM is already used for prototype constructions associated with significant savings
of time compared to traditional processes. Likewise, development advances, prices decrease,
and customers´ attention increases resulting in higher willingness to pay for customized
products. Additionally, digital models can be exchanged globally via internet, processed,
individualized, and printed locally. Eventually, this is associated with many possibilities for
novel BMs based on decentralized manufacturing (Kalva, 2015).
Despite increasing influences of IT and ICT systems, cloud manufacturing, AM, and software
integration, Spath et al. (2013) emphasize that human beings assume important responsibilities
in the context of the fourth industrial revolution. People are required as decision makers,
684
sensors, and actuators forming the core of each BM. Due to the complete and network-like
connectivity of production plants, single objects and smart components can cause problems,
whereupon human beings have to intervene with the aid of assistance systems. For all with
intelligent sensors equipped CPS, there may emerge gaps, which can exclusively be closed by
keen human senses. Nevertheless, the influence of the IIoT on the work environment is not
without consequences. The advancing automation of administrative and manufacturing
processes indeed results in productivity increases, but, in return, a highly qualified workforce
is required for consequent activities such as data analyses. Victims affected by downsizing are
probably employees of lower and middle qualification and wage levels.
Key activities
According to Herterich et al. (2015a; 2015b), customer integration plays an important role
when it comes to a BM´s essential activities in the context of the IIoT. Customers become codesigners participating in development processes to design products in compliance with their
needs. These possibilities are enabled by changing key resources as described before.
With regard to higher levels of IT and software integration, e.g. by implementing CPS and
cross-company connectivity, enterprises are facing respective activities in terms of IT and
software optimization and adjustments in order to establish an appropriate reference
architecture (Ramsauer, 2013).
Our literature review indicates that extant literature is convinced of the importance of analyzing
big data (Bulger et al., 2014; Markl et al., 2013; Stockinger et al., 2015). The collection of
process and machine condition data as well as their analysis by applying algorithms and data
mining results in an early error detection and correction, reduction of downtimes, and
efficiency increases. Additionally, the use and analysis of big data provides new business
opportunities and data-driven products. Here, the growing fitness and health market serves as
a good example (Bulger et al., 2014; Markl et al., 2013; Stockinger et al., 2015).
Eventually, as per Matt et al. (2015), the trend of distributed manufacturing, i.e. an IT-driven
production network containing geographically distributed production plants, establishes the
basis for customized products, shortened delivery times, and production on demand.
Key partners
In the context of the IIoT, customers are collaborative partners and co-designers, which are
integrated via open source or open innovation processes. The basic idea of open innovation is
the extension of the traditional inside-out perspective with an outside-in perspective, which
specifically opens up companies` boarders to the market environment. Thus, external players
may participate in product development processes, supported by the implementation of cloud
technologies (Ehrenberg-Silies et al., 2014).
Moreover, according to Porter and Heppelmann (2014), the IIoT influences key partners with
regard to changing established supply relationships. Since products are increasingly digitized,
suppliers are less expected to provide OEMs with physical components but rather with virtual
sensors, software, and cloud-based platforms. In addition, data analyses and data mining
requires specialized analysts as well as IT experts from outside, if not available at one´s
corporation. Markl et al. (2013) clarify, that manufacturers should make an effort to transform
685
IT departments into data analytics divisions to benefit from big data. Another possibility is to
outsource these activities to specialized service providers in order to reduce labor costs, which
is defined as “data analytics-as-a-service” (Bulger et al., 2014; Stockinger et al., 2015).
In general, Weiner et al. (2010) emphasize the relevance of a powerful, reliable, and platformbased partner network constituted by IT suppliers and respective service providers.
Revenue streams
Eventually, a BM has to be operated profitably. Fleisch et al. (2014) refer to an increasingly
consequent service-orientation, enabled by the integration of physical and digital components
and the subsequent offering of hybrid solutions. According to them, companies become able to
generate novel revenues streams by establishing up-to-date BM types, e.g. “multi-sided”,
“freemium”, or “add-on” BMs.
Cost structure
The literature review reveals several cost saving potentials. Computer-based tests and
simulations enable reductions of product and prototype development expenses (Posada et al.,
2015). Moreover, according to Bauernhansl (2014), the IT-driven optimization of value chains
results in significant savings in terms of operating costs, e.g. energy, labor, and capital costs.
Here, smart grids serve as a good example, since they intelligently identify production
downtimes, e.g. at weekends or nights, and consequently activate energy-saving modes.
Further cost savings are possible with regard to inventory, logistics, quality, maintenance, and
complexity, reaching up to 60-70 percent.
Concluding Discussion
In the following sections, we concisely address the four goals of the paper at hand, defined in
the introduction:
With regard to the most frequently analyzed BM components in literature, we reveal that until
now authors mainly focused on the key resources (78 out of 87 articles) and the key activities
(75 out of 87 articles) associated with a successful adaption of manufacturing BMs to the IIoT.
The next most prevalent BM component subject to IIoT-triggered changes is the value
proposition, which is considered by 40 out of 87 articles. As table 1 clearly illustrates, the
remaining six components constituting the BM Canvas play a subordinate role in the academic
works between 2011 and 2015. Particularly, channels (14 out of 87 articles), customer
relationships (11 out of 87 articles), and customer segments (7 out of 87 articles) hardly attract
scholars´ attention.
686
Channels
Customer
relationships
Key resources
Key activities
Key partners
Revenue
streams
Cost structure
Overall results
Customer
segments
Author(s) (year)
Value
proposition
Table 1: Evaluation of the 87 selected articles from 2011 to 2015
40
7
14
11
78
75
20
21
25
Adolph (2014)
X
Bauernhansl (2014)
X
Baum (2013)
X
X
Becker (2015)
Belahcen et al. (2015)
X
Bildstein and Seidelmann (2014)
BMWi (2015)
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
Bochum (2015)
Bollhöfer et al. (2015)
X
X
X
X
Bracht et al. (2013)
X
X
X
X
X
Bulger et al. (2014)
Büttner and Brück (2014)
X
Dais (2014)
X
Davis et al. (2012)
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
Diemer (2014)
X
X
Drath and Horch (2014)
X
X
X
X
X
X
X
X
Fallenbeck and Eckert (2014)
X
Forstner and Dümmler (2014)
X
Gaziulusoy and Twomey (2014)
X
Gobble (2013)
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
Haag (2015)
X
X
Hartmann (2015)
X
X
X
X
X
X
X
X
Herterich et al. (2015a)
X
X
Herterich et al. (2015b)
Hirsch-Kreinsen (2015)
X
X
X
X
Hoffmann (2014)
Hoppe (2014)
X
X
Gorecky et al. (2014)
Günthner et al. (2014)
X
X
X
Fleisch et al. (2014)
X
X
Deuse et al. (2015)
Ehrenberg-Silies et al. (2014)
X
X
Bridges et al. (2015)
Brody and Pureswaran (2015)
X
X
X
Botthof (2015)
Brettel et al. (2014)
X
X
X
X
X
X
(continued)
687
X
78
75
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
Ittermann et al. (2015)
Jazdi (2014)
X
Kagermann (2014)
X
Kalva (2015)
X
X
X
Kärcher (2015)
Kleinemeier (2014)
X
X
Krückhans and Meier (2013)
Kuehnle (2014)
X
Leminen et al. (2015)
X
X
X
X
Liggesmeyer and Trapp (2014)
Cost structure
Huber and Kaiser (2015)
11
Revenue
streams
14
Key partners
7
Key activities
Channels
40
Key resources
Customer
segments
Overall results
Author(s) (year)
Customer
relationships
Value
proposition
20
21
25
X
X
X
X
Lüder (2014)
X
Lüdtke (2015)
X
X
X
X
X
X
X
X
X
X
X
X
X
X
Markl et al. (2013)
X
Marston et al. (2011)
X
Matt et al. (2015)
X
X
X
Mayer and Pantförder (2014)
X
X
Naumann et al. (2014)
X
X
Niggemann et al. (2014)
X
X
Ovtcharova et al. (2015)
X
Pantförder et al. (2014)
X
X
Perera et al. (2015)
X
Pisano et al. (2015)
X
X
Porter and Heppelmann (2014)
X
X
X
X
X
X
X
X
X
X
X
X
Posada et al. (2015)
X
X
X
Roos and Fusco (2014)
X
X
X
X
X
X
X
X
X
X
X
X
Schlick et al. (2014)
X
X
X
Schließmann (2014)
X
X
X
Schöning and Dorchain (2014)
X
X
X
X
X
X
X
Sendler (2013)
X
X
X
X
X
688
X
X
X
Russwurm (2013)
Schuh et al. (2014)
X
X
Ramsauer (2013)
Repschläger et al. (2010)
X
X
Petschow et al. (2014)
Pötter et al. (2014)
X
X
X
X
X
X
X
X
(continued)
X
Steegmüller and Zürn (2014)
X
Cost structure
Spath et al. (2013)
Revenue
streams
14
Key partners
7
Key activities
Channels
40
Key resources
Customer
segments
Overall results
Author(s) (year)
Customer
relationships
Value
proposition
11
78
75
20
21
25
X
X
X
X
X
X
X
X
X
Stockinger et al. (2015)
ten Hompel and Henke (2014)
X
Tödter et al. (2015)
X
Verl and Lechler (2014)
X
Vogel-Heuser (2014)
X
Weiner et al. (2010)
Wende and Kiradjiev (2014)
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
Wieland et al. (2013)
Wu et al. (2014)
Würtz et al. (2015)
X
X
Xu (2012)
Zhang et al. (2014)
X
Zülch (2013)
X
X
X
X
X
X
Concerning the comprehensiveness of BM component considerations in literature, our paper
shows that most reviewed articles (21 out of 87 articles) examine exactly two of the nine BM
components (based on our applied analytical framework). In addition, more than half of the
analyzed articles (49 out of 87 articles) merely deal with a maximum of three BM components,
representing only a minor part of an entire BM. As opposed to this, we identified just seven
articles referring to more than five BM components. Particularly important, it has to be noticed
that there is no article comprising all of the nine components defined by the BM Canvas. In
other words, academic literature lacks a comprehensive analysis of IIoT-triggered BM changes.
Having a closer look at the most prominent qualitative BM changes in literature, the review at
hand identifies customized and individualized value propositions as well as smart products and
services, associated by a consequent service-orientation, as critical changes in the context of
the IIoT. Thereby, manufacturers are not only able to change existing, but rather create new
markets and customer segments. These are approached by the increasing application of social
media and online platforms/communities. Eventually, the enhanced offering of service
solutions results in collaborative and intensified relationships with customers. Consequently,
the latter represent critical key partners with regard to developing products and services jointly.
Hence, customer integration into value creation processes plays an important role. However,
customers are not the only players integrated from the outside, but are supplemented with
specialized data analysts and IT experts capable of mastering big data. This outside-in process
is particularly important, if such experts and capabilities are not available at one´s corporation.
Otherwise, one´s IT departments have to adapt respective competences and conduct targeted
big data analyses by themselves, which plays a crucial role since the integration of key
resources such as IT/ICT systems and cloud technologies produces enormous amounts of
undirected information and data. For all those intelligent systems, human beings associated
689
with their keen senses are still resources of particular importance. With regard to the financial
perspective of a BM, we learned that a consequent service-orientation is associated with novel
revenue sources and that the IIoT implicates far-reaching cost saving potentials. The
abovementioned insights can likewise be regarded as managerial implications for
manufacturing enterprises facing the implementation of the IIoT into their value creation.
Since one of this paper´s research goals is to derive need for further research, the review
enables us to identify six areas to be examined more extensively in future:
(i) Due to the IIoT´s degree of novelty in research, we observed that there are far too few
scientific works dealing with an economic perspective. Compared to mature research areas
such as BMs, industrial services, or technological diffusion, the quantity of high-quality
published journal articles has to do a lot of catching-up.
(ii) Not only the financial perspective of a BM in terms of how to generate novel revenue
streams as well as how to realize cost saving potentials, but also the crucial network of
partners has to be analyzed further and in greater depth. The latter is particularly important
against the IIoT´s background of an across-company network and connectivity idea.
Additionally, the abovementioned lacking consideration of the channels, customer
relationships, and customer segments, which constitute the entire customer perspective of
a BM, requires research efforts by e.g. strategic and marketing scholars.
(iii) Since none of the reviewed articles deals with all of the nine BM components, we suggest
a more comprehensive understanding and examination of the BM as an entire system
entailing interplaying components. It is of high importance to consider BM components
not only in isolation, but also with regard to their mutual influences and interdependencies.
Besides these needs for future research identified in the course of our review, further points of
contact for further research, which result from the fact that our study is, like all research, not
without limitations, are derived below:
(iv) Despite the fact that a systematic literature review is an appropriate and powerful method
for the provision of a comprehensive overview by structuring, refining, and clustering the
existing knowledge on a specific topic (Carter and Ellram, 2003), it does not allow us to
elaborate on the specific papers. Consequently, we suggest further qualitative, explorative
studies on IIoT/BM in combination with quantitative methods to understand respective
issues better.
(v) In the context of the last-named research gap, we recommend future research projects
addressing the change management and BM innovation process required to achieve the
BM changes revealed by the review at hand successfully. Associated, critical success
factors regarding the implementation of the IIoT are of particular interest. Moreover, future
research should strive for answering the questions “how to realize IIoT-based
opportunities?” and “how to meet IIoT-based challenges?”.
(vi) Since this paper merely reviews articles dealing with IIoT-triggered changes of and
influences on established manufacturing BMs, we are convinced of the interestingness and
importance of further examining, which completely new and highly innovative BMs will
probably emerge. Associated, academia has to understand, which are potential novel
market players in this context.
In conclusion, our systematic review of 87 academic articles from 2011 until 2015 contributes
to a better understanding of IIoT-triggered BM changes by working up the existing body of
relevant literature. As such, this paper provides key insights on where research has been and
where additional future research is needed.
690
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