Analyzing Industry 4.

0 Models with Focus

on Lean Production Aspects

Christian Leyh1(&), Stefan Martin1, and Thomas Schäffer2

1
Technische Universität Dresden, Chair of Information Systems,
Esp. IS in Manufacturing and Commerce, Helmholtzstr. 10,
01069 Dresden, Germany
Christian.Leyh@tu-dresden.de
2
Faculty of Business Administration, University of Applied Sciences Heilbronn,
Max-Planck-Str. 39, 74081 Heilbronn, Germany
Thomas.Schaeffer@hs-heilbronn.de

Abstract. Nearly all enterprises have to face enormous challenges when

dealing with digitalization topics such as Industry 4.0/Industrial Internet. To
support companies to handle these challenges and therefore, to be able to
“move” in an Industry 4.0 environment several frameworks or reference models
already exist. Within this paper we provide results of a detailed analysis of
selected Industry 4.0 models. However, we show that not all models are dealing
with this topic in a holistic way but rather focusing on specific aspects or
requirements of Industry 4.0. Additionally, we focus in our analysis on lean
production aspects since the basic principles of lean management/lean produc-
tion offer since the 1980s appropriate measures to optimize production and
therefore, can be/should be addressed by Industry 4.0 models as well. Hence, it
became obvious that those principles are not often addressed in Industry 4.0
models. Despite the fact that those aspects are often seen as a basis for Industry
4.0 implementation this is mostly not integrated in the respective models.
Therefore, the contribution of our paper consists of the classification of 31
Industry 4.0 models/frameworks as well as the identification of needs for further
research to enhance existing Industry 4.0 models to a more holistic approach.

Keywords: Lean management
Lean production
Industry 4.0

Industrial Internet
Digitalization
Digital transformation
Reference models
Maturity models

1 Motivation and Objectives

Since the beginning of the first Industrial Revolution in the middle of the 18th century
and the development of steam engines as well as the increased use of hydropower,
strong efforts have been made by industrial nations such as Germany or the United
States of America to create a basis for their economy’s growth [1]. One of the most
important foundations was and still is the linking of value chains within a factory and
even beyond the companies’ borders [2]. To achieve this in an appropriate way still large
planning effort is necessary which can be supported by lean management concepts.
However, these concepts can only partially deal with the challenges of appropriate

© Springer International Publishing AG, part of Springer Nature 2018

E. Ziemba (Ed.): AITM 2017/ISM 2017, LNBIP 311, pp. 114–130, 2018.
https://doi.org/10.1007/978-3-319-77721-4_7
 Analyzing Industry 4.0 Models 115

linking value chains. When focusing lean management especially in light of the ongoing
digitalization of business the need for new and adequate forms of communication in
order to support these value chains or even whole value networks within and throughout
all partners in the value networks still emerges up to date [2]. Hence, the implementation
of lean management concepts is not primarily based on technical solutions, which are
nevertheless elementary, but rather on structures in the form of architectures and stan-
dards that simplify and standardize business processes.
However, considering the evolution of technology, digitalization provides manifold
opportunities to support or even renew business processes. These advanced techno-
logical opportunities, especially the merging of the physical with the digital world,
result in new fundamental paradigm shifts affecting all industry sectors. Companies
must handle global digital networks, improve automation of individual or even of all
business processes, and reengineer existing business models to gain momentum in
digital innovation. The prevailing and steady high dynamics in everyday business show
that constant changes and adjustments (to which also digitalization belongs) will be no
exception, but rather the rule in the future economy [3–6].
To appropriately deal with this, adjusted management and communication concepts
have become highly important. In broad parts of society, the Internet of Things
(IoT) has already established itself as an interlinked communication network to connect
people, “things” and also whole value chains. Examples include package tracking, vital
data logging via Smart watch or Smart Home control within the domestic environment.
This development is accompanied by increasingly short and individual life cycles of
products which consequently lead to new production requirements. Transferring the
approaches of IoT to companies resulted in the concept of Industry 4.0 by connecting
especially the production itself with the internet; leading to an increasing digitization of
products and systems associated with their interconnectedness [7–9].
An analysis using the “Google Trends” tool (Fig. 1) shows that the interest in the
field of Industry 4.0 has never been as significant as in the last couple of years. This
also indicates the increasing international perception of the term Industry 4.0.
However, especially for those companies willing to use/integrate Industry 4.0
aspects in their production this is not a trivial task. Therefore, to support companies
acting in the field of Industry 4.0 different reference models, frameworks or Industry
4.0 architectures have emerged. Using these “tools” should enable companies to
structure their business processes appropriate regarding Industry 4.0 requirements.
This is where the present paper comes in. As extended paper of Leyh et al. [10] we
set up a study to analyze selected architectural/reference models of Industry 4.0.
Moreover, those models should be characterized according to the basic principles of
lean management/lean production since these approaches offer since the 1980’s
appropriate measures to optimize production. Thus, in our opinion, those principles
should be addressed by/included in Industry 4.0 models as well. Our study was driven
by two research questions:
Q1: What organizational and technical reference models for Industry 4.0 exist?
Q2: What relationship can be established between the encountered models and Lean
Production?
116 C. Leyh et al.

100
as a percentage of the total number
Proportion of search queries

80

60

40

20

0
2012 2013 2014 2015 2016 Years
Industrie 4.0 Industry 4.0

Fig. 1. Search queries for the terms “Industrie 4.0” and “Industry 4.0” on Google since 2012

In order to answer these questions, we set up a study based on a systematic

literature analysis. The aim of the literature analysis is to describe, summarize, eval-
uate, clarify or integrate aspects focusing Industry 4.0 and/or lean production. Selected
study results will be presented in this paper. Therefore, the paper is composed of four
sections: Following this introduction, the second section provides a conceptual back-
ground of the key terms Industry 4.0 and lean production. The third section will be the
main part of this paper where our literature review is described in its methodology and
selected review results are presented and discussed. The paper concludes with a short
summary, as well as an outlook for future research.

2 Conceptual Background

2.1 Industry 4.0

The term Industry 4.0 or the Industrial Internet is characterized as the fourth stage of
the industrial revolution (Fig. 2) and consists of an increasing interconnectedness of
products and systems. Focusing on the enhancement of the automation, flexibility, and
individualization of products, production, and the connected business processes,
Industry 4.0 aims at connecting the physical and virtual worlds [11, 12]. From a
production perspective, Industry 4.0 is understood as the movement of intelligent
workpieces that independently coordinate their paths through the factory. Machines are
able to “realize” these tracks and communicate in real time with the corresponding
warehouse. If necessary, orders are automatically triggered by means of targeted cal-
culations, errors can be computed at an early stage of production, and the right
interpretation enables a machine to change its production order. Information is pri-
marily used to assess and control current processes [13]. Thus, an essential feature of
Industry 4.0 can be seen in information aggregation in engineering and operations
across different projects, plants, and plant operators [2].
 Analyzing Industry 4.0 Models 117

Networked/linke
d and
Use of communicating
electronics (enterprise)
Introduction of assembly and IT systems using
work-sharing for automation Internet
mass production purposes technology
Introduction of
mechanical using electric
production energy Starting at the
Starting in the
plants using mid of the 20th beginning of the
water and steam century 21st century
power Starting at the
end the 19th
century
Starting in mid
of the 18th
century

Fig. 2. Historical development of the industrial revolutions [14]

A universal definition for the term Industry 4.0 does not exist. Therefore, we
deduced a working definition to serve as the foundation for our research which we also
used in other related Industry 4.0 studies of ours (e.g. [3, 8]):
Industry 4.0 describes the transition from centralized production towards one that is very
flexible and self-controlled. Within this production, the products and all affected systems, as
well as all process steps of the engineering, are digitized and interconnected to share and pass
information and to distribute this along the vertical and horizontal value chains and beyond in
extensive value networks.

However, the fact that companies have not yet implemented many parts of Industry
4.0 is shown in Table 1.

Table 1. Comparison of today’s factory and an Industry 4.0 factory [15]

Characteristics Today’s manufacturing Industry 4.0 manufacturing
Component Key Precision Independent action based on own
(e.g. sensor) attributes predictions
Key Smart sensors and fault Degradation monitoring and
technologies detection remaining useful life prediction
Machine Key Producibility and Independent action based on own
(e.g. controller) attributes performance (quality predictions and comparison with
and throughput) inventory data
Key Condition-based Operating time recording with
technologies monitoring and predictive health monitoring
diagnostics
Manufacturing Key Productivity and overall Independent configure, maintain and
System attributes equipment effectiveness organize
(e.g. MES) (OEE)
Key Lean operations: work Low-maintenance, self-adapting
technologies and waste reduction production systems
118 C. Leyh et al.

2.2 Lean Production

Lean production/lean management already existed before the concept of Industry 4.0
was introduced/has been established. This form of the production management was first
seized by Taiichi Ōno in 1978, who was responsible for the production of the Japanese
automotive manufacturer Toyota [16]. After the end of World War II, Toyota noticed
that the American car manufacturers were able to produce nine times more in the same
time because they manufactured large batch sizes in order to compensate long set-up
times. This was not possible for Toyota at this time because their production volumes
were too small. Thus, Toyota adjudicated to develop their own philosophy that con-
forms the high quality of the label “Made in Germany” and competes with the faster
and higher productivity levels of the US manufacturers at the same time [17]. After
successfully implementing the respective measure to achieve a leaner production, a
study including over 90 production centers that was conducted by the Massachusetts
Institute of Technology in 1985 showed that Japanese car manufacturers performed
better in all performance and quality parameters than the American and European
manufacturers [18]. In total, this concept led to a paradigm shift and to the fact that lean
production is now defined as a third production system design, since it is neither mass
production nor manual work [19].
The basic principle of lean production is based on the avoidance of eight causes of
waste. These are summarized by Ōno as transport, storage, accessibility of processes,
unnecessary movement, waiting times, overproduction, tight tolerances, defects and,
above all, unused skills of the employees [17]. In addition, Oeltjenbruns [20] classifies
three central principles of lean production. This classification, shown in Fig. 3, mainly
refers to the influence of these basic ideas on the company.

Benefits for
high incremental fundamental high
the company

BPR
Management
commitment
the company
Impact on

TQM/
Lean Mgt.

Kaizen

low low

Fig. 3. Mutual classification of Kaizen, TQM/Lean Management and BPR [20]

 Analyzing Industry 4.0 Models 119

The term Kaizen describes the continuous improvement process, which never ends
and the sum of the changes contributes to the long-term success of the company. The
Total Quality Management (TQM) is also leads to a long-term change in values of the
entire workforce, which contributes to process improvement. The approach of Business
Process Reengineering (BPR) requires the highest management deployment but brings
the greatest benefit to the company at the same time. It refers to the radical change and
a fundamental reorganization of processes with the aim of achieving greater synergies
and the avoidance of waste. For the reorganization, the organizational structure of the
company plays a central role. Due to the size of the changes, it can be assumed that this
approach is only used for major strategic transformations such as the introduction of
completely new product ranges, a fundamental reorientation of the company or even a
structural change in the entire production system [20].

2.3 From Lean Production to Lean Automation to Industry 4.0

Kolberg and Zühlke [21] describe Industry 4.0 as a further development of Computer
Integrated Manufacturing (CIM) and thus as a network approach, which is comple-
mented by CIM through communication and information technology. This approach is
supported by the integration of Cyber Physical Systems (CPS) [21]. These systems are
a combination of two essential elements. These elements are the control of processes
with the help of integrated software systems and the network of these software systems.
However, the network is not limited to a single production line, factory or company,
but to the global value chain or whole value networks [22].
With these systems lean automation can be implemented in order to support and
expand the approaches and concepts of lean production. Lean automation automates a
process with as little waste as possible. The objectives of short lead times with minimal
costs and the highest quality remain unchanged. Consequently, it is possible to provide
a company-wide representation of the actual situation in real time and to enable
simulation-based optimization measures based on decentralized control systems. Each
workpiece is thus clearly identifiable and the enterprise systems have information of the
customer-specific mass product. Optimization measures and new services can be cre-
ated from the resulting data collection. Ōno also describes the fact that lean production
and automation are not mutually exclusive but the monitoring and use by the employee
is elementary and does not work as a replacement [16]. The employee becomes the
smart operator of production. The smart operator is, for example, notified by means of
e-mail or SMS in the event of a fault reported by sensors. Thereby, the time from the
occurrence of the error to the noticing of the error reduces. At the same time, the system
makes suggestions for troubleshooting. In addition, modern technology such as Aug-
mented Reality can provide a better representation of the process flows. The employee
has access to previously available data such as cycle times in his immediate field of
view [21].
However, the variety of possibilities in the linking, networking and interconnect-
edness requires a systematization of Industry 4.0 aspects, which shows the possibilities
for the support and adapting of lean production.
120 C. Leyh et al.

3 Literature Analysis

As shown in Sect. 2 Industry 4.0 and lean management/lean production are both
complex concepts which seem to have some connecting points and similarities. To
investigate these aspects, we set up a study approach to contrast those two concepts.
Since lean production is a mature concept and Industry 4.0 is an emerging topic we set
up a systematic literature review to identify current papers dealing with the aspects of
Industry 4.0. More specifically, a distinction is made between narrative articles and
those that examine statistical and mathematical questions according to [23]. Narrative
articles provide definitions for the most important terms and concepts.
Mathematical-statistical papers provide comprehensive insights into existing research
results and support a more deductive approach [24]. This resulted in a systematic
literature review that is explained in the following subsection. After identifying and
analyzing the Industry 4.0 articles we compared and contrasted identified Industry 4.0
frameworks and models with important aspects and approaches of lean
management/lean production which is discussed later-on.

3.1 Methodology
The systematic literature analysis is intended to answer research question 1 and is based
on four steps according to [25] and [26]:
Step 1 – Selection of databases and search terms: To get a broad overview of the
topic we selected the databases ScienceDirect (www.sciencedirect.com) as well as
Academic Search Complete (www.ebsco.com/products/research-databases/academic-
search-complete) and Business Source Complete (www.ebsco.com/products/research-
databases/business-source-complete). In addition, we used Google Scholar to identify
articles may be not listed in scientific databases. The search fields for the database
search were limited to abstract, title and keywords. The search terms themselves
resulted from a short preliminary search according [26] and were afterwards discussed
with researchers at the respective university institutes. This resulted in the following
search string:
TITLE-ABSTR-KEY(“industrie 4.0” OR “industry 4.0” OR “fourth industrial
revolution” OR “smart factory” OR “digital factory”) and TITLE-ABSTR-KEY
(“framework” OR “scheme” OR “structure” OR “model”)
Step 2 – Implementation of practical screening criteria: With the help of step 2 and
step 3, journal papers, conference papers and reports shall be classified. Considering
the practical screening criteria, no temporal restriction was applied. In addition, the
search was focused on articles in German as well as English, in which a general
reference model for Industry 4.0 (or at least to a large extent Industry 4.0 concepts,
frameworks or approaches) are presented. Therefore, articles were excluded which deal
only indirectly with Industry 4.0 or only with a partial specific aspect of Industry 4.0
such as Big Data and do not classify this into a reference scheme. All identified papers
were transferred in the literature management program Zotero (www.zotero.org).
Afterwards, using the tools functionality a duplication check was performed.
 Analyzing Industry 4.0 Models 121

Step 3 – Implementation of methodological screening criteria and Step 4 –

Synthesis of the results: In these steps, a deeper analysis of the articles, that were not
excluded during practical screening was conducted. First, the papers were classified
according basic criteria:

1. Manufacturing environment: Does the model/the paper focus the manufacturing

industry?
2. Industry 4.0 concept: Does the paper present/discuss/evaluate a reference model
which covers the aspects of Industry 4.0 in total? Or are solely partial aspects of
Industry 4.0 addressed?
3. Does the model address software and/or hardware aspects of Industry 4.0
requirements?
4. To what extent are lean production principles included and addressed in the ref-
erence model?
5. To what extent are business applications or enterprise systems explicitly addressed
in the model?
6. Can the paper be classified as narrative article or merely as examining statistical and
mathematical aspects (according to [23])?
7. Is an evaluation presented and discussed regarding the suitability and fit of the
model in terms of Industry 4.0 requirements?
To assess the papers according these criteria/questions we used Harvey Balls with
the differentiation shown in Table 2.
In addition to the seven merely general criteria we also assessed the models
regarding four specific implementation requirements of Industry 4.0 postulated by
different German national associations (e.g., VDMA – Mechanical Engineering Industry
Association; Bitkom – Federal Association for Information Technology, Telecommu-
nications and New Media; ZVEI – German Electrical and Electronic Manufacturers’
Association) [9]:
8. The extent of horizontal integration across value networks
9. The extent of vertical integration in the company
10. The extent of product lifecycle management (PLCM) and consistency of
engineering
11. The extent of the “human factor” – the employee as a conductor in the value
networks
The results of the literature review and the analysis of the papers and models will be
presented according to our classification scheme in the next section.
Table 2. Criteria classification
Symbol Description
○ Criterion is not addressed
◔ Criterion is addressed indirectly
◑ Criterion is mentioned
◕ Criterion is partially addressed
● Criterion is fully addressed
122 C. Leyh et al.

3.2 Selected Results

The search in the aforementioned databases with the presented search string scored a
total of 166 papers. 96 of the articles found are published in conference proceedings
which would be excluded by focusing the search solely on high ranked journals.
Therefore, this is a first approval of our selected search methodology. In addition, nine
out of the 166 were duplicates listed in more than one database. Therefore, those papers
were excluded from deeper screening. After the practical screening of the remaining
157 papers, 31 papers could be identified that deal with an Industry 4.0 framework or
model according to our criteria. Those 31 articles than were screened in depth to assess
the criteria of step 3. Those articles are published not earlier than 2012 which again
emphasizes the relevance and topicality of Industry 4.0.
Selected results will be discussed in the following paragraphs. Table 3 gives a short
summarization of the results.
The total assessment of the remaining articles as well as the reference list for those
papers are provided in the Appendix. As shown in Table 4 in the Appendix, nearly all
articles (27 out of the 31) are addressing the manufacturing industry. The remaining
articles deal with, for example, the service sector (Tables 4 and 5, No. 18) or the
construction sector (Tables 4 and 5, No. 16). During the deeper analysis, it became
clear that 15 of the 31 articles presented or discussed an Industry 4.0 approach with a
holistic focus (see column 2 in Table 4) whereas 16 papers addressed specific partial
aspects of Industry 4.0. In addition, it is striking that a discussion of software archi-
tectures is mainly provided in the articles. Hardware issues and aspects are not dis-
cussed without software aspects. Furthermore, it becomes apparent that most of the
articles address the topic of enterprise architecture at least somehow. Therefore,
emphasizing the enterprise and business process structure is important when a company
wants to successfully “move” in an Industry 4.0 environment. These considerations of
a generally valid architecture are accompanied by a few statistical or mathematical
models, as those discussions often constitute a higher degree of detail as in papers with
a more narrative focus (according to [23]).

Table 3. Short Categorization of the identified articles

Category of articles Number of
papers
Relevant in the sense of the research questions 31
↳No holistic Industry 4.0 reference model included 16
↳Holistic Industry 4.0 reference model included 15
↳Lean Production principles as main topic addressed 3
↳No Lean Production principles as main topic addressed 11
↳Lean Production principles are addressed in a medium to high extent but 1
not as main topic
 Analyzing Industry 4.0 Models 123

Considering lean production only three articles (Tables 4 and 5 in Appendix, Nos.
4, 6, and 11) are actively addressing those principles and incorporating them into an
Industry 4.0 setting to a full extent (in regard to our criteria). Although lean production
is often seen (as discussed in Sect. 2) as one of the foundations for Industry 4.0 most
related concepts in the identified 31 articles are either taken on only marginal aspects of
lean production or are not at all addressing these principles in combination with
Industry 4.0. However, only one article (Tables 4 and 5, No. 11) out of the mentioned
three articles fosters a production environment and actually incorporates lean pro-
duction principles and approaches in an Industry 4.0 reference model. Also those
authors present those principles as main point of their model.
In the second part of the in-depth analysis of the papers regarding the Industry 4.0
implementation requirements (criteria 8–11) it is noticeable that considering all articles,
that provide a holistic Industry 4.0 model, vertical integration is the main subject in 13
out of those 15 articles. Whereas, the integration and consideration of employees as the
main paper topic is addressed the least. Thus, it can be assumed that the automation and
therefore often the replacement of human labor are often given more emphasis.
However, in the discussion of the concept of Industry 4.0 human replacement is not the
main goal. Moreover, the employees should be qualified to work with the Industry 4.0
technologies and instruments and be a “valuable support and cooperation partner” for
the smart production floor. The aspects of product life cycle management and espe-
cially the consistency of engineering from factory planning to the final stage of the
product life cycle are included and discussed regarding at least a certain
section-by-section consistency in four articles with a really high emphasis (Tables 4
and 5, Nos. 4, 13, 15 and 18). Especially in the description of the Digital Eco-Factory
by Matsuda et al. (Tables 4 and 5, No. 13 and 15) this is especially explained as the
main subject in the production sector. However, this small number of hits shows that
there is still a need for further discussion and higher emphasis of the specific aspect of
Industry 4.0.

4 Discussion and Conclusion

Summing this up, we could identify several models and frameworks addressing the
complex field of Industry 4.0 and therefore, provide a first answer to research question
Q1. However, not all models are dealing with this topic in a holistic way but rather
focusing on specific aspects or requirements of Industry 4.0. Hence, a common goal
could be identified throughout all papers. The (explicit or indirect) stated goal is always
to reduce the cost per unit produced. It was also crucial for all models and often
discussed in the papers that for a further development and appropriate implementation
of Industry 4.0, the communication in the three relationships man-man, machine-man
and, above all, machine-machine are seen as especially important. From this, the
machine-machine communication gains an even larger impact since in Industry 4.0
environments this communication and information sharing are the essential foundation
for the autonomous machine decisions. As a first conclusion from these aspects the use
of appropriate information and communication technology (ICT) is a crucial factor in
Industry 4.0 environments as it is also stated by several authors (e.g., [4, 6, 8, 11]).
124 C. Leyh et al.

Regarding our research question Q2 it became obvious that lean management/lean

production principles are not often addressed in Industry 4.0 models. Despite the fact
that those aspects are often seen as a basis for Industry 4.0 implementation this is not
integrated in the respective models nor is it discussed in connection with these models.
Thereby, as shown in Table 4 merely the vertical integration is the main aspect in the
identified models mostly also in combination with horizontal integration aspects. This
also supports the fact that appropriate ICT is essential for Industry 4.0 environments.
From this, further research needs arise. First of all, considering the ICT it will be a
challenge for enterprises which want to “move” in the field Industry 4.0 to identify and
implement the appropriate ICT for the own company. Therefore, in addition to the
identified models general maturity models are needed (with regard to Industry 4.0
requirements). Several models regarding these issues already exist. Those models deal
for example with enterprise system landscapes for Industry 4.0 (e.g., [8]), with orga-
nizational aspects (e.g., [13]) or system-specific aspects in detail (e.g., [27]). However,
a mapping of these maturity models would be necessary to combine their different
points of view. For example, different maturity level assignments and dimensions
between these models should be developed to enable companies to fully classify
themselves in terms of Industry 4.0 requirements in all levels of their enterprise. With
this work, companies would be able to determine their overall maturity in the field of
Industry 4.0.
Furthermore, since the aspects of consistency of engineering and the employees
themselves as well as the evolution of work (often named as Work 4.0) should be
addressed in those models as well since they are essential issues of Industry 4.0.
Therefore, also for further research it would be interesting to enhance and further
develop existing lean production methodologies such as Kanban or Kaizen with regard
to their Industry 4.0 suitability, since these approaches are already designed in their
structure for self-organization and automation (that is a key issue of Industry 4.0). In
addition to automation, the human factor must be better integrated into existing models,
since employees will remain an essential part of the business processes. Therefore, the
cooperation between employees and automated machines should be addressed in more
detail in future research.
Closing this, despite the fact that there exist already several frameworks and ref-
erence models that are considering Industry 4.0 environments, there are still a couple of
issues that can be seen as unsolved or at least not adequately addressed. Therefore, still
further research is necessary to combine the existing approaches with additional key
aspects of Industry 4.0 that are not addressed in a good extent yet.

Appendix

Additional possibility to download the assessment of all articles and the respective ref-
erence list: https://tu-dresden.de/bu/wirtschaft/isih/ressourcen/dateien/isih_team/pdfs_
team/Supplementary-Material.pdf.
 Analyzing Industry 4.0 Models 125

Table 4. Categorization of the identified articles according to the classification criteria

Industry 4.0
General criteria implementation
aspects ([9])

Employees as a conductor
Lean production principle

Vertical integration (e.g.,

Mathematical / statistical

across the value network

Assessment of Industry
Software (S) / hardware

PLCM / consistency of
Horizontal integration

in the value networks

Holistic Industry 4.0

Business application
(H) consideration
Manufacturing

4.0 suitability
environment

in a factory)

engineering
References

concept

aspects
No.

1 Ayadi et al. 2013 S&H

2 Azevedo et al. 2010 S

3 Bagheri et al. 2015 S

4 Brettel et al. 2016 S

5 Debevec et al. 2014 S&H

6 Diez et al. 2015 S

Flatscher and Riel

7 S
2016
Francalanza et al.
8 S
2017

9 Ivanov et al. 2016 S

10 Jufer et al. 2012 S&H

Kolberg and Zühlke

11 S&H
2015

12 Long et al. 2016 S

Matsuda and
13 S
Kimura 2015

14 Matsuda et al. 2012 S

15 Matsuda et al. 2016 S

126 C. Leyh et al.
 Analyzing Industry 4.0 Models 127

Table 5. List of identified articles

No. Reference
Ayadi, M., Costa Affonso, R., Cheutet, V., Masmoudi, F., Riviere, A., Haddar, M.:
Conceptual Model for Management of Digital Factory Simulation Information.
1
International Journal of Simulation Modelling 12(2), 107-119 (2013).
doi: 10.2507/IJSIMM12(2)4.233
Azevedo, A., Francisco, R.P., Bastos, J., Almeida, A.: Virtual Factory Framework:
An Innovative Approach to Support the Planning and Optimization of the Next
2
Generation Factories. IFAC Proceedings 43(17), 320-325 (2010).
doi: 10.3182/20100908-3-PT-3007.00069
Bagheri, B., Yang, S., Kao, H.-A., Lee, J.: Cyber-physical Systems Architecture for
3 Self-Aware Machines in Industry 4.0. IFAC-PapersOnLine 48(3), 1622–1627 (2015).
doi: 10.1016/j.ifacol.2015.06.318
Brettel, M., Klein, M., Friederichsen, N.:The Relevance of Manufacturing Flexibility
4 in the Context of Industrie 4.0. Procedia CIRP 41, 105–110 (2016).
doi: 10.1016/j.procir.2015.12.047
Debevec, M., Simic, M., Herakovic, N.: Virtual Factory as an Advanced Approach
5 for Production Process Optimization. International Journal of Simulation Modelling
13(1), 66-78 (2014). doi: 10.2507/IJSIMM13(1)6.260
Diez, J.V., Ordieres-Mere, J., Nuber, G.: The HOSHIN KANRI TREE. Cross-plant
6 Lean Shopfloor Management. Procedia CIRP 32, 150-155 (2015).
doi: 10.1016/j.procir.2015.02.120
Flatscher, M., Riel, A.: Stakeholder integration for the successful product–process co-
7 design for next-generation manufacturing technologies. CIRP Annals -
Manufacturing Technology 65(1), 181–184 (2016). doi: 10.1016/j.cirp.2016.04.055
Francalanza, E., Borg, J., Constantinescu, C.: A knowledge- based tool for designing
8 cyber physical production systems. Computers in Industry 84, 39–58 (2017).
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