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Exploring industry 4.0 A readiness assessment for SMEs

Thesis · June 2020

DOI: 10.13140/RG.2.2.12170.08647

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 Exploring industry 4.0
A readiness assessment for SMEs

Natalie Grufman & Sinéad Lyons

Department of Computer
and Systems Sciences
Degree project 30 HE credits
Computer and Systems Sciences
Degree project at the master level
Spring term 2020
Supervisor: Eriks Sneiders
Abstract
Industry 4.0 is considered to be the fourth industrial revolution and involves virtual and physical systems
that are interconnected and collaborate together in an autonomous way. This thesis presents a readiness
assessment of small and medium enterprises (SMEs) regarding industry 4.0 implementation. Industry 4.0 is
a relatively new concept within the computer science field and it raises the interest on how to make use of
technologies included in the concept and profit from them. In order for SMEs to prepare for industry 4.0
implementation, a readiness assessment is needed. The research problem is that there is a limited amount of
readiness assessments available that covers challenges and specific requirements for SMEs in industry 4.0.
The research question has been formulated to be What is the level of readiness of SMEs to embrace the
benefits and tackle the challenges of industry 4.0? and will be answered by using a survey approach as a
research strategy. The data collection and analysis method selected is to utilize a literature review with a
grounded theory approach. The literature review was used as the foundation for conducting a readiness
assessment method - the IMPULS model. The IMPULS model has been used with a modified approach to
fit the research question.
The results show that SMEs receive no higher level than level two out of six levels (0-5) according to the
IMPULS model in the modified IMPULS approach, indicating they are at a learning state of industry 4.0
implementation. The conclusion is that SMEs are at the intermediate level which indicates that some SMEs
have to some extent started to implement industry 4.0 in their strategies. The intermediate level is equivalent
to level two which is a rather low readiness level. Intermediate is categorised by IMPULS into Learners,
which describes well what state SMEs according to the readiness assessment are.

Keywords: Industry 4.0, SME, IMPULS, Readiness assessment, Literature review

Synopsis
Background
Industry 4.0 is the new industrial revolution involving smart technologies and sophisticated business
strategies. Industry 4.0 is in a fast evolving state and is something both large and small companies want to
take advantage of. There is plenty of literature about industry 4.0 and its main pillars, but not so many
covering industry 4.0’s impact on small-medium enterprises. Today there is a need for a readiness
assessment to show how small-medium enterprises can prepare for industry 4.0.

Problem
Readiness assessments can be of great help to clarify for SMEs how to prepare for an organizational
transformation towards industry 4.0. The research problem is that there is a limited amount of readiness
assessments related to industry 4.0 for SMEs to utilize.

Research Question
The research question for this thesis is What is the level of readiness of SMEs to embrace the benefits and
tackle the challenges of industry 4.0? and will unfold in a readiness assessment.

Method
This study will use a survey as the overall strategy and a literature review will be the data collection
method combined with grounded theory as the data analysis method. The readiness assessment is based on
the literature review and the IMPULS model with some modifications.

Result
SMEs have no higher level than intermediates which is level two out of six (0-5). The challenges and
opportunities found in the literature review gives an understanding to why SMEs are at this readiness
level.
Discussion
The limitation in this study is only considering IMPULS. The ethical aspects are considered throughout
the paper by following ethical guidelines. SMEs take a substantial part of the global economy, hence, the
study is of both high value and originality by adding to the field industry 4.0 regarding SMEs as a group.
The main societal implications are a new readiness assessment of SMEs based on IMPULS, most
important dimensions "Cost" and "Smart Factory" and industry 4.0 challenges and benefits. These
contribute to researchers and business people who want to conduct readiness assessments on their
companies.
Acknowledgement
Our studies at Stockholm University have brought us to this point where we are proud to have finalized our
master thesis. It has been a great journey for us and has given us memories and knowledge for the future.We
are grateful to the DSV department of Stockholm University, for providing an excellent study environment
and a great online platform allowing students to easier work on distance and provide new opportunities and
space to grow. Finally, we would like to thank the professor Eriks Sneiders for being our supervisor during
this thesis, providing us with feedback and assistance and for being a completely fenomenal supervisor. He
has not only inspired us to write a good thesis, but also sparked our curiosity for writing research on a high
academic level.
Table of Contents
Table of Contents ........................................................................................................................................... 6
1 Introduction ............................................................................................................................................ 1
1.1 Background ................................................................................................................................... 1
1.1.1 Industry 4.0? ............................................................................................................................. 1
1.1.2 Previous research of industry 4.0 ............................................................................................. 1
1.1.3 Fundamentals of Industry 4.0 ................................................................................................... 2
1.1.4 Definitions of SMEs ................................................................................................................. 4
1.1.5 SMEs: readiness for industry 4.0 ............................................................................................. 4
1.2 Research problem ......................................................................................................................... 5
1.3 Research question ......................................................................................................................... 5
2 Methodology .......................................................................................................................................... 6
2.1 Research strategy .......................................................................................................................... 6
2.1.1 Selected research strategy ........................................................................................................ 6
2.1.2 Alternative research strategy .................................................................................................... 6
2.2 Data collection and data analysis method ..................................................................................... 7
2.2.1 Define ....................................................................................................................................... 8
2.2.2 Search ....................................................................................................................................... 8
2.2.3 Select ........................................................................................................................................ 9
2.2.4 Analyze ..................................................................................................................................... 9
2.2.5 Present ...................................................................................................................................... 9
2.3 Alternative data collection methods ........................................................................................... 10
2.4 Alternative data analysis methods .............................................................................................. 10
2.5 Ethics and research quality ......................................................................................................... 11
3 Readiness assessment ........................................................................................................................... 13
3.1 Dimensions and associated fields ............................................................................................... 14
3.1.1 Smart factory .......................................................................................................................... 14
3.1.2 Smart operations ..................................................................................................................... 15
3.1.3 Smart products........................................................................................................................ 15
3.1.4 Data-driven services ............................................................................................................... 15
3.1.5 Employees .............................................................................................................................. 15
3.1.6 Strategies and organizations ................................................................................................... 15
3.2 Readiness levels .......................................................................................................................... 16
3.3 IMPULS original process ........................................................................................................... 17
3.4 Modifications of IMPULS .......................................................................................................... 18
3.4.1 Chosen dimensions and fields ................................................................................................ 18
3.4.2 Minimum requirements .......................................................................................................... 20
 3.4.3 Assessing levels approach ...................................................................................................... 22
3.4.4 Assessing scores approach ..................................................................................................... 22
3.5 Planned approach ........................................................................................................................ 22
4 Research process .................................................................................................................................. 24
4.1 Pilot study ................................................................................................................................... 24
4.2 Literature review ......................................................................................................................... 24
4.3 Assessing readiness .................................................................................................................... 25
4.4 Ethical issues .............................................................................................................................. 26
5 Results .................................................................................................................................................. 27
5.1 Geographical spread ................................................................................................................... 27
5.2 Literature review findings........................................................................................................... 28
5.2.1 Selective code: challenges ...................................................................................................... 28
5.2.2 Selective code: opportunities.................................................................................................. 29
5.2.3 Selective code: readiness ........................................................................................................ 29
5.3 Readiness assessment result ....................................................................................................... 29
5.3.1 Level 0: Outsider .................................................................................................................... 30
5.3.2 Level 1: Beginner ................................................................................................................... 31
5.3.3 Level 2: Intermediate.............................................................................................................. 32
5.3.4 Readiness score ...................................................................................................................... 33
6 Discussion ............................................................................................................................................ 34
6.1 Tackle challenges........................................................................................................................ 34
6.1.1 Technological issues............................................................................................................... 34
6.1.2 Cost ......................................................................................................................................... 35
6.1.3 Organizational issues.............................................................................................................. 35
6.1.4 Lack of competencies ............................................................................................................. 36
6.2 Embracing the benefits ............................................................................................................... 36
6.2.1 Mass customization ................................................................................................................ 36
6.2.2 Corporate collaboration .......................................................................................................... 37
6.2.3 Good preconditions ................................................................................................................ 37
7 Conclusion............................................................................................................................................ 38
7.1 Societal consequences ................................................................................................................ 38
7.2 Delimitations............................................................................................................................... 39
7.3 Future research............................................................................................................................ 39
References .................................................................................................................................................... 40
Appendices ................................................................................................................................................... 42
Appendix A – Mapping process details ................................................................................................... 43
Appendix B – Literature review references ............................................................................................. 45
Appendix C – Quotes and fields .............................................................................................................. 48
Appendix D – Reflection Document Natalie ........................................................................................... 53
Appendix E – Reflection Document Sinéad ............................................................................................ 54
List of Figures
Figure 1 Overview of top 100 research papers from Google Scholar, year 2016 and forward. .................... 2
Figure 2 Aazam et al (2018) visualizing IoT, IIoT, and Industry 4.0. ........................................................... 3
Figure 3 IMPULS model by Lichtblau et al (2015). .................................................................................... 14
Figure 4 IMPULS levels by (Lichtblau et al, 2015)..................................................................................... 17
Figure 5 Geographical spread ...................................................................................................................... 27
Figure 6 Readiness assessment results. ........................................................................................................ 30
List of Tables
Table 1 Wolfswinkel et al (2013), Five-stage grounded-theory method for reviewing the literature in an
area. ................................................................................................................................................................ 7
Table 2 Minimum requirements. .................................................................................................................. 21
Table 3 Planned approach for readiness assessment. ................................................................................... 23
Table 4 Literature review process. ............................................................................................................... 25
Table 5 Literature review findings. .............................................................................................................. 28
Table 6 Scores for each dimension. ............................................................................................................. 33
Table 7 Found challenges (readiness score rounded to whole numbers). .................................................... 34
1 Introduction
The fourth industrial revolution is here and it is transforming the technologies, economies and the society
itself (Schwab, 2017). This study attempts to answer the research question What is the level of readiness of
SMEs to embrace the benefits and tackle the challenges of industry 4.0? by providing a readiness assessment
based on literature review. The aim of the study is to receive a greater knowledge regarding the research
question and gather in-depth knowledge of important aspects of industry 4.0 in the field computer science.

1.1 Background
This chapter is structured in the way of first bringing up what industry 4.0 is, followed by previous research
of the topic. After that, the main parts of industry 4.0 will be briefly explained and then comes an overview
of small-medium enterprises (SMEs). This leads up to the research problem and research question.

1.1.1 Industry 4.0?

The development of technology and smart solutions is in a fast-evolving state without indications of slowing
down. There is a need for a highly specialized industry that supports high global competitiveness, and
according to Schwab (2017), a German professor in economics and the founder of the World Economic
Forum, industry 4.0 is no longer in the future, but in the present. Industry 4.0, also called the Fourth
Generation Industrial Revolution, was first introduced in Germany 2011 at the Hanover Fair. At the fair,
discussions were made to describe how industry 4.0 will revolutionize the creation of global value chains
(Popkova, Ragulina & Bogoviz, 2019). Industry 4.0 is a concept built on the digital revolution where smart
machines communicate with each other using wireless connections and are connected to a system that can
make decisions on its own by visualizing the entire manufacturing process (Schwab, 2017). Hence,
computers can make decisions without human involvement. The virtual and physical systems are
interconnected and collaborate together in a flexible way. With these new intelligent solutions, industry 4.0
can benefit any industry worldwide that is ready to make use of the technologies. Industry 4.0 is a term
originally from the German word "industrie 4.0" and started as an initiative from the German government
to increase competitiveness of the manufacturing industry in Germany (Stentoft, Jensen, Philipsen & Haug,
2019). Multiple countries have now followed Germany and started their own initiatives for moving towards
industry 4.0. Even Sweden launched its strategy for the new smart industrialization in 2015 which states
that industry 4.0 is one of the main focus areas. The goal is that Swedish industries should be leaders of the
digital transformation worldwide and by this strengthen their competitiveness in the global market (Swedish
Ministry of Enterprise and Innovation, 2016; Machadoa et al, 2019).

1.1.2 Previous research of industry 4.0

Previous research often described the pillars of industry 4.0 and focus mainly on the definition of the term
and where it was coined, rather than on the impact and the use of the actual technology (Liao, Deschamps,
Loures & Ramos, 2017; Lu, 2017; Zhong, Xu, Klotz & Newman, 2017; Vaidya, Ambad & Bhosle, 2018;
Xu, Xu & Li, 2018). For this study, the interest is not to add more regarding definitions or heritage, but to
investigate more concretely the impact of industry 4.0. When reviewing literature about industry 4.0, the
results among the top 100 hits on Google Scholar using the keyword "industry 4.0" show that from 2016 it
is mainly state of the art articles (25.5%), the fundamentals of industry 4.0 (26.5%), some case studies
(2.9%) and discussions about how it impacts in larger companies and in society (18.6%), and organisational

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transformation strategies on how to adapt to using industry 4.0 on a high level (26.5%). The data collection
for figure 1 is part of a pilot study, which will be described in chapter 4 Research process, heading 4.1 Pilot
study.

Figure 1 Overview of top 100 research papers from Google Scholar, year 2016 and forward.

1.1.3 Fundamentals of Industry 4.0

Industry 4.0 includes many technologies and acts as an umbrella term. The techniques and technologies
included can be called fundamentals of industry 4.0. Others state that the four main drivers are cyber-
physical systems (CPS), Internet of Services (IoS) and then IoT and smart manufacturing mentioned above
(Roblek, Meško and Krapež, 2016). There is some lack of concreteness regarding what the exact parts of
industry 4.0 are, however, it can be said to be a collection of disruptive technologies. Disruptive technologies
are considered to be new technology that changes how things are done. According to the Cambridge
Dictionary disruptive technology is defined as something that "... overturns a traditional business model,
which makes it much harder for an established firm to embrace" (Cambridge Dictionary, 2020). In regards
of industry 4.0, it is a revolution containing new technologies, that changes traditional business models in
terms of using technologies in new ways, justifying industry 4.0 to be called a collection of disruptive
technologies. Many authors put more weight on CPS or CPPS (cyber-physical production system) to be a
major part of the term. Vaidya et al (2018) mention in their article that industry 4.0 has four main drivers,
namely, Internet of Things (IoT), Industrial Internet of Things (IIoT), Cloud based manufacturing and also
smart manufacturing. They are also describing the nine pillars of industry 4.0 for applying the concept and
making use of it. The nine pillars of industry 4.0 according to Vaidya et al (2018) are:

Big Data and Analytics

Big Data consists of four V’s, namely; volume of data, variety of data, value of data and velocity of
generation of new data and analysis. Analysis is the data analysis of previous data used to forecast and
find patterns.

Autonomous Robots
Autonomous robots are used for doing repetitive and autonomous production work in a precise manner,
but also doing tasks where humans are not fit, such as restricted work. The robots can be used to complete

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tasks within given circumstances and put focus on important aspects, which could be safety, versatility,
flexibility and collaboration with other robots or humans.

Simulation
Simulations can be in the shape of 2D or 3D simulations for creating a virtual reality of parts of a
production. It can be used to simulate reality in a virtual space, including machines, products and humans
and thus enable for a higher level of effectiveness via decrease setup time and increase quality at the same
time, amongst other aspects.

System Integration: Horizontal and Vertical Integration

System integration can be horizontal, vertical or in an end to end manner, meaning across the value
creation network, vertically down networked manufacturing systems or end to end across an entire
lifecycle of a product.

The Industrial Internet of Things (IIoT)

Aazam, Zeadally and Harras (2018), describes the differences between IoT and IIoT and their differences.
Simply put, IoT brings the internet to a "thing", whereas IIoT is when data is collected from sensors,
actuators, and other machines in an environment of industrial character (Aazam et al, 2018).

Figure 2 Aazam et al (2018) visualizing IoT, IIoT, and Industry 4.0.

These technologies are enabling a connected network, making the connections intelligent and agile and
thus setting a good foundation for industry 4.0.

Cyber Security and Cyber-Physical Systems (CPS)

To keep up with the demanding nature of connected networks, cyber security must be a part of the process
to secure reliable communications and advanced access management is working. This could also be done
with the help of cyber-physical systems where humans and machines are closely working together. A CPS
is good for decentralization and autonomous behavior and can be used to monitor and overview a network
of physical and cyber connections.

The Cloud
Cloud is a technique used for sharing data across an enterprise, and for storage. Cloud solutions are good
for performance while retrieving data and could be used for linking multiple sensors at once to the cloud,
connecting data together and sharing data between devices.

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Additive Manufacturing
Additive manufacturing means an enterprise can work with small batches of products with a high level of
customization. Transportations distances can be reduced along with the need for storing many products at
once.

Augmented Reality (AR)

For service and repair, AR is bringing big opportunities, allowing for repair to happen on the spot, by
regular personnel. This is something the movement of industry 4.0 can leverage and reduce time spent on
waiting for repairs or for expertise.

This list can be extended to include blockchain and adding to the fourth pillar with industrial integration
which includes enterprise architecture and application integration in the sense that industrial integration is
a part of the fourth revolution process (Xu et al, 2018). Disruptive technologies as the ones mentioned
being a part of industry 4.0 above, are not excluded to big and large companies, but are also considered in
smaller enterprises. Small-medium enterprises (SMEs) are a substantial part of the global economy (EU
Commission, 2020), and industry 4.0 being a revolution affecting the entire globe indicates not only larger
companies are touching upon these technologies, but SMEs too.

1.1.4 Definitions of SMEs

The European Commission defines SMEs based on the company’s staff headcount and its annual turnover
or balance sheet total. The criteria for being a small company is less than 50 employees and an annual
turnover of less or equal to 10 million euros. A medium sized company should have less than 250 employees
and an annual turnover of less or equal to 50 million euros. In the European Union, SMEs represent 99% of
all companies (EU Commission, 2020). According to some researchers like Schröder (2016) and Sommer
(2015), the size of the company matters when it comes to implementing industry 4.0 (Sommer, 2015;
Schröder, 2016). Sommer (2015) argues that the smaller the company is the more challenging it will be to
benefit from industry 4.0 and are more likely to become victims of it.

1.1.5 SMEs: readiness for industry 4.0

Research in the area of industry 4.0 readiness is a way that can aid companies to prepare for challenges that
may appear when transforming towards the new revolution (Hofmann and Rüsch, 2017). According to
Schumacher, Erol, and Sihn (2016), who studied and analyzed multiple readiness and maturity models for
industry 4.0 implementation, say that the difference between a maturity model and readiness model is that
the readiness assessment occurs before the maturing process starts. Hence the readiness model should
prepare the company and clarify whether the company is ready to initialize a transformation process for
industry 4.0 or not. While a maturity assessment aims to clarify which maturity level the company is in.
Schumacher et al (2016) defines the term maturity as the state of a company being complete or ready. Mittal,
Khan, Romero, and Wuest (2018) defines readiness assessments as "evaluation tools to analyze and
determine the level of preparedness of the conditions, attitudes, and resources, at all levels of a system,
needed for achieving its goal" (Mittal et al, 2018, p. 199). There is a limited amount of readiness assessments
available that covers challenges and specific requirements for SMEs in industry 4.0. A readiness assessment
is needed to demonstrate the SME’s readiness (Mittal et al, 2018).

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1.2 Research problem
Industry 4.0 is in the present, hence it is not a matter of maturity in an organization, but rather its readiness
for implementing it. To Hofman and Rüsch (2017) "... it becomes apparent that the concept of Industry 4.0
still lacks a clear understanding and is not fully established in practice yet" (Hoffmann & Rüsch, 2017, p.
23). Sommer (2015) states that it can be challenging for smaller companies to benefit from industry 4.0 and
could impact them in a negative way. This gives the impression that there exists a knowledge gap in the
research about SMEs and how their transformation towards industry 4.0 can impact them. It is important
that companies understand the new industrial trends and how they can exploit them in the best way.
Readiness assessments can be of great help to clarify for the companies how to prepare for an organizational
transformation towards industry 4.0.

The research problem in this study is that there is a limited amount of readiness assessments available that
covers challenges and specific requirements for SMEs in industry 4.0. An assessment is needed to
demonstrate the SME’s readiness for industry 4.0 implementation (Mittal et al, 2018).

1.3 Research question

With the research problem stated, the main research question for this study is What is the level of readiness
of SMEs to embrace the benefits and tackle the challenges of industry 4.0? and will unfold in a readiness
assessment.

The results are presented in a readiness assessment. It will cover what readiness level SMEs have for
implementing industry 4.0, how benefits are embraced and how challenges are avoided, mitigated or
resolved.

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2 Methodology
This study will be based on a literature review as the data collection method of choice, with a survey as the
overall research strategy. The data analysis method will be to use grounded theory in combination with
literature review to give an exploratory perspective whilst conducting the review.

2.1 Research strategy

To maintain a good structure of the study, a strategy is useful to apply. In this section, selected research
strategy and alternatives are discussed.

2.1.1 Selected research strategy

The chosen research strategy for this study is to utilize a survey strategy. A survey is used when something
is to be looked upon or to map out either something physical or social. It is a strategy often used when the
aim is to gather large quantities of data, and when the aim is narrow and specifically defined (Johannesson
& Perjons, 2014). There are different ways of doing a survey, such as traditional postal, internet survey or
an observation where a behaviour is investigated in a survey manner. A strength with this strategy and a
reason for why it is chosen is that it is good for gathering large amounts of data during a short time and
allows for both qualitative and quantitative data collection. A disadvantage with the strategy is that it can
be difficult to engage and avoid superficial data (Johannesson & Perjons, 2014).

For this study, the strategy is a good fit since the research question is both narrow and well defined.
Johannesson and Perjons (2014) states that the approach is ill suited for investigating feelings regarding a
topic, but more suited for investigating attitudes, which seems fit to this study's research question. A
document survey seems appropriate, which is to gather and make use of documents (Johannesson & Perjons,
2014). Since this study has no intentions to gather data directly from individuals, but rather mainly from
scientific research, the disadvantage becomes irrelevant.

2.1.2 Alternative research strategy

One alternative could be to use case studies. A case study gives deep knowledge and insights about the
specific topic being investigated and focuses on depth and the context. In comparison with surveys, a case
study can give great details of the topic whereas surveys are more for a broad and shallow perspective. It is
a strategy very well suited for small-scale research since it focuses on depth of information (Johannesson &
Perjons, 2014). For this study, the approach could be to conduct a case study with one or a few companies
and apply the research goal. The main reasons against this is that in the term SME, a company can look
quite different in size, causing the need for multiple case studies to be conducted. Another impractical reason
for not choosing case studies where the location of the authors, being spread out in the world. Also, case
studies are well suited for small-scale research which this study most likely will grow out of, striving to be
more generalizable by more shallowly approaching the topic with a survey strategy. The strength of a case
study is the deep knowledge it can entail, however, the disadvantage of having difficulties when it comes to
generalisability outweighs the strength and causes it to not be the main strategy for this study (Johannesson
& Perjons, 2014).

Another alternative could be to use action research as strategy for the study. Action research is a strategy
for practical problems in real world settings and could, according to Johannesson and Perjons (2014) be

6
used "... to solve important problems that people experience in their practices" (Johannesson & Perjons,
2014, p. 49). This strategy differs from others since it typically involves practitioners of the field themselves
and then uses results to change their practice. The strategy could be in the form of practical action research
and means that the goal is to improve practitioners' insights of themselves and be able to improve in their
field (Johannesson & Perjons, 2014). This strategy could have been a suitable option in the sense that the
research question involves benefits and challenges, but not so much regarding the readiness level. Also,
action research often includes practitioners themselves conducting the research, giving the appearance of
being a next step after this study and could then possibly focus on maturity level instead of readiness.

A challenge for action research which is similar to what case studies have, is the ability to generalize the
results. It is a strategy often used in a local setting with practitioners themselves, thus being hard to
generalize and could be affected by the researchers (Johannesson & Perjons, 2014). Both discussed
alternatives could be a suggested future approach for future research within the topic since a survey is more
shallow and broad to its extent.

2.2 Data collection and data analysis method

When conducting a literature review, grounded theory is a suitable analysis method for analyzing a set of
chosen literature (Wolfswinkel, Furtmueller & Wilderom, 2013). Grounded theory is a useful analysis
method to receive a thorough and relevant analysis for the research topic. Since literature review involves a
high volume of research materials it can be quite difficult to search and choose the most important and
relevant sources for the study. According to Wolfswinkel et al (2013) there is a need for more precise and
rigor analysis in the field of information systems, which is the field that this study belongs to. This study
will therefore follow Wolfswinkel et al (2013) guide for reviewing the literature of industry 4.0 in order to
provide a high-quality analysis of the topic.

Stage Task

Define Define the criteria for inclusion/exclusion

Identify the fields of research

Determine the appropriate sources

Decide on the specific terms

Search Search
Select Refine the sample
Analyze Open coding

Axial coding

Selective coding

Present Represent and structure the content

Structure the article
Table 1 Wolfswinkel et al (2013), Five-stage grounded-theory method for reviewing the literature in an area.

The data collection method will be five stages of literature study combined with the data analysis method
grounded theory. The figure above presents the five stages of reviewing literature using the grounded theory

7
analysis method including the tasks for each step. This guide is supposed to be used in an iterative way
throughout the whole research (Wolfswinkel et al, 2013). Each stage is described below.

2.2.1 Define
a. Define the criteria for inclusion/exclusion
The first step is to define the inclusion and exclusion criteria of the literature, meaning restrict the sampling
of the literature by setting certain thresholds on the documents. The character of this study is to explore a
selected area, which justifies the choice of using an exploratory sample and to be purposeful about the
sampling (Johannesson & Perjons, 2014). According to Johannesson and Perjons (2014) documents can be
of different types: government publications, organizational records, academic publications, newspapers and
magazines, personal communications and social media streams. The inclusion criteria will be academic
publications and research articles where a scientific database for research papers will be used. The exclusion
criteria will be documents of the type newspapers and magazines, personal communications and social
media streams. The chosen criteria can be revisited in order to relax or further limit them. When sampling
literature concerning industry 4.0, only literature from the year 2016 and forward will be taken into
consideration. The reasoning for this is that the majority of articles found on the database Google Scholar
when searching on the term industry 4.0 were from the year 2016 and onward.

b. Identify the fields of research

The second step is to identify the research field. The choice of an appropriate field depends on the research
topic and research questions, also it helps if the researchers select a field they feel mostly familiar with. The
research question of this study is What is the level of readiness of SMEs to embrace the benefits and tackle
the challenges of industry 4.0?, therefore the research field in this study will be industry 4.0 in the discipline
of information systems. This is why certain fields like business marketing and economy will be discarded
because they do not relate to the research topic and question.

c. Determine the appropriate sources

The third step is to determine the appropriate sources. Since the chosen type of documents will be academic
publications, a scientific database on the internet will be used for gathering the data. Databases of scientific
character are appropriate sources because they have documents of the chosen type and usually contain a
large volume of publications. To use an online database is potentially not the most reliable place to look for
documents, however, if the database is a commonly used tool on a global scale, image and reputation are at
stakes for the provider of the database. Also, the amount of users could entail to some extent if the database
is of good quality. When choosing a database as a source, the criteria for this study the database has to meet
are global recognition, large volume of data and easy to navigate.

d. Decide on the specific search terms

The final step is to define the specific search terms. These should be reflective of the entire scope of the
research field which in this case is industry 4.0. The principles for the search queries will be terms related
to the research topic and support the main targets in the research question.

2.2.2 Search
In this stage the actual search for all the identified sources is carried out. 100 top links on the database will
be looked at to gather a sufficient amount of information. Searching for the most suitable literature can be
time consuming and the search hits may result in duplicate texts. If duplicate texts are detected, the previous
stage Define will be revisited where earlier set sampling and related criteria will be checked. All the searches

8
and search terms including their dates and results will be documented for the sake of transparency when
writing the review.

2.2.3 Select
The third stage involves the task of refining the sample. In order to make a proper selection of the literature,
criterias for the selection will be defined. To begin with, all the duplicates will be filtered out and literature
that does not fit the criteria will be set aside. When reading the headings, all literature that does not include
any of the search terms will be discarded. Abstracts that mention at least two of the search terms will be
considered. If the heading and abstract of a paper both uphold these requirements, they will be downloaded
for closer inspection. If the downloaded literature turns out to be irrelevant in regards to the research
question or are of the wrong type of document, it will not be included in the analysis.

2.2.4 Analyze
Here is where the key principles of grounded theory take place. To start analyzing the papers, a random one
will be selected to go through and every interesting and relevant aspect of it will be highlighted. Each word,
sentence and paragraph that has been highlighted makes the paper a relevant excerpt. The fourth stage
contains three steps of coding principles to analyze the texts. The first step is open coding.

a. Open coding
Open coding will be executed by re-reading all excerpts and documenting any new concept or associated
insights that have been found. These concepts and insights will shape the niche of the study. The goal with
open coding is to understand the big picture of the findings and identify categories.

b. Axial coding
Axial coding is about finding interrelations between the categories and newly found subcategories in the
literature. Meaning if four papers are defined as one type of category, re-reading these papers will allow for
finding new subcategories. The key categories will eventually represent the study’s main themes of the
findings.

c. Selective coding
The last step is selective coding. In this step the identified categories will be integrated and refined. By
doing this, relationships between the categories can be found along with new theories and concepts that fit
the scope of the study. It is during this step that explanation of new theories and concepts of the study can
be anchored to the research question.

2.2.5 Present
a. Represent and structure the content
The first step is to represent and structure the content. The content will be based on all the empirical findings,
previous notations and logs of the associated insights of the research field. The accumulated knowledge will
be documented in a readiness assessment. The model will be developed to illustrate and present the data in
a comprehensible way. It will address the main targets of the research question which are benefits,
challenges and SMEs’ readiness for them.

b. Structure the article

The second step is structuring the article which in this case is the structure of the readiness assessment. The
readiness assessment will present the results of the study which will then be followed by a discussion and
conclusion. It will be presented with the help of a table followed by an analysis explaining its contents.

9
2.3 Alternative data collection methods
An alternative data collection method could be interviews. The method interviews involve an interaction
between the researchers and the respondents where a predefined protocol of a list of open-end questions
are asked to the respondents, to discover unique information about a certain domain. It is a suitable
approach if the purpose of the research is to gather complex and sensitive information about a subject and
elicit the respondents’ opinions and emotions about it (Johannesson & Perjons, 2014). Disadvantages with
interviews are that they can be time consuming and there is a risk of the outcome being affected by the
researchers’ personal attributes (Johannesson & Perjons, 2014). In this study interviews could be an
appropriate method to see how personal feelings and opinions are affecting the topic in the research field.
However, the limitations in this study would be to find enough SMEs to interview in only one country like
for instance Sweden, or the difficulty to reach out to SMEs in multiple countries. If the study would focus
on a national level, for instance Sweden, preferably six SMEs would have needed to be found and agreed
to be interviewed in order to meet the standards of a masters study. In the authors opinion, to produce high
quality research, the method interview would not support this because there seems to be too few SMEs
who either have implemented industry 4.0 or plan to do so. Another reason is that, the aim of this study is
not to research about different feelings about industry 4.0 but rather finding facts about its impact in a
wide range of materials which the method documents allows (Johannesson & Perjons, 2014).
Furthermore, another reason for not choosing the interviews method is because it can be time consuming
and difficult to find respondents who want to participate in such a study.

A second alternative data collection method could be questionnaires. In this method a written document is
produced containing a list of questions which is distributed to multiple respondents (Johannesson & Perjons,
2014). Questionnaires are used to gather brief data that is unambiguous, which could be a good choice for
this study. However, this study's aim is not to gather personal opinions from selected respondents, which
questionnaires can be appropriate for. Rather, the goal is to investigate a whole field regarding a specific
topic and thus, makes questionnaire not as suitable as a literature review. An advantage with using the
method questionnaires is that it is an inexpensive and quite easy way to gather straightforward data from a
large number of people. The disadvantage with using questionnaires is that it can be hard to find many
respondents who are willing to answer the questions (Johannesson & Perjons, 2014). In this case this
disadvantage can become more prone to happen since this study involves SMEs which perhaps can be a
reason for acting reluctant towards a questionnaire.

2.4 Alternative data analysis methods

An alternative method for data analysis could be content analysis. Content analysis is a data analysis method
of qualitative research. It is carried out in six phases where elements in a text are put in different categories
and then the frequency of the elements are calculated in each category (Johannesson & Perjons, 2014). The
main difference between content analysis and grounded theory as data analysis methods, is that the codes
in grounded theory are not based on pre-existing theories. In grounded theory the researchers need to have
an open mind and the codes will emerge from the texts that are used. One disadvantage with content analysis
method is that it does not take contexts into consideration, it only analyzes each unit individually
(Johannesson & Perjons, 2014). Since this research is about exploring industry 4.0 in the context of SMEs,
the method grounded theory is more appropriate. Furthermore, this study is in the form of a literature review
and grounded theory is recommended and used by several scientists as an appropriate method for producing
a legitimate and in-depth analysis (Wolfswinkel et al, 2013).

10
2.5 Ethics and research quality
The ACM Code of Ethics and Professional Conduct, also called "The Code" (ACM Code Task Force,
2018), have been used to make sure the study follows good ethical principles and maintains high research
quality during the study and in any connection to the study. The Code presents some general ethical
principles to follow, namely:

To contribute to society and to human well-being, acknowledging that all people are stakeholders in
computing
This principle is encouraging researchers to minimize negative consequences of computing research
towards society, having in mind that the results should respect diversity, be socially and broadly accepted
and contribute to the public good. Also, researches should promote sustainability. This study strives to
uphold this principle by conducting research relevant to society and in the spirit of the age, respect
diversity by taking a broad approach to the topic and promote sustainability by using technology and
methods in a favorable manner in regards to the environment.

Avoid harm
This principle describes more in-depth that researchers should avoid negative consequences of different
characters. It is said that to minimize harm, best precis should be followed unless there are very good
reasons not to. For this study, no direct contact with individuals will occur and thus, lowers negative
consequences automatically. Regarding results and best practice, well established methods will be used for
conducting the research. It is encouraged that researchers report any sign of harm caused by systems,
which will be followed if applicable to this study.

Be honest and trustworthy

This principle is simply a principle about being honest, trustworthy and truthful whilst conducting the
research. It is this study’s full intention to do so, to apply full disclosure, honesty and trustworthiness
throughout the research and honor commitments being made.

Be fair and take action not to discriminate

This principle states that fairness is required in all circumstances and that no discrimination is applied.
Since no individuals will take part of this research, discrimination is already partly avoided. Selection of
specific data to investigate in this research will be done in caution of not discriminating a particular field
within the area of interest.

Respect the work required to produce new ideas, inventions, creative works, and computing artifacts
This principle is about making sure works of others are respected and recognized. In this study, this is
honored by using proper referencing measurements and making sure all statements and contributions by
others are recognized and referenced to original work.

Respect privacy
This principle states that personal information only should be used for legitimate reasons and during no
circumstances be violated. It also states that only the least amount of personal information for a purpose
should be gathered and not be used for anything else than an advanced stated purpose. Due to the
character of this study being not on an individual level, it is most likely not a threat from this study to
violate this principle. However, the principle will be respected and in mind whilst conducting the research.

Honor confidentiality

11
This principle says that researchers often are commended with information of confidential character and
that it should stay that way during any circumstances except when it violates the law, organizational
regulations or of the Code. Since this study is applying a literature review as a data collection method, it
implies a great amount of data will be at hand, however, data will only be taken from open databases,
making the risk of violating the confidentiality of data gathered quite small.

The Swedish Scientific Board (Vetenskapsrådet, "VR") brings up four main principles, namely Reliability,
Honesty, Respect and Accountability (Vetenskapsrådet, 2017). The Code above more or less includes them
all, but to clarify and ensure high research quality of this study, reliability and accountability could be
elaborated. In regards of reliability, the study will strive to uphold high reliability by reaching for elaborating
the area of interest until saturation has been achieved. This will be done by other than maintaining a good
study structure, including all scientific data being found in selected databases when applying search terms
and phrases and not being exclusive. Regarding accountability, the authors of this study will take full
responsibility regarding the entire process of planning, executing and summarizing the results of the
research, as well as consequences of the research made to a reasonable extent.

Regarding generalisability, this study is gaining strengths by having surveys as the research strategy and
thus being more general by default. The goal is to be general and to conduct research with great
generalisability, however, to some extent. This study is most likely generalisable to a global perspective to
some extent since the research aim is to explore the chosen topic without a selected geographical place. To
be selective of a country could limit the global perspective of the study, but perhaps show results good
enough to compare on a global scale. Maybe that the results of the study could be said to reach a
generalisation of global level to some extent.

12
3 Readiness assessment
A readiness assessment is an evaluation tool used to analyze and determine the level of the company’s
preparedness needed to achieve its goals (Mittal et al, 2018). Several maturity models and a few readiness
models for industry 4.0 implementation have been published. Schumacher et al (2016) created their own
maturity and readiness model by evaluating five different previously published models. One of them is a
readiness assessment called IMPULS – Industrie 4.0 Readiness (Lichtblau et al, 2015). This readiness
assessment presents six dimensions associated with 18 fields of industry 4.0. According to Schumacher et
al (2016) the IMPULS model is scientifically well grounded and its structure is well explained.

The other models Schumacher et al, (2016) looked at are less detailed and does not cover as many
organizational aspects as the IMPULS assessment does. An alternative model could be PwC’s Industry
4.0/Digital Operations Self-Assessment which is an online tool that contains 33 questions related to different
areas of the business. The result of the self-assessment is an action plan which lets the company reach a
higher maturity level for industry 4.0 (Akdil, Ustundag & Cevikan 2018). Another alternative model could
be Schumacher’s model, but it focuses only on manufacturing enterprises (Sony & Naik, 2018).

Since this study’s research question is about the level of readiness for SMEs, IMPULS is arguably the most
suitable model for this study because it measures the company’s readiness level for industry 4.0
implementation. IMPULS is one of the most well-known readiness models to initialize a development
process and it defines the barriers and recommendations to overcome them. According to Sony and Naik
(2018) IMPULS is a suitable model for several types of industries and therefore is chosen for this study. In
the remaining parts of this chapter, the IMPULS framework applied in the study "IMPULS - industry 4.0
readiness" by Lichtblau et al (2015) is used as reference (Lichtblau et al, 2015). This study will not develop

13
its own research model but make a readiness assessment based on the literature review, and the IMPULS
assessment model will be used to measure the readiness level of SMEs.

Figure 3 IMPULS model by Lichtblau et al (2015).

3.1 Dimensions and associated fields

The six dimensions represented in the inner ring of the model are used to measure the readiness for industry
4.0. Each dimension has associated fields of industry 4.0 on the outer ring (Lichtblau et al, 2015; Akdil et
al, 2018). The dimensions reflect business areas of the organization and the fields are areas of industry 4.0
which are associated with the dimensions. These fields and dimensions are used as measurements for
readiness levels, which are described in 3.2.

3.1.1 Smart factory

A smart factory is one of the key concepts of industry 4.0 where an intelligent and interconnected factory
produces products through smart operations by using smart products. In other words the factory should be
self-regulated when it comes to all business processes especially production. This dimension includes the
fields: Digital modeling, Equipment infrastructure, Data usage, IT systems. According to Lichtblau et al
(2015), the field Digital modeling involves "... smart gathering, storage, and processing of data", Equipment
infrastructure means that "The smart factory relies on cyber-physical systems (CPS), which link the physical
and virtual worlds by communicating through an IT infrastructure, the Internet of Things", Data usage
involve "... integrated systems produce huge amounts of data that are processed, analyzed, and integrated

14
into decision-making models" and lastly, IT systems "... requires the real-time, cross-enterprise
collaboration between production systems, information systems, and people." (Lichtblau et al, 2015, p.13).

3.1.2 Smart operations

Smart operations mean components and systems are all integrated both horizontally and vertically. This
results in a cross-enterprise network which enhances the planning and control of the product’s lifecycle.
This dimension includes the fields: Cloud usage, IT security, Autonomous processes, Information sharing.
Cloud usage means that cloud services are used in the business and IT security indicates that measurements
towards IT security is taken. According to Lichtblau et al (2015), Autonomous processes are "... technical
requirements in production and production planning necessary to realize the self-controlling workpiece ..."
and Information sharing is "... the enterprise-wide and cross-enterprise integration of the physical and virtual
worlds." (Lichtblau et al, 2015, p. 13).

3.1.3 Smart products

Smart products can be described as physical objects that use information communication technologies
(ICTs). In industry 4.0 the products should be able to carry out their own work. This means that they should
have a unique identification so they can interact with its environment and do recordings of it through sensors.
For instance, a physical object should be self-guided and communicate to other machines which worksteps
need to be done. This dimension includes the fields: ICT add-on functionalities, Data analytics in usage
phase. According to Lichtblau et al (2015), ICT add-ons are "Physical products are equipped with ICT
components (sensors, RFID, communications interface, etc.) to collect data on their environment and their
own status." and Data analytics in usage phase is when it is "... possible to monitor and optimize the status
of the individual products." (Lichtblau et al, 2015, p. 13).

3.1.4 Data-driven services

Data collection and analysis can provide valuable information to produce new business models that enhance
benefits to the customers. With the help from new data-driven services companies can digitize their old
conventional business models and develop new ones. By combining the company’s products and services
the added value increases to the customer. This dimension includes the fields: Data-driven services, Share
of revenues, Share of data used. These fields involve according to Lichtblau et al (2015) "The after-sales
and services business will be based more and more on the evaluation and analysis of collected data and rely
on enterprise-wide integration." (Lichtblau et al, 2015, p. 13).

3.1.5 Employees
Employees play a key role in organizational changes. This is because they are the ones who are affected the
most by the changes in the work environment. Requirements for new skills and qualifications will arise. In
this dimension it becomes important to evaluate the readiness of not only employees’ skills but also their
willingness to learn and take actions. This dimension includes the fields: Employee skill sets and Skill
acquisition. According to Lichtblau et al (2015), the two fields in this dimension are about "Their direct
working environment is altered, requiring them to acquire new skills and qualifications. This makes it more
and more critical that companies prepare their employees for these changes through appropriate training and
continuing education." (Lichtblau et al, 2015, p. 52).

3.1.6 Strategies and organizations

This dimension covers the strategies needed for companies to develop new business models that will support
the industry 4.0 implementation. It is important for companies to know how to go about when, for example,
investing in new technologies that will change its business process. Hence, without a proper strategy for

15
implementation, the invested technologies become difficult to grasp. In other words, having new strategies
is of great importance in order to kick start the organizational transformation towards industry 4.0. This
dimension includes the fields: Strategy, Investments and Innovation management. According to Lichtblau
et al (2015), Strategy "... offers the opportunity to develop entirely new business models." (Lichtblau et al,
2015, p. 29). Investment and Innovation management is about where the business allocates its economical
resources and how much effort is put into innovation.

3.2 Readiness levels

In IMPULS there are six levels (0-5) used for assessing a readiness level. The levels are divided into three
groups consisting of newcomers (0-1), learners (2) and leaders (3-5) (Lichtblau et al, 2015).

○ Level 0 Outsider indicates that a company either does not know of industry 4.0, thinks it is irrelevant
or has not taken any steps towards an implementation.

○ Level 1 Beginner involves some steps taken towards industry 4.0, such as doing pilot studies and
having some system compatibility for industry 4.0, along with very little competence in the
organization and only planned IT security.

○ Level 2 Intermediate, companies have implemented industry 4.0 to some extent into their strategies,
some investments are being made, the infrastructure is to some extent using industry 4.0, inhouse
sharing of information, there are competencies in the company and sufficient IT security.

○ Level 3 Experienced is assigned to companies that have an industry 4.0 strategy, makes investments
in more than a few areas, promotes industry 4.0 via the innovation department, have information
sharing inhouse and partly external and have connected infrastructure with future expansion in mind
that collects data automatically. Also, necessary IT security is implemented, cloud is used for future
expansions and major steps are taken to make sure competencies for all this already exists in the
company or making efforts to achieve it.

○ Level 4 Expert is for companies already using and monitoring industry 4.0, makes investments in
almost all areas, supported by interdepartmental innovation, IT-systems supports almost all
production and collects vast amounts of data also used for optimization. Here, future expansions can
easily be made due to already supporting systems, information sharing is on both internal and
business level, IT security is applied and scalability is not a problem, data-driven services are used
and the company has all necessary skills inhouse.

○ Level 5 Top performer is for companies that have already implemented their industry 4.0 strategy and
monitor implementations of other projects in the company, which is supported by investments across
the company. The innovation department is covering the entire company, IT systems are fully
implemented along with autonomous processes, collecting vast amounts of relevant data. The
infrastructure fulfills all needs for integration, across the company's system and for both internal and
business information sharing. The IT-architecture is flexible, IT security is at a comprehensive level
and the competencies in the company is all expertise they need.

16
Figure 4 IMPULS levels by (Lichtblau et al, 2015).

3.3 IMPULS original process

The IMPULS model process (Lichtblau et al, 2015) is conducted by assessing the readiness level of a
company by using the dimensions and fields. Each dimension and its associated fields is assessed and a
level for each field is determined. The first step is to look at the six dimensions in the inner circle and
identify which dimension is applicable from the model. The purpose of this step is to go through the inner
circle of IMPULS in order to identify which of the six dimensions of industry 4.0 is applicable to a company.
It could be all or just a few, depending on the company. The input for this step is the information about the
company, which could be retrieved in numerous ways such as questionnaires or literature reviews. The
output is the identified dimensions for the company.

The next step is to determine the fields for each dimension which will be measuring industry 4.0 readiness
level for the company. The fields will be analyzed in a similar manner as dimensions, with the purpose to
find applicable fields in the identified dimension. The input is derived from the previous step and the output
is the suitable fields.

After this, the assessment of levels starts by utilising the levels 0 - 5 which need to meet specific minimum
requirements. Minimum requirements are described by IMPULS for each dimension and its associated
fields. These requirements are related to the levels and must be fully met in order to complete a level. For
example, in the dimension "Employees" and in the field "Employee skill set", level 2 can only be achieved
by meeting the minimum requirement of "Employees have low skill levels in one relevant area". Once the
level is determined for each field, the lowest level in the fields determines the overall level for the dimension.
This means that a dimension with level 5 in one field and level 1 in another field will get the overall level
of 1.

Each dimension has a different weight of importance depending on the organisation and situation. For
example for some industries the dimension "Smart operations" could be more important than "Smart
products". The dimensions are weighted by allocating a total of 100 points. The decision of how many points
each dimension receives can be determined according to Lichtblau et al (2015) via a survey or another
similar prioritization activity, where top managers or business experts are asked questions about industry
4.0. The purpose of the importance points is to see which dimension is the most important one to a company
for industry 4.0 implementation. Hence, it points out which dimensions are the main focus area that needs

17
attention. The importance points are used to calculate the readiness score for a company. The difference
between levels and importance points is that the level is a measurement of readiness, meaning how well a
company meets a minimum requirement for a dimension, whereas the importance point is a measurement
of how important that specific dimension is to a company. Importance points are also used to calculate the
readiness score.

According to Lichtblau et al (2015) the readiness score is based on the dimension-levels and the average of
importance points for assigning readiness levels for multiple companies in different industries. To calculate
the readiness score for a dimension, one dimension could for instance receive 25 out of 100 points, then
taking the level of the dimension (the lowest level of all fields in one dimension) and multiplying it by the
points. For example, if the lowest level in the dimension is 2, then the score for the dimension of 25 points
is 25/100 times 2, which is 0.5.

Level Importance Readiness

(2) points score

The final step is to summarize the results and present them in a comprehensible manner. This can be done
by creating tables for each dimension and its fields with determined levels and scores. The score represents
the overall readiness for each dimension.

3.4 Modifications of IMPULS

The judgment of a dimension and stating its level will be based on the codes derived from the grounded
theory method and the researchers’ common sense and reasoning of the codes. The deviation this study will
then make is to assess multiple SMEs into one overall level instead of assessing SMEs on an individual
level assessment. In IMPULS the score of 100 points is used to assess a readiness score for each dimension.
This task becomes difficult to manage in the same way as the original method implies since the foundation
for the scores is not the same. Therefore, some modifications to the scores will also be made.

3.4.1 Chosen dimensions and fields

Some findings from the literature review differed from the dimensions and fields in IMPULS and resulted
in an ill fit, causing some of them to be excluded and others to be added. As mentioned earlier in the
grounded theory method, three types of coding of the literature review was made. The axial codes are
supported by quotes taken from the literature, which are used to match the fields and the dimensions in
IMPULS to the selective codes from the literature review. This led to modifying the IMPULS model making
it more suitable to use in this study. See table in Appendix A for more details about how the axial codes are
mapped into dimensions and fields.

The chosen dimensions and associated fields for this study’s readiness assessment are:
○ Smart factory
○ Digital modeling
○ Equipment infrastructure
○ Data usage
○ IT systems

○ Smart operations
○ Cloud usage

18
 ○ IT security
○ Autonomous processes
○ Information sharing

○ Smart products
○ Data analytics in usage phase
○ ICT add-on functionalities

○ Data-driven services
○ Data-driven services
○ Share of revenues
○ Share of data used

○ Employees
○ Employee skill set
○ Skill acquisition

○ Strategy and organization

○ Strategy
○ Investments
○ Innovation management

○ Cost
○ Financial aid
○ Financial resources
○ Funding strategy

The dimensions and fields from IMPULS that were not used in this study were; the dimension "Smart
products", the field "Information sharing" from the dimension "Smart operations", the field "Digital
modeling" from the dimension "Smart factory" and finally the field "Share of revenues" from the dimension
"Data-driven services". The dimension "Smart products" was discarded because it seemed to be already
embedded in the dimensions "Smart factory" and "Smart operations". The definition of "Smart products"
as stated earlier, showed that its fields overlapped with the other two dimensions. Also, no codes showed
sufficient information about the use of industry 4.0 physical technologies explicitly to keep the dimension.
"Information sharing" was discarded because the codes did not show enough details regarding specific
systems that are integrated and share information. "Digital modeling" was left out due to not finding any
supporting quotes for codes in the literature review. "Share of revenues" was discarded due to irrelevance
to this study in terms of the code findings from the literature review.

An added dimension to the model was the dimension "Cost" with three fields. It was added because in the
literature findings it was evident that the economical factor played a big role for SMEs’ readiness to invest
in industry 4.0. The three fields for "Cost" are "Financial aid", "Financial resources" and "Funding strategy".
"Financial aid" means any subsidies or financial support from governmental organizations or other corporate
projects that are aimed to help, among others, SMEs to move towards industry 4.0. "Financial resources"
means the available economic resources that a company can allocate in the business towards industry 4.0.
Finally, "Funding strategy" means the company’s economical strategy to invest in industry 4.0 concepts.

19
3.4.2 Minimum requirements
In the table below (table 2), all minimum requirements for all dimensions, fields and levels are stated. The
majority of the requirements are taken from the original IMPULS model. The IMPULS model does not
provide a description of the process for how to create minimum requirements. Therefore, the reasoning for
creating new minimum requirements have been based on the axial codes and quotes. The grey areas indicate
requirements that have been created by the authors due to either being absent from the IMPULS model (this
is the case for the fields "Skill acquisition") or being a new dimension created by the authors (the "Cost"
dimension). In order to create minimum requirements, analysis is made on the axial codes and quotes from
the literature review. These are then applied with the same logic of levels as in the original IMPULS model.
For instance, looking at the dimension "Cost" which was chosen by the authors, the levels 0-3 have the
minimum requirement "No aid" for the field "Financial aid". Meaning no financial aid for SMEs exists in
terms of financing for industry 4.0. This reasoning is based on the analysis of the related axial codes and the
way IMPULS have formulated its requirements.

Dimension Fields Level 0 Level 1 Level 2 Level 3 Level 4 Level 5

Smart factory Equipment Machine and Some machines Machine and Machine and Machinery can Machines and
infrastructure system can system system be controlled systems can be
infrastructure be controlled infrastructure infrastructure completely controlled
cannot be through IT, are can be can be through IT, is almost
controlled interoperable, or controlled to controlled partially completely
through IT, no have M2M some extent through IT and integrated through IT and
integration capability through IT, is is partially (M2M) or are fully
(M2M) interoperable or integrated interoperable integrated
integrated (M2M)
Data usage No data No data Data is used for Some data used Data used in Data used for
available for available for a few select to optimize several areas for comprehensive
further use further use purposes processes optimization process
(greater (predictive optimization
transparency, maintenance,
etc.) etc.)

IT systems No support Main business Some areas of Some areas of Complete IT IT systems
through IT process the business are the business are support of support all
systems supported by IT supported by IT supported by IT processes, full company
systems systems and systems and integration processes and
integrated integrated with are integrated
one another
Smart operations Cloud usage
Cloud solutions Cloud solutions Cloud solutions Initial solutions Initial solutions Multiple
not in use not in use not in use planned for implemented solutions
cloud-based implemented
software, data
storage, and
data analysis

IT security
No IT security Initial IT Multiple IT IT security Comprehensive IT security
solutions in security security solutions have IT security solutions have
development or solutions solutions are been partially solutions have been
implemented planned planned or implemented been implemented for
initial solutions implemented, all relevant
are in existing gaps areas
development are being closed

20
 Autonomous
processes Autonomously Autonomously Autonomously Autonomously Experiments in Use in selected
guided guided guided guided test and pilot areas or even
workpieces not workpieces not workpieces not workpieces not phase cross-enterprise
in use. Self- in use. Self- in use. Self- in use. Self-
reacting reacting reacting reacting
processes not in processes not in processes not in processes not in
use use use use

Data-driven Data-driven
services services No data-driven Data-driven Data-driven Data-driven Data-driven Data-driven
services offered services are services are services are services are services are
offered, but offered, but offered, but offered with fully integrated
without without without customer into the business
customer customer customer integration model
integration integration integration (integration
with the
customers)

Share of data
used Data not used Data not used 0–20% of 20–50% of 20–50% of More than 50%
collected data is collected data is collected data is of collected data
used used used is used

Employees Employee skill

set No skills Employees have Employees have Employees have Employees have Employees
low skill levels low skill levels adequate skill adequate skill possess all
in one relevant in a few levels in some levels in several necessary skills
area relevant areas relevant areas relevant areas in several
relevant areas

Skill acquisition No plans to hire Investigation Some plans to Some new Multiple new Sufficient
new competent regarding hire hire new competent competent competent
employees nor new competent competent employees hired employees hired employees hired
train current employees or employees or or trained or training of or continuation
employees train current train current current current of training
employees employees employees employees in current
progress employees
Strategy and Strategy
organization Industry 4.0 is Industry 4.0 is Industry 4.0 is An industry 4.0 An industry 4.0 An industry 4.0
not part of the an issue at the part of the strategy has strategy is in strategy has
strategic process departmental strategic been defined implementation been
level but is not process, and a implemented
integrated into strategy is being enterprise wide
the strategy developed

Innovation
management No innovation No innovation No innovation Innovation Innovation Uniform, inter-
management management management management in management departmental
isolated areas implemented in innovation
several management
departments has been
established

Cost Financial aid No aid No aid No aid Some aid Some aid Multiple aids

Financial No sufficient No sufficient Some resources Some resources Sufficient Resources not
resources resources resources resources an issue
Funding No funding No funding Some strategy Some strategy Good strategy Well established
strategy strategy strategy towards towards towards strategy towards
investing in investing in investing in investing in
industry 4.0 industry 4.0 industry 4.0 industry 4.0
Table 2 Minimum requirements.

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3.4.3 Assessing levels approach
In the IMPULS method, levels are set due to which minimum requirement is fully met in each field. For
this study, the axial codes are supported by quotes from the literature, which will be used in order to assess
which minimum requirement is met and then be able to assess a level for that field. The quotes are used
since they are more descriptive than the axial codes itself and thus more easily used as a measurement for
the minimum requirements. When all fields in a dimension have a set level, the overall level for the
dimension will be the lowest existing level for all fields included in the dimension.

In addition to what is stated above, another approach for assessing levels will be used in parallel. Since the
IMPULS method implies that the lowest level for a field in a dimension represents the overall level for the
entire dimension, it could potentially give a very low overall level if a field in a dimension receives a low
level. To counter a very low overall level for a dimension that could have received a higher overall level if
only one field were ignored, an alternative approach will be used. The alternative approach for assessing
levels is to place out each quote in a field where it fits the best, and then look at which level has the majority
of quotes. This will result in another overall level for an entire dimension to compare with. The criteria for
placing the quotes were; to have both authors analyzing the quotes together, use a top to down approach
where the highest requirement to the lowest requirement were looked at and finally the specific words and
terms in the quote were looked at. A quote would never appear in more than one place, meaning no cross-
use of quotes between fields.

3.4.4 Assessing scores approach

The assessment of the total readiness score in this study will deviate from what Lichblau et al (2015) did in
the original IMPULS model as stated in section 3.3. This is because this study takes multiple SMEs from a
global perspective into account, whereas Lichblau et al (2015) studied German companies in two industries
which they gathered all their data from. Hence, this study does not have the same foundation as in the
original IMPULS model. However, the importance score can be used to see what dimensions possibly
should be looked at firstly in tackling challenges and embracing benefits for SMEs. Since this study is of
qualitative character and the original process is more of a quantitative research, where surveys have been
used to gather data to allocate the importance points, it becomes difficult to do the same. To counter that,
this study will use quotes from the literature review to justify a score for each dimension. The total number
of quotes in a dimension will be divided with the total number of quotes and then multiplied by 100 to get
an importance score for each dimension.

Therefore, the purpose of calculating the total readiness score in this study will be to see which dimension
is the main focus area for a SME when implementing industry 4.0. The level does not explain which
dimension is the most important area for a SMEs to look at, it only tells the current readiness state. Therefore
the readiness score (level * importance points = readiness score) complements the level by pointing out
which dimension is important. This will later on be evaluated in terms of challenges and opportunities.

3.5 Planned approach

In conclusion these are the methods including their goals that will be executed to produce a readiness
assessment.

Section Activity Method Goal

3.4.1 Choose the dimensions Based on the axial codes and quotes from the literature The goal is to finalize the suitable dimensions
and fields review with grounded theory and content from the original and fields for SMEs based on the axial codes
IMPULS process. and quotes.

22
3.4.2 Create new minimum Based on the axial codes and quotes from the literature The goal is to create new minimum
requirements review with grounded theory and content from the original requirements. for dimensions and fields that
IMPULS process. have occured.
3.4.3 Assess levels Using the new minimum requirements for all fields by The goal is to produce an overall level for all
placing quotes into fields. dimensions.
3.4.4 Assess scores: Counting quotes in dimensions and divide them by the The goal is to allocate importance points.
Importance points total number of quotes and multiply by 100.
3.4.4 Assess scores: (Level) * (Importance points) = Readiness score The goal is to calculate a modified readiness
Readiness score score.
Table 3 Planned approach for readiness assessment.

23
4 Research process
In this chapter the research process is described. It covers how the pilot study, literature review and readiness
assessment were performed. The selected research strategy is to make use of a document survey approach
to gather large amounts of data. Hence, a literature review was made with the grounded theory method and
the results of it were mapped out to compare it with the IMPULS readiness model. The tool used for
structuring data and creating tables in order to fully survey the research at hand, have been to use Google
Sheets. In Google Sheets, a collaborative approach has been used amongst the researchers.

4.1 Pilot study

Figure 1 is produced in a pilot study to grasp the extent of the industry 4.0 field and to receive a basic
understanding of the topic. The search term used was "industry 4.0" and top 100 publications were gathered
from Google Scholar from 2016 and forward. The year threshold 2016 and forward was used due to a trend
of many research papers being published from 2016, giving the impression of having relevant data from that
year and beyond. After going through headings and abstracts briefly, a table was made to categorize and
code the articles. The codes found were "Fundamentals of i4.0", "Case study", "Societal impact",
"Transformation towards i4.0" and "State of the art". These codes showed a lack of research about SMEs
and their readiness level for industry 4.0 implementation, being a reason for the chosen research question.

4.2 Literature review

The research process began with an implementation of the Wolfswinkel’s 5 stages. The literature was
collected from the database Google Scholar which contains academic publications. The execution of the
stages are presented in the table below.

Stage Task

Define Inclusion criteria: year 2016 - 2020, academic publications and research articles, top 100
hits if available.
Exclusion criteria: Newspapers, magazines, personal communications, social media
streams
Fields of research: industry 4.0, information systems

Appropriate sources: Google Scholar

Specific terms: (SME OR "Small and medium enterprises") AND "industry 4.0" AND
(readiness OR benefits OR challenges) AND Sweden

(SME OR SMF OR "små och medelstora företag") AND ("industri 4.0" OR "industry 4.0")
AND (beredskap OR fördelar OR utmaningar)

(SME OR "Small and medium enterprises") AND "industry 4.0" AND

(readiness OR benefits OR challenges)
Search 100 top links on Google Scholar were looked at to gather a sufficient amount of
information. The search term in Swedish only generated 50 hits. The searches were
documented in a Google sheet with a new sheet for each search term. All duplicates and
irrelevant literature was discarded at this stage.

24
Select The sample was refined by firstly reading the headings and checked if it contained key
words like industry 4.0, SME and readiness. If the abstract contained two out of these three
words it was selected. Secondly, the abstracts were read and those which contained at least
two of the search terms were selected. The selected literature was then downloaded in the
Google sheet file for further reading.
Analyze Open coding: all selected articles were reread and categories were identified. The
categories were documented in the Google sheet file including some highlights from the
text.
Axial coding: two columns were added in Google sheet named "subcategories" and "axial
codes" which represented interrelations between the subcategories and the axial codes.
Highlighted words and sentences were added which supported the categories.
Selective coding: all documented main categories and subcategories were looked on again
and then were refined to adapt to the research topic.
Present The whole literature review is represented in the Appendix A. The findings are discussed in
the Results chapter.
The literature review was structured in a Google sheet with the columns; article name,
main category which is related to subcategory, highlights and notes.
Table 4 Literature review process.

In the Define stage inclusion criteria and exclusion criteria were defined, see table 4 above. After criteria
had been decided, the field of research was decided along with deciding what search terms were relevant
for the research question. Next stage was the Search stage where the top 100 links on Google Scholar were
gathered. Since the Swedish search term only generated 50 links in total, all links were initially included. In
this stage, all duplicates and irrelevant literature were sorted out and discarded. After the Search stage, the
Select stage was initiated. During this stage the headings, abstracts and keywords of the articles were read
through and the ones which seemed most relevant to the research question and research field were selected.
After finalizing the Select stage in Wolfswinkel’s model the Analyze stage was started. The first step was
Open coding where all the gathered literature was pasted into a Google sheet. All literature was briefly read
through to grasp any new insights and concepts which were then documented along with identified
categories. Next step was Axial coding, here interrelations were found between the categories. Words,
phrases and paragraphs in the litterature were highlighted. The axial codes were derived from quotes found
in the literature. The quotes were gathered in a list and given a number to structure them with the letter "Q"
and then a number (see list of quotes in Appendix C). Some subcategories were derived from the literature
which were interrelated with the main categories. In the last step Selective coding, all discovered categories
were refined and determined. These three substeps of the Analyze stage were put into a table (see table in
Appendix A, more specifically column 1-3 ).

4.3 Assessing readiness

To assess the readiness level of SMEs in terms of industry 4.0, the readiness assessment IMPULS was used
with some modifications. The targets in the research question were always kept in mind when analyzing the
literature in order to derive the most essential aspects.

When identifying which dimensions and fields are appropriate for SMEs, the inner circle of dimensions and
the outer circle of fields in IMPULS were analyzed. Here, this study utilized a literature review to gather a
broad and global perspective of SMEs and their challenges and opportunities of industry 4.0. This means
that this study does not have a single SME in mind, but multiple. Here, changes to dimensions and fields in
IMPULS were made, with the coding and quotes from the literature review as foundation. Hence, the
IMPULS dimensions and fields were mapped out in a Google Sheets table with the codes and quotes from

25
the literature review. Dimensions and fields were chosen based on the axial codes with their quotes to
motivate which dimension and fields would be kept, removed or created (see Appendix A, column 3-5).

To assess levels 0-5, each field in a dimension was looked at to see which minimum requirement was met
(as described in section 3.4.2). In this study the axial codes and quotes from the literature review were used.
As mentioned in the previous section literature review, a list of quotes were gathered (see Appendix C) and
these were placed in a corresponding level in a matrix created for each field in a dimension (see figure 5 in
chapter 5.2). Each quote contains descriptive information that justifies which minimum requirements were
met. After quotes have been allocated to levels in each field, the overall level for the dimension is
determined, which according to IMPULS is the lowest level present in the dimension. Here, the alternative
level approach was also used where the overall dimension level was determined by the level that had the
most quotes.

The dimensions are then given importance points (see section 3.3). This was done by counting the quotes
in each level for each dimension. The quotes were then divided by the total number of quotes and multiplied
by 100 to get the point for the dimensions. To calculate the readiness score, two approaches were used. The
first approach was based on the IMPULS method where the overall level is the lowest level in a field. Then
the overall level was multiplied with the importance points of the dimension to receive a readiness score.
The second approach was to calculate the readiness score based on multiplying the importance points with
the alternative overall level, being the level in a dimension with the most quotes.

4.4 Ethical issues

The ethical issues from section 2.5 were taken into account during the research. Some were more relevant
than others in regards to the research process. Looking at the principle Be honest and trustworthy and the
principle Avoid harm, these principles have been honored by documenting the research process data and
adding raw data to the appendix to achieve transparency. Furthermore, well-established methods were used
to increase the quality of the study. Accountability is supported by partly similar reasons, but also by paying
attention to the correctness of following the methods and to take responsibility for any consequences of the
research to a reasonable extent. Reliability has been met by using a well-known database as foundation for
the data collection and to strive for saturation in the fields by having well-defined criteria and a suitable
amount of articles.

26
5 Results
This study answers the research question: What is the level of readiness of SMEs to embrace the benefits
and tackle the challenges of industry 4.0? – To do this a literature review was done using the grounded
theory method followed by a readiness assessment based on IMPULS. The selective codes found in the
grounded theory method, which are the main and subcategories in the literature review results, turned out
to be almost identical as the targets in the research question, namely "Challenges", "Opportunities" and
"Readiness". This chapter presents the results of the literature review as well as the readiness assessment
based on IMPULS. An analysis is made to explain the findings of the study.

5.1 Geographical spread

The literature used in the literature review had a quite large geographical spread. Since this study aims to
answer the research question from a global perspective, it must be shown that not an entirely global
perspective is met when looking into what countries are mentioned by the literature used. The figure 5 below
gives an estimation of the countries mentioned in the 44 publications that were chosen from the literature
review. In other words, the authors estimated how many times a country, continent or type of region were
mentioned in each article. Important to say is that it is difficult to specify which country a study aims at or
which country is thought of in some cases. Therefore, there are three variables in the figure named "Europe",
"Global" and "Western countries" where an estimation has been made based on the judgment of the authors
while reading the literature. Since this literature review covers studies in multiple countries worldwide, it
can be said that this study is looking at SMEs and industry 4.0 from a global perspective, however, leaning
towards a more western perspective.

Figure 5 Geographical spread

27
5.2 Literature review findings
The literature review resulted in three types of codes from the grounded theory method (see Appendix A).
The selective codes are the main categories and subcategories. The axial codes are based on the selected
quotes from the literature. The quotes are sentences and phrases which describe the relevance of the
subcategories. It is interesting to see that the selective codes are unintentionally almost the same as the
targets in the research question. These categories are the foundation of the readiness assessment (see
Appendix A for the mapping process). A full list of articles used in the literature review can be found in
Appendix (B). The quotes used from the articles can be found in Appendix (C). The table below shows the
selective codes and supporting quotes, which is used for analysis further below.

Main categories Subcategories Quotes

Cost Q6, Q5, Q7, Q3, Q4

Lack of competencies Q30, Q26, Q22, Q23, Q29, Q27, Q28

Challenges
Technological issues Q14, Q10, Q13, Q12, Q15, Q48

Organizational issues Q31, Q33, Q41, Q42, Q32, Q37

Corporate collaboration Q11, Q24, Q25, Q2, Q20

Good preconditions Q43, Q45, Q44, Q39

Opportunities

Mass customization Q46, Q40, Q16, Q47, Q17, Q18, Q19, Q8

Governmental influence Q9, Q1, Q38

Knowledge level Q34, Q21

Readiness

SME requirement specification Q36, Q35

Table 5 Literature review findings.

5.2.1 Selective code: challenges

Looking at the main category "Challenges" (in the table 5 above) the subcategories found in the literature
were Cost, Lack of competencies, Technological issues and Organizational issues. Cost turned out to be a
challenge which involves economical aspects for SMEs in terms of affording an implementation of industry
4.0. Examples of such Costs are not sufficient funds for the latest technology (Q4) and fewer resources in
terms of budget and qualified work forces (Q5). Lack of competencies is about employees’ skills already at
hand and skills required for using industry 4.0 technologies within the organization. Examples of Lack of
competencies are lack of knowledge or expertise when it comes to technologies and its applications (Q29),
the challenge of having many and new IT areas (Q26) and the high cost and competition of staff (Q27).
Technological issues cover the challenges SMEs face with integration and configuration of various industry
4.0 technologies in the business. Examples of Technological issues are the challenge of having new concepts
that are not so simple for SMEs to adapt (Q12) and also the issues regarding data security (Q10). Lastly, the
organizational issues cover the challenges for SMEs like the size and complexity of the business and finding
a suitable implementation strategy (Q37).

28
5.2.2 Selective code: opportunities
The second main category is "Opportunities" and it has three related subcategories. Corporate collaboration
means collaborations or formed partnerships for example between SMEs and governments. Examples of
Corporate collaboration are multiple governmental initiatives ongoing in Europe (Q1) as well as globally
(Q20, Q22) and could also be collaborations between established enterprises and small companies. Good
preconditions means strengths SMEs hold for an organizational transformation like industry 4.0
implementation, by simply being a SME. SMEs typically are smaller in size, thus more flexible, have less
complexity and have an advantage when it comes to changes due to these characteristics (Q43). Mass
customization is a combination of flexibility and personalization which industry 4.0 supports by using a
combination of smart technologies and big data analysis. This is a great opportunity for SMEs to optimize
their business offers to customers and stay competitive (Q8, Q40).

5.2.3 Selective code: readiness

The last main category, "Readiness", which relates to the company’s current preparedness and maturity
level for a digital transformation such as industry 4.0 implementation, according to the literature. Readiness
is a common term found in the literature review, and it is categorized into three subcategories. Governmental
influence, on a national level such as organizational initiatives or governmental projects influence SMEs’
approach for industry 4.0 implementation. It is evident that developed countries and developing countries
have different approaches to implementing industry 4.0 depending on the type of government (Q38).
Knowledge level for a SME could be the awareness of some areas required for implementation of industry
4.0. SMEs need to have a certain knowledge level to be sure when and how they should implement industry
4.0 (Q34). Hence, it affects the drive for an SME to initiate an implementation process. A SME requirement
specification is a kind of requirement evaluation needed to define the vision, aims, goals, available resources
and standards in order to start preparing and adapt to an implementation of industry 4.0. SMEs can use
requirement specifications to structure what the business needs, however, it is a very difficult task for SMEs
to do and they struggle with the task (Q35). SMEs are in need to do these activities, but are currently not
able to (Q36).

5.3 Readiness assessment result

The IMPULS readiness assessment resulted in a summarized assessment of SMEs presented below. Each
dimension is to the left with its associated fields and the levels 0-5 presented on the top row of each
dimension. The level for the fields are to the right with an overall level for the entire dimension to the far
right. The quotes used in the assessment can be found in Appendix C.

29
Figure 6 Readiness assessment results.

Two approaches, AP1 and AP2, (described in section 3.4.4), have been used. The first approach (AP1)
describes the overall level based on the lowest level in the dimension. The alternative approach (AP2) to
decide the overall level is to select the level with the most quotes in a dimension. Looking at the figure 5
above, each dimension is assessed from the two approaches at an overall level.

5.3.1 Level 0: Outsider

According to AP1, SMEs have the readiness level 0 in the dimensions "Smart operations", "Employees"
and "Strategy and organization". This means that SMEs are generally outsiders in these business areas
related to industry 4.0, meaning they have no knowledge about industry 4.0 or have not yet done anything
to move towards it. According to AP2, there are two dimensions with the level 0, namely "Smart
operations" and "Employees". This indicates that SMEs are considered outsiders for these two dimensions
as well.

What can be determined in the results of the two approaches is that "Smart Operations" and "Employees"
are definitely on level 0. "Strategy and organization" differs in both approaches, AP2 results in a higher
level than AP1. The low level for "Smart operations" according to AP1 is supported by the selective code
Technological issues, which is about SMEs’ challenge to integrate and configure technologies that would
lead to autonomous processes which is one pillar of industry 4.0.
Q13: "... the degree of automation in SMEs is on average currently rather low, which results in a high dependency of employees’ expertise, which
has grown over the years and cannot easily be externalized and transferred into program code (Bracht, Geckler, & Wenzel, 2011)."

Why dimension "Employees" has such a low level can be understood through the challenge Lack of
competencies which the quote Q29 describes as:
Q29: "[...] the lack of knowledge or expertise regarding the possibility and potential of using the current technology and its applications. This has
been a major problem with SMEs where more than 50% of the companies having faced difficulties to fill vacancies f or IT specialists in 2016 and
about 30% of companies working without their own websites [25]."

30
"Strategy and organization" according to AP1 is lower than AP2 but both approaches do not have a big
difference since they both result in relatively low levels 0 and 1. The reason for level 0 can be argued by
Organizational issues, meaning SMEs’ challenges to form a comprehensive strategy for industry 4.0 and it
may be due to its small size. This is confirmed by:
Q37: "[...] many leaders of SMEs do not have a comprehensive strategy regarding Industry 4.0 to gain an appropriate maturity level (Schröder,
2017). The smaller the company size, the more likely this is to be the case (Sommer, 2015)."

5.3.2 Level 1: Beginner

When it comes to the dimensions "Cost", "Smart factory" and "Data-driven services" for AP1, the level is
1. This means that SMEs are on the beginner level where they have taken some actions towards industry
4.0, for instance pilot studies and having system compatibility to industry 4.0 with some planned IT security.
The results of AP2 shows that "Smart operations" and "Strategy and organization" are also on the beginner
level. It is interesting to note that "Smart operations" and "Strategy and organization" have a higher level
here than in AP1.

Why the dimension "Cost" is on level 1 and not higher could be because the selective code Cost is one of
SMEs challenges when it comes to investing in industry 4.0 implementation, meaning the majority of SMEs
simply cannot afford it.
Q4: "Generally, all over the world, small and medium-sized enterprises often do not have sufficient funds to invest in the latest technologies and
must allocate capital very effectively and carefully."

However, since the dimension "Cost" is not on the lowest level but on the beginner one, it means there is
some progress being made in this dimension in terms of the readiness. Looking at the selective code
Opportunities and Corporate collaboration, SMEs have possibilities to create partnerships with other
established companies and work with several governmental initiatives that aim to support SMEs’ shift to
industry 4.0 in the future. Furthermore, looking at the selective code Readiness and Governmental influence,
it is supported again by another quote where it is stated that there are already many initiatives established
for SMEs.
Q2: "The European Commission has presented the Digital Europe programme for the next EU financial period 2021-2027, which plans to invest in
five main areas: supercomputers, Artificial Intelligence (AI), cybersecurity and trust, digital skills and ensuring a wide use of technologies across
the economy and the society"

Q1: "An overview of the European Commission shows that there are more than 30 national and regional initiatives at European level: e.g., Plattform
Industrie 4.0 in Germany Catapult in UK, Fabbrica Digitale in Italy, Made Different in Belgium, Industry du Futur in France, Produktion 2030 in
Sweden, Made in Denmark, Smart Industry in Netherlands, Produtech in Portugal, Industria Conectada 4.0 in Spain, Production of the Future in
Austria, Průmysl 4.0 in Czech Republic, Smart Industry SK in Slovakia and many others (Plattform Industrie 4.0 2019)."

Q40: "Innovative and agile start-ups and SMEs with no need to defend legacy business are widely entering the circular economy and Industry 4.0
field by providing new digital platforms and disruptive service solutions to maximise the value of products and materials. Partnerships between
established and small companies hold great promise for disruptive new solutions.

The dimension "Smart factory" receives according to AP1 level 1 which indicates SMEs are beginners in
this dimension. According to AP2, the dimension receives a higher level which is discussed in the next level
section (see section 5.2.3). Why level 1 is met in AP1 could be supported by the selective codes Readiness
and Knowledge level where it is found that SMEs possibly only follow trends and are not quite ready to
implement industry 4.0.
Q21: "[...] more than 50% of companies do not have well defined goals when adopting Industries 4.0 technologies, and just follow trends or what
competitors are doing, and in doing so, adopt technologies that are not appropriate to its need".

"Data-driven services" receives level 1 in AP1 and level 2 in AP2. Why it received level 1 can be justified
as previous studies show that industry 4.0 can be helpful to solve problems faster by enabling autonomous

31
processes. Hence, SMEs are beginning to see the benefits of industry 4.0 rather than just the challenges of
it.
Q47: "I 4.0 help firms to identify the problem in realistic approach known as digital lean. By reducing the waste through eliminating non-value
added activities using sensors, robots, data analytics and automation ..."

When following AP2, "Smart operations" and "Strategy and organization" receive level 1 instead of level
0. Here it is difficult to tell which level is appropriate for "Smart operations" since it is divided between the
two levels. A possible argument for why this dimension is on level 1 could be that governments have already
begun to help SMEs to adopt digital transformation.
Q20: "Digital transformation has been for a long time on the EU agenda and the initiatives adopted at European level facilitate the access to finance,
technologies, knowledges for enterprises, notably for SMEs."

For "Strategy and organization", the higher level according to AP2 could be supported with the quote Q36
below, stating that investments are being made, but are often evaded. Also, the quote is placed in the
selective codes Readiness and SME requirement specification, indicating that potentially the requirement
specification process is lacking.
Q36: "Moreover, SMEs tend to avoid technologies with uncertain results (Hirsch-Kreinsen, 2016), so investments as early adopters are often evaded,
due to the risk of investing in the wrong technologies (Faller and Feldmüller, 2015). This conservative investment strategy has shortcomings, as
researchers highlight the importance of identifying new technological trends early and of promptly responding to them"

In the selective codes Opportunities and Good preconditions it can perhaps show that SMEs are although
opportunistic when it comes to strategy and organization. As quote Q45 below implies, a good precondition
SMEs have is their ability to implement digital transformation much faster than larger companies. This can
be because of SMEs’ flexibility and small size.
Q45: "SMEs are most likely to be the big winners from the shift; they are often able to implement the digital transformation more rapidly than large
enterprises, because they can develop and implement new IT structures from scratch more easily (Deloitte 2015). Many small- and medium-sized
companies are already focusing on digitized products in order to stand out in the market (PWC 2015). The integration of information and
communication technology (ICT) and modern Industry 4.0 technologies would transform today’s SME factories into smart factories with significant
economic potential (Lee and Lapira 2013; Gualtieri et al. 2018)."

5.3.3 Level 2: Intermediate

According to AP1, no dimension receives level 2, while according to AP2 the dimensions "Cost", "Smart
factory" and "Data-driven services" receive level 2. The intermediate level indicates that SMEs have
implemented a strategy for industry 4.0 and initiated investments, infrastructure, inhouse information
sharing and some skills exist to some extent and also have sufficient IT security. It is interesting to note that
if AP1 solely were used, SMEs would not have reached a higher level than 1 (newcomers).

The reason why dimension "Cost" has received level 2 in AP2 can be because SMEs are at the learning
stage when it comes to Mass customization. Previous studies have been made on the main advantages of
mass customization, which is supported by implementing industry 4.0.
Q8: "Main advantages of mass customization: ...o Lower cost of material waste and inventory - it is a contract production, it is not necessary for the
company to have a stock of finished products; ... o Faster cash flow: quick production - quick turnaround; ... o The manufacturer's ability to offer a
wide range of products with low production costs - various product types with the same basic components but different final design will allow
manufacturers to offer a whole range of products to satisfy every customer."

Since dimension "Smart factory" is one of the key concepts of industry 4.0, it might not be surprising it is
on level 2 in AP2. An argument for this could be due to the rise of technologies such as machine learning.
It can be an opportunity for SMEs, which supports Mass customization where the quote below states
opportunities with machine learning.
Q18: "The most innovative element of Industry 4.0 is the capacity of machines to solve a problem faster than before, due to the increasing information
captured by the system: the so-called machine learning."

32
For the dimension "Data-driven services" the level 2 is reached since SMEs seem to be gathering
information, but face some challenges in putting the information to use. In the selective code Challenges
and Technological issues the quote Q48 states that investments in information gathering are made, but
difficulties appear when trying to do something with the gathered data.
Q48: "[...] value creation challenges can develop into value offer challenges. This is reflected by companies, which invest in gathering information
through Industry 4.0 technologies, while facing challenges in putting the information to commercial use."

5.3.4 Readiness score

The readiness score is calculated by the formula (level * importance points = readiness score). To assess
how important a dimension is, importance points are distributed. The importance points of a dimension is
the total amount of quotes in a dimension, divided by the total amount of quotes and then multiplied by 100.
The importance points can be seen in "Importance P" in the table 6 below. The readiness score is the final
readiness result for each dimension and is presented for both AP1 and AP2. The readiness score represents
the readiness of the dimension to SMEs, when it comes to industry 4.0. For simplifying, "Smart operations"
have been given the level 1 out of 0/1 in AP2, since no significant difference implies due to not being the
most ready dimension in either case.

A high importance point versus a low importance point means how important a dimension is for SMEs,
where high is better of a maximum of 100. A high readiness score versus a low readiness score means how
ready a dimension is considered to be. The higher readiness score is equal to being more ready. For both
AP1 and AP2, the most ready dimension is a tie between two dimensions, "Cost" and "Smart factory". This
could possibly tell that SMEs are focusing most on these two areas when implementing industry 4.0.

Dimensions Importance P Overall level AP1 Readiness score AP1 Overall level AP2 Readiness score AP2
Cost 16.67 1 16.67 2 32.5
Smart operations 10.4 0 0 1 10.4
Smart factory 16.67 1 16.67 2 32.5
Employees 18.75 0 0 0 0
Strategy and organization 31.25 0 0 1 31.25
Data-driven services 6.25 1 6.25 2 12.5
Table 6 Scores for each dimension.

33
6 Discussion
The readiness assessment made in this study presents main focus areas that cover challenges and
opportunities to show how SMEs can prepare for industry 4.0, which according to Mittal et al (2018) there
is a need for. Schwab (2017) argues that industry 4.0 is already in the present which means that more
companies need to follow this industrial trend in order to stay competitive. As Machado et al (2019) also
states, especially SMEs need to strengthen their competitiveness because they have more at stake than large
enterprises when it comes to the impact of their business. The issue with industry 4.0 is that it lacks
concreteness since it is new. Therefore this study aimed to contribute with new knowledge to this research
field in terms of a readiness assessment using IMPULS. The answer to the research question is that SMEs
have a rather low readiness level where some have started to learn about the industry 4.0 concept. This
chapter discusses the challenges and benefits SMEs face which explains the readiness level for industry 4.0
found in the results chapter.

6.1 Tackle challenges

For SMEs to tackle challenges, it must be clear to understand what the challenges are. The selective code
Challenges was subcategorized into Cost (economical perspectives of industry 4.0 implementation),
Organizational issues (lack of comprehensive strategy for organizational change regarding industry 4.0)
and Technological issues (issues with new 4.0 technologies). Both approaches for assessing scores in the
result chapter (AP1 and AP2, section 5.2) are used in the table below.

To measure the order of difficulty for each challenge is a difficult task, since the readiness level results for
each dimension is quite homogeneous. A judgement is made based on the readiness score combined with
the meaning of the quotes. Hence, the highest readiness score indicates a high interest in tackling the
challenge and the meaning of quotes exemplifies the grade of difficulty.

Challenge in the order of difficulty Corresponding dimension Quotes in common Readiness score AP1 Readiness score AP2
1. Technological issues Smart factory, Q10, Q12, Q13, Q14, SF: 17 SF: 33
Smart operations, Data-driven Q15, Q48 SO: 0 SO: 10
services DDS: 6 DDS: 13
Total score: 23 Total score: 56
2. Cost Cost Q3, Q4, Q5, Q6, Q7 17 33
3. Organizational issues Strategy and organization Q31, Q32, Q33, Q37, 0 31
Q41, Q42,
4. Lack of competencies Employees Q30, Q26, Q22, Q23, 0 0
Q29, Q27, Q28

Table 7 Found challenges (readiness score rounded to whole numbers).

6.1.1 Technological issues

The challenge Technological issues covers three dimensions, thus having a large impact. However, it does
have a high total readiness score indicating a higher readiness than the other dimensions have with the
corresponding challenges. Regarding the meaning of the quotes about technology, it was evident that there
exist some hurdles in how to implement industry 4.0 technology. For instance, integrating and configuring
technology seems to be difficult, along with the data gathered and not really knowing how to make use of
the gathered data (Q48). Examples of these technologies could be CPS, autonomous robots and IoT as
mentioned in the background. Technological issues were present at both level 0 and level 2, meaning that

34
SMEs are moving towards a higher level, but have not fully left the lowest level. SMEs cannot in a simple
way adapt to new technologies in a similar way as companies with large-scale production, as stated by Q12:
"Current Industry 4.0 concepts and technologies, such as cyber-physical production systems, focus on
large-scale production with autonomous modifications of the internal and external supply chain. These
concepts cannot simply be adapted by SME, as they operate in niche markets and often create individual
pieces in accordance with special customer requirements.". Because of technological issues the degree of
automation in SMEs’ business processes is currently low (Q13), and automation is a key concept in industry
4.0 technologies. Arguable, new technologies are hard to adapt to since integration (such as horizontal and
vertical integration) and configuration of systems are needed, which could change the companies traditional
business models. This is why this challenge becomes the hardest to overcome. Perhaps a way to tackle the
challenge of technology is to continue investigating gathered data and make use of collaborations.

6.1.2 Cost
The second challenge, Cost, indicates that SMEs do not have sufficient financial resources, like large
companies might have. It is difficult for SMEs to tackle this and there are no straight forward answers on
how to overcome the challenge completely. This challenge is placed after the technological challenge since
financial resources probably always will be a challenge for SMEs. SMEs all over the world often do not
have enough financial resources to invest in new technologies, therefore it is quite a big challenge to tackle,
which is mentioned in Q4: "Generally, all over the world, small and medium-sized enterprises often do not
have sufficient funds to invest in the latest technologies and must allocate capital very effectively and
carefully.". Also, many industry 4.0 technologies are still under development meaning SMEs fear to invest
in them and prefer to wait until they are available at an affordable cost (Q3). However, there is financial
support that could help SMEs financially to some extent. For instance, there are multiple initiatives aimed
for helping SMEs, such as projects from the EU commission (Q1). Cost is present in the levels 1 and 2, this
implies that financial support exists for SMEs, but SMEs themselves do not have sufficient financial
resources to invest in industry 4.0.

6.1.3 Organizational issues

Organizational issues are present in level 0 and 1, with a high readiness score in AP2, but 0 in AP1. It shows
that the dimension "Strategy and organization" seems to be very important for SMEs, but not close to being
ready for industry 4.0 implementation. This challenge implies that a comprehensive strategy is needed for
top managers of SMEs to adapt to the industry 4.0 implementation. Reasonable to say, a strategy seems to
be lacking for implementing industry 4.0, indicating SMEs do not know how to take steps towards an
implementation and thus are falling behind in readiness (Q37). It is argued that the smaller a SME is, the
harder it is for it to implement industry 4.0 (Q31). A big organizational issue for SMEs is an inappropriate
strategy and standardization for making an organizational change towards industry 4.0, which can be seen
in Q33: "They emphasized specific barriers to Industry 4.0 implementation such as missing standardization
and an inappropriate company strategy.". An argument for this can be that CIOs and top managers of SMEs
often lack technological competencies thus the technological issues become greater than organizational
issues. Larger and established enterprises often have a specific IT department that are specialized in solving
the technological issues.

There is so far no best practice available for implementing industry 4.0 by SMEs which is a reason why it
may be hard to develop a strategy for digital transformation. A readiness assessment can assist SMEs to find
some vital aspects of the organization so that the vision becomes clear, and a strategy for implementing
industry 4.0 can be developed more easily.

35
6.1.4 Lack of competencies
From the results, it is evident that SMEs are not ready when it comes to the skills needed for industry 4.0
implementation, since the dimension "Employees" have level 0 in both approaches. The challenge Lack of
competencies is also present in the level 0 which is not a surprising result. Arguably, this is because industry
4.0 is quite a new concept and many employees of SMEs do not know what industry 4.0 means. When it
comes to implementing industry 4.0 many SMEs have difficulties in hiring highly skilled staff since it is a
matter of costs and a competition with larger enterprises (Q27). Having highly skilled staff and the right
competencies for industry 4.0 is important since "... the results showed that education and qualification of
employees is one of the main requirements for the implementation of Industry 4.0." (Q30). A suggestion
could be to look into collaborating with others or to investigate skill acquisition via perhaps workshops and
learning factories.

6.2 Embracing the benefits

For SMEs to embrace the benefits of industry 4.0, the selective code Opportunities of industry 4.0
implementation must be clarified. In this section the subcategories of Opportunities are defined as the
benefits SMEs can embrace. Thus, the three benefits are: Corporate collaboration, Good precondition and
Mass customization. When assessing SMEs, it was clear that all the benefits potentially are difficult to
embrace since SMEs are at highest at level 2 in the results of the readiness assessment, meaning they are at
a learning stage.

Measuring the benefits could not be done in the same way as the challenges in the previous section, because
the benefits could not be mapped into the dimensions in a clear way. To measure which of these benefits is
the strongest one, quotes have been counted from table 5 combined with a subjective judgement made on
the meaning of quotes from the literature review. Each benefit is then related to the challenges mentioned
above. It was clear that Mass customization had more quotes than the other two, hence was decided to be
the strongest benefit for SMEs to embrace.

6.2.1 Mass customization

The major identified benefit of industry 4.0 for SMEs is to embrace Mass customization, which has eight
quotes in total in the table 5. As mentioned earlier, Mass customization is a combination of flexibility and
personalization which industry 4.0 supports by using a combination of smart technologies and big data
analysis. If SMEs successfully implement industry 4.0 this benefit could be achieved, there are several
opportunities with mass customization for SMEs: "Main advantages of mass customization: ...o Lower cost
of material waste and inventory - it is a contract production, it is not necessary for the company to have a
stock of finished products; ... o Faster cash flow: quick production - quick turnaround; ... o The
manufacturer's ability to offer a wide range of products with low production costs - various product types
with the same basic components but different final design will allow manufacturers to offer a whole range
of products to satisfy every customer." (Q8). This allows SMEs to follow the digital trend and gain
competitive advantages. As mentioned in the background, additive manufacturing is a technology that
enables customization of products in the industry 4.0 concept by working with personalized small batches.
By having multiple personalized small batches, steps towards mass customization can be made.

Another opportunity with Mass customization as a strategy it relates to servitization. It is shown in a study
that servitization is a worthy pursuit for SMEs by enabling the creation of new innovative business models,
allowing for maintenance and other value creating activities (Q46). Also, by embracing this benefit, possibly

36
make problem solving faster by innovation (Q18). It is a crucial benefit since, as noted by Schwab (2017),
industry 4.0 has arrived thus adoption of industry 4.0 must happen now.

This benefit may be affected by all the challenges. Technological issues is a challenge where SMEs have to
overcome the issues with integrating and configuring industry 4.0 technologies with their own IT systems,
in order to embrace Mass customization. Moreover, having enough financial resources (Cost) to pay for the
implementation and maintenance is obviously an affecting factor too.

6.2.2 Corporate collaboration

The second strongest benefit is Corporate collaboration which has 5 quotes. This involves either
partnerships with other established firms or to make use of the available global governmental initiatives that
aim to support SMEs in the movement towards industry 4.0: "An overview of the European Commission
shows that there are more than 30 national and regional initiatives at European level: e.g., Plattform
Industrie 4.0 in Germany Catapult in UK, Fabbrica Digitale in Italy, Made Different in Belgium, Industry
du Futur in France, Produktion 2030 in Sweden, Made in Denmark, Smart Industry in Netherlands,
Produtech in Portugal, Industria Conectada 4.0 in Spain, Production of the Future in Austria, Průmysl 4.0
in Czech Republic, Smart Industry SK in Slovakia and many others" (Q1). These initiatives can help SMEs
embrace the benefits from industry 4.0 through financial support, education or other similar support when
needed. The challenge Cost can possibly be overcome by embracing the benefit of collaborating with others
and receiving financial support. Collaboration can be in the form of sharing data, share expertise and
experiences to achieve a win-win relationship.

6.2.3 Good preconditions

The third benefit with 4 quotes is Good preconditions meaning the knowledge of the strengths SMEs have
before deciding to implement industry 4.0. This benefit is of special character since it revolves around a
state before implementing industry 4.0. According to Vaidya et al (2018), the pillars of industry 4.0 are a
collection of disruptive technologies. As mentioned in the background, disruptive technologies are harder
for established companies to embrace. For instance, large enterprises may arguably have more difficulties
because of their size and complexity, whereas SMEs could embrace the fact that they are smaller in size
meaning they can be more flexible and agile when implementing complex systems: "… SMEs are most
likely to be the big winners from the shift; they are often able to implement the digital transformation more
rapidly than large enterprises, because they can develop and implement new IT structures from scratch
more easily …" (Q45). Another precondition, is that SMEs are often in niche markets which means they
can easily identify their needs in terms of which technologies should be invested in (Q12). SMEs could also
benefit from this precondition by possibly taking on one or a few industry 4.0 technologies at a time to
match their needs instead of investing in a large volume of new technologies at once. In order to embrace
Good preconditions, SMEs should have awareness of them, meaning if they know their own strengths they
are one step ahead of being ready for moving towards industry 4.0. Moreover, small enterprises usually
have fewer inhouse IT competencies than large enterprises. To implement industry 4.0, some skills are
needed for the new technologies. Reasonable to say, competencies of all types for all new technologies
might be missing in a smaller company. This puts the challenge Lack of competencies in correlation to the
benefit Good precondition. If SMEs are aware of what competencies are needed and have to be acquired,
the benefit can be embraced.

37
7 Conclusion
In conclusion, the main challenges and benefits impact the readiness level of SMEs regarding industry 4.0
implementation. The most important benefit of industry 4.0 is Mass customization, to embrace it the main
challenges technological issues and cost need to be tackled first. Furthermore, looking at the results chapter
it can be said that for AP1 the level never exceeds 1 and for AP2 the level is never higher than 2, leading to
the concluding thought that SMEs are at highest at level 2 ("Intermediate") in terms of readiness level.

A readiness score was calculated both according to the original model approach and according to the
modified approach. The dimensions "Cost" and "Smart factory" received the highest scores in both
approaches. It is interesting to see that there is not much that supports these two dimensions for being the
most ready regarding industry 4.0 implementation. Possibly, one interpretation could be that those two
dimensions are the ones SMEs should start looking at when moving towards industry 4.0, since they are
important to SMEs.

This conclusion implies that the answer to the research question What is the level of readiness of SMEs to
embrace the benefits and tackle the challenges of industry 4.0? is currently rather low and places SMEs no
higher than the level "Intermediate", hence they are learners when it comes to industry 4.0.

7.1 Societal consequences

The societal consequences of the research contribute to the research field industry 4.0 by showing the current
status of SMEs. This is important because SMEs play a big role in the global economy and industry 4.0
being the fourth revolution already present. Below is a list of societal consequences and its related
stakeholders.

For researchers targeting SMEs

● Readiness assessment:
This study contributes to the research field industry 4.0 by presenting a readiness assessment of
SMEs from a global perspective. The results show that the readiness level is rather low, but they
start to learn more about this new concept.

● Choice of readiness model:

The readiness assessment is based on IMPULS, with minor changes. The results may have had a
different outcome if another model was chosen. This study proves that IMPULS can be applicable
to SMEs.

For business people working at or evolving an SME

● Two important dimensions:
The two important dimensions SMEs should start to look at are "Cost" and "Smart factory".
Cost implies that the company should set up a financial strategy, use available financial aid and
establish the financial resources. Smart factory means that the company should define the needed
IT systems (industry 4.0 technologies), set up the equipment infrastructure and ensure all data that
is valuable information is used.

● Main challenges and benefits:

This study shows the main challenges and benefits SMEs have from a global perspective. Also, it
presents which are the most important and how they are linked to each other meaning what
challenge needs to be tackled in order to embrace the benefit.

38
7.2 Delimitations
A limitation in this study was lack of scientific base found about SMEs’ readiness in Sweden, which initially
was included in the study’s research question. However, a large volume of literature about SMEs’s readiness
in a global perspective was found. Therefore this study focused on SMEs globally instead of limiting it to
only one country which also leads to greater generalizability of the results. The delimitations in the design
of the study are related to mainly the choice of methods. This study is delimited to the chosen readiness
assessment IMPULS, meaning if the study had chosen another readiness model the results may have been
different. The readiness assessment method in the study strives to achieve reproducibility by having sections
about how the readiness assessment was built, how it was assessed and the results of it which are described
with transparency. Validity is met in this study through measuring the readiness level for SMEs which is
the study’s research question. To have high reliability this study followed scientifically grounded methods.
Since this study is of qualitative nature, it is important that subjectivity can be limited by focusing on actual
collected data. Regarding extensibility, this study could potentially be extended to more research areas,
since it covers aspects not only in computer and system science, but also economical aspects. A high
credibility has been strived for by using a scientific base for references and presenting raw data in appendix.

Ethical aspects have been considered throughout the research by following the principles stated in the
introduction chapter. One principle in particular to consider is To contribute to society and to human well-
being, acknowledging that all people are stakeholders in computing which has been followed by
contributing to the public good via conducting research in a relevant field with technologies possibly
affecting the future way of industries.

7.3 Future research

This study has defined SMEs readiness level for industry 4.0 from a global perspective (with a leaning
towards a western perspective) based on a literature review. Future research can be made where this study’s
readiness assessment can be used in practice for specific SMEs in different industries. As mentioned in the
background, Sweden has made industry 4.0 as one of the main focus areas to strengthen their
competitiveness in the global market. It would therefore be interesting to produce case studies in Swedish
SMEs using this readiness assessment to assess their readiness level. Other future studies can focus on
identifying strategies to depict how SMEs can approach industry 4.0 in the best way, to optimize SMEs’s
benefits and limit their challenges.

Regarding the list of societal consequences, future research can be made in each point of the list. For
readiness assessment, future research can investigate different readiness assessments on national level
instead of a global perspective to gather more in-depth details. A suggestion is to base the readiness
assessment on national level by using case study as method. For the point about choice of method, future
research can be made on developing a new model that is customized for a specific SME. A case study or
survey approach could be utilized for gathering data to the new model. For the point about the two important
dimensions, future research possibly in combination with developing a new model could result in other
important dimensions, which would be interesting to look further into. What is learned from this study is
the main challenges and benefits for SMEs. Future research could focus on these lessons learned to
investigate in more detail on how to best prepare for industry 4.0

39
References
References for the literature review are to be found in Appendix B.

Aazam, M., Zeadally, S. & Harras, K. A. (2018). Deploying Fog Computing in Industrial Internet of Things and
Industry 4.0, IEEE Transactions on Industrial Informatics, 14(10), pp. 4674–4682. DOI 10.1109/TII.2018.2855198.

ACM Code Task Force. (2018, June 22). Code of Ethics, The Code. Retrieved from https://www.acm.org/code-of-
ethics [Accessed: 6 March 2020].

Akdil, K. Y., Ustundag, A., & Cevikcan, E. (2018). Maturity and readiness model for industry 4.0 strategy. A.
Ustundag & E, Cevikan (Eds.). Industry 4.0: Managing the digital transformation. (pp. 61–94). Cham: Springer.

Cambridge Dictionary. (2020). Cambridge Dictionary. Retrieved from https://dictionary.cambridge.org/ [Accessed:

16 March 2020].

EU Commission. (2020). What is an SME? Retrieved from https://ec.europa.eu/growth/smes/business-friendly-

environment/sme-definition_en [Accessed: 9 March 2020].

Hofmann, E. & Rüsch, M. (2017). Industry 4.0 and the current status as well as future prospects on logistics.
Computers in Industry, 89, pp. 23–34. DOI 10.1016/j.compind.2017.04.002.

Johannesson, P. & Perjons, E. (2014). An Introduction to Design Science. Cham: Springer.

Liao, Y., Deschamps, F., Loures, E. D. F. R., & Ramos, L. F. P. (2017). Past, present and future of Industry 4.0 – a
systematic literature review and research agenda proposal. International Journal of Production Research, 55(12), pp.
3609–3629. DOI 10.1080/00207543.2017.1308576.

Lichtblau, K., Stich, V., Bertenrath, R., Blum, M., Bleider, M., Millack, A., Schmitt, K., Schmitz, E. & Schröter, M.
(2015). IMPULS-industrie 4.0-readiness. Impuls-Stiftung des VDMA, Aachen-Köln. Retrieved from
https://industrie40.vdma.org/documents/4214230/26342484/Industrie_40_Readiness_Study_1529498007918.pdf/0b
5fd521-9ee2-2de0-f377-93bdd01ed1c8 [Accessed: 15 March 2020].

Lu, Y. (2017). Industry 4.0: A survey on technologies, applications and open research issues.. Journal of Industrial
Information Integration, 6, pp. 1–10. DOI 10.1016/j.jii.2017.04.005.

Machadoa, C. G., Winrotha, M., Carlsson, D., Almströma, P., Centerholtb, V., & Hallin, M. (2019). Industry 4.0
readiness in manufacturing companies: challenges and enablers towards increased digitalization. P. Butala, E.
Govekar & R. Vrabič (Eds.). 52nd CIRP Conference on Manufacturing Systems. (pp. 1113–1118). DOI
10.1016/j.procir.2019.03.262.

Mittal, S., Khan, M.A., Romero, D. & Wuest, T. (2018). A critical review of smart manufacturing & Industry 4.0
maturity models: Implications for small and medium-sized enterprises (SMEs). Journal of Manufacturing Systems,
49, pp. 194–214. DOI 10.1016/j.jmsy.2018.10.005.

Popkova, E. G., Ragulina, Y. V. & Bogoviz, A. V. (Eds.). (2019). Industry 4.0: Industrial Revolution of the 21st
Century. Springer.

Roblek, V., Meško, M. & Krapež, A. (2016). A Complex View of Industry 4.0. SAGE Open, 6(2), DOI
10.1177/2158244016653987.

Schwab, K. (2017). The Fourth Industrial Revolution. NY: Crown Business.

Schröder, C. (2016). The Challenges of Industry 4.0 for Small and Medium-sized Enterprises. Friedrich-Ebert-
Stiftung: Bonn, Germany. Retrieved from www.fes-2017plus.de [Accessed: 9 March 2020].

Schumacher, A., Erol, S. & Sihn, W. (2016). A maturity model for assessing Industry 4.0 readiness and maturity of
manufacturing enterprises. Procedia CIRP, 52(1), pp. 161–166. DOI 10.1016/j.procir.2016.07.040.

40
Sommer, L. (2015). Journal of Industrial Engineering and Management Industrial Revolution-Industry 4.0: Are
German Manufacturing SMEs the First Victims of this Revolution?, 8(5), pp. 1512–1532. DOI 10.3926/jiem.1470.

Sony, M., & Naik, S. (2019). Key ingredients for evaluating Industry 4.0 readiness for organizations: a literature
review. Benchmarking: An International Journal. DOI 10.1108/BIJ-09-2018-0284.

Stentoft, J., Jensen, K. W., Philipsen, K., & Haug, A. (2019) Drivers and Barriers for Industry 4.0 Readiness and
Practice: A SME Perspective with Empirical Evidence. Proceedings of the 52nd Hawaii International Conference on
System Sciences. Retrieved from https://hdl.handle.net/10125/59952 [Accessed: 9 March 2020].

Swedish Ministry of Enterprise and Innovation. (2016). Smart industri. Retrieved from
https://www.regeringen.se/contentassets/869c75f458fc4585ab4ec8c13b250a07/informationsmaterial-smart-industri--
-en-nyindustrialiseringsstrategi-for-sverige [Accessed: 15 February 2020].

Vaidya, S., Ambad, P. & Bhosle, S. (2018). Industry 4.0 - A Glimpse. Procedia Manufacturing, 20, pp. 233–238.
DOI 10.1016/j.promfg.2018.02.034.

Vetenskapsrådet (2017) God forskningssed - Vetenskapsrådet. Retrieved from

https://www.vr.se/analys/rapporter/vara-rapporter/2017-08-29-god-forskningssed.html [Accessed: 9 March 2020].

Wolfswinkel, J. F., Furtmueller, E. & Wilderom, C. P. M. (2013). Using grounded theory as a method for rigorously
reviewing literature. European Journal of Information Systems. Nature Publishing Group, 22(1), pp. 45–55. DOI
10.1057/ejis.2011.51.

Xu, L. Da, Xu, E. L. & Li, L. (2018). Industry 4.0: State of the art and future trends. International Journal of
Production Research. Taylor and Francis Ltd., 56(8), pp. 2941–2962. DOI 10.1080/00207543.2018.1444806.

Zhong, R. Y., Xu, X., Klotz, E., & Newman, S. T. (2017). Intelligent Manufacturing in the Context of Industry 4.0:
A Review. Engineering, 3(5), pp. 616–630. DOI 10.1016/J.ENG.2017.05.015.

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Appendices
Appendix A – Mapping process details
Appendix B – Literature review references
Appendix C – Quotes and fields

42
Appendix A – Mapping process details
In the table below, the mapping of the chosen IMPULS dimensions can be seen in the 5th column.
Column 1 and 2 represents the selective codes, which are defined as main categories found in the literature
review process along with the subcategories. The categories were used to compare with the content in
IMPULS. The axial codes are supported with quotes and mapped into fields that could be mapped into
suitable dimensions (column 3-5).
Column 1 Column 2 Column 3 Column 4 Column 5

Main categories Subcategories Axial codes Quotes IMPULS fields and new fields IMPULS dimensions
and new dimensions
Cost Costs, Q6, Funding strategy, COST (New
Costs, Q5, Financial resources, dimension, not
Adaptability of CPS Q7, Funding strategy, original from
and Rollout Strategies, Q3, Financial resources, IMPULS)
Cost, Q4 Financial resources
Cost
Lack of competencies Lack of competencies, Q30, Skill acquisition, EMPLOYEE
Lack of competencies, Q26, Skill acquisition,
Job losses, Q22, Employee skill set,
Job losses, Q23, Employee skill set,
Lack of competencies, Q29, Skill acquisition,
Hiring, Q27, Skill acquisition,
Hiring Q28 Skill acquisition
Technological issues Small amount of IT Q14, Equipment infrastructure, Smart factory / Smart
Challenges systems, Q10, IT security, operations
IT security issues, Q13, Autonomous processes,
Adaptability of CPS Q12, Autonomous processes,
and Rollout Strategies, Q15, Equipment infrastructure,
Adaptability of CPS Q48 Share of data used
and Rollout Strategies,
Lessons learned,
Data issues
Organizational issues Size matters, Q31, Strategy, STRATEGY AND
Lacking structure, Q33, Innovation management, ORGANIZATION
Lacking structure, Q41, Innovation management,
Lacking structure, Q42, Strategy,
Strategy issues, Q32, Strategy
Adaptability of CPS Q37
and Rollout Strategies
Corporate collaboration Collaboration with Q11, IT security, Employee / Smart
large companies, Q24, Employee skill set, factory / Smart
Collaboration with Q25, Employee skill set, operations / Cost
large companies, Q2, Financial aid,
Collaboration with Q20 IT systems
large companies,
EU financial
programme,
Digital transformation
Good preconditions Flexibility, Q43, Innovation management, STRATEGY AND
Flexibility, Q45, Innovation management, ORGANIZATION
Opportunities
Flexibility, Q44, Innovation management,
Capture new Q39 Innovation management
opportunities
Mass customization Servitization, Q46, Data-driven services, Data-driven services /
Innovative SMEs, Q40, Innovation management, Smart operations
Data analysis, Q16, Data usage,
Lean data approach, Q47, Share of data used,
Supply chain, Q17, Data usage,
Machine learning, Q18, Data usage,
AI, Q19, IT security,
Mass customization Q8 Funding strategy
Governmental influence Use of Cloud services, Q9, Cloud usage, Smart factory / Cost /
Aiding project, Q1, Financial aid, Strategy and
Policies Q38 Strategy organization
Readiness
Knowledge level Low readiness, Q34, Strategy, Strategy and
Keeping up with trends Q21 IT systems organization / Smart
operations

43
SME requirement Identify requirements, Q36, Strategy, STRATEGY AND
specification Identify requirements Q35 Strategy ORGANIZATION

44
Appendix B – Literature review references
Below is a list of the literature included in the literature review.

1. Ulewicz, R., Novy, F., & Sethanan, K. (2019). The Challenges of Industry 4.0 for Small and Medium
Enterprises in Poland and Slovakia. Quality Production Improvement-QPI (pp. 147–154). Sciendo. DOI
10.2478/9783110680591-020.
2. Müller, J. M., Buliga, O., & Voigt, K. I. (2018). Fortune favors the prepared: How SMEs approach business
model innovations in Industry 4.0. Technological Forecasting and Social Change, 132, pp. 2–17. DOI
10.1016/j.techfore.2017.12.019.
3. Raj, A., Dwivedi, G., Sharma, A., de Sousa Jabbour, A. B. L., & Rajak, S. (2019). Barriers to the adoption
of industry 4.0 technologies in the manufacturing sector: An inter-country comparative perspective.
International Journal of Production Economics. DOI 10.1016/j.ijpe.2019.107546.
4. Matt, D. T., & Rauch, E. (2020). SME 4.0: The Role of Small-and Medium-Sized Enterprises in the Digital
Transformation. Industry 4.0 for SMEs (pp. 3–36). Palgrave Macmillan, Cham.
5. Rauch, E., Vickery, A. R., Brown, C. A., & Matt, D. T. (2020). SME Requirements and Guidelines for the
Design of Smart and Highly Adaptable Manufacturing Systems. Industry 4.0 for SMEs (pp. 39–72).
Palgrave Macmillan, Cham.
6. Rojas, R. A., & Garcia, M. A. R. (2020). Implementation of Industrial Internet of Things and Cyber-
Physical Systems in SMEs for Distributed and Service-Oriented Control. Industry 4.0 for SMEs (pp. 73–
103). Palgrave Macmillan, Cham.
7. Dallasega, P., Woschank, M., Zsifkovits, H., Tippayawong, K., & Brown, C. A. (2020). Requirement
Analysis for the Design of Smart Logistics in SMEs. Industry 4.0 for SMEs (pp. 147–162). Palgrave
Macmillan, Cham.
8. Modrák, V., & Šoltysová, Z. (2020). Development of an Organizational Maturity Model in Terms of Mass
Customization. Industry 4.0 for SMEs (pp. 215–250). Palgrave Macmillan, Cham.
9. Orzes, G., Poklemba, R., & Towner, W. T. (2020). Implementing Industry 4.0 in SMEs: A Focus Group
Study on Organizational Requirements. Industry 4.0 for SMEs (pp. 251–277). Palgrave Macmillan, Cham.
10. Sopadang, A., Chonsawat, N., & Ramingwong, S. (2020). Smart SME 4.0 Implementation Toolkit. Industry
4.0 for SMEs (pp. 279–302). Palgrave Macmillan, Cham.
11. Castelo-Branco, I., Cruz-Jesus, F., & Oliveira, T. (2019). Assessing Industry 4.0 readiness in
manufacturing: Evidence for the European Union. Computers in Industry, 107, pp. 22–32. DOI
10.1016/j.compind.2019.01.007.
12. Mittal, S., Khan, M. A., Romero, D., & Wuest, T. (2018). A critical review of smart manufacturing &
Industry 4.0 maturity models: Implications for small and medium-sized enterprises (SMEs). Journal of
manufacturing systems, 49, pp. 194–214. DOI 10.1016/j.jmsy.2018.10.005.
13. Hamzeh, R., Zhong, R., & Xu, X. W. (2018). A survey study on industry 4.0 for New Zealand
manufacturing. Procedia Manufacturing, 26, pp. 49–57. DOI 10.1016/j.promfg.2018.07.007.
14. Villa, A., & Taurino, T. (2019). SME Innovation and Development in the Context of Industry 4.0. Procedia
Manufacturing, 39, pp. 1415–1420. DOI 10.1016/j.promfg.2020.01.311.
15. Taurino, T., & Villa, A. (2019). A method for applying Industry 4.0 in Small Enterprises. IFAC-
PapersOnLine, 52(13), pp. 439–444. DOI 10.1016/j.ifacol.2019.11.099.
16. Anbumozhi, V., & Kimura, F. (2018). industry 4.0: What Does it Mean for the Circular Economy in
ASEAN?. industry, 4, pp. 1–35.
17. Gabriel, M., & Pessl, E. (2016). Industry 4.0 and sustainability impacts: Critical discussion of sustainability
aspects with a special focus on future of work and ecological consequences. Annals of the Faculty of
Engineering Hunedoara, 14(2), 131.
18. Stentoft, J., Wickstrøm, K. A., Haug, A., & Philipsen, K. (2020). Cost-driven motives to relocate
manufacturing abroad among small-and medium-sized manufacturers. Journal of Manufacturing
Technology Management.
19. Ghobakhloo, M., & Fathi, M. (2019). Corporate survival in Industry 4.0 era: the enabling role of lean-
digitized manufacturing. Journal of Manufacturing Technology Management.

45
20. Maisiri, W., & van Dyk, L. (2019). Industry 4.0 Readiness Assessment For South African Industries. South
African Journal of Industrial Engineering, 30(3), pp. 134-148. DOI 10.7166/30-3-2231.
21. Stentoft, J., Jensen, K. W., Philipsen, K., & Haug, A. (2019). Drivers and Barriers for Industry 4.0
Readiness and Practice: A SME Perspective with Empirical Evidence. Proceedings of the 52nd Hawaii
International Conference on System Sciences.
22. Bossen, H., & Ingemansson, J. (2016). Digitalisering av svensk industri-kartläggning av svenska styrkor
och utmaningar. Roland Berger AB på uppdrag av Vinnova, Stockholm.
23. Moeuf, A., Lamouri, S., Pellerin, R., Eburdy, R., & Tamayo, S. (2017). Industry 4.0 and the SME: a
technology-focused review of the empirical literature. 7th International Conference on Industrial
Engineering and Systems Management IESM.
24. Nwaiwu, F., Duduci, M., Chromjakova, F., & Otekhile, C. A. F. (2020). Industry 4.0 concepts within the
Czech SME manufacturing sector: an empirical assessment of critical success factors. Business: Theory and
Practice, 21(1), pp. 58–70.
25. Ludwig, T., Kotthaus, C., Stein, M., Pipek, V., & Wulf, V. (2018). Revive old discussions! Socio-technical
challenges for small and medium enterprises within industry 4.0. Proceedings of 16th European Conference
on Computer-Supported Cooperative Work-Exploratory Papers. European Society for Socially Embedded
Technologies (EUSSET).
26. Schröder, C. (2016). The Challenges of Industry 4.0 for Small and Medium-sized Enterprises. Friedrich-
Ebert-Stiftung: Bonn, Germany.
27. Machadoa, C. G., Winrotha, M., Carlsson, D., Almströma, P., Centerholtb, V., & Hallin, M. (2019).
Industry 4.0 readiness in manufacturing companies: challenges and enablers towards increased
digitalization. P. Butala, E. Govekar & R. Vrabič (Eds.). 52nd CIRP Conference on Manufacturing Systems.
(pp. 1113–1118). DOI 10.1016/j.procir.2019.03.262.
28. Arcidiacono, F., Ancarani, A., Di Mauro, C., & Schupp, F. (2019). Where the rubber meets the road.
Industry 4.0 among SMEs in the automotive sector. IEEE Engineering Management Review.
29. Jones, M., Zarzycki, L., & Murray, G. (2018). Does Industry 4.0 Pose a Challenge for the SME Machine
Builder? A Case Study and Reflection of Readiness for a UK SME. International Precision Assembly
Seminar (pp. 183–197). Cham: Springer.
30. Suresh, N., Hemamala, K., & Ashok, N. (2018). Challenges in implementing industry revolution 4.0 in
INDIAN manufacturing SMES: insights from five case studies. International Journal of Engineering &
Technology, 7(2.4), pp. 136–139. DOI: 10.14419/ijet.v7i2.4.13024.
31. Bär, K., Herbert-Hansen, Z. N. L., & Khalid, W. (2018). Considering Industry 4.0 aspects in the supply
chain for an SME. Production Engineering, 12(6), pp. 747–758. DOI https://doi.org/10.1007/s11740-018-
0851-y.
32. Andulkar, M., Le, D. T., & Berger, U. (2018). A multi-case study on Industry 4.0 for SME’s in
Brandenburg, Germany. Proceedings of the 51st Hawaii International Conference on System Sciences, 2.
33. Pérez, J. D. C., Buitrón, R. E. C., & Melo, J. I. G. (2018). Methodology for the Retrofitting of
Manufacturing Resources for Migration of SME Towards Industry 4.0. International Conference on
Applied Informatics (pp. 337–351). Cham: Springer.
34. Dassisti, M., Panetto, H., Lezoche, M., Merla, P., Semeraro, C., Giovannini, A., & Chimienti, M. (2017).
Industry 4.0 paradigm: The viewpoint of the small and medium enterprises.
35. Ristuccia, C. (2019). Industry 4.0: SMEs Challenges and Opportunities in the Era of Digitalization. ZEI
Discussion Paper C 252/2019.
36. Grube, D., Malik, A. A., & Bilberg, A. (2017). GENERIC CHALLENGES AND AUTOMATION
SOLUTIONS IN MANUFACTURING SMES. Annals of DAAAM & Proceedings, 28. 161. DOI:
10.2507/28th.daaam.proceedings.
37. Gergin, Z., Üney-Yüksektepe, F., Gençyılmaz, M. G., Aktin, A. T., Gülen, K. G., İlhan, D. A., Dündar, U.,
Cebeci, Ö. & Çavdarlı, A. İ. (2018). Industry 4.0 scorecard of Turkish SMEs. The International Symposium
for Production Research, (pp. 426–437). Cham: Springer.
38. Rodič, B. (2017). Industry 4.0 and the new simulation modelling paradigm. Organizacija, 50(3), pp. 193–
207.
39. Müller, J., & Voigt, K. I. (2017). Industry 4.0—Integration Strategies for Small and Medium-sized
Enterprises. Proceedings of the 26th International Association for Management of Technology (IAMOT)
Conference, Vienna, Austria, (pp. 14–18).
40. Safar, L., Sopko, J., Bednar, S., & Poklemba, R. (2018). Concept of SME business model for industry 4.0

46
 environment. TEM Journal, 7(3), 626.
41. Beliveau, M., De Santa-Eulalia, L. A., Mosconi, E., & Cadieux, N. (2018). How Can SME Embark in a
Digital Transformation in the Context of the 4th Industrial Revolution?. 2nd International Symposium on
Supply Chain, (pp. 37–42).
42. de Sousa, T. B., Guerrini, F. M., & Coghi, M. (2018). Main objectives and barriers of the enterprise
adaptation project to Industry 4.0: a case study in a technologies supplier company. 2nd International
Symposium on Supply Chain 4.0, (pp. 43–48).
43. de Campos Martins, F., & Simon, A. T. (2018). Supply Chain 4.0 Challenges. 2nd International Symposium
on Supply Chain 4.0, (pp. 50–57).
44. Ingaldi, M., & Ulewicz, R. (2019). Problems with the Implementation of Industry 4.0 in Enterprises from
the SME Sector. Sustainability, MDPI, Open Access Journal, 12(1), pp. 1–18.

47
 Appendix C – Quotes and fields

Fields Quote list Quote Article Reference

number
Financial aid page 10: "An overview of the European Commission Q1 Industry 4.0 for SMEs - Challenges, Matt, D. T., & Rauch, E. (2020). SME 4.0:
shows that there are more than 30 national and Opportunities and Requirements [ARTICLE 1: The Role of Small-and Medium-Sized
regional initiatives at European level: e.g., Plattform SME 4.0: The Role of Small- and Medium- Enterprises in the Digital Transformation.
Industrie 4.0 in Germany Catapult in UK, Fabbrica Sized Enterprises in the Digital In Industry 4.0 for SMEs (pp. 3-36).
Digitale in Italy, Made Different in Belgium, Industry Transformation] Palgrave Macmillan, Cham.
du Futur in France, Produktion 2030 in Sweden, Made
in Denmark, Smart Industry in Netherlands, Produtech
in Portugal, Industria Conectada 4.0 in Spain,
Production of the Future in Austria, Průmysl 4.0 in
Czech Republic, Smart Industry SK in Slovakia and
many others (Plattform Industrie 4.0 2019)."
Financial aid page 2: "The European Commission has presented the Q2 Industry 4.0: SMEs Challenges and Ristuccia, C. (2019). Industry 4.0: SMEs
Digital Europe programme for the next EU financial Opportunities in the Era of Digitalization Challenges and Opportunities in the Era of
period 2021-2027, which plans to invest in five main Digitalization. Zentrum für Europäische
areas: supercomputers, Artificial Intelligence (AI), Integrationsforschung, Rheinische
cybersecurity and trust, digital skills and ensuring a Friedrich-Wilhelms Universität Bonn.
wide use of technologies across the economy and the
society"
Financial page 4551: "Moreover, several i4.0 technologies are Q3 A multi-case study on Industry 4.0 for SME’s Andulkar, M., Le, D. T., & Berger, U.
resources still under development and it is challenging for small in Brandenburg, Germany (2018, January). A multi-case study on
and medium-sized enterprises to dedicate resources for Industry 4.0 for SME’s in Brandenburg,
these technologies. They would in several cases prefer Germany. In Proceedings of the 51st
to use these technologies as off-the-shelf products Hawaii International Conference on
(instead of developing in-house) to achieve product System Sciences.
innovation."
Financial page 4: "Generally, all over the world, small and Q4 Problems with the Implementation of Industry Ingaldi, M., & Ulewicz, R. (2019).
resources medium-sized enterprises often do not have sufficient 4.0 in Enterprises from the SME Sector Problems with the Implementation of
funds to invest in the latest technologies and must Industry 4.0 in Enterprises from the SME
allocate capital very effectively and carefully." Sector. Sustainability, 12(1), 1-18.
Financial page 150: "Compared to bigger companies, SMEs Q5 Industry 4.0 for SMEs - Challenges, Dallasega, P., Woschank, M., Zsifkovits,
resources have at their disposal fewer resources in terms of Opportunities and Requirements [ARTICLE 5: H., Tippayawong, K., & Brown, C. A.
budget and qualified workforces for doing research Requirement Analysis for the Design of Smart Requirement Analysis for the Design of
and innovation actions." Logistics in SMEs] Smart Logistics in SMEs. Industry 4.0 for
SMEs, 147.
Funding page 85: "Although there are some efforts of providers Q6 Industry 4.0 for SMEs - Challenges, Rojas, R. A., & Garcia, M. A. R. (2020).
strategy to adapt their offerings to the needs of small- and Opportunities and Requirements [ARTICLE 3: Implementation of Industrial Internet of
medium-sized enterprises (SMEs), usually their Implementation of Industrial Internet of Things Things and Cyber-Physical Systems in
primary customers are large organizations able to and Cyber-Physical Systems in SMEs for SMEs for Distributed and Service-
afford such costs (Cruz-Cunha 2009)." Distributed and Service-Oriented Control] Oriented Control. In Industry 4.0 for
SMEs (pp. 73-103). Palgrave Macmillan,
Cham.
Funding page 5: "SMEs doubt that the investment in CPPS and Q7 Revive Old Discussions! Socio-technical Ludwig, T., Kotthaus, C., Stein, M., Pipek,
strategy Industry-4.0 technologies will amortize within an Challenges for Small and Medium Enterprises V., & Wulf, V. (2018). Revive old
economically acceptable time span" within Industry 4.0 discussions! Socio-technical challenges for
small and medium enterprises within
industry 4.0. In Proceedings of 16th
European Conference on Computer-
Supported Cooperative Work-Exploratory
Papers. European Society for Socially
Embedded Technologies (EUSSET).
Funding page 628: "Main advantages of mass customization: Q8 Concept of SME Business Model for Industry Safar, L., Sopko, J., Bednar, S., &
strategy ...o Lower cost of material waste and inventory - it is a 4.0 Environment Poklemba, R. (2018). Concept of SME
contract production, it is not necessary for the Business Model for Industry 4.0
company to have a stock of finished products; ... o Environment.
Faster cash flow: quick production - quick turnaround;
... o The manufacturer's ability to offer a wide range of
products with low production costs - various product
types with the same basic components but different
final design will allow manufacturers to offer a whole
range of products to satisfy every customer."
Cloud usage page 8: "[...] clouds assist in the outsourcing of data Q9 Fortune favors the prepared: How SMEs Müller, J. M., Buliga, O., & Voigt, K. I.
storage and data sharing with partners (Xu, 2012). In approach business model innovations in (2018). Fortune favors the prepared: How
order to receive widespread acceptance in SMEs, such Industry 4.0 SMEs approach business model
platforms need to be easily accessible, secure, and innovations in Industry 4.0. Technological

48
 efficient in usage." Forecasting and Social Change, 132, 2-17.
IT security page 252: "At the same time, this industrial revolution Q10 Industry 4.0 for SMEs - Challenges, Orzes, G., Poklemba, R., & Towner, W. T.
brings some challenges regarding data security, Opportunities and Requirements [ARTICLE 9: (2020). Implementing Industry 4.0 in
finding the needed capital, developing a strategy for Implementing Industry 4.0 in SMEs: A Focus SMEs: A Focus Group Study on
implementing it and finding qualified employees Group Study on Organizational Requirements] Organizational Requirements. In Industry
(Schröder 2016)." 4.0 for SMEs (pp. 251-277). Palgrave
Macmillan, Cham.
IT security page 4: "This enables a much higher degree of Q11 Fortune favors the prepared: How SMEs Müller, J. M., Buliga, O., & Voigt, K. I.
transparency and efficiency in transactions compared approach business model innovations in (2018). Fortune favors the prepared: How
to the third Industrial Revolution and brings new Industry 4.0 SMEs approach business model
questions in the already established debate on cyber innovations in Industry 4.0. Technological
security" Forecasting and Social Change, 132, 2-17.
Autonomous page 5: "Current Industry 4.0 concepts and Q12 Revive Old Discussions! Socio-technical Ludwig, T., Kotthaus, C., Stein, M., Pipek,
processes technologies, such as cyber-physical production Challenges for Small and Medium Enterprises V., & Wulf, V. (2018). Revive old
systems, focus on large-scale production with within Industry 4.0 discussions! Socio-technical challenges for
autonomous modifications of the internal and external small and medium enterprises within
supply chain. These concepts cannot simply be industry 4.0. In Proceedings of 16th
adapted by SME, as they operate in niche markets and European Conference on Computer-
often create individual pieces in accordance with Supported Cooperative Work-Exploratory
special customer requirements." Papers. European Society for Socially
Embedded Technologies (EUSSET).
Autonomous page 5: "... the degree of automation in SMEs is on Q13 Revive Old Discussions! Socio-technical Ludwig, T., Kotthaus, C., Stein, M., Pipek,
processes average currently rather low, which results in a high Challenges for Small and Medium Enterprises V., & Wulf, V. (2018). Revive old
dependency of employees’ expertise, which has grown within Industry 4.0 discussions! Socio-technical challenges for
over the years and cannot easily be externalized and small and medium enterprises within
transferred into program code (Bracht, Geckler, & industry 4.0. In Proceedings of 16th
Wenzel, 2011)." European Conference on Computer-
Supported Cooperative Work-Exploratory
Papers. European Society for Socially
Embedded Technologies (EUSSET).
Equipment page 85: "... we suggest that OSS and open standards Q14 Industry 4.0 for SMEs - Challenges, Rojas, R. A., & Garcia, M. A. R. (2020).
infrastructur are also key enablers of IIoT in SME. To support this Opportunities and Requirements [ARTICLE 3: Implementation of Industrial Internet of
e statement, we observe that SMEs have a relatively Implementation of Industrial Internet of Things Things and Cyber-Physical Systems in
small amount of digital systems and processes to be and Cyber-Physical Systems in SMEs for SMEs for Distributed and Service-
integrated, thus they may be able to afford to develop Distributed and Service-Oriented Control] Oriented Control. In Industry 4.0 for
in-house software applications. Moreover, thanks to SMEs (pp. 73-103). Palgrave Macmillan,
the source code availability, OSS gives SMEs the Cham.
possibility of developing highly customized and lean
solutions based on their know-how."
Equipment page 1165: "• SME’s must work strategically with Q15 GENERIC CHALLENGES AND Grube, D., Malik, A. A., & Bilberg, A.
infrastructur production that makes more long-term planning AUTOMATION SOLUTIONS IN (2017). GENERIC CHALLENGES AND
e possible and consistent. • The planning and control MANUFACTURING SMES AUTOMATION SOLUTIONS IN
systems must be simple and efficient solutions to MANUFACTURING SMES. Annals of
support personal at the shop floor, to make right DAAAM & Proceedings, 28.
decisions. • Focus at Lean tools to reduce waste and
create flow, to prepare for automation. • SME’s must
think smart in simple Lean Automation solutions, that
may be specific to the individual companies, but still
there are some general learnings and benchmarking
between companies."
Data usage page 137: "The key areas where data analysis helps I Q16 Industry 4.0: SMEs Challenges and Ristuccia, C. (2019). Industry 4.0: SMEs
4.0 are predicting machine health, transparent supply Opportunities in the Era of Digitalization Challenges and Opportunities in the Era of
chain, reduction labor cost, better working Digitalization. Zentrum für Europäische
environment, energy saving and maintenance schedule Integrationsforschung, Rheinische
[11]." Friedrich-Wilhelms Universität Bonn.
Data usage page 6: "The availability of such data generates an Q17 Industry 4.0: SMEs Challenges and Ristuccia, C. (2019). Industry 4.0: SMEs
added value in terms of range of services and Opportunities in the Era of Digitalization Challenges and Opportunities in the Era of
efficiency in managing supply chains. Furthermore, Digitalization. Zentrum für Europäische
businesses can define customer needs in real time and Integrationsforschung, Rheinische
respond to specific trends." Friedrich-Wilhelms Universität Bonn.
Data usage page 6: "The most innovative element of Industry 4.0 Q18 Industry 4.0: SMEs Challenges and Ristuccia, C. (2019). Industry 4.0: SMEs
is the capacity of machines to solve a problem faster Opportunities in the Era of Digitalization Challenges and Opportunities in the Era of
than before, due to the increasing information captured Digitalization. Zentrum für Europäische
by the system: the so-called machine learning." Integrationsforschung, Rheinische
Friedrich-Wilhelms Universität Bonn.
IT systems page 7: "AI could be an important tool to address Q19 Industry 4.0: SMEs Challenges and Ristuccia, C. (2019). Industry 4.0: SMEs
socio-economic challenges and to pursue the human Opportunities in the Era of Digitalization Challenges and Opportunities in the Era of
development, which raises new ethical, legal and even Digitalization. Zentrum für Europäische
democratic issues. This technology will make Integrationsforschung, Rheinische
machines and robots able to interact with each other Friedrich-Wilhelms Universität Bonn.

49
 and above all will enable them to learn from the
various situations. Automotive, aerospace, energy,
pharmaceutical are the main sectors in which AI has
been already applied. "
IT systems page 13: "Digital transformation has been for a long Q20 Industry 4.0: SMEs Challenges and Ristuccia, C. (2019). Industry 4.0: SMEs
time on the EU agenda and the initiatives adopted at Opportunities in the Era of Digitalization Challenges and Opportunities in the Era of
European level facilitate the access to finance, Digitalization. Zentrum für Europäische
technologies, knowledges for enterprises, notably for Integrationsforschung, Rheinische
SMEs." Friedrich-Wilhelms Universität Bonn.
IT systems page 47: "[...] more than 50% of companies do not Q21 2nd International Symposium on Supply Chain de Sousa, T. B., Guerrini, F. M., & Coghi,
have well-defined goals when adopting Industries 4.0 4.0 (ISSC4): Main objectives and barriers of M. (2018). Main objectives and barriers of
technologies, and just follow trends or what the enterprise adaptation project to Industry 4.0: the enterprise adaptation project to
competitors are doing, and in doing so, adopt a case study in a technologies supplier company Industry 4.0: a case study in a technologies
technologies that are not appropriate to its need". [6] supplier company. In 2nd International
Symposium on Supply Chain 4.0 (p. 43).
Employee page 20: "Industry 4.0 technologies are also rapidly Q22 Industry 4.0: What Does It Mean for the Anbumozhi, V., & Kimura, F. (2018).
skill set increasing jobs that can be performed better and faster Circular Economy in ASEAN? industry 4.0: What Does it Mean for the
by machines rather than by people. While these may Circular Economy in ASEAN?. industry,
reduce costs and raise productivity, they will also 4, 1-35.
threaten jobs, and some members of ASEAN will be
more affected than others. The immediate threats are
to low-skilled, repetitive jobs such as those by
assembly line workers"
Employee page 193: "...9. Loss of many jobs to automatic Q23 Does Industry 4.0 Pose a Challenge for the Jones, M., Zarzycki, L., & Murray, G.
skill set processes and IT-controlled processes, especially for SME Machine Builder? A Case Study and (2018, January). Does Industry 4.0 Pose a
lower educated parts of society" (Lavanya et al. Reflection of Readiness for a UK SME Challenge for the SME Machine Builder?
2017)." A Case Study and Reflection of Readiness
for a UK SME. In International Precision
Assembly Seminar (pp. 183-197).
Springer, Cham.
Employee page 8: " the results show that while most companies Q24 Fortune favors the prepared: How SMEs Müller, J. M., Buliga, O., & Voigt, K. I.
skill set in the sample lack expertise in implementing Industry approach business model innovations in (2018). Fortune favors the prepared: How
4.0 (Kowalkowski et al., 2013), this deficiency can Industry 4.0 SMEs approach business model
open new doors for cooperation and value creation innovations in Industry 4.0. Technological
innovation with partnering companies and institutions" Forecasting and Social Change, 132, 2-17.
Employee page 6: "The SMEs revealed their lack of expertise for Q25 Fortune favors the prepared: How SMEs Müller, J. M., Buliga, O., & Voigt, K. I.
skill set mastering such challenges as well as their derived approach business model innovations in (2018). Fortune favors the prepared: How
need for external assistance, which can be provided by Industry 4.0 SMEs approach business model
governmental institutions and by work groups of the innovations in Industry 4.0. Technological
trade community." Forecasting and Social Change, 132, 2-17.
Skill page 19: "Utmaningen för dessa företag består i att de Q26 DIGITALISERING AV SVENSK INDUSTRI Bossen, H., & Ingemansson, J. (2016).
acquisition måste hantera ett stort antal teknikområden som i - Kartläggning av svenska styrkor och Digitalisering av svensk industri-
många fall dessutom är nya för dem." utmaningar kartläggning av svenska styrkor och
utmaningar. Roland Berger AB på uppdrag
av Vinnova, Stockholm.
Skill page 4: "Other problems are difficulties in hiring staff, Q27 Problems with the Implementation of Industry Ingaldi, M., & Ulewicz, R. (2019).
acquisition the high cost of staff and big competition." 4.0 in Enterprises from the SME Sector Problems with the Implementation of
Industry 4.0 in Enterprises from the SME
Sector. Sustainability, 12(1), 1-18.
Skill page 4: "... lack of employees." Q28 Problems with the Implementation of Industry Ingaldi, M., & Ulewicz, R. (2019).
acquisition 4.0 in Enterprises from the SME Sector Problems with the Implementation of
Industry 4.0 in Enterprises from the SME
Sector. Sustainability, 12(1), 1-18.
Skill page 4552: "Another factor is about the lack of Q29 A multi-case study on Industry 4.0 for SME’s Andulkar, M., Le, D. T., & Berger, U.
acquisition knowledge or expertise regarding the possibility and in Brandenburg, Germany (2018, January). A multi-case study on
potential of using the current technology and its Industry 4.0 for SME’s in Brandenburg,
applications. This has been a major problem with Germany. In Proceedings of the 51st
SMEs where more than 50% of the companies having Hawaii International Conference on
faced difficulties to fill vacancies for IT specialists in System Sciences.
2016 and about 30% of companies working without
their own websites [25]."
Skill page 150: "... the results showed that education and Q30 Industry 4.0 for SMEs - Challenges, Dallasega, P., Woschank, M., Zsifkovits,
acquisition qualification of employees is one of the main Opportunities and Requirements [ARTICLE 5: H., Tippayawong, K., & Brown, C. A.
requirements for the implementation of Industry 4.0." Requirement Analysis for the Design of Smart (2020). Requirement Analysis for the
Logistics in SMEs] Design of Smart Logistics in SMEs. In
Industry 4.0 for SMEs (pp. 147-162).
Palgrave Macmillan, Cham.
Strategy page 5: "The smaller SMEs are, the higher the risk that Q31 Industry 4.0 for SMEs - Challenges, Matt, D. T., & Rauch, E. (2020). SME 4.0:
they will not be able to benefit from this revolution." Opportunities and Requirements [ARTICLE 1: The Role of Small-and Medium-Sized

50
 SME 4.0: The Role of Small- and Medium- Enterprises in the Digital Transformation.
Sized Enterprises in the Digital In Industry 4.0 for SMEs (pp. 3-36).
Transformation] Palgrave Macmillan, Cham.
Strategy page 288: "... It is obviously difficult to apply all Q32 Industry 4.0 for SMEs - Challenges, Sopadang, A., Chonsawat, N., &
Industry 4.0 concepts to SMEs due to the limitation of Opportunities and Requirements [ARTICLE Ramingwong, S. (2020). Smart SME 4.0
human resources, technology, and financial potential. 10: Smart SME 4.0 Implementation Toolkit] Implementation Toolkit. In Industry 4.0
Thus, SMEs should start their implementation of for SMEs (pp. 279-302). Palgrave
SMEs 4.0 concept with prioritized and appropriate Macmillan, Cham.
measures."
Strategy page 150: "They emphasized specific barriers to Q33 Industry 4.0 for SMEs - Challenges, Dallasega, P., Woschank, M., Zsifkovits,
Industry 4.0 implementation such as missing Opportunities and Requirements [ARTICLE 5: H., Tippayawong, K., & Brown, C. A.
standardization and an inappropriate company Requirement Analysis for the Design of Smart (2020). Requirement Analysis for the
strategy." Logistics in SMEs] Design of Smart Logistics in SMEs. In
Industry 4.0 for SMEs (pp. 147-162).
Palgrave Macmillan, Cham.
Strategy page 17: "Decker (2017) analyzes the readiness of Q34 Industry 4.0 for SMEs - Challenges, Matt, D. T., & Rauch, E. (2020). SME 4.0:
Danish SMEs from the metal processing sector for Opportunities and Requirements [ARTICLE 1: The Role of Small-and Medium-Sized
Industry 4.0 using case study research. Up to this SME 4.0: The Role of Small- and Medium- Enterprises in the Digital Transformation.
point, there was no maturity or readiness model Sized Enterprises in the Digital In Industry 4.0 for SMEs (pp. 3-36).
available and thus the analysis was conducted Transformation] Palgrave Macmillan, Cham.
basically on a qualitative level. The basic outcome is
that SMEs at this time were not sure if, when and how
they should start to introduce Industry 4.0 in their
firms."
Strategy page 6: "While many of those companies aim to Q35 Fortune favors the prepared: How SMEs Müller, J. M., Buliga, O., & Voigt, K. I.
establish an IT-facilitated, automated interconnection approach business model innovations in (2018). Fortune favors the prepared: How
with suppliers and customers, they struggle with the Industry 4.0 SMEs approach business model
resulting uncertainties and complexities, for instance innovations in Industry 4.0. Technological
in case of disturbances. " Forecasting and Social Change, 132, 2-17.
Strategy page 8: "Moreover, SMEs tend to avoid technologies Q36 Fortune favors the prepared: How SMEs Müller, J. M., Buliga, O., & Voigt, K. I.
with uncertain results (Hirsch-Kreinsen, 2016), so approach business model innovations in (2018). Fortune favors the prepared: How
investments as early adopters are often evaded, due to Industry 4.0 SMEs approach business model
the risk of investing in the wrong technologies (Faller innovations in Industry 4.0. Technological
and Feldmüller, 2015). This conservative investment Forecasting and Social Change, 132, 2-17.
strategy has shortcomings, as researchers highlight the
importance of identifying new technological trends
early and of promptly responding to them"
Strategy page 5: "[...] many leaders of SMEs do not have a Q37 Revive Old Discussions! Socio-technical Ludwig, T., Kotthaus, C., Stein, M., Pipek,
comprehensive strategy regarding Industry 4.0 to gain Challenges for Small and Medium Enterprises V., & Wulf, V. (2018). Revive old
an appropriate maturity level (Schröder, 2017). The within Industry 4.0 discussions! Socio-technical challenges for
smaller the company size, the more likely this is to be small and medium enterprises within
the case (Sommer, 2015)." industry 4.0. In Proceedings of 16th
European Conference on Computer-
Supported Cooperative Work-Exploratory
Papers. European Society for Socially
Embedded Technologies (EUSSET).
Strategy page 15: "[...] developed countries have formulated Q38 Barriers to the adoption of industry 4.0 Raj, A., Dwivedi, G., Sharma, A., de
national strategies and policies for incentivizing technologies in the manufacturing sector: An Sousa Jabbour, A. B. L., & Rajak, S.
Industry 4.0 technologies, whereas developing inter-country comparative perspective (2019). Barriers to the adoption of industry
countries have adopted Industry 4.0 technologies on a 4.0 technologies in the manufacturing
corporate level, relying on individual corporate sector: An inter-country comparative
initiatives rather than national and coordinated perspective. International Journal of
policies. The influencing barriers in developed and Production Economics, 107546.
developing countries differ due to divergent policies
for the advancement of Industry 4.0."
Innovation page 25: "Innovative SMEs and start-ups will be Q39 Industry 4.0: What Does It Mean for the Anbumozhi, V., & Kimura, F. (2018).
management critical in capturing new opportunities offered by Circular Economy in ASEAN? industry 4.0: What Does it Mean for the
Industry 4.0. Many ASEAN countries already have Circular Economy in ASEAN?. industry,
innovation hubs and incubators at national level. To be 4, 1-35.
competitive, however, new businesses will need to
operate at scale and reach it rapidly".
Innovation page 19: "Innovative and agile start-ups and SMEs Q40 Industry 4.0: What Does It Mean for the Anbumozhi, V., & Kimura, F. (2018).
management with no need to defend legacy business are widely Circular Economy in ASEAN? industry 4.0: What Does it Mean for the
entering the circular economy and Industry 4.0 field Circular Economy in ASEAN?. industry,
by providing new digital platforms and disruptive 4, 1-35.
service solutions to maximise the value of products
and materials. Partnerships between established and
small companies hold great promise for disruptive new
solutions.

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Innovation page 225: "... it can be stated that larger companies can Q41 Industry 4.0 for SMEs - Challenges, Modrák, V., & Šoltysová, Z. (2020).
management follow the higher maturity levels in the technological Opportunities and Requirements [ARTICLE 8: Development of an Organizational
domain for this concept more quickly than SMEs." Development of an Organizational Maturity Maturity Model in Terms of Mass
Model in Terms of Mass Customization] Customization. In Industry 4.0 for SMEs
(pp. 215-250). Palgrave Macmillan, Cham.

Innovation page 216: "Although there is high potential from Q42 Industry 4.0 for SMEs - Challenges, Modrák, V., & Šoltysová, Z. (2020).
management Industry 4.0 in SMEs, the main limit lies in a lack of Opportunities and Requirements [ARTICLE 8: Development of an Organizational
methodological frameworks for its introduction and Development of an Organizational Maturity Maturity Model in Terms of Mass
wide implementation." Model in Terms of Mass Customization] Customization. In Industry 4.0 for SMEs
(pp. 215-250). Palgrave Macmillan, Cham.
Innovation page 5: "Due to their flexibility, the entrepreneurial Q43 Revive Old Discussions! Socio-technical Ludwig, T., Kotthaus, C., Stein, M., Pipek,
management spirit, and the innovation capabilities, SMEs have Challenges for Small and Medium Enterprises V., & Wulf, V. (2018). Revive old
proved to be more robust than large and multinational within Industry 4.0 discussions! Socio-technical challenges for
enterprises, as the previous financial and economic small and medium enterprises within
crisis showed (Matt 2007; Matt et al. 2016)." industry 4.0. In Proceedings of 16th
European Conference on Computer-
Supported Cooperative Work-Exploratory
Papers. European Society for Socially
Embedded Technologies (EUSSET).
Innovation page 5: "SMEs are not only adaptive and innovative in Q44 Revive Old Discussions! Socio-technical Ludwig, T., Kotthaus, C., Stein, M., Pipek,
management terms of their products, but also in terms of their Challenges for Small and Medium Enterprises V., & Wulf, V. (2018). Revive old
manufacturing practices." within Industry 4.0 discussions! Socio-technical challenges for
small and medium enterprises within
industry 4.0. In Proceedings of 16th
European Conference on Computer-
Supported Cooperative Work-Exploratory
Papers. European Society for Socially
Embedded Technologies (EUSSET).
Innovation page 5: "SMEs are most likely to be the big winners Q45 Revive Old Discussions! Socio-technical Ludwig, T., Kotthaus, C., Stein, M., Pipek,
management from the shift; they are often able to implement the Challenges for Small and Medium Enterprises V., & Wulf, V. (2018). Revive old
digital transformation more rapidly than large within Industry 4.0 discussions! Socio-technical challenges for
enterprises, because they can develop and implement small and medium enterprises within
new IT structures from scratch more easily (Deloitte industry 4.0. In Proceedings of 16th
2015). Many small- and medium-sized companies are European Conference on Computer-
already focusing on digitized products in order to Supported Cooperative Work-Exploratory
stand out in the market (PWC 2015). The integration Papers. European Society for Socially
of information and communication technology (ICT) Embedded Technologies (EUSSET).
and modern Industry 4.0 technologies would transform
today’s SME factories into smart factories with
signifcant economic potential (Lee and Lapira 2013;
Gualtieri et al. 2018)."
Data-driven page 9: "[...] the present paper shows that servitization Q46 Fortune favors the prepared: How SMEs Müller, J. M., Buliga, O., & Voigt, K. I.
services is a worthy pursuit for SMEs, leading to innovative approach business model innovations in (2018). Fortune favors the prepared: How
business models, beginning with repair and Industry 4.0 SMEs approach business model
maintenance, followed by technological trainings and innovations in Industry 4.0. Technological
consulting as well as CPS-related services, such as Forecasting and Social Change, 132, 2-17.
digitization of processes, real-time product co-
development or data processing and analysis (Eggert
et al., 2014; Mathieu, 2001)"
Share of data page 137: "I 4.0 help firms to identify the problem in Q47 Challenges in implementing industry revolution Suresh, N., Hemamala, K., & Ashok, N.
used realistic approach known as digital lean. By reducing 4.0 in INDIAN manufacturing SMES: insights (2018). Challenges in implementing
the waste through eliminating non-value added from five case studies industry revolution 4.0 in INDIAN
activities using sensors, robots, data analytics and manufacturing SMES: insights from five
automation ..." case studies. International Journal of
Engineering & Technology, 7(2.4), 136-
139.
Share of data page 6: "[...] value creation challenges can develop Q48 Fortune favors the prepared: How SMEs Müller, J. M., Buliga, O., & Voigt, K. I.
used into value offer challenges. This is reflected by approach business model innovations in (2018). Fortune favors the prepared: How
companies, which invest in gathering information Industry 4.0 SMEs approach business model
through Industry 4.0 technologies, while facing innovations in Industry 4.0. Technological
challenges in putting the information to commercial Forecasting and Social Change, 132, 2-17.
use."

52
Appendix D – Reflection Document Natalie
How does your study correspond to the goals of the thesis course? Why? Focus on the goals that were
achieved especially well and those that were not well achieved.
The main goal of this thesis was to answer the research question with a literature review and a readiness
assessment, which I believe have been achieved. The thesis fulfils the goals of the thesis course well by
being independently made by myself and my thesis partner and contributes to the field of computer and
system science with research both original and of significance. The goals of correctly using relevant
scientific methods and to critically reflect about ethical and societal aspects especially have been fulfilled.
Also, the goal to be able to analyze and criticize relevant scientific literature has been fulfilled by conducting
a literature review of high quality.

Regarding goals not being well achieved, I have a hard time pointing out a specific goal not really well
achieved. In my humble opinion, I do think our work fulfills all goals quite well. Perhaps we could have
worked harder on building upon relevant literature, however, there was a shortage of scientific papers with
the same angle as the approach we chose which means it was a difficult task to begin with.

How did the planning of your study work? What could you have done better?
Both my thesis partner and I are ambitious and organized by default, meaning we had no issues at all
organizing ourselves and creating a plan of action for this thesis. We early decided to work hard from the
very beginning and set our own deadlines slightly ahead of the deadlines given by the course. Our supervisor
has been an excellent supervisor, being super quick with feedback and thus allowing us to work ahead more
often than not. We could have been better prepared for the mandatory seminars to attend to since we ended
up in a situation where all seminars were full for many days. It was a stressful state to be in the belief that
we would have to wait for a long time to do the mandatory tasks. In the end, many seminars were posted at
once and there probably would not have been an issue to wait either, however we did not know that at the
time and stress can be quite exhausting. Especially when it is easily avoided.

How does the thesis work relate to your education? Which courses and areas have been most relevant
for your thesis work?
This thesis covers a quite new concept, but filled with technologies I would be unfamiliar with without my
education. It has been of great help to have an education in the computer science field to be able to more
quickly grasp the extent of the literature, the technologies and to understand IT related issues. It also gives
a more deep understanding of underlying causes. For obvious reasons, all courses regarding scientific
methods have helped a lot to create a thesis of good structure and good content, but also the courses about
cyber security (INTROSEC), big data (OBIDAM) and digital business (DIBU) have been great. Knowledge
in those courses made me more secure about the topic at hand.

How valuable is the thesis for your future work and/or studies?
For my future work I believe I can make use of the knowledge gathered from this thesis by being informed
about SMEs, their importance and how important the industry 4.0 concept really is. If i were to continue
towards a phd later in life, this certainly is a topic I am keen to dig deeper in.

How satisfied are you with your thesis work and its results? Why?
I am very satisfied with our thesis due to being an important topic on a global level. I am also very satisfied
by the simple knowledge that we made this, and it turned out really good. I am proud. I hope that researchers
can build upon this thesis and produce more research in the field and investigate more. It would be
interesting to see if the level of readiness for SMEs is changing and if similar challenges are discovered.
Also, I hope multiple case studies are being conducted already so that more details on individual SMEs and
their industry 4.0 implementation soon can be accessed.

53
 Appendix E – Reflection Document Sinéad
• How does your study correspond to the goals of the thesis course? Why? Focus on the goals
that were achieved especially well and those that were not well achieved.
The main goals of this study were to produce a literature review and conduct a readiness assessment in order
to answer the question of what readiness level SMEs have for industry 4.0. This has been done successfully
by using relevant scientific literature and critically analyzing and documenting them using a professional
language. Furthermore, the literature review was made by following a scientifically grounded method called
“Wolfvswinkel’s five stages” with the analysis method grounded theory. By following each stage and
discussing the content of collected data among the authors lead to achieve the goal of selecting and using a
scientific method correctly. Since this research method allows reading a large volume of scientific papers,
the authors were able to accumulate knowledge of the research topic and think critically about what was
found.

A goal that was the most challenging to achieve, was to meet the requirements of some ethical aspects.
Some struggles appeared when trying to motivate with high transparency and clear formulations of how the
chosen readiness model IMPULS was supposed to be used. The creators of IMPULS applied different
research methods than those applied in this study. This meant that the authors had to be innovative when
using the collected data from the literature review to modify IMPULS in order to fit the study better. To
achieve the goal of making a readiness assessment using IMPULS, the authors chose to use their common
senses and reasonings of selected codes and quotes from the literature. The ethical aspects of being
transparent and showing credibility were thus challenging, but achieved by mainly following the process
presented in the IMPULS source, and presenting the exact process steps taken by the authors of how to
assess the readiness level and present raw data in appendices.

• How did the planning of your study work? What could you have done better?
From the beginning the authors set out a schedule containing all deadlines and time estimations of each
essay section. This worked well and both authors had a constant open communication when changes needed
to be done in the schedule.

• How does the thesis work relate to your education? Which courses and areas have been
most relevant for your thesis work?
This study allowed the authors to look at both the organizational aspects of an SME as well as the IT aspects.
It relates well to the program Information Systems Management which includes both of these areas where
one learns about how to understand, analyze and manage certain business cases related to IT. In this study,
it was interesting to see which dimensions and fields are most relevant to SMEs in terms of transforming
the organization towards industry 4.0 The most relevant courses have been DIGMAN, FMVEK, BPCM,
SERDES, SYSTOIT and GOVSYS.

• How valuable is the thesis for your future work and/or studies?
Very valuable since it shows the authors’ rigorous and hard work as well as critical thinking about a “hot
topic” in the computer science field. Industry 4.0 is a broad concept and this study has reflected on it from
different perspectives which opens the door for a variety of future jobs/educations.

• How satisfied are you with your thesis work and its results? Why?
The results give a clear answer to the research topic and the conclusion shows the relevant societal
consequences. This study gives the reader an idea of what the current status of SMEs’ readiness is for
industry 4.0 and how they can go about implementing it. Also, it provides an outlook of how it looks from
a geographical perspective, meaning which parts of the world are studying about SMEs in relation to
industry 4.0. This is quite original since not many studies do not take geography into the factor to discuss
the results. Hence, this study presents both analytical arguments and a practical research which can be
replicated by a future researcher. Therefore, I’m very satisfied with this thesis thanks to a great collaboration
between the authors and helpful guidance from the supervisor.

54

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