strategy for digital transformation that supports
TRANSCRIPT
Strategy for digital transformation that supports manufacturing business value
Master thesis work
30 Credits, Advanced level
Product and process development
Production and Logistics
Jens Brauer & Alexander Stenfeldt
Commissioned by: MITC
Tutor (Company): Erik Andersen
Tutor (University): Anna Granlund and Mats Ahlskog
Examiner: Antti Salonen
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ABSTRACT
The emergence of new digital technologies and their potential strategic contribution is currently one of
the most prominent concerns for organizations. Despite the opportunity brought on by modern digital
technology, an estimated 66-84% of digital transformation projects end up failing. Therefore, a research
question was formulated as; How to formulate a strategy for digital transformation that supports
manufacturing business value?
An abductive research approach was utilized to reach two main research objectives; Creating a description
of how the current and future digital landscape is understood and creating a description of how digital
transformation can be constructed and what strategies and/or strategical elements exist within that
construct. Furthermore, one manufacturing case company within the automotive industry and specifically
its formal initiative and efforts to conduct a digital transformation were examined. By creating this
comprehensive context, it was believed that existing knowledge could be solidified, and new knowledge
could be created to answer the research question.
Scattered and diverse findings regarding a multitude of elements are presented in the frame of reference,
in order to build a strong basis for analysis. Distinguished levels of conceptual strategies within the
construct of digital transformation are recognized as “Digital Business Strategy” and “Digital
Transformation Strategy”. An empirical study is presented that examines present elements such as a
conceptual roadmap, task teams, and an overall description of a formal initiative towards digital
transformation, as well as how these elements are interpreted and understood by leaders within the
organization.
The analysis consists of a comparison between the presented literature and the empirical findings. This is
viewed through the lens of the research objectives in order to show commonalities and differences
between the literature and the case company, and to corroborate findings. Most prominent is the absence
of a digital business strategy at the case company in contrast to the expressed necessity for one in the
literature.
In a presented discussion, strategy as a linkage between business value and digital transformation is
argued to be direct through a digital business strategy. The answer to the stated research question is that
digital transformation and strategy formulation can not be understood as a linear, but rather an iterative
and evolving process involving building an absorptive capacity, assessing maturity, formulating a digital
business strategy, and creating a roadmap and a digital transformation strategy that support the digital
business strategy and the maturity level.
Conclusively, the findings of this report are corroborated by each other but lack empirical evidence as to
the meaning of being successful and valuable manufacturing business in the setting of the fourth industrial
revolution is yet unknown. However, practitioners are encouraged to employ a conscious business model
perspective, while researchers are discouraged of thinking about digital transformation as a linear and
sequential process.
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ACKNOWLEDGEMENTS
First and foremost, we would like to express our upmost gratitude towards the case company
representative who initiated this thesis project and trusted us with and supported us through the task.
Secondly, we want to thank Mälardalen Industrial Technology Center (MITC). Partly, for sponsoring the
work, but more importantly for welcoming us into your facilities and supporting, spawning new ideas and
angles, challenging, and questioning the research continuously. In our mind, you facilitate a fascinating
playground for ideas and have been an invaluable source of inspiration. We have enjoyed every minute.
Thirdly, we are deeply thankful to our academic tutors Anna Granlund and Mats Ahlskog, who have
provided brilliant feedback and support throughout the entire process. Your support and encouragement
to clarify and crystallize arguments and ideas have been one of the most important assets throughout the
writing of this thesis.
Fourthly we would like to thank the respondents of the study for taking the time and really engaging in
our conversations and interviews. We are very grateful for your engagement and enthusiasm.
Eskilstuna, 2021-01-06
Jens Brauer & Alexander Stenfeldt
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Table of Content
1. Introduction ...................................................................................................................................1 1.1 Background ......................................................................................................................................... 1 1.2 Aim and Research question ................................................................................................................ 2 1.3 Research scope and limitations .......................................................................................................... 3
2. Research Methodology ...................................................................................................................4 2.1 Research approach.............................................................................................................................. 4 2.2 Data collection .................................................................................................................................... 4
2.2.1 Literature review .......................................................................................................................... 5 2.2.2 Observation .................................................................................................................................. 6 2.2.4 Interview ...................................................................................................................................... 7
2.3 Data analysis ....................................................................................................................................... 7 2.4 Quality of the research ....................................................................................................................... 8
3. Frame of reference ....................................................................................................................... 10 3.1 Industry 4.0 - The current technological era ..................................................................................... 10
3.1.1 Technologies .............................................................................................................................. 11 3.1.2 Design principles ........................................................................................................................ 11 3.1.3 Applications ................................................................................................................................ 12 3.1.4 Challenges .................................................................................................................................. 13
3.2 Digital transformation ....................................................................................................................... 14 3.2.1 Evaluation of the Digital Maturity .............................................................................................. 16 3.2.2 Strategies in the context of digital transformation ................................................................... 17 3.2.3 Methodological Approaches ...................................................................................................... 20
4. Empirical findings ......................................................................................................................... 22 4.1 Introduction to the case.................................................................................................................... 22 4.2 Digital Transformation Initiative ....................................................................................................... 22 4.3 Roadmap ........................................................................................................................................... 24 4.4 Task Team ......................................................................................................................................... 25 4.5 Maturity Assessment Model ............................................................................................................. 26
5. Analysis ....................................................................................................................................... 28 5.1 How is the current and future digital landscape understood? ......................................................... 28 5.2 How is the digital transformation constructed? ............................................................................... 30
5.2.1 Evaluation of maturity ............................................................................................................... 31 5.2.2 Strategic Orientation .................................................................................................................. 31 5.2.3 Implementation ......................................................................................................................... 33
6. Discussion .................................................................................................................................... 35 6.1 How to formulate a strategy for digital transformation that supports manufacturing business value? ...................................................................................................................................................... 35
7. Conclusion ................................................................................................................................... 37 References ....................................................................................................................................... 39 Appendices ...................................................................................................................................... 43
Appendix 1 – Framework by Correani, et al. (2020) ............................................................................... 43 Appendix 2 – Framework for digital strategy (Lipsmeier, et al., 2020) .................................................. 43
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List of tables
Table 1 Keywords and Alternative terms ...................................................................................................... 5 Table 2 Illustration of search strings, data bases and results ....................................................................... 6 Table 3 Categories of factors enabling Digital Transformation initiation (Muehlburger, et al., 2019) ...... 16 Table 4 Business Model Led and Technology Led Digital Transformation ................................................. 19
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1. Introduction
In this section a background to the problem is explained leading to the aim and research question for this
thesis. The section sums up with a description of the scope and limitations for the research.
1.1 Background
The emergence of new digital technologies and their potential strategic contribution, is currently one of
the most prominent concerns for organizations (Korachi & Bounabat, 2019). The 4th industrial revolution
signified by the adoption of cyber-physical-systems and new digital capability, is predicted to enable fully
autonomous and intelligent production systems, leading to immense impacts on the global economy
(Piccarozzi, et al., 2018). Embracing the digital technology brought on by the fourth industrial revolution
and driving digital transformation can enable strategic advantage for individual companies, in terms of
aligning products and services with customer demands as well as enabling better internal processes for
example with faster innovation and shorter time to market (Brown & Brown, 2019; Correani, et al., 2020).
As digital transformation in the current technological environment is described as bringing massive
business improvement that enable future competitiveness (Brown & Brown, 2019), there is also a
perceived urgency to transform as the disruptiveness may limit the time frame of opportunity (Tekic &
Koroteev, 2019).
Despite the opportunity brought on by the modern digital technology, an estimated 66-84% of digital
transformation projects end up failing. For example, General Electric’s software operations facility
intended to support their digital strategy, ended up firing 100 employees and Nike ended up discontinuing
their Nike+ products, even though the transformational initiatives in both cases arguably made strategical
sense (Correani, et al., 2020).
Another example that provides some clues to the high failure rate and the problematic nature of
technology adoption, is the enabled information sharing across supply chains brought on by the
technological advancement of the industrial fourth revolution. It is a claimed advantage from a logistics
and supply chain management perspective that information and knowledge travels at a higher speed and
enable more accurate planning and decision making. At the same time, one of the most prominent
barriers for adoption of the corresponding innovation is the unwillingness from participants within the
supply chain to share information with each other. I. e. technology enables pivotal strategic advantage
that is not in alignment with the organizations information sharing policy (Preindl, et al., 2020).
The observed effects of management activities to drive digital transformation towards an Industry 4.0
maturity, substantiate the meaning of these observations. For example, “Mission and Vision Statements”
are found to be positively associated with industry 4.0 readiness while “Strategic Planning” is found to be
negatively associated with industry 4.0 factors, even though they are closely related. This would indicate
that strategic plans and roadmaps are poorly developed and generally fail to increase digital maturity. This
finding is suggested to reflect a lack of strategic focus and poor definition of goals and understanding of
implementation (Črešnar, et al., 2020).
One key finding in literature, is the seemingly paradoxical observation that organizations that are digitally
more mature, do not treat digital advancement as a goal but a tool or means to reach strategic objectives
(Tillväxtverket, 2017). Along the same lines, digital maturity of a company can be understood as a
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company and its leaders having a well understood vision for how to benefit from digital technology (Brown
& Brown, 2019).
Research on distinct success cases of ABB, CNH Industrial and Vodafone indicate the importance of
consistency between strategy formulation and strategy implementation while providing a generic
framework that guides digital transformation efforts through a description of the given business model
(Correani, et al., 2020). The view, that the coherence between business objectives and transformational
efforts is important is widely shared (Brown & Brown, 2019; Zineb & Bouchaib, 2020). According to Kane,
et al. (2015) it is not necessarily the technology that drives a digital transformation, it is the strategy.
The topic of digital transformation strategies is extensively researched although there is still conflict in the
literature regarding what success factors and key elements that are required for successful digital
transformation, as well as the barriers and problems that companies are faced with (Korachi & Bounabat,
2019).
Common features that correlate with the above-described examples and issues, can however be
distinguished. One obvious example is the common recommendation, that is often suggested to be at the
core of transformation management, of an overall vision for a strategically beneficial future state
(Albukhitan, 2020; Brunetti, et al., 2020; Korachi & Bounabat, 2019). Similarly, some conclude that in silo
and stand-alone initiatives are futile in conducting an organizational digital transformation (Zaoui &
Souissi, 2020; Brunetti, et al., 2020) and that lack of strategy and vision is a major barrier to overcome
(Vogelsang, et al., 2019). Parallel to the notion of the importance of vision and strategy, is the observation
that the development of such a vision and strategy is a struggle for many organizations (Mushore & Kyobe,
2019; Fischer, et al., 2020). It is a challenge in terms of incorporating interrelations between processes,
work practices and stakeholders and creating alignment in objectives (Mushore & Kyobe, 2019), as well
as being able to design material and strategic objectives from overwhelming opportunity and challenge
(Fischer, et al., 2020).
The topic and phenomena of digital transformation is documented and presented as frameworks (Korachi
& Bounabat, 2019; Tekic & Koroteev, 2019; Correani, et al., 2020), broken down into fundamental
interrelated concepts (Brown & Brown, 2019), interrelated building blocks (Zineb & Bouchaib, 2020), as
well as step by step guides (Albukhitan, 2020), varying in methodological approaches, purposes, levels of
abstraction, focal topics and depth.
1.2 Aim and Research question
This thesis builds on the idea that digital transformation is not merely a digital technology adoption, but
rather an organizational change that aims to adapt to a digital landscape, as proposed by Brown and
Brown (2019). Furthermore, it is assumed that digital maturity and business excellence is overlapping and
dependent on the value that can be extracted from the technological paradigm of industry 4.0. Implicitly,
it is assumed that digital maturity in the Industry 4.0 era will not be achieved within the manufacturing
realm, unless it is understood how it supports business goals in manufacturing companies.
As the rich literature on the topic of digital transformation is built by examining empirical findings and
synthesizing ideas based on common features and described success stories, this thesis aims to provide a
novel perspective by comparing compiled literature to the digital transformation efforts of a
manufacturing company within the automotive industry, in order to understand the formulated research
question;
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RQ: How to formulate a strategy for digital transformation that supports manufacturing business value?
1.3 Research scope and limitations
In order to answer the formulated research question, two main research objectives were formulated to
guide a literature review. First, to create a context for the phenomenon of digital transformation and
highlight its relevance, the study aims to develop a description of how the current and future digital
landscape is understood. Second, to create an understanding of the actual phenomenon of digital
transformation itself, the study aimed to create a description of how digital transformation can be
constructed and what strategies and/or strategical elements exist within that construct. Furthermore, one
manufacturing case company within the automotive industry and specifically its initiative and efforts to
conduct a digital transformation is examined, in order to create an empirical reference, to add to the
current body of academic work. By creating this comprehensive context to support the analysis and
elaboration on specific ideas about strategies for digital transformation, it is believed that existing
knowledge can be solidified, and new knowledge can be created to answer the research question.
The empirical study at the case company is limited to the formal initiative and efforts produced by the
company. The case company is a global organization that has factories around the world. The global
organization has initiated the digital transformation and prescribed certain activities for local plants. One
of those activities is to create a local task team at each plant, responsible for investigating and exploring
digital technologies to apply in manufacturing. This study was conducted in cooperation with one of the
local plants and with some engagement from the global organization. Specifically, the leader of the local
task team is the initiator of the thesis project and the main respondent for the study. Conclusively, there
could be informal events and ICT-initiatives within the local plant, as well as throughout the global
organization that are outside of the scope of the study.
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2. Research Methodology
In this chapter, the methodology behind the thesis is described in depth. For each method used or chosen,
a description and arguments on how and why the method has been utilized are presented. The chapter
starts with an introduction to the research approach and case study method. It is followed by the data
collection which describes how data were collected for the Frame of Reference and Empirical evidence.
Finally, an analysis of the gathered data is presented, and the quality of the research is deliberated in terms
of validity and reliability.
2.1 Research approach
In this thesis a single case study was conducted. The choice is based on being able to describe a
phenomenon with in-depth knowledge and the formulation of the research question include a "How"
question (Yin, 2014). Data collection methods used in this thesis such as literature review, observation
and interviews are commonly used in a case study (Patel & Davidson, 2011) and the choice of a case
study were suitable. As the focal topic of the thesis is the phenomenon of digital transformation and
elements of strategy, the case study was designed to examine a manufacturing case company that has
initiated and is actively engaged in a digital transformation. Hence, the phenomenon can be described
based on firsthand observations. By combining the mentioned techniques for data collection (more
described in section 2.2) it was possible to complete a triangulation of data. It was an important step to
ensure the quality of the data. When employing a triangulation, Safsten and Gustavsson (2020) mentions
weaknesses from one technique can be compensated from a strength from another technique.
When choosing a research approach for the thesis, it was a choice between performing a deductive or an
abductive approach. The importance of this thesis for the case company was to gain insights from
academic research and its knowledge in the field of digital transformation. To carry out a deductive
approach, a hypothesis is set up that is based on the theory and then tests it against reality. In this case,
it could be an appropriate choice as what is to be answered must be substantiated against current
research. However, the weakness of developing a hypothesis from theory and then testing it is the risk
that it does not correspond at all with what is expected to be investigated (Saunders, et al., 2015).
Therefore, this thesis is based on an abductive approach to be able to go from data to theory and theory
to data to ensure not to end up in the wrong with what is to be answered (Bryman & Bell, 2015). With the
possibility to adapt the literature afterwards the empirical data collection was an enabler to have a
complete frame of reference and support a clear structure.
2.2 Data collection
Data collection has been used to create a perception of the academic research that is available at present
time and the collection of empirical data has been done by using various forms of collection techniques
such as interviews and observations. As this thesis leans towards a qualitative research rather than a
quantitative, capturing the perceptions and experiences of employees was an important step (Safsten &
Gustavsson, 2020). Both primary data and secondary data have been used in this thesis. The primary data
collected has originated from the use of the different data collection techniques which is described later
in this section and the secondary data collected has consisted of different control documents and
statistical documents that have been important for the work with the empirical part of the study. Using
both primary and secondary data is common and the difference between primary and secondary data is
that the primary is a phenomenon that is happening at the time while the study is ongoing and secondary
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data consists of such content that has already happened, for example statistical documents (Bell and
Waters, 2016). For this thesis, it has been useful to have both types of data as they have been able to
complement each other. Using historical data with data from the current situation facilitates an analysis
of why it looks the way it does. The secondary data collected has been made through email
correspondence with the contact persons who have been involved from the case study company.
Secondary data that was requested from the case company in this thesis was the following:
• Information about the case companies’ global initiative of digital transformation
• The model for their maturity assessment
• Organizational structure
• Statistics on KPI: s
• Information about conducted and upcoming pilots
• Business goals
All data in this thesis was collected between 2020-08-31 and 2020-12-11.
2.2.1 Literature review
A literature review was conducted to create a theoretical framework to support argumentation and
conclusions. The use of the literature review provided a good basis for understanding what previous
research in the same field has discovered (Saunders, et al., 2015). The literature review began at the start
of this thesis work. The phenomenon that was to be studied is not a new phenomenon but it belongs to
an area that many are currently investigating and then it was a necessary step to formulate the research
problem to know what the latest research has shown in the area (Safsten & Gustavsson, 2020). When
formulating the research question, keywords for the study were also identified and the keywords were
used in the search to set parameters and to limit the search result as only articles in the area to be studied
were important to find (Table 1). To have a broader search for articles and to enable the literature that
could be important for the study to be found, alternative terms were added to the search for each
keyword. The choice to add alternative terms was based on getting several hits in the search for literature
and that the terms that were added were words that appeared in the first search where only the keywords
were used.
Table 1 Keywords and Alternative terms
KEYWORDS ALTERNATIVE TERMS
DIGITAL Digitalization TRANSFORMATION Alignment STRATEGY Business Model INDUSTRY 4.0 Smart factory, Smart Production
The databases used to find literature were ScienceDirect and Scopus. Other databases were also used as
a complement as certain articles found via Scopus or ScienceDirect was labeled as "Article in press". What
was done then was to search further on the article to identify whether it has been published or not. Many
of the articles found as "Article in press" were published. As an additional complement to databases, the
snowball effect was applied as a search strategy. When reading articles, some quoted sentences could be
interesting which resulted in going to the original source that was referenced and then using it in the
literature review. As the snowball effect was used, no restrictions were set for the published year, a
language limit was used, and it was English. Table 2 illustrates the search strings, which databases and
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how many hits the searches returned. The search string was built up by using the keywords and alternative
terms and by using "OR" and "AND" the result of the search can be that it finds articles with either a
combination of certain keywords but also between the keywords and the alternative terms. Since the case
company is a manufacturing company, it was tested to narrow the search to find items closer to the case
company. This was done by choosing as an option in the search that manufacturing would be included in
the title of the articles. Another attempt to capture more articles with the search was to test wildcards.
An example might be whether a search should include either manufacture, manufacturer, or manufacture
and then by using a wildcard, it is sufficient to formulate the search string "manufact *". Then all results
with the beginning of "manufact" come up. ScienceDirect did not support wildcard searches, but Scopus
did.
Table 2 Illustration of search strings, data bases and results
Search String Database Matches Scanned Selected
(Digital OR Digitalization) AND (Transformation OR “Business model”) AND (“Industry 4.0” OR “Smart
Factory” OR “Smart Production”) AND (Strategy OR Alignment)
ScienceDirect 1478 138 51
Scopus 156 75 24
With manufacturing in title ScienceDirect 319 64 12
Scopus 27 13 8
Once the search strings were structured and the various tests on how the search was to be formulated,
the next step was to start selecting articles from the search. This was divided into different parts and at
the beginning to select articles with relevance, the summary of the articles and the introduction were
read. The next step was to go through the articles that were first selected by also reading the conclusion
given in the article. In order to finally be able to decide which articles to select, the entire article was
scanned through. When scanning the articles, an Excel sheet was created to sort the relevance and to
categorize the content. A color system was used to maintain a good structure when sorting and
categorizing the items (Bryman & Bell, 2015)
2.2.2 Observation
The observations for this thesis would be classified as unstructured, mainly because the nature of the
thesis is qualitative data and the observations done has been by attending different meetings. The
meetings have been both informative meetings and some of them included practical demonstration on
new technology the case company is reviewing for application. Before attending the meetings no specific
agenda for the meeting was announced, the main purpose was to listen and learn how the smart team
worked. By attending the meetings, discussion afterward was held and gave a better insight into the case
company, and empirical data was contained. Why this type of observation would classify as an
unstructured is based on that the authors did not control the environment or had a specific phenomenon
to observe, the outcome was unknown. The main purpose of the observations has been to capture a
realistic view on how they are working instead of asking how they conduct their work, what people say
and what they actually do is not always the same thing (Safsten & Gustavsson, 2020).
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2.2.4 Interview
In the progress of analyzing data that had been collected, it was realized that the data needed to be
clarified or verified. Interviews were conducted as a step to ensure the quality of the documents and to
gain new perspective of how others within the company would explain the data received for the thesis.
The interviews in this thesis have been both unstructured and semi-structured. When the phenomenon
was known and more knowledge about it was needed, semi-structured interviews were suitable to fill the
knowledge gap. The interviews were made through a snowball selection were the interviewees were
selected by recommendation. The choices of interviewees were made by recommendation, and on what
questions needed to be answered. To know that the one who got the questions could answer them, a
recommendation was needed. When the interviews were conducted, there was already some knowledge
in the subject and each interview had a purpose linked to the research question. The interviewees were
selected based on their connection to or involvement in the case company’s digital transformation thus
the possibility to acquire the necessary information or data that needed to be clarified or verified. The
interviewees had the following positions within the case company:
• Director Manufacturing Engineering (ME)
• Global Director Manufacturing Engineering
• Project Manager
The interview with the Director of Manufacturing Engineering was held to receive information on how the
top management for the local plant think they should work with digital transformation. There was some
critical information needed to fully gain an understanding if there were a current strategy and vision in
place for their work with digital transformation.
The purpose behind the interview with the Global Director ME was to understand the background of the
digital transformation initiative, as he is the owner and co-creator of it. It gave the opportunity to obtain
information on the global management perspective on industry 4.0 and its significance to the company.
The Project Manager leads the local task team and has a responsibility to both the local and the global
management. Before the interview, he had provided different data and through the interview it was
possible to obtain more information on how the local task team work with digital transformation and how
they currently work to align efforts with the sites vision for industry 4.0.
2.3 Data analysis
In general, the data analysis was done with the research question in mind to support decisions on what is
relevant for the thesis (Saunders, et al., 2015). During the search for scientific articles, a coding system
was applied to facilitate the work of sorting and categorizing the articles collected (Bryman, and Bell,
2015). In parallel with identifying which articles were relevant to the study, the color code system was
additionally used to identify themes in the selected articles. The color code system was used in both
Mendeley and in an Excel spreadsheet. After the scanning process when the final selection was made,
most of the articles had been read through. When several articles had been read and analyzed it became
clear that most of the articles had similarities in their structure and some patterns was identified. These
similarities were then used in the literature review to create a good structure. The technique used to
analyze the scientific articles is called thematic analysis. A decision on what was an appropriate theme
where done by analyzing and visualize how the themes was connected to each articles purpose. Safsten
and Gustavsson (2020) mentions that it is important for the researcher to determining what could
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characterize as a theme and a theme may be something that occurs frequently. However, they mention
furthermore that a theme should be capturing something essential regarding the research question. For
each article, to grasp the underlying theme that was related to the purpose of the study, the coding system
was supporting a systematic approach to the identification of the patterns.
At the point when empirical data was collected, it was still important to connect what type of information
which necessary for the purpose of the thesis. By receiving a great amount of data, described in section
2.2, sorting and categorizing was a necessary step to support the analysis of the raw data. At each
observation, notes were written, and those notes can easily become unclear after a while and hard to
interpret (Safsten & Gustavsson, 2020). To ensure not to lose any information, all notes were discussed
and rewritten into a more comprehensive text.
As mentioned, when analyzing the literature, a thematic analysis was used, and themes could be
identified. Those themes were useful when collecting the empirical data. By using the themes, an
identification of what type of data was critical for an analysis and they supported the work of reduce data.
However, by working with abduction approach for this thesis, an iterative process between collecting
theoretical and empirical data resulted in a massive data collection. There were at multiple occasions a
change of direction how the research question should be answered in a suitable way. This happened due
to new knowledge attained from continuously analyzing the literature and at the same time the empirical
data. If this thesis had focused on the empirical part at first and a hypothesis where drawn, the outcome
from the analysis of the empirical data would have impact on how to analyze the literature or the other
way around. Although, it could be confusing, the knowledge attained by working with an iterative
approach produced a deeper understanding within both literature and empirical data. Finally, a decision
was made on how to answer the research question, and an alignment between literature and empirical
data was found. By localizing similar themes, based both on literature and empirical findings, it was
possible to sort literature and empirical data into categories and produce an analysis that synthesized the
components to answer the research question.
2.4 Quality of the research
According to Safsten and Gustavsson (2020) validity and reliability are two factors when ensuring the
quality of the scientific study. When talking about the validity of a study, you examine whether what has
been studied really corresponds to what was predestined to study. Validity can also be divided into
internal validity and external validity where the internal focuses, for example, on whether the study
delivers the answers needed to answer the study's research question and the external validation is about
how the study is generalizable to similar studies in the same research area.
At different times where the collected data was analyzed and where there were certain parts needed to
be interpreted by the authors, the interpretations was strengthened by having a continuous discussion
throughout the study with the case company. Some of the secondary data received consisted of data files
in PowerPoint format or in Excel spreadsheets, and those type of data files where not always fully
informative and descriptive. In situations where information was deficient or was not entirely descriptive
to interpret, complementary questions were formulated and sent to the case company. By updating and
reconciling that the interpretations corresponded to reality, the possibility that the interpretations would
deviate from what the current situation looked like was reduced (Bryman, 2018). By combining the
primary and secondary data and that the collection of primary data has been done by different techniques,
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triangulation was done. This technique is accordingly to Yin (2018) a step to strengthen the validity of the
study.
Bryman (2011) describes reliability to the extent that a study can be repeated or be similar when the same
measurement or studied phenomenon gives the same results as a previous study. The reliability can be
affected by random event and Safsten and Gustavsson (2020) mentions it can be caused by either the
research subject, the researchers, or the study situation. As this study is mainly of a qualitative nature and
is concerned with a phenomenon that is progressing and ongoing, the reliability concerns are somewhat
obvious. The interviewees and the studied organization are in a process of change and the participants of
the organization and the interviewees of this study is learning and adapting to new knowledge. It seems
plausible rather than possible that the interviewees as well as the studied organization will change and
therefore the results of this study will, by nature, be hard if not impossible to replicate. Furthermore, the
literature review has been conducted with an emphasis on breadth and context rather than with a narrow
and deep focus. Therefore, it is possible that other researchers would present the subject differently and
emphasize other aspects of the body of research. This concern has been addressed to some extent by
trying to show significance of statements and findings through highlighting where there are discrepancies,
similar views held by several authors, and in some cases tracking down cited articles and original sources,
in order to comprehensively present the current academic knowledge on the topic as honest, exhaustive,
and accurate as possible.
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3. Frame of reference
This section is divided into two main subsections with the purpose of giving a comprehensive view of the
subject and context; firstly, describing the current industrial context and paradigm in which manufacturing
industry is operating and also the terminology and ideas that are present in the academic discourse.
Secondly, describing the ideas and concepts that aim to deal with strategies by which to operate under to
successfully transform the manufacturing enterprise into compliance with the current and future era.
3.1 Industry 4.0 - The current technological era
Since 2011, industries globally have been working towards a digital transformation which is a part of
and/or referred to as the fourth industrial revolution, Industry 4.0. It was in 2011 in Germany that the
concept of industry 4.0 emerged (Gaspar & Juliao, 2020). The concept is a broad term that describes a
technological transformation of industry that have a significant impact on the global economy (Piccarozzi,
et al., 2018) and change the way in which industrial enterprise operates (Piccarozzi, et al., 2018; Narula,
et al., 2020). Industry 4.0 realization may require changes in operations of production systems (Yin, et al.,
2018) and changes can be considered driven by IT (Mantravadi & Møller, 2019). This resonates with the
definition of digital transformation being an adapting of an organization to a digital landscape (Verhoef,
et al., 2019; Saarikko, et al., 2020), and the statement by Sarvari, et al. (2019) makes sense, that Industry
4.0 is driven by digital transformation. Furthermore, Osterrieder, et al. (2020) identify “Digital
Transformation” as one of eight distinct research topics within the field of the smart factory concept.
In historical context the demand for the fourth industrial revolution is driven by customer demand
dimensions namely Variety (multiple models per product), Time (short requested delivery time and
uncertain length of life cycle) and Volume (fluctuating and uncertain demands) (Yin, et al., 2018).
Within the realm of Industry 4.0, there is a variety of concepts or paradigms that are used to describe and
materialize the vision of what the current technological era will realize, even though they are more or less
synonymous (Cohen, et al., 2019). “Smart manufacturing”, “Cyber-physical systems”, “Industry 4.0”
(Cohen, et al., 2019), “industrial internet” (Büchi, et al., 2020), as well as “digital manufacturing” or “digital
machine-building production” (Kholopov, et al., 2018) are all examples of concepts that share the goal of
responding to real time conditions and a market demand for highly customized products, through the use
of a digitalized, flexible, reconfigurable, intelligent and fully autonomous production systems (Kholopov,
et al., 2018; Piccarozzi, et al., 2018; Cohen, et al., 2019). There is some discrepancy in the extension of the
formulated definitions and explanations of the concepts, as the inclusion of services (Cohen, et al., 2019)
and the value chain (Piccarozzi, et al., 2018), as well as changes in governance and stakeholder relations
(Büchi, et al., 2020) are not always explicitly included. There is also discrepancy that can perhaps be
understood as a variation in depth and level of abstraction. For example, Gaspar and Juliao (2020) see
Industry 4.0 as being based on the integration of information and communication technology, which is a
description that is perhaps a coinciding but not necessarily a comprehensive understanding of the Industry
4.0 construct as described above.
Regardless, it is popularly argued that Industry 4.0 is a fundamental change to the current state.
Osterrieder, et al. (2020) for example, refer to the notion of how the production plant is now viewed as a
construct of four distinct layers of physical, data, cloud & intelligence, and control layer, rather than a
plane of activities.
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3.1.1 Technologies
The significance placed on separate technologies within the Industry 4.0concept vary to some extent
throughout literature. Impacts on the current era and competitive environment for businesses are
analyzed based on the inclusion of different clusters of technologies. The digitization and namely IoT and
Big Data can be understood as central to the digitalization of organizations and imperative to master in
order to thrive through business digitalization (Sestino, et al., 2020). Similarly, Artificial Intelligence,
Blockchain, Cloud and Data Analytics are studied based on them being perceived as being interconnected
and driving data to support business decisions (Akter, et al., 2020).
Even within the focal topic of manufacturing industry, the technical understanding of Industry 4.0 and
basis for discussion vary to some extent. However, a reoccurring and comprehensive view of the Industry
4.0 concept includes nine pillars; Advanced Robotics, Additive Manufacturing, Augmented Reality,
Simulation, Vertical and Horizontal System Integration, Industrial Internet of Things, Cloud Computing,
Cybersecurity and big data analytics (Alcácer & Cruz-Machado, 2019; Büchi, et al., 2020; Narula, et al.,
2020; Ribeiro da Silva, et al., 2020). It can however be noted that this is not an exclusive definition and for
example a tenth pillar of “other enabling technologies” is sometimes proposed (Büchi, et al., 2020). The
description and distinction of technologies and tools etc. are not always clear and is presented differently.
For example, the concept of Digital Twin shows up as an enabling technology (Mantravadi & Møller, 2019),
as well as a digital manufacturing tool (Ribeiro da Silva, et al., 2020).
The technologies can be distinguished as front-end- and base-technologies. Front-end technologies
consider four dimensions (smart manufacturing, smart products, smart supply chain and smart working)
and are considered to have an end-application for the value chain and address needs of the market and
environment. Base-technologies consider four elements (internet of things, cloud services, big data and
analytics) described as information and communication technologies that are utilized by multiple front-
end technologies and support all dimensions. (Frank, et al., 2019a)
Demeter, et al. (2020) however, point out the interrelatedness of the technologies in the sense that for
example that functional use of big data analytics, rest upon the use of IoT, sufficient data storage
processing capacity and human knowledge. Saarikko, et al. (2020) point out this same feature, that
technologies need to work together in a hierarchy from sensor to server and system. Furthermore,
Saarikko, et al. (2020) elaborate on this point and highlight the importance of the sociocultural aspect of
integration as the large system can be difficult to oversee if it penetrates an organization through
individual and sound use cases resulting in a large non-cohesive patchwork.
3.1.2 Design principles
Industry 4.0 and its implications for individual businesses and industries are also proposed to be more
clearly understood through design principles as they are the base for development and can act as a guide
towards the benefits of technology adoption (Ghobakhloo, 2018; Santos & Martinho, 2019). Similar to the
technologies, some discrepancy exist in literature about the significance of principles and which to
include. Ghobakhloo (2018) describe 12 design principles; Service orientation, Smart product, Smart
factory, Interoperability, Modularity, Decentralization, Virtualization, Real-time capability, Vertical
integration, Horizontal integration, Product personalization, Corporate social responsibility. Santos and
Martinho (2019) propose; digitalization, connectivity, interoperability, adaptability, scalability, efficiency,
12
predictive capability, reconfigurability. Worth noting is the overlap with other distinctions, where for
example, horizontal and vertical integration is included in the nine pillars (Alcácer & Cruz-Machado, 2019;
Büchi, et al., 2020; Narula, et al., 2020; Ribeiro da Silva, et al., 2020) and smart product are included as a
dimensions by Frank, et al. (2019a) as well.
3.1.3 Application areas
The applicability of Industry 4.0 is researched, elaborated on, and understood in varying depth and
comprehension. Büchi, et al. (2020) suggest that Industry 4.0 opportunities adhere to one of six main
typologies being; production flexibility through manufacturing of small lots, the speed of serial prototypes,
greater output capacity, reduced set-up costs and fewer errors and machine downtimes, higher product
quality and less rejected production, and customers’ improved opinion of products.
Frank, et al. (2019b) elaborate on how servitization and Industry 4.0 are not just creating value for
customers but can lead to a faster time to market and elevate internal processes such as inventory
management, production planning and control.
Through stratification, Narula, et al. (2020) show the end to end manufacturing aspects responsible for
Industry 4.0 application to be; Design and development, environmental protection, customer support,
logistic management, supply chain management, quality assurance, production planning and control,
process control, maintenance of equipment and tooling, traceability, strategy leadership and culture,
technology integration and cybersecurity. For each of the aspects, the authors provide sub factors
highlighting some interesting application areas.
Ribeiro da Silva, et al. (2020) show application domains for technologies through a map of a manufacturing
life cycle areas; Production requirements, production (re)design, engineering of manufacturing systems,
manufacturing system integration and ramp up, production, re-use/recycle. For each of the areas, the
authors present relevant technology and applications for them.
Fritschy and Spinler (2019) argue that autonomous trucks will change the relations within the
automotive and logistics industry and reconfigure the relations of logistic service providers, OEMs and
tier 1 suppliers.
Schumacher, et al. (2020) highlight the interrelation of Industry 4.0 and lean production system and that
Industry 4.0 is commonly suggested to support lean production systems and Hoellthaler, et al. (2018)
suggest that a sound approach for digitalization is to implement digital technology, where lean has
reached its limits and digital technology can overcome them.
Kristoffersen, et al. (2020) elaborate on how digital technology can support circular economy strategies
(in extension sustainability) and should be part of the Industry 4.0 agenda. Maffei, et al. (2019) stress this
point even further and highlight that digitalization has an overwhelmingly positive impact on the
transition towards circular economies. Bag, et al. (2020) add to this field and argues that a strong
procurement strategy as well as a procurement 4.0 review process enable the enhancement of circular
economy performance. Li, et al. (2020) conclude that digital technology has a positive impact on
environmental and economic performance and that supply chain platforms are a mechanism that explains
the linkage. Along the lines of sustainability, Shrouf, et al. (2014) present an approach to enhance energy
efficiency with the use of IoT enabled energy management.
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3.1.4 Challenges
During the adoption of digital technology, there will be challenges in the implementation. This arises
through the difficulty of understanding the new technology and which leads to the importance of attaining
the knowledge before implementing (Parviainen, et al., 2017). Albukhitan (2020) mentions challenges
such as Traditional processes, Resistance to Change, Legacy Business Mode, Limited Automation, Budget
restrictions, Absence of relevant knowledge, Inflexible company structure, and Security. The challenges
mentioned relate partly to organizational culture and to the existent production systems. How ready is
the organization for a change and not only the culture, how is the readiness of the production processes
to handle new technology embedded into the existent systems and is it possible to connect older
machines. It is important to understand the investment cost through a digital transformation and what
kind of knowledge is essential for integrating the technology (Albukhitan, 2020). At the same time, Sarvari
et al, (2019) bring up similar challenges for an Industry 4.0 transformation. They emphasize the challenge
of lack of knowledge that is necessary to understand the technologies and the opportunities. In a similar
way, it is the same with the benefits of the Industry 4.0 transformation, it is important to have functional
communication through the organization c mentioned the challenge of budget restrictions, Sarvari et al,
(2019) shares the opinion and mentions a possible challenge of human and financial resources. To meet
the challenges and overcome the integration of the new technology, roadmapping is an appropriate
approach for the strategic planning. Roadmapping has been widely used since it first was introduced in
the late 1970s and it is considered as a complex long-term planning instrument. It is a useful approach for
estimating the potential of new technologies, products and services (Vishnevskiy, et al., 2016).
When the new technology is implemented such as IoT, computer vision, digital twins, additive
manufacturing and augmented reality (AR), challenges in both manufacturing and assembly will take place
(Cohen, et al., 2019). Technologies such as mentioned comes with different applications and some of
those may not be planned for and expertise could be missing as Sarvari, et al. (2019) mentions. Cohen, et
al. (2019) highlights some challenges regarding the assembly of products in contexture to the new digital
era. In an assembly line there is existing technology and when adopting new additional technology, it is
critical to fully utilize the potential of the technology and consider the aspect of synergetic integration
between existing and new technology. Some other effects of adopting new technology is the possibility
to create new smart assembly line were the assembly line can handle different products due to increase
flexibility and a challenge would be how to design the line when multiple products can be mounted on
the same line without creating a high level of reconfiguration for each product. Another challenge
mentioned is an open issue with new collaborated robots, the solution how the integration between the
human operator and the robot would be is stated as a major challenge for the assembly line. The last
challenge mentioned by Cohen, et al. (2019) connects with the challenge both Albukhitan (2020) and
Sarvari, et al. (2019) mention about knowledge. Cohen, et al. (2019) stresses the importance of having
human specialist in maintaining the new smart assembly 4.0 systems. Even tough more assembly
operation becomes automated, skilled, and trained personnel are still needed to maintain and operate
the assembly systems and will be throughout the Industry 4.0 era.
Cohen, et al. (2019) do highlight challenges for the manufacturing in a similar context to the assembly and
describing the challenges as almost the same. New challenges mentioned for manufacturing would be the
part of all data handling, due to the digitalization of manufacturing along with the development of
computers, information and communication technologies (ICT), and artificial intelligence (AI), larger
amount of data will be created. This data would be categorized as industrial big data and with the sorting
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of the 4Vs (Volume, Velocity, Variety and Value) new manufacturing challenges arise. The challenge comes
from how the structure of the data is different from how it was before and it will be necessary for the
data collected to be processed efficiently (Cohen, et al., 2019; Singh, 2020). As for the assembly
integration issues for manufacturing is consider as a challenge by Cohen, et al. (2019) in the way how they
highlight the vertical, horizontal, and end-to-end integration in cyber-physical production system and
social cyber-physical production system how these integrations challenge the traditional.
3.2 Digital transformation
Three domains for understanding the diffusion of digital technology are digitization, digitalization, and
digital transformation. Digitization is presented as the converting of analog signals to digital signals,
digitalization is a leveraging of digitized products or processes while digital transformation refers to the
process of adapting organizations or firms to the digital landscape (Verhoef, et al., 2019; Saarikko, et al.,
2020).
Verhoef, et al. (2019) mentions three different external drivers of digital transformation that make
companies consider transforming; Digital Technology, Digital Competition and Digital Customer Behavior.
Savastano, et al. (2019) show that digital manufacturing applicability is industry-dependent even within
the manufacturing sector and that there is a growing widespread of tools, strategies and applications tied
to the specificity of their needs. This align with the point raised by Zaoui and Souissi (2020) that there are
different input variables like size, activities and targets linked to companies approaching digital
transformation and therefore there is a need for adapting transformation instructions.
Although, the phenomenon of digital transformation is studied closely by researchers and experts, there
is still no consensus on how to successfully accomplish the journey of transformation (Zaoui & Souissi,
2020; Zapata, et al., 2020). Similarly, Zapata, et al. (2020) state that there is still a knowledge gap in the
literature of empirical evidence on how manufacturing companies have adopted a successful digital
transformation. Correani, et al. (2020) point out that even though the implementation phase of a digital
transformation is strategically planned, there is still risk and uncertainty due to the disruptiveness of
technology to the business. Frank, et al. (2019a) also highlight uncertainty being associated with digital
transformation in regard to both technology requirements and potential benefits. Büchi, et al. (2020)
further add to this point as the necessary investments in organizational development through innovation
research and acquiring competence and skill can only be assessed in the long term while literature lack
confirmatory studies at this point. Santos and Martinho (2019) share the same view and argue that the
high complexity of the organizational change likely leads to years of planning and implementation of
incremental changes to realize positive impacts on profitability. Along the same lines, investments in
digital technologies of the sort associated with industry 4.0 require significant up-front costs before any
major benefits can be realized (Demeter, et al., 2020; Saarikko, et al., 2020).
Lipsmeier, et al. (2020) argue that a digital transformation is a dynamic and continuous process and should
be coordinated by a digital strategy that is integrated with and central in strategic management. Matt, et
al. (2015) argue that the transformational progress and the digital transformation strategies should be
evaluated and reassessed continuously. Similarly, referring to the business model being a description of a
company, Tekic and Koroteev (2019) point out that the development of a business model is an iterative
process and that it is rarely correctly blueprinted but rather evolve over time. To this point it should also
be added that seeing a business model as a description of a company’s activities and digital transformation
as an organizational adaption to digital technology, makes digital transformation a business model
15
development, per definition. Sarvari, et al. (2019) also point to the inability of many enterprises to launch
roadmaps because of a lack of qualified staff and that long term planning should be handled by specialists.
However, suggestions for methods and tools for roadmapping and planning activities exist. According to
Osterrieder, et al. (2020), initiation of activities involving developing a smart production, is advised to start
with selected use cases, which align somewhat with for example Lipsmeier, et al. (2020), who propose
that identifying use cases is a part of developing a digital strategy.
Technology development through the last years may be the obvious factor to transform an organization
nonetheless to understand the transformation, the aspect of culture and leadership are as important as
technology (McKinsey, 2018; Nadkarni & Prügl, 2020). Saarikko, et al. (2020) notes that it is necessary for
companies to develop competencies to learn how to use or implement digital technology for business
purposes. To further increase the meaning of this, Sarvari, et al. (2019) suggest that many enterprises are
even unable to launch roadmaps due to lack of competence.
Furthermore, it is not only a matter of transforming the organization to successfully adopt technology,
but also to transform the organization into a changeable organization. Gaspar and Juliao (2020) develop
arguments related to operations management in the Industry 4.0 era, suggest that the data streams and
information flow will increasingly demand organizational change and that operations management will
need to increase flexibility and responsiveness to meet the future needs of the market. Verhoef, et al.
(2019) follows this line of reasoning and suggest that digital agility (the ability to sense and seize
opportunity provided by digital technology) is vital even for market incumbents survival. Verhoef, et al.
(2019) argues that organizations must allow for repeated switching of organizational roles, introduce new
technologies to meet customer needs and to respond to intensified competition as market boundaries
and barriers are blurred and removed. Along the same lines, Mohelska and Sokolova (2018) argue that
Industry 4.0 implementation require continuous innovation and education, they stress that involvement
from top management is a necessity for promoting comprehensive change management.
Tekic and Koroteev (2019) suggest that there are different kinds of leaderships in organizations based on
their digital transformation strategy. They propose that technology led digital transformation is general
coordinated bottom-up and leadership from top management is more passive and “allow” initiatives,
while organizations that employ a business model led digital transformation are prone to an active top
management leadership communicating and supporting the overall initiative (Tekic & Koroteev, 2019).
The business model led digital transformation also generally employ entrepreneurial leaders that drive
disruption and new thinking (Tekic & Koroteev, 2019). Tekic and Koroteev (2019) also suggest activities
such as innovation labs and hackathons in order for firms leading a business model led digital
transformation to scout for entrepreneurial talent as it is often in scarce demand. This might be key
initiatives as collaborative, explorative and entrepreneurial mind-sets in employees is a success factor for
Industry 4.0 implementation i. e. digital transformation (Muehlburger, et al., 2019).
Haffke, et al. (2016) identify that there may be a need for a Chief Digital Officer (CDO) leading the overall
digital initiative in organizations with a digital business strategy. Haffke, et al. (2016) define 4 different
CDO roles (Digital Evangelist, Digitization Coordinator, Digital innovator, Digital Advocate) based on a
matrix with axises of supply/demand side of IT and Implications of digitization.
Muehlburger, et al. (2019) present a framework of categorized enabling factors for initiation of digital
transformation which all relate to organizational features (see Table 3).
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Table 3 Categories of factors enabling Digital Transformation initiation (Muehlburger, et al., 2019)
Category Enabling Factors
Workforce capabilities
Individual Creativity and Innovation Capabilities
ICT literacy
Organizational values Innovative Organizational Culture
Internal and External Collaboration
Organizational infrastructure Digital Platform Infrastructures
IT-Agility
Institutionalized Innovation Process
Bimodal Organization
Organizational Agility
Management capabilities Strategic Embeddedness
Digital Leadership
Forming Strategic Alliances
Strategic Customer Focus
Müller, et al. (2020) point to the absorptive capacity of organizations i. e. their ability to utilize new
external information. Müllers, et al. (2020) view is that partners in value creation as well as innovation
partners in ecosystems are vital in order to acquire information and not only business transactions. Müller,
et al. (2020) means that this involves engaging with research institutes and universities as well as growing
a culture of cross-functional thinking and collaboration, as Industry 4.0 potential is not siloed in individual
functions. In the context of external partners for firms, Tschandl, et al. (2020) elaborate on the role and
meaning of learning and research factories in terms of applied research and development. Zangiacomi, et
al. (2020) elaborate on the utility of sharing skills and knowledge with research institutes and universities
as important points. Burchardt and Maisch (2019) address the need for changeability and a collaborative
culture by suggesting employing concepts like agile principles and open innovation.
Through a literature review, in which reviewed material distinguish information and communications
technology-integration from digital transformation and consider technology integration as a subset of
digital transformation, three indispensable pillars make up the digital transformation; Evaluation of the
digital transformation, definition of the strategic orientation of the digital transformation, and
implementation of the digital transformation (Zaoui & Souissi, 2020).
3.2.1 Evaluation of the Digital Maturity
Zapata, et al. (2020) view maturity evaluation as a key component of digital transformation and suggest
that dimensions and levels of the maturity models must be specific to the users. Santos and Martinho
(2019) propose a model with five dimensions; organizational strategy, structure and culture, smart
products and services, workforce, smart factories, and smart processes, and four levels spanning from 0
(No degree of implementation) to 5 (reference in applying concepts and implement Industry 4.0
technologies. The proposed framework is bifunctional as it is suggested to both serve firms in evaluation
of their own transformational strategy as well as being a functional tool to compare firms across industry
boundaries and benchmarking competition.
Brown and Brown (2019) elaborate on a classification of firms in different digital transformation scenarios
and approaches; Beginners who are not in a hurry to advance beyond internet and e-mail, Conservatives
who have foresight and capability in place but stall implementation, Fashionistas who rush do adopt
17
technologies without a strategic orientation or vision, and Digiratis who have leadership committed to a
clear and well understood vision that will leverage the business. As a guidance for considering digital
transformation, Brown and Brown (2019) cite Schumacher, et al. (2016) and suggest considering eight
elements being; Strategy, Leadership, Products, Operations, Culture, People, Governance and
Technology.
Schumacher, et al. (2019) cite Schumacher, et al. (2016) and described how their maturity model was
further developed, resulting eight similar dimensions; Technology, Products, Customers and Partners,
Value creation process, Data and information, Strategy and Leadership, Corporate standards and
Employees. This framework by Schumacher, et al. (2019) is specifically linked to the assessment of
individual firms to derive action fields and roadmaps to develop Industry 4.0 maturity.
Issa, et al. (2018) propose a structure where individual focal areas are assessed and compiled based on
three dimensions; technology, organization and process. Tonelli, et al. (2016) assess and compile
individual processes and activities without reference to dimensions. Both Issa, et al. (2018) and Tonelli, et
al. (2016) use the assessments as a tool to derive and prioritize actions into roadmaps.
3.2.2 Strategies in the context of digital transformation
Kane, et al., (2015) implies that companies with a clear digital strategy combined with a culture and
leadership who strives to accomplish added value from digital transformation is what separate digital
leaders from the rest. Lipsmeier, et al. (2020) show that there is not a clear consensus on the position of
a digital strategy within the organization or if it should be separated or integrated with the business
strategy. Lipsmeier, et al. (2020) refer to Bharadwaj, et al. (2013) on this point and propose that it should
be placed on the business level of a company and also that it should be separated from the business
strategy at first, but integrate over time as digital maturity develops. However, it is important to realize
that Lipsmeier, et al. (2020) is referring to a digital strategy while Bharadwaj, et al. (2013) is referring to a
digital business strategy. Winkelhake (2019) emphasizes the importance of not preserving the digitization
strategy isolated, when formulating a strategy that emphasize all key business processes it should be
integrated into a strategic corporate planning process. As shown by Verhoef, et al. (2019) and Saarikko,
et al. (2020), digitization can be understood as a subset of digital transformation, and Winkelhake (2019)
actually suggest business model review, should precede digitization. This seems to substantiate the point
that from the very start, a digital transformation and its business impacts should at least be considered
and reviewed at a corporate level.
The definition of the strategic orientation of the digital transformation can be further unpacked, as it
relates both to defining the objectives and what is to be achieved through the digital transformation as
well as the planning and unpacking of the transformational efforts and activities needed to achieve those
objectives (Zaoui & Souissi, 2020). This unpacking resonates with (Brown & Brown, 2019; Tekic &
Koroteev, 2019) that separate two distinct levels of strategy; digital business strategy and digital
transformation strategy.
Digital Business Strategy can be understood as an organizational strategy that combines business strategy
and information system strategy to exploit digital technology for business value (Brown & Brown, 2019;
Tekic & Koroteev, 2019). Many authors cite Bharadwaj, et al. (2013) when discussing the topic of Digital
Business Strategy and hence understand the concept as being different from regular business strategies
through four elements being speed, scope, scale and source (Tillväxtverket, 2017; Tekic, and Koroteev,
2019a; Lipsmeier, et al., 2020). Scope refers to the extension of a company’s direct control and ownership,
18
in terms of activities and portfolio of business and products. A digital business strategy should transcend
functional areas and even traditional business boundaries. An example could be Frank, et al. (2019b) and
their display of how servitization in Industry 4.0 serves internal processes. This shows how supply chain
and logistics adapt their activities through product and service data and how the digital transformation of
the product and service portfolio serves departments that do not have direct influence over them. Scale
refers to the dynamic capability to scale business up and down. Cloud computing is an example of how
pay per use and on demand access to computing capability can be accessed, enabling scaling up and down
digital capability seamlessly. It is also perceived that network effects will play a major role in the new
digital landscape and that business partnerships and business ecosystems will be increasingly important
and understanding how to utilize and access channels of huge data streams for business value. Speed
refers to the speed of decision making, product launching, supply chain, networks, and adaption to
circumstances. It builds on the network aspect in the sense that faster decision making is possible due to
the access of channels of information, from both internal and external resources. It is noted that within
supply chain, digital networks are far superior to the traditional value chain, especially in the automotive,
chemistry and textile industry. Source refers to the nature of business value creation and capture in digital
business models. This involves for example the ability to personalize products and offering based on
customer preferences, multisided and multidimensional business models where value is created and
offered for free in order to capture some other value, coordinating business models in networks, or value
creation from control of industry architecture. (Bharadwaj, et al., 2013; Tillväxtverket, 2017)
Business model as a construct to understand the digital transformation is a popular theme. According to
Winkelhake (2019), initiatives within the digital transformation should align with the business model and
consider all key business processes. Savastano, et al. (2019) highlight that technology is advanced enough
and mature to the point that the keys issue of adoption is the design of business models rather than
technological innovation. Savastano, et al. (2019) argue that firms need to first, outline explicit strategic
objectives and ensure commitment by all functions. Second, identify innovations that can improve process
and add business value. Third, prioritize by business benefits and ease of implementation in accordance
with market and long-term objectives.
Correani, et al. (2020) provides an example of a framework presented in appendix 1, that is used to
investigate business model components and apply a business model perspective to the digital
transformation. It is worth mentioning that the utility of a business model perspective is not exclusively
serving the digital business strategy, but is an important perspective to incorporate in order to understand
the effects of implementation and how to formulate a digital transformation strategy (Correani, et al.,
2020).
Savastano, et al. (2019) notion that it is important to ensure commitment from all functions coincide with
Saarikko, et al. (2020) recommendation. Ritter and Pedersen (2020) elaborate on this by pointing to a
feature of digitization which is that digitization of one business model component has effects on other
components. This arguably underscore the need for a transfunctional digital business strategy, described
by Bharadwaj, et al. (2013), and Ritter and Pedersen (2020) suggest that it could be more beneficial to
study the effects of digitization by looking at relations between business model components rather than
components in isolation. For example, Gaspar and Juliao (2020) also highlight the interrelated nature of
digitization from another angle when researching Industry 4.0 impact on operations management, and
argue that there are implications for operations management strategizing based on emergence of data
from external as well as internal channels.
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Digital Transformation Strategy can be understood as a map of, and a guide through the process of digital
transformation towards a beneficial future state (Brown & Brown, 2019; Tekic & Koroteev, 2019). Tekic
and Koroteev (2019) propose that the concept of business models, being a holistic description of a
company, can be used to characterize four generic digital transformation strategies being “Disruptive”,
“Business Model Led”, “Technology led” and “Proud to be analog”. These strategies are crystalized based
on two dimensions being; the relevance of digital technology mastery within its sector and business model
readiness for digital operations (Tekic & Koroteev 2019). The “disruptive” category is exclusively practiced
by industry newcomers on the basic logic that they can start developing from zero and are not bound by
legacy. The “Proud to be analog” category is distinguished by companies that will decrease the value of
their business by turning to digital technology, as the characteristics of being analog is appreciated by
their customers (Tekic & Koroteev, 2019). Consequently, the remaining two categories include established
businesses that are likely to be positively affected by digital transformation and have an existing
organization to transform. Their characteristics are summarized in table 4. While for example, Santos and
Martinho (2019) suggest that manufacturing companies must develop new strategic orientations and
adapt business models, Tekic and Koroteev (2019) perhaps give a more flexible generalization model and
explain that there are difference even within manufacturing industry.
Table 4 Business Model Led and Technology Led Digital Transformation
Digital transformation Features Recommended tactics
Business Model Led
Primary target is to find new opportunities and schemes
Learn from disrupters and utilize existing advantages, build capacity for open innovation and educate extensively with the target of displaying benefits with digital technology (Tekic & Koroteev, 2019)
Strong leadership from top to empower, resolve, communicate and support
Entrepreneurial spirits are in scarce demand
Risk of disruption and inertia and challenge to identify useful and useless components of business model
Technology Led
Primary target is cost optimization and improvements are likely short term, limited and marginal
Allow and promote a bottom-up approach. Empower small groups of employees to experiment with new opportunities and pathways that go beyond technology adoption. A caveat for the firms adopting a technology led digital transformation is that they should consider a move to a business model led transformation as a plan B (Tekic & Koroteev, 2019)
Risk avoiding leadership based on lack of perceived need to change
Entrepreneurial leaders are underutilized and can create crises by proposing change that is organizationally unmanageable
Risk that technology adoptions becomes false positives and a false perception of change
There are further suggestions that address digital technology adoption, for example Hoellthaler, et al.
(2018) who propose that digital technologies can be adopted more or less solely on the idea that they can
20
overcome limits of lean production systems. Demeter, et al. (2020) argue that though it is likely that digital
technology adoption is initially be handled by lean-teams, Industry 4.0 adoption require greater
capabilities then these types of teams possess.
Digital Strategy is a third term used throughout literature with somewhat varying meanings;
• A strategy that describes the overall digitalization vision as well as strategic measures to achieve
it. Including concrete short-, medium- and long-term goals and initiatives (Lipsmeier, et al., 2020).
• A guiding policy for creation and appropriation of value by exploiting digital technologies to
achieve long term objectives- which include factors related to the external environment, the
technological potential in the current competitive scenario, and the evolution of the market.
(Correani, et al., 2020)
Lipsmeier, et al. (2020) cite Matt, et al. (2015) and suggest that their proposed framework is one for
“Digital Strategy” while the original article by Matt, et al. (2015) clearly state that it is a framework for
“Digital Transformation Strategies”. On the other hand, the definition of digital strategy by Lipsmeier, et
al. (2020) corresponds with Matt, et al. (2015) view that digital transformation strategies consist of two
dimensions being firstly the action plan to implement a digital transformation, and secondly a guiding
strategic orientation. This implies that the term “Digital Strategy” is comprehensive strategy that envelope
both “Digital Business Strategy” and a “Digital Transformation Strategy”. This does not however,
correspond with the definition posed by Correani, et al. (2020) as they distinguish “Digital Transformation
Strategy” as its own level of strategy and thereby seem to understand “Digital Strategy” as a synonym to
“Digital Business Strategy”.
Furthermore, the term “Roadmap” is present throughout literature and can be understood as a tool based
on strategies and activities that brings an organization from a current state to a future state, by mapping
the path (Sarvari, et al., 2019). Lipsmeier, et al. (2020) place the roadmap within the digital strategy and
see the roadmap as being a part of the digital strategy and a time plan of the activities that the digital
strategy entails.
3.2.3 Methodological Approaches
Systematic approaches that can be stated to recognize the comprehensiveness nature of digital
transformation and adhering to the view of Zaoui and Souissi (2020) which entails three stages where
transformation and strategy is defined based on the maturity assessment. In Schumacher, et al. (2019)
proposed model for transformation and roadmapping, the activities to transform the organization are
derived by developing an “as-it-is” and “target-state” along the dimensions of the maturity model. The
dimensions span from no “recognition of Industry 4.0” to “state of the art”. Furthermore, the dimensions
and targeted actions are weighted on significance and thereby prioritized in the roadmap.
Tonelli, et al. (2016) propose a similar methodology, however the dimensions are not based on an Industry
4.0-generic framework like Schumacher, et al. (2019), but a “Gartner Maturity Model”, where company
stakeholders evaluate maturity in each activity and process. These data are then compiled to create a
comprehensive company view.
Similar to Tonelli, et al. (2016), in a model proposed by Issa, et al. (2018), the process of assessing maturity
is not one based on a generic Industry 4.0 maturity assessment tool, but rather targeted areas for
digitalization are derived by self-assessment of stakeholders focus areas on the dimensions of technology,
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organization and process. Interestingly, it is found by Issa, et al. (2018), that the maturity assessment was
of little or no use to the firms that the model was tested on.
Both Issa, et al. (2018) and Tonelli, et al. (2016) present use cases where strategically aligned digitalization
projects are generated. Schumacher, et al. (2019) cite both Issa, et al. (2018) and Tonelli, et al. (2016) and
raise general criticism of existing models. Schumacher, et al. (2019) consider their generic Industry 4.0
maturity framework to be their significant contribution, as this framework allows to expand the
technology centric view and envelope organizational features in a broader sense.
The products of each model are actions and roadmaps. In Schumacher, et al. (2019) one of the action
fields that is identified is “the development of Industry 4.0 leadership”. In both Issa, et al. (2018) and
Tonelli, et al. (2016) strategic business orientation are input variables in the models. It is hence suggested,
that practices to understand the business model for example like as proposed by Correani, et al. (2020),
and methodological approaches for roadmapping and creating actionable activities based on models
proposed by Tonelli, et al. (2016), Issa, et al. (2018) and Schumacher, et al. (2019), are not exclusive but
rather synergetic or interrelated.
Lipsmeier, et al. (2020) also present a methodical step-by-step process that is not based on maturity
assessment but rather the assessment of the business model and its domain. Like the other models,
Lipsmeier, et al. (2020) model produce digital projects/programs and a roadmap, but with a specific step
dedicated to a distinguished digital strategy, see appendix 2. The lighter blue segments (Digital guiding
principles, Strategic direction, Measures, and organization) are primary strategic elements and the darker
segments (digital culture, Digital Competencies, IT/OT Architecture, Value creation network) are
secondary strategic elements. In this model the primary elements are defined as guiding and the
secondary elements are subject to transformation and must be mapped out in accordance with the
primary elements (Lipsmeier, et al., 2020).
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4. Empirical findings
This chapter will present empirical findings from the case study. First an introduction to the case company
followed by a brief description of the case. Further a current state analysis is presented and explained.
4.1 Introduction to the case
The case study was conducted at a manufacturing company that can be considered a major player in the
automotive industry. In 2019, the company had a global turnover of approximately SEK 88,000 million.
The company has many sites around the world and the case study took place at one of the local plants in
Sweden. This plant specializes in producing transmissions and axles, and include functions such as Sales &
Marketing, Technology, purchasing and IT. The plant is one of Europe's largest machine process plants
and has about 2100 employees.
The case company is currently working with the transformation towards Industry 4.0. To be able to realize
the journey towards Industry 4.0 and become more digital in their production, the case company has
created local task teams at each site that are responsible for exploring and evaluating digital technologies
and their applicability in the production settings. These teams consist of members from various functions
within the local sites. At the studied local plant, the team is led by a project manager who identified the
need for a strategy to guide the digital transformation toward business value, and therefore initiated this
thesis project.
To support the case company with their development of a strategy for digital transformation, their formal
transformation efforts have been studied in order to evaluate and relate them to the academic literature
and knowledge in the field of digital transformation.
4.2 Digital Transformation Initiative
Within the local plant, the focus of the production system development has shifted towards how to
implement new technology to the current production system. They have launched several pilot projects
to try and evaluate new digital technologies and how they can be integrated with their production. So far,
emphasis has been placed on evaluation of the technologies, if they are mature enough and can act as
support to solve current obstacles and enable improvements in OEE, information sharing, quality, safety
etc.
To make progress in the work towards Industry 4.0, the case company has formulated an initiative which
aims to enable and guide a digital transformation throughout the organization. This is the only formal
initiative within the studied local plant, however, there are other informal initiatives outside of the
initiative, that could be categorized as work towards industry 4.0. Those have been implemented without
any specific initiative for working with a strategic digital transformation but has sprung out as an effect
when awareness of technology has dispersed throughout the organization.
The formal initiative is meant to kickstart the transformation towards their interpretation of Industry 4.0,
but the case company consider it a journey, rather than a project. Each individual site has been given the
responsibility to put together its own a task team of employees to work with the development of the
digital transformation. The global initiative has given each site the responsibility of working with the
guidelines created and developing their own vision and objectives of how to create value. The digital
transformation initiative as mentioned was created at a global level within the company. The global ME
initiated and became the owner of the initiative but distributed a local ownership of the work and results
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to the individual sites. The role of the global organization has been to engage and make all sites around
the world active participants in the initiative. Although, each site is working with the same guidelines, all
sites adopt the initiative into their organization individually. All the sites have their own responsibility to
decide how they want to work with the initiative, as the global leadership has not set the initiative as a
requirement, but rather an encouragement and a guidance and support for the sites. What resources each
site chose to use is completely up to them. From the global perspective, they see benefits from following
the initiative for gradually becoming more aware of digitalization opportunities.
Though the case company recognize the importance of staying competitive in their market, when
discussing competitive advantage within Industry 4.0 there is no notion of competitive pressure being a
major driver of digital transformation, neither from the global nor local management, and the factor of
how their competitors are making progress with their digital transformation is not stressed. When
discussing the progress of digital transformation during the interview with the local director ME, it was
stated that it is more important to learn and adopt new technology in their own pace and that they should
not stress the development because a competitor has progressed faster. The global director ME shared
this notion and expressed how they see sharing ideas with competitors as a possibility and that learning
from each other could be a step in the development in Industry 4.0.
As communicated by the global director ME, it is meant to drive a digital transformation in manufacturing
that will enable an acceleration of the lean journey to achieve operational excellence. The studied local
site has successfully and continuously worked with a lean approach within production. The company
globally has a vision that with their lean principles and Industry 4.0 combined, it is feasible to reach >40
% in cost reduction over the course of the upcoming 3-5 years. Previously, the case company has built up
their own approach of lean principles and the owner of digital transformation initiative states that he
would like to see similar work with digital transformation. It is also mentioned that the initiative has a
higher purpose than achieving operational excellence which is to “build the place we want to work in”.
That shows that they perceive a digital transformation to be an opportunity to build an employee centric
workplace that place the employees in the best conditions to perform and execute high adding value task.
The creation of the task teams is only meant to start the digital transformation and become aware of new
technology and help each site to make progress with the digital transformation journey. In a few years,
the task teams are not meant to exist in the same way. The hope of the global director ME is that
digitalization will become embedded in the organizational culture and when working with improvements,
Industry 4.0 “thinking” will be in all employees’ “backbones”. Within the company some education on the
topic of Industry 4.0 has been available for the employees. Industrial internet of Things is one course that
have been available at the company’s internal “university” and a cyber security course has been
mandatory to take.
The initiative is constructed by categories that are identified as areas that will be affected by the digital
transformation journey. For each category, a general idea of a future state has been communicated by
the owner of the initiative. At the core of the digital transformation is the formal initiative and its
associated leaders, while the categories surround and contribute to the success of the initiative and the
achievement of the digital transformation itself. Smart governance, and a smart way of thinking are
considered the two main elements to the core of the initiative. Within smart governance, it is explained
that it is the case company´s business strategies and targets that should drive the digital transformation
and make the initiative successful. I. e. the initiative is considered to be a top-down challenge with
bottom-up solutions. At a global level they have a forum for top management, “Manufacturing
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Technology Development Board” (MTDB) that includes executives and other leaders. Their function is to
support and give guidance, directions, and priority for the project portfolios.
The first category is considered as a foundation to succeed with implementing a digital transformation
and it is IT and data security. In the category, IT-infrastructure is highlighted and how communication
networks, application platforms and databases are needed to be standardized. By standardizing the
infrastructure, it will support the connection to operational equipment, advanced analytics, collection,
and storage of data. Once connecting operational equipment and a massive amount of data pour through
their networks, cybersecurity will be critical. Cyber security will play the part of protecting their facility
and automated operations from cyber-attacks and improper use of data.
The second category, operations data collection, describes how collecting data should be conducted. The
goal is that data from production equipment, tools and operators are collected without any human
intervention nor manipulation. Data from Integrated sensors and PLCs as well as all other relevant data
should be stored on a single platform. In this category it is also mentioned how not only data from the
processes of equipment is important to gather, but also data such as operators’ motions, voice of the
operator, sound from operator or equipment and images of work in progress. The company emphasizes
in this category, the importance of data reliably reflecting the reality.
Next category in the model is operation data usage. All data collected should be used in some productive
way for example real-time monitoring. Operational data and business transactions will be analyzed and
correlated to simplify the work of identifying obstacles, supporting predictive decisions, and creating
opportunity to improve fact-based business decisions.
Advanced manufacturing is the next topic in the model and this category addresses the goal of identifying
new technology that can be integrated into the production environment when the technology is
considered mature and can create value for the company.
Model based Manufacturing is formulated by the case company as follows: “Model based Manufacturing
uses annotated 3D models of the product & production process in order to design, visualize, simulate,
optimize, verify and dry-run all aspects of the manufacturing process.”
Automation and support of manual labor address the work with automation and how it can increase the
competitiveness by more efficient manufacturing. Furthermore, it addresses how automation can lead to
a decrease in heavy lifting tasks and how the company can become more attractive with a high technology
level.
Autonomous control of manufacturing should provide the production flow with an optimized and
adaptive way of managing manufacturing in real time. It should include a data collection and a real time
analyzing of the data from the shop floor and resources. Also included in the future for autonomous
control is an identification mechanism that recognize every component and final product, intelligent and
predictive quality control, automated and autonomous logistics flow, and real time location system.
4.3 Roadmap
To guide the journey of digital transformation, the initiative has created its own global, abstract, and high-
level, roadmap described as three steps:
Connected Manufacturing → Virtual Manufacturing → Autonomous Manufacturing
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The roadmap describes how the categories in the initiative are linked in a logical sequential way.
Connected manufacturing includes the first three categories (IT and data security, operations data
collection and operations data usage) from the model described in the previous section. The global goal
of connected manufacturing is to have all elements of man, machine, and data connected. This would
enable the real time data and together with organized structure of the data, business decision would be
supported through analysis from artificial intelligence.
The next step of the roadmap is to achieve virtual manufacturing, which includes the next two categories,
(model based manufacturing and advanced manufacturing technologies). In this step, the factory should
be sufficiently mature to create a digital twin with the ability to run simulations of each process. By
enabling simulations, optimization and predictions for their products and processes becomes a reality.
This in turn enables understanding the outcome of events and decisions in a virtual reality before it
manifests in the physical world.
Finally, Autonomous manufacturing is last step in the road map. With both connectivity and virtual
manufacturing deployed, the vision of new level of flexibility, predictability and efficiency is reachable
through autonomous control of manufacturing.
4.4 Task Team
The task team at the local plant is cross-functional and consists of representatives from production
development, maintenance development, IT, Logistics development, Union, project management,
machining, quality, production, and method development. The task team was assembled in 2019, and
managers from each department chose candidates for the team based on interest in technology and
development. Joining the team was voluntarily and there were no specific requirements to join in terms
of competence. The task team is placed under the department Production Development and Project
Management, which in its turn is placed under Manufacturing Engineering.
The formal purpose of the task team is to identify new innovative production solutions for the plant, by
combining technology and IT. Each member of the team dedicates about 5-10% of their time to the task
team. The smart factory team meets up once a week where they discuss what they have done the past
week and what they should do the upcoming week.
No specific guidelines exist on what the team should investigate or how they should coordinate their work.
The team has the liberty to explore what they believe is suitable and can create value for production.
However, the task team needs to request funding for example to invest in a new machine or technology,
and a business value needs to be presented to get approval. Projects and investments are funded both by
the local management and the global organization. The global management has decided a specific amount
each site can receive, and the investment should be in the interest of digital transformation. Most of their
current pilots are created based on known issues and ideas for improvements, of the current production
environment. Though there is no specific strategic direction or prioritization from local and global
management on what the task team should explore, their activities are guided by some sense of strategic
direction as they need to comply with the plant’s overall priorities. The factory's overall priorities are
safety, quality, reduction of waste, increase productivity, success in strategic projects, and continuous
improvements. A general perception from the local director ME is that it is not necessarily the top
management that has all the answers to what the solutions would be to implement the transformation.
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The local director ME stated in the interview that the employees who work with daily activities on, for
example, the shop floor have more knowledge of how or what should be developed or implemented to
succeed. However, top management should be responsible for providing guidelines, and directions on
what should be given priority. Aligning pilots to the priorities is not perceived as a major concern as these
priorities have a great breadth. As an example, continuous improvements can be linked to all the pilots
that show a value improvement and hence, all pilots could be placed within the specific priority. In the
work with continuous improvements, the studied local plant has a variety of key performance indicators
(KPIs) to consider and they have targeted some that have a larger impact and are critical for their business
to improve. There is currently a new business area that the studied plant is trying to absorb and one of
their KPIs are critical to improve to even be considered as a valid supplier of products to this specific
market. However, this is not an explicitly expressed goal or issue that is asked of the task team to take
into consideration.
The task team has conducted some pilot projects, for example one project involves setting up a virtual
training station in order to reduce quality issues. Additional to following the local plant's priorities, the
task team is aligning their pilots to the global roadmap presented in section 4.3. There are differences in
how the roadmap is perceived, as it is described in section 4.3 the global road map is created in sequential
order, but the leader of the task team believes that pilots can be developed simultaneously in all three
steps. Some of the technology which could be included in Virtual manufacturing can be tested in an
"island" where connected manufacturing can be bypassed in the sense of running a pilot. The virtual
training station is such an example that is categorized as a project within virtual manufacturing, though it
is not based on any production data sources and connected manufacturing.
After a pilot has been completed it is still responsibility of the task team to evaluate the results. A group
of engaged executives are partly included in the evaluation and they participate in overseeing the
completed pilot. Information about the pilots is shared in a global internal online platform where a global
team evaluate the maturity level. If a pilot is a success, the team take the responsibility of moving the
pilot forward to a strategic preparation and a project is created.
4.5 Maturity Assessment Model
The case company has produced a maturity assessment model for supporting the company both globally
and locally. The assessment model is though considered incomplete at this point and a work in progress
by the global director ME due to feedback provided from the first try of conducting the assessment. The
model is a tool to create a view of the current maturity and readiness state, a view of a desired maturity
and readiness state and thereby implicitly provide and deliver a gap analysis that can act as a guide for
focusing the transformation activities on what is to be achieved during the upcoming 3-5 years.
The maturity assessment model consists of four themes; Smart Governance and Strategy, Connected
Manufacturing, Virtual Manufacturing, and Autonomous Manufacturing. Those themes in turn, consist of
subthemes on which diagnostic results are based. For each subtheme, a set of questions are provided to
guide and support the analysis. The questions should be answered by evaluating the current state as well
as setting a desired state based on levels on a scale from 1 (No Knowledge) to 10 (Lighthouse).
Furthermore, the levels are group into three categories that labels the site as “Newcomers”, “Learners”
or “Leaders” within that subtheme.
27
The studied local plant has not used the model though the company has encouraged it and expected that
it should have been done already. Consequently, there is no current state analysis present, nor a vision of
what the factory should achieve in the upcoming years. The view of the leader of the local task team, is
that it is the responsibility of the local management to utilize the model and that they should be the owner
of the results. However, according to the local Director of ME who is a member of the local management
team, the management for the local site is currently working on a strategic plan and a vision for the
upcoming 3-5 years that is independent of the model, yet they have set a date in Q1 2021 to conduct the
maturity assessment with the use of the provided model.
In the theme “Smart Governance and Strategy”, the subthemes are Strategy, Leadership & Culture,
Customer & Society, Business Performance Management, Risk Management, Collaboration, Skills &
Competence, and Communication. The content of the subthemes can be described as below;
Strategy: Focusing on questions regarding how the case company understand their digital transformation,
is it clear why they want to go smart and connected? Also, how is it aligned with their plant and global
visions? Is the case company´s priorities connected with the digital transformation initiative and how is
the digital transformation initiative connected with the business needs? Investment policies and
processes for global and local funding, are those aligned and in place with the vision? Finally, is there a
roadmap for implementation of their activities?
Leadership and Culture: How is the leadership and the organizational culture engaging in question such
as promoting staff to be involved in digital transformation activities, to try new solutions? To which extent
is the leaders themselves early adopters? Do leadership encourage everyone, follow up and learn from
initiatives? Is there an approach to nurture the creative capabilities (bottom-up), and is there a shared
view of the value of digital transformation?
Customer & Society: This subtheme mentions the extent to which they are prepared to understand new
opportunities with transparency towards customers in their operations? It also mentions if the plant/site
is prepared for new business models.
Business Performance Management (KPI’s): The case company want to understand to what extent they
have defined clear goals and targets for business, and how the global initiative can support. Also, how can
success projects and efforts within the digital transformation be measured?
Risk Management: Are all factors that can be classified as a risk to the business known and are the
initiatives evaluated from a risk and business continuity perspective?
Collaboration: The case company strives for a collaboration within their company both internal and
external. The local task teams from different sites should collaborate within the Volvo group as well as
with universities and customers. Another aspect to consider is the purpose of collaboration.
Skills & Competences: Are the critical competences for succeeding in the transformation present, or is
there a plan to acquire them? The case company also consider what is required for the future industrial
worker.
Communication: This subtheme wants to establish how their internal communication is with conveying
the digital transformation initiative to all employees. Also, the local plant itself needs to consider its
workplace and its visual appearance.
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5. Analysis
The analysis consists of a comparison between the presented literature in the frame of reference and the
empirical findings. This is presented through the lens of the research objectives in order to show
commonalities and differences between the literature and the case company. From the analysis, a
discussion can be held and an answer to the research question can be finalized.
5.1 How is the current and future digital landscape understood?
As the comprehensive description of the Industry 4.0 concept shows, the discrepancy in academic
literature is obvious and it should be noted that what is included in the concept is not obvious if even
distinguishable. In light of this, notions about “adopting Industry 4.0” or “becoming Industry 4.0” does not
seem meaningful or make much sense. As shown, there is a broad set of technologies, design principles,
application domains with specific challenges. In terms of understanding what the digital landscape of the
era consists of, perhaps a broad understanding of what the concept of Industry 4.0 is, and consisting of,
is meaningful. However, in the context of digitally transforming an organization, the useful employment
of technology is highly specific, and it depends on the needs and variables of the business. Hence, staying
relevant and competitive as the fourth industrial revolution takes place seems to be a more sensible
pursuit and useful idea. In the literature on digital transformation, this is further highlighted. Sector, size,
and activities are highly relevant to the applicability of the broad concept, and the organizational ability
to change as well as the financial position to overcome budget restrictions and bare the extensive cost,
risk, and uncertainty of development and innovation efforts.
At the case company, the digital transformation initiative and description of categories show a
comprehensive view and understanding of technological potential when it is examined under the lights of
the literature on the Industry 4.0 concept presented in section 4.1. The communicated construct of the
digital transformation initiative, as described by the global organization, provides some vision for what is
possible and how fundamental changes in the manufacturing organization are to be expected, relating to
the view of the literature (Piccarozzi, et al., 2018; Yin, et al., 2018). While the initiative roadmap express
that the final stage of the journey is achieving autonomous manufacturing coinciding with the Industry
4.0 concept, there is not however, any mentioning about reconfigurability, which is generally included in
the academic perspective (Kholopov, et al., 2018; Piccarozzi, et al., 2018; Cohen, et al., 2019).
Furthermore, in no way is the initiative branded as an Industry 4.0 adoption, but rather an overall
commitment to building a future production environment. This captures the point that it is not based on
a fixed concept, but a development of a factory that is relevant in the future, whatever and however that
factory may look like and operate.
The case company also show an awareness of the challenges that face the implementation and adoption
of Industry 4.0. It is both visible in the description of the initiative, and in the way they have set up their
structure in order to support local sites and plants. For example, they have set up the forums where sites
are encouraged to share information about ideas and projects in order to make sites with financial
challenges and little or no budget for technology experimentation, reap the benefits of the lessons learned
in sites where the projects are generously funded. Furthermore, the company has set up a system where
local sites can request funding for projects. In those regards, the global organization support the local sites
through challenges such as absence of relevant knowledge and budget restrictions that are prominent in
literature (Albukhitan, 2020; Sarvari et al., 2019).
29
The variety and volume dimensions presented by Yin, et al. (2018) as needs for Industry 4.0 are not
explicitly addressed in the construct of the initiative, however the time dimension is addressed and
shorter time to market is explicitly mentioned as a value for the case company’s customers. The idea that
data is a driver in order to make better business decisions is present both in literature (Akter, et al., 2020)
as well as in the company´s description of the initiative.
It is noteworthy that the leader for the local task team at the case company does not see the roadmap as
being a linear process with connected sequences. However, according to the global organizations
perspective it is created as a sequential stepwise roadmap. This perhaps highlights a too narrow
perspective by the local task team. Even if, for example, their projects within VR is arguably a part of
virtual manufacturing, the idea of having a virtual manufacturing that is built upon a foundation of
connected manufacturing might be exceedingly more valuable and deliver far more than one application.
In literature the interrelatedness is considered of high importance (Demeter, et al., 2020) and individual
applications should be considered a part of the larger system from the start in order to build cohesiveness
(Saarikko, et al., 2020). In this regard, the four layer model that Osterrieder, et al. (2020) refer to, is
evidently a useful perspective in order to move beyond the single application focus.
What is interesting is the scope of the company´s digital transformation initiative, in the light of the
literature presented in topics of application. The initiative is mainly focused on the factory and
manufacturing which can perhaps be interpreted as a limiting of the scope, as it is described by Bharadwaj,
et al. (2013). As the local plant is responsible for manufacturing of axles and transmission, and their
customer is part of the same organization, there are arguably some limiting factors to their degree of
freedom to reinvent their value proposition and radically change their business model, within the scope
of their own factory. Consequently, it is hard to pair some of the topics of application to match the specific
plant. For example the servitization in order to elevate internal processes mentioned by Frank, et al.
(2019b) could be less applicable as the factory´s customer is not an end user. Narula, et al. (2020) mention
traceability, which is a feature of the production that would preferably be applied across factories of the
complete value chain as well as within factories. The whole topic of circular economy, as well as the
production life cycle, is likely better orchestrated by the coordination of the complete final product value
chain rather than in isolation by a manufacturer of individual components. This is interesting as it latches
on to idea of transfunctionality of a digital business model as well as business model coordination in value
networks that is discussed within the source dimension by Bharadwaj, et al. (2013). Furthermore, this
goes to highlight Ritters and Pedersens (2020) point about how digitization of business model components
have impacts on other components. No doubt, the digitization of one local plant has effects for the rest
of the organization revolving the final product of the value network. When considering Fritschys and
Spinlers (2019) ideas on autonomous trucks having implications for relations between OEM:s and Tier 1
suppliers, is obviously something that is more effectively addressed as a joint venture between the case
company’s plants. Consequently, it has great implications whether the digital transformation is addressed
as individual plants in silo, as the factories will probably need to assess their position and purpose in the
value network, or if it is addressed as a joint value network where factories probably have lots to offer
each other in knowledge and data, and where mutually beneficial applications can be spawned and
developed.
In the literature, there are numerous statements on how leadership and culture are two factors that are
as important in a digital transformation as technology . The case company displays an understanding of
this based on the part of “culture & leadership” in the maturity evaluation model. There is a fundamental
30
view of how their employees will grow and be the most important asset in the digital transformation. In
the description of the initiative, the case company states that they have a great opportunity to create an
employee-centric workplace, which is seen as more important than or perhaps requisite to achieving
operational excellence. The case company also encourages leadership that enables its employees to dare
exploring and not feel pressured for “right” results. This view is corroborated by statements such as that
the digital transformation is not exclusively technology-driven and would require a deep culture change
(Nadkarni & Prügl, 2020), development of competences is necessary for the implementation and
understanding of new technology (Saarikko, et al., 2020), and companies with a clear digital strategy
combined with culture and leadership who strives for accomplishing added-value from the digital
transformation is what separate the digital leaders from the rest (Kane, et al., 2015). The fundamental
believe of the case company on how organizational culture is one of their most important pillars in their
digital transformation journey relate to a statement mentioned by Burchardt and Maisch (2019) who cite
Peter Drucker: “culture eats strategy for breakfast”. This speaks for the importance of having an
organizational culture that is mature and involved with a corporate strategy that includes continuous
changes (Burchardt & Maisch, 2019). Further highlighted by Gaspar and Juliao (2020) who shows
operations managements need for an increased flexibility and responsiveness from to meet the future
needs of the market. Competences and knowledge are considered challenging in the transformation
(Sarvari, et al., 2019; Saeed Albukhitan, 2020) and the case company shows awareness of this, as the
subtheme “Skills & Competences” address this and can be used to evaluate if the company has the
competences that are critical to succeed with the digital transformation.
The case company and the literature both emphasize the aspect of leadership and organizational culture
as pillars in the digital transformation. The task teams are considered a driver to jumpstart the progress
and raise the awareness of new technology. The task team’s responsibility is to share the knowledge and
drive different pilots that should include new innovated processes that everyone can learn from. One way
of sharing the knowledge is through the digital platforms the case company has set up. The platforms
work as forums were employees can give comments and share their knowledge with each other.
Something that relates to the open innovation ideas proposed by Burchardt and Maisch, (2019) who
describes that open innovation entails that all employees can liberate their own internal knowledge and
profit from sharing. The case company does not see the task teams as a long-term solution and hope that
they are not as important in 3-5 years as they are today. This is because, the case company strives for a
culture change and hope that digital applications of Industry 4.0 should be in every employee´s mind- and
toolset. The director of ME stated that the top management is not the ones who know how to implement
all new digital solutions. This relates to what Burchardt and Maisch (2019) mentions, on how the power
and knowledge are no longer with the individual executives, but rather the employees more often bring
the solutions and knowledge. Also mentioned by the Director ME was how top management can provide
guidance and objectives on what the company should strive for, but solutions should evolve bottom-up.
5.2 How is the digital transformation constructed?
To understand and break down the initiative of the case company and elaborate on the comprehensive
view of digital transformation, the three pillars that make up the digital transformation; Evaluation of the
digital transformation, definition of the strategic orientation of the digital transformation, and
implementation of the digital transformation proposed by Zaoui & Souissi (2020) serves as a structure.
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5.2.1 Evaluation of maturity
Repeatedly through literature, it is proposed that evaluation of maturity is a key component of digital
transformation (Schumacher, et al., 2016, 2019; Tonelli, et al., 2016; Issa, et al., 2018; Brown & Brown,
2019; Ifenthaler & Egloffstein, 2020; Zineb & Bouchaib, 2020; Zapata, et al., 2020). As shown, the
importance when creating a framework for evaluating the maturity is to capture the company’s own
organizational structure. When initiating a digital transformation, companies struggle with both
operational and organizational challenges such as legacy equipment, integration with old technology,
culture changes and leadership (Muehlburger, et al., 2019; Sarvari, et al., 2019). The understanding of the
literature seems to be that organizations should assess the current state and have a target state to create
a gap analysis. The identified gaps clarify steps needed to move in the direction of wanted future state
(Tonelli, et al., 2016). This is exactly the way in which the case company´s tool is intended to be used. The
company´s tool is constructed with four themes, Smart Governance and Strategy, Connected
Manufacturing, Virtual Manufacturing, and Autonomous Manufacturing. Those themes together capture
the state of strategy thinking, leadership and culture aspects, risk and business management, and
technology progression. The themes broadly cover the generic dimensions corroborated by literature;
technology, customer and partners, strategy and leadership, operations, culture, governance and
technology (Brown & Brown, 2019; Schumacher, et al., 2019). Yet, the tool has not been utilized by the
studied plant. However, the case company has operationalized and evaluated the tool in another plant in
their organization which provided feedback to its applicability and there seem to have been much trouble
in the sensemaking of the model, its dimensions and how the questions and issues should be answered.
5.2.2 Strategic Orientation
There are some key findings in the empirical study that imply that the case company is currently adopting
what is described by Tekic and Koroteev (2019) as a technology led digital transformation strategy. The
assembled task teams are small sets of members that represent different departments and stakeholders,
operating under no strategic direction and that are free to explore any innovations and ideas. It is explicitly
expressed by the local director ME that top management rely on the team and the employees of the
organization to provide an answer to “how” the organization should operate and change. Cost reduction
is an explicit goal, and perhaps even a primary goal, however not an exclusive one. Though the local
director ME sees it as imperative that the vision for the plant includes an ambition to become a smart
plant, there is no notion of urgency. The comments from the global director ME that competitive pressure
is not something that has been a significant driver in this initiative and hardly even considered. This could
be interpreted as a lack of perceived need to change. The comments from the local director ME indicates
that there is not an active involvement in the activities of the task team from top management, as would
be expected in a business model led digital transformation. Though, top management view their
responsibility as setting guiding priorities, the leader of the task team states that these priorities are broad
stroke enough to fit basically any project they can think of. The priorities are formulated in a way to reflect
the values of the organization; however the perception seems to be that they do not reflect a strategic
orientation that enables task team to prioritize and evaluate projects in order to create alignment with an
overall business strategy. The strongest indication of a technology led transformation strategy is that
there is an absence of a digital business strategy or a reference to the business model. While there is an
awareness of this being relevant to the digital transformation in the maturity evaluation tool, judging by
the themes, the local plant shows no indication of applying this perspective. For example, there has been
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no consideration from the local director ME about how product portfolio relates to the digital
transformation.
The only indication of a formulated strategic orientation taking root in the local plant that has been found
during the study is the global roadmap and the top managements priorities. The roadmap does not
however, resemble what a roadmap is, in the understanding of literature as it does not display actions or
strategies (Sarvari, et al., 2019). Though the leader of the local task team makes use of the roadmap by
categorizing their activities based on it, he does not see the parts of the roadmap to be linear sequences
which further undermines the strategic importance of it as a roadmap in the literary sense. When asked
about the global roadmap, the global director ME, agrees with the statement that it should be understood
as a guidance and a way of thinking about the digital transformation rather than an expectation or
instruction. In light of the framework presented by for example Correani, et al. (2020) or the four
dimension presented by Bharadwaj, et al. (2013) the priorities expressed by top management as their
strategic guide in the smart factory initiative, cannot be considered an indication of a business model
perspective being applied or a digital business model being present or considered in the literary sense.
The claims throughout literature about the importance and utility of the digital business strategy concept
and/or a business model perspective seem warranted. Notions like Ritters and Pedersens (2020) point
about how digitization of one business model components have impacts on other components and
Bharadwaj, et al. (2013) elaboration on the transfunctionality of digital transformation finds practical
context in Franks, et al. (2019b) elaboration on how servitization elevate efficiency in internal processes,
and as highlighted by Gaspar and Juliao (2020) who argue that operations management will need to
effectively operate and act swiftly based on market data. As it has been shown Industry 4.0 adoption (if
granted to be a useful term), is implicitly a digital transformation, as both are related to adoption of digital
technologies and organizational changes. In light of a business model being a map or a description of a
company as stated by Tekic and Koroteev (2019), digital transformation and Industry 4.0 adoption is
implicitly a redesign of the given organizations business model. Hence, if an organization commits to a
digital transformation in the literary sense, the only interesting question is if they redesign their business
model consciously or unconsciously. Furthermore, as Correani, et al. (2020) point out, their model is not
only a model to use in order to redesign or innovate the business model to develop a digital business
strategy, but also a tool that enables an understanding of the necessary actions needed for an actual
implementation of a digital transformation. To a deeper point, regardless of the method applied to
understand digital transformation of the business model, this again feeds back to Ritters and Pedersens
(2020) point that the effects of digitization leads to ripple effects in the business model components, and
seem to warrant the claimed importance of using a holistic business model perspective when committing
to a digital transformation.
An interesting finding is in the proposed methodological approaches that suggest assessment of maturity
and gap analysis in order to extract actions and roadmaps, for example the one employed by the case
company and also the one prescribed by Schumacher, et al. (2019). One of the resulting action fields that
is generated by the model in the use case presented by Schumacher, et al. (2019) is “the development of
Industry 4.0 leadership”. It is hence understood that the development of “correct leadership” is something
to be developed by the organization itself, and not provided by the model. Considering the dimension of
“Strategy” it can be understood that a strategy is not generated by the model, but rather a need for one
is identified. Furthermore, in the light of statements about many firms lacking the competencies to launch
roadmaps by Sarvari, et al. (2019), it could be called into question if firms have the competencies to
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“correctly” or competently asses their maturity and identify the need for a strategy. In some sense, as the
gap analysis is based on the company’s own perception of what the aim should be for a target state in any
given dimension, there is no guarantee that the users of the model realize the importance of a strategy
and make it a high priority to develop one. And as previously implied, if they make it a high priority, they
are still stuck with the hassle of developing one. Other models like the ones proposed by Tonelli, et al.
(2016), Issa, et al. (2018), or Lipsmeier, et al. (2020), seem to suppose the engagement of management in
the process to develop a strategic business-oriented strategy, or assume that a strategic business
orientation is identified or formulated in advance. Consequently, regardless of if the maturity assessment
is utilized or not, defining a desired future state, I.e., a digital business strategy, is evidently useful and a
gap analysis should not be considered a substitute but a complement.
Noting on the caveat that Tekic and Koroteev (2019) leave in the recommendations for a technology led
digital transformation, that a plan B should be to move to a business model led digital transformation on
time. It would seem as if the task of determining if the time is right, might be best understood by
examining the business model in order to understand if significant benefits can be achieved through a
redesign. Furthermore, even if a technology led digital transformation is being applied, the management
of funding for projects still need to be evaluated on some metric that represent business impacts. If the
statement is then warranted (as it seems to be) that a business model perspective is useful to understand
relations among components and effects of digitization, then its applicability is not dependent on the
transformation strategy either.
The empirical study in this paper did not include a map of the organizational structure, business model or
the relations between sites within the company structure. However, the analysis presented above should
support a claim about the applicability of a business model perspective and a digital business strategy to
understand and develop a digital strategy and/or a digital transformation strategy.
5.2.3 Implementation
As the term digital transformation refers to an organizational change that aims to utilize digital technology
and adapt to a digital landscape (Verhoef, et al., 2019; Saarikko, et al., 2020), the part of the digital
transformation that can be considered implemented, is the creation of the task teams and the
organizational setup around them. As described above, the strategy can be usefully described as being
what Tekic and Koroteev (2019) describe as a technology led digital transformation.
Discarding a business model led digital transformation and a digital business strategy comes at some risk
and vulnerability. Tekic and Koroteev (2019) note on the risk that the technology led digital transformation
strategy runs the risk of delivering false positives and that changes and adoption is mistaken for actual
progress in the transformation. Similarly, Lipsmeier, et al. (2020) note that the bottom-up approach may
lead to heterogeneity of solutions as well as a focus on incremental changes causing suggested projects
and solutions to fail in delivering synergy effects. These concerns seem pressing, as synergy effects and a
wide perspective seems imperative, to meet the challenges in building a cohesive and comprehensive
digital production system. This calls the task teams role into question. Tekic and Koroteev (2019) as well
as Lipsmeier, et al. (2020) emphasize the importance of not limiting the scope to daily problems. Hence,
it seems warranted to claim that either the task team or management, must focus some work towards
creating a holistic and strategic view and objective that goes beyond continuous improvements. The
perception of the local director ME that the employees should deliver solutions might be warranted. But
in a technology driven era, that holds a promise for disruption, perhaps it is the role of management to
34
consider the role of the task team and question if the employees and task team are making significant and
sufficient progress, rather than just progress.
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6. Discussion
In this chapter, strategy as a linkage between business value and digital transformation is presented,
leading to a motivation and a proposed answer to the stated research question.
As it is presented in the Frame of Reference, a digital strategy can be considered an inclusive term that
envelope both digital business strategy and digital transformation strategy.
Business value is directly linked to Digital Business Strategy. Given the presentation of challenges and
envisioned structural changes that Industry 4.0 poses in order to achieve value and strategic benefits, we
consider statements about the utility of a strategic and holistic approach warranted and doubt that any
major benefits will be achieved with a narrow focus on current processes and issues. Partly because of
the comprehensive nature of digital transformation and also because it seems that a just cost/benefit
ratio that motivates a disruptive project, is improbably achieved without understanding the business
impact through a holistic and transfunctional perspective. As is also highlighted, the transformation is
dependent on size, sector and activities which indicates that only resolving to scanning the external
environment for opportunities and practices to adopt, is probably futile as the solutions to problems are
likely highly specific and penetration into the organization is complex and comprehensive.
Business value is indirectly linked to Digital Transformation Strategy in the sense that you need to develop
your organization and absorptive capacity, as well as changeability to create a competent Digital Business
Strategy, Digital Transformation Strategy and/or Digital Strategy as well as to realize it. Naturally, a digital
transformation strategy determines the success of a digital transformation. But following that logic, any
event associated with an organization is directly linked and the statement becomes meaningless. The idea
here of indirectly and directly linked should be understood in a specific manner. A digital business strategy
is deliberately and solely aimed and formulated to extract business value creating a direct linkage. A digital
transformation strategy is deliberately aimed at the activities that facilitate organizational change that
leads to business value. It is either subject to a digital business strategy or to experimentation with
technology in order to innovate and find new opportunities. Hence, we argue that a digital transformation
strategy is indirectly linked through uncertain development and innovation activities or through a digital
business strategy.
6.1 How to formulate a strategy for digital transformation that supports manufacturing
business value?
The changes in the global digital landscape and the emergence of the fourth industrial revolution seem to
show the potential of disrupting the manufacturing industry. It should therefore be considered a business
goal to stay relevant and competitive under the new external circumstances and industry- or sector-
specific challenges and needs that come with it. In order to strategize based on that commitment, we
suggest the following elements to align the strategic initiative with business value.
Build an absorptive capacity in the organization and develop competence and ICT literacy. We argue
that as soon as the commitment is made to a business goal that is based on being relevant in the future
digital landscape, the starting point should be to develop an absorptive capacity. This is imperative to be
able to develop an understanding of the impact that the fourth industrial revolution will have on the
organization and how it can be utilized and create advantage.
36
Develop a digital business strategy or at least a business model perspective. We argue that, based on
the presented areas of applications, challenges, design principles and the complex interrelation of the
digital technologies within the Industry 4.0 construct, as well as the uncertainty, risk and upfront cost of
investments required for technology adoption and digital transformation, the statements about the
necessity for a holistic and strategical perspective are warranted. A Digital Business Strategy should at
least be a work in progress by some function of the firm as well as a commitment to develop it over time.
Even if the work is conducted with an awareness of the immaturity of the results and its only function is
to support the evaluation of projects to distribute funding.
Evaluate the maturity of the organization along dimensions that are relevant to the organization and
the digital landscape. The evaluation of maturity and the practice of reviewing the progress and state of
the organization seem to be a validated as it is corroborated both by empirical findings and in literature.
The idea of creating a gap analysis based on a current state evaluation and a desired state formulation, to
determine what activities and actions to prioritize is most likely a useful practice. However, it does not
seem to be a substitute for developing the activities and performing them, but rather systematically clarify
their priority. Therefore, the proposed evaluation tools should probably be used multiple times
throughout the journey, as an understanding evolve, about what the current state and desired state is
and should be.
Develop a roadmap and a digital transformation strategy that is based on the maturity evaluation and
the digital business strategy. Adhering to the previous points, this step becomes implicit from granting
the validity of the maturity evaluation practice and the digital business strategy. The necessity of
prioritizing activities and creating a time plan is corroborated by the literature and can perhaps even be
assumed. As previously stated, gap analysis from the maturity evaluation tool and a digital business
strategy are both powerful tools to evaluate the level of priority for activities. Furthermore, they do not
exclude each other but rather seem to mutually create an exhaustive perspective on the time plan for the
transformation.
Iterate and revisit sequences of the process and continuously reassess the commitments and axioms as
maturity and competence grows within the organization. Based both on the literature study and the
empirical findings, we question the perception of digital transformation as a linear and stepwise model.
While we consider elements and components of the digital transformation from literature to be relevant
and descriptive, we suggest that developing strategies needs to be understood as a dynamic process and
that iteration, reevaluation, and redesigning strategies and roadmaps should be a practiced continuously.
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7. Conclusion
This chapter will conclude the findings from the analysis and discussion which has elaborated from the
frame of reference and Empirical Evidence. To finalize the thesis, future recommendations are presented.
The contribution of this thesis is firstly the recognition that Industry 4.0 adoption is a digital
transformation as well as a redesign of the business model. Secondly, this thesis identifies distinguished
levels of strategy, their different purposes, as well as how they interrelate with each other and produce
business value from digital transformation directly and indirectly. Thirdly, this thesis, by its empirical
findings and analysis of the distinguished definitions can show that the construct of a digital
transformation proposed by for example Zaoui and Souissi (2020), should not be understood as a linear
process. We suggest that digital transformation starts with implementation of actions in order to develop
an organization that is able and competent enough to perform evaluation of maturity and develop a digital
business strategy as well as a digital transformation strategy. Due to the complexity of the challenge and
the lack of knowledge around efficiently transforming an organization to become digital and ”smart”,
building changeability and absorptive capacity should be a parallel process to developing a guiding
strategy.
The comprehensiveness of the literature review and the complementary empirical study have enabled
triangulation of information and different concepts and ideas have, in analysis, been displayed to support
theoretical claims about the necessity for a holistic business model perspective to understand the
business value of digital transformation as well as the construct of a digital transformation strategy and
roadmap. It could be seen as a semantical argument as the definition of digital transformation is defined
as an organizational change or organizational adaption, rather than an adoption of digital technology.
However, we claim to have been true to the literary consensus about the definitions presented. It has also
been shown how approaches that focus on the topic that we would define as technological adoption, is
likely to fail in delivering the benefits described and envisioned when handling the complexity and
comprehensiveness of the fourth industrial revolution and its implications for manufacturing companies.
We believe that the findings of this thesis have significant value for both practitioners and researchers. As
described and repeated, practitioners are urged to consider a business model perspective when dealing
with digital transformation and understand that the effects of digitization and digitalization initiatives are
likely to have transfunctional effects if considered and utilized properly. Furthermore, increasing
absorptive capacity to develop a competent digital transformation initiative as well as to gain access to
valuable data channels to thrive in the future digital landscape, should be a top priority parallel to creating
strategic direction.
For researchers and academia, this paper offers a comprehensive view of the topics of digital
transformation and Industry 4.0. The terminology and definitions presented can be useful in order to
identify specific areas of research and their interrelations with other topics. Furthermore, the
triangulation of different pieces of information spread across the literature has enabled a strengthening
of some theoretical arguments. As argued, a theoretical view of the phenomenon of digital transformation
can not be practically reduced to a linear process of evaluation, strategic orientation and implementation.
Rather, these phases within the construct seem to occur, and arguably should occur iteratively and
parallelly. However, while themes, ideas and results within this thesis seem to be corroborating each
other, they can not be considered evidence that show what actions and ideas create success and business
value as the fourth industrial revolution emerges.
38
For the case company, the findings are relevant as the choice between a business model led and
technology led digital transformation changes the role of the task teams significantly and the expectations
from management should be examined. If a technology led digital transformation is consciously
employed, then perhaps the scope should be expanded beyond immediate and short-term business
impact. If a business model led digital transformation is considered and employed, then who should
develop and design the new business model and the digital business strategy? Is the task team equipped
for this task in regard to simple resources such as time and authority? How should the team evaluate their
position and work to coordinate with other plants and sites within the company?
While this study has provided a view of the case company´s digital transformation initiative, the strategies
are limited to being studied by what is operationalized and communicated throughout the organization.
This limitation is meaningful as many of the activities and recommendations throughout literature are
often aimed at management activities and the strategic work that is done within and by top management.
We therefore recommend that future studies within the field involve top management participation.
39
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9. Appendices
Appendix 1 – Framework by Correani, et al. (2020)
Appendix 2 – Framework for digital strategy (Lipsmeier, et al., 2020)