Effects of Digitalization in the Steel Industry.pdf

I
DEGREE PROJECT IN INDUSTRIAL ENGINEERING AND
MANAGEMENT,
SECOND CYCLE, 30 CREDITS
STOCKHOLM, SWEDEN 2020
Effects of Digitalization in Steel Industry
Economic Impacts & Investment Model
JENNY CHENG
JOSEFIN WESTMAN
KTH ROYAL INSTITUTE OF TECHNOLOGY
SCHOOL OF INDUSTRIAL ENGINEERING AND MANAGEMENT
www.kth.se

III
Economic Impacts & Investment Model
by
Jenny Cheng
Josefin Westman
Master of Science Thesis TRITA-ITM-EX 2020:280
KTH Industrial Engineering and Management
Industrial Management
SE-100 44 STOCKHOLM

IV
Effekter av digitalisering i stålindustrin
Ekonomisk påverkan & investeringsmodell
av
Jenny Cheng
Josefin Westman
Examensarbete TRITA-ITM-EX 2020:280
KTH Industriell teknik och management
Industriell ekonomi och organisation
SE-100 44 STOCKHOLM

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Master of Science Thesis TRITA-ITM-EX 2020:280
Jenny Cheng
Josefin Westman
Approved
2020-06-12
Examiner
Hans Lööf
Supervisor
Gustav Martinsson
Commissioner
SSAB
Contact person
Abstract
The awareness and interest for digitalization have increased tremendously during recent years.
However, many companies are struggling to identify the economic benefits and often face long
payback time and large initial investment costs. This study aims to assess the potential economic
effects from digitalization projects in the steel production industry. The study begins by
elucidating central concept like, digitization, digitalization, digital transform and the link between
digitalization and automation. Furthermore, the study identifies effects of digitization at
production level from an internal efficiency perspective, based on existing literature. On this basis,
an investment tool for digitization projects has been developed, consisting of three different
analyzes; a level of automation analysis, a quantitative analysis and a qualitative analysis.
To continue, the investment model has been applied to a potential investment of a smart automatic
crane. The results from all three analyses provided positive results and incentives to initiate the
project. As a result of poor data collection and rigid data, only one effect could be accounted for
in the quantitative analysis, which generated a net present value of nearly 12 MSEK over a ten-
year period. The most critical parameter proved to be the timing of initiating the project.
Key Words: digitalization, automation, steel production, level of automation, discounted cash
flow, multicriteria analysis

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Examensarbete TRITA-ITM-EX 2020:280
Effekter av digitalisering i stålindustrin
Jenny Cheng
Josefin Westman
Godkänt
2020-06-12
Examinator
Hans Lööf
Handledare
Gustav Martinsson
Uppdragsgivare
SSAB
Kontaktperson
Sammanfattning
Medvetenheten och intresset för digitalisering har ökat enormt under de senaste åren. Många
företag kämpar emellertid med att identifiera de ekonomiska fördelarna och möter ofta långa
återbetalningstider och stora initiala investeringskostnader. Denna studie syftar till att utvärdera
de potentiella ekonomiska effekterna av digitaliseringsprojekt i stålproduktionsindustrin. Studien
börjar med att redogöra för vad digitalisering är samt kopplingen mellan digitalisering och
automation. Vidare identifierar studien effekter av digitalisering på produktionsnivå ur ett internt
effektivitetsperspektiv baserat på befintlig litteratur. Baserat på detta har ett investeringsverktyg
för digitaliseringsprojekt utvecklats, bestående av tre olika analyser; en automationsgradsanalys,
en kvantitativ analys och en kvalitativ analys.
Investeringsmodellen har dessutom tillämpats på en potentiell investering i form av en smart
automatkran. Resultaten från samtliga tre analyser var positiva och utgjorde incitament till att
initiera projektet. Som ett resultat av bristande datainsamling och ostrukturerade data kunde
kostnadsbesparingen från endast en effekt redovisas i den kvantitativa analysen, vilken genererade
ett nuvärde på nästan 12 MSEK under en tioårsperiod. Den mest kritiska parametern visade sig
vara tidpunkten för att implementera projektet.
Nyckelord: digitalisering, automation, stålproduktion, automationsgrad, ”discounted cash flow”,
multikriterieanalys

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Foreword
This Master Thesis report was conducted by Jenny Cheng and Josefin Westman at the
Royal Institute of Technology (KTH) at the department of Industrial Engineering and
Management, Stockholm, Sweden. The authors are both majoring in Industrial
Engineering and Management but with different masters respectively; financial
mathematics and sustainable power production. The idea was to combine the diversified
competencies and create an outlet for both management and finance. Furthermore, this
Master Thesis work was carried out in collaboration with a European special steels
company over a five-month period during spring 2020.
Acknowledgments
Firstly, we would like to thank our supervisor at KTH, Gustav Martinsson, Associate
Professor in Financial Economics, for always being accessible when we have been in need
of support and feedback; both regarding advise on formalities but also in logical reasoning
and ensuring the academical level of the work.
Secondly, we would also like to express our gratitude to everyone at the commission
company who has been involved in this project, one way or another. Especially, we want
to thank our supervisor; thank you for taking your time to have continuous meetings with
us and providing useful data. It has been a pleasure to get to know you and the company,
and this work would never have been completed without you.
Last but not least, we want to send a great thank you to our friends and classmates at KTH
who supported us not only throughout this period, but all five years at KTH, making every
day a bit more enjoyable.
We are incredibly grateful for everything you have contributed to enable or facilitate this
journey!
Jenny Cheng & Josefin Westman
June 2020
Stockholm, Sweden

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List of Abbreviations
AHP Analytical Hierarchy Process
AI Artificial Intelligence
CF Cash Flow
DCF Discounted Cash Flow
FTE Full-time Equivalent
H2M Human-to-Machine
IoS Internet of Services
IoT Internet of Things
ICT Information and Communication Technologies
IRR Internal Rate of Return
IT Information Technology
KET Key Enabling Technologies
KPI Key Performance Indicator
LoA Level of Automation
MCA Multicriteria Analysis
M2H Machine-to-Human
M2M Machine-to-Machine
NPV Net Present Value
OAT One-at-the-time
OED Oxford English Dictionary
PB Payback Period
ROI Return on Investment
RRR Required Rate of Return
SME Small and medium-size enterprise
SoPI Square of Possible Improvements
TTM Time-to-market
WACC Weighted Average Cost of Capital

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Table of Contents
1 Introduction .............................................................................................................. 1
1.1 Background................................................................................................................... 1
1.2 Problematization .......................................................................................................... 3
1.3 Purpose and Research Questions................................................................................ 3
1.4 Delimitations................................................................................................................. 4
1.5 Outline of Thesis........................................................................................................... 5
2 Method....................................................................................................................... 7
2.1 Research Design ........................................................................................................... 7
2.2 Research Method.......................................................................................................... 7
2.3 Data Collection ............................................................................................................. 8
3 Literature Review..................................................................................................... 9
3.1 Digital Definitions......................................................................................................... 9
3.1.1 Digitization.............................................................................................................................. 9
3.1.2 Digitalization......................................................................................................................... 10
3.1.3 Digital Transformation.......................................................................................................... 10
3.2 Automation and Digitalization.................................................................................. 10
3.3 History of Industrial Revolution............................................................................... 12
3.3.1 Industry 4.0............................................................................................................................ 14
3.4 Steel Industry.............................................................................................................. 14
3.4.1 Production Process................................................................................................................ 16
3.3.2 Current State.......................................................................................................................... 17
3.5 Assessment Methods .................................................................................................. 19
3.5.1 LoA Framework.................................................................................................................... 19
3.5.2 Discounted Cash Flow .......................................................................................................... 22
3.5.3 Multicriteria Analysis............................................................................................................ 25
4 Effects of Digitalization.......................................................................................... 28
4.1 Approach..................................................................................................................... 28
4.2 Quantitative Effects ................................................................................................... 31
4.2.1 Quantified Quantitative Impacts ........................................................................................... 36
4.3 Qualitative Effects...................................................................................................... 37
5 Investment Model ................................................................................................... 40
5.1 Conceptual Overview................................................................................................. 40
5.2 LoA Analysis............................................................................................................... 41
5.3 Quantitative Analysis................................................................................................. 42
5.3.1 Initial Investment Data.......................................................................................................... 44
5.3.2 Cost saving factors ................................................................................................................ 45
5.3.3 Discount rate ......................................................................................................................... 49
5.3.4 Sensitivity Analysis............................................................................................................... 50
5.4 Qualitative Analysis ................................................................................................... 51
6 Application of Investment Model.......................................................................... 58

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6.1 Project “Smart Crane”.............................................................................................. 58
6.2 LoA Analysis............................................................................................................... 58
7 Results...................................................................................................................... 67
7.1 LoA Analysis............................................................................................................... 67
8 Analysis of Results.................................................................................................. 75
9 Discussion ................................................................................................................ 77
9.1 Discussion of Method................................................................................................. 77
9.2 Reliability & Validity................................................................................................. 77
9.3 Generalizability .......................................................................................................... 78
10 Conclusion........................................................................................................... 79
10.1 Answer of Research Question 1 ................................................................................ 79
10.4 General Conclusion.................................................................................................... 80
10.5 Recommendation & Future Research...................................................................... 81
References ....................................................................................................................... 82
Appendix A – Investment Model................................................................................... 86

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List of Figures
Figure 1 Research Process.............................................................................................................................. 8
Figure 2 History of Industrialization............................................................................................................. 13
Figure 3 Industry Chart ................................................................................................................................ 15
Figure 4 Steel Production Process ................................................................................................................ 17
Figure 5 Mechanical-Information-LoA Diagram Showing SoPI.................................................................... 22
Figure 6 Levels of Digitalization ................................................................................................................... 29
Figure 7 Viewpoints for Analyzing Digitalization Impact ............................................................................. 30
Figure 8 Classification of Effects .................................................................................................................. 31
Figure 9 Summary of Quantitative Effects................................................................................................... 35
Figure 10 Summary of Qualitative Effects.................................................................................................... 39
Figure 11 Conceptual Overview of Investment Model ................................................................................. 41
Figure 12 Overview of Initial Investment Data............................................................................................. 60
Figure 13 Overview of Maintenance Savings............................................................................................... 61
Figure 14 Overview of Productivity Savings................................................................................................. 62
Figure 15 Overview of Personnel Savings .................................................................................................... 63
Figure 16 Overview of Quality Savings........................................................................................................ 63
Figure 17 Overview of Downtime Savings.................................................................................................... 64
Figure 18 LoA Chart over Investment Potential ........................................................................................... 68
Figure 19 SoPI Results.................................................................................................................................. 69
Figure 20 Saving Potential ........................................................................................................................... 71
Figure 21 Savings Per Factor........................................................................................................................ 71
Figure 22 Savings Pie Chart.......................................................................................................................... 71
Figure 23 Quantitative Sensitivity Analysis Result ....................................................................................... 72
Figure 24 Discount Rate Tornado Diagram.................................................................................................. 73
Figure 24 Qualitative Sensitivity Analysis Results ........................................................................................ 74
List of Tables
Table 1 Levels of Automation Reference Scale ............................................................................................ 20
Table 2 Summary of Quantified Quantitative Impacts.................................................................................. 37
Table 3 Overview of Qualitative Analysis..................................................................................................... 66
Table 4 LoA Mapping.................................................................................................................................... 67
Table 5 Quantitative KPIs Results ................................................................................................................ 70
Table 6 Sensitivity Analysis Summary........................................................................................................... 73
Table 7 Qualitative Analysis Results............................................................................................................. 74

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1 Introduction
This chapter provides the background information about the research area and aims to
increase the understanding of the problem. The purpose of the study is explained, and
research questions defined, followed by a presentation of delimitations and the outline of
the thesis.
1.1 Background
Rapid changes in the digital technology is revolutionizing the industries and the society
(Snabe Hagemann & Weinelt, 2016). The impact of digitalization is major, and many
companies believe it is vital to follow the digitalization trend in order stay competitive in
terms of effectiveness, growth and prosperity (Vernersson et al., 2015). There are several
consequences, but also possibilities, followed by the industrial digital transformation.
Today we are currently entering a new technological paradigm, the next industrial
revolution, Industry 4.0, where we transform towards an industrial internet with smart
devices, higher flexibility and larger applications (Vernersson et al., 2015).
The steel industry is no exception and is undergoing tremendous digital transformations
today, even though it seems like the steel industry in many aspects lag behind other
industries when it comes to digitalization. The steel industry alone accounted for 3.8% of
the annual global GDP in 2017 and contributed to over 6 million employments the same
year (Oxford Economics, 2019). The industry is both capital and human capital intensive,
resulting in certain rigidity. Therefore, it seems only reasonable that transformations within
steel industry would require more time. On the other hand, large corporations hold some
benefits over small and medium-size enterprises (SMEs), where they can utilize scale
advantages and afford knowledgeable IT specialist to accelerate the transformation. In
order to reach higher production efficiency, more competitive products and better business
models, Key Enabling Technologies (KET) such as; Artificial Intelligence (AI), Internet
of Things (IoT), Internet of Services (IoS), Mechatronics and Advanced Robotics, Cloud
Computing, Cybersecurity, Additive Manufacturing and Digital Twin has been or will be
used. These KETs together build the foundation of digitalization, which in turn is the core
of Industry 4.0 and has become more popular than ever. (Murri et al., 2019)

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The importance of digitalization and Industry 4.0 are well known and the technological
shift in the industries is inevitable. Bill Ruh, Chief Executive Officer, GE Digital, USA
believes it is a now or never chance to act (Snabe Hagemann & Weinelt, 2016), but the
question is what the benefits from these actions are. It is rather easy to find both articles
and other studies dealing with the subject digitalization. However, it is difficult to find
studies that examine the economic impacts of digitalization and more specifically the
economic impacts of digitalization in steel industry. A big contributing factor to this fact
is that it is hard to identify the economic impacts from digitalization projects. Projects are
often very costly and require large capital investments while it is expected to meet short
payback requirements set by stakeholders. (Murri et al., 2019) According to the European
Steel Skills Agenda, the steel industry faces several barriers; difficulty in integrating new
technologies and processes among site workers, a strong age gap between current
employees and prospective employees creates knowledge transfer issues and lack of
investment in training and education from steelmaking companies as well as an insufficient
amount of in-house training provided by companies (Henriette et al., 2015).
Digital transforms affect the entire organization including the business model, operational
process and both internal and external stakeholders (Stolterman & Fors, 2004). Even
though the challenges are many and it is shown that technical barriers are less crucial than
organizational issues (Branca et al., 2020), digitalization is still something highly valued.
Companies must try to find ways to quantify the benefits of these kind of projects, but if it
cannot be done, the companies should ask themselves what they lose by not adopting to
the new technological shift rather than what they gain (Bossen & Ingemansson, 2016).
To conclude, production managers often foresees high potentials with new digital
solutions, while management is struggling to identify potential profit, preventing rapid
digitalization progress. Therefore, the desire for economic reason behind digitalization is
undeniably great in most industries. Increased popularity and utilization of digital
technologies leads to an incentive for several well-known journals and consultancy
companies to explore the topic and address the economic benefits it provides. However,
by studying the existing literature it can be confirmed that quantification of digitalization
in monetary terms is extremely difficult. For example, large initial investments and long
payback times puts spanners in the works. Hence, there is a great need of studies that aim
to concretize and quantify the economic effects of digitalization.

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1.2 Problematization
There is no doubt the majority has a strong belief that digitalization has a net positive effect
on the entire organization. The unlimited number of reports, case studies and articles
addressing positive impacts of digitalization creates a thrive for companies to follow the
trend. However, papers dealing with quantifying economic aspects of digitalization are
scarce. Furthermore, studies on current state of digitalization in steel industry in particular
are limited as well. Therefore, researchers and steel companies find it difficult to quantify
the actual effects of digitalization.
Furthermore, the notation digitalization is widely used in everyday language, contributing
to a confusion regarding what it actually comprises. Therefore, it is important to factorize,
concretize and specify the definition of digitalization in order to estimate the potential
economic impacts. The quantification of these impacts is obstructed by the uncertainty of
possible aggregated effects enabled by extension projects as well as the difficulty to
identify synergies from future integration of subprojects. As we currently are in the middle
of the digital transformation, the opportunity to compare potential outcomes with historical
data is highly limited and further increasing the level of difficulty.
At last, it is proven that digitalization projects have both long pay back times and contribute
to many soft term consequences, implying even higher uncertainty in calculations. For all
reasons stated above, it seems difficult to quantify obvious impacts and to address less
prominent varying soft term factors. This leads to financial uncertainties and difficulties to
justify the implementation of these projects.
1.3 Purpose and Research Questions
The overall purpose of this paper is to partly solve few of the obstacles digitalization
brings, described in the background and problematization sections. This study aims to
identify potential impacts of digitalization within the special steels industry, in order to
address relevant saving opportunities and finally draw strategic conclusions. We aspire to
develop an investment model where the relationship between future digitalization projects
in a delimited steel manufacturing process and different cost saving factors will be
carefully examined through the lens of economic KPIs and other qualitative metrics. The

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intention of the model is to be used as a tool to help steel companies make well-grounded
digitalization investment decisions, taking not only the most obvious but all possible
effects into account.
With the problematization and purpose as a foundation, the following research questions
will be considered:
Q1: What are the potential impacts of digitalization in a delimited steel production section?
Q2: How can potential impacts from digitalization projects be quantified?
Q3: What potential cost savings can be expected from digitalization projects?
1.4 Delimitations
This report mainly focuses on digitalization projects at Process level which will be studied
from an Internal Efficiency perspective, based on the two frameworks developed by
Tihinen et al. (2017). Digitalization is implemented at Process level when it facilitates the
adoption of digital tools and streamlining processes by reducing manual steps. Process
level is thereby directly connected to the production department of a firm. When
digitalization is studied from an Internal Efficiency perspective, it is analyzed with regards
to how it improves the ways of working through digital means and by re-planning of
internal processes, see section 4.1 for further explanations. Digital implementations at any
other levels will not be considered, and projects will mainly be evaluated from this certain
perspective.
The investment model developed in this paper have been designed for valuation of
potential digitalization projects in a delimited production process within the special steels
industry. Projects that change or affect the organization in its whole and projects only
utilizing digital technology without generating a higher level of digitalization are not
considered as a part of the scope.
The intention of the model is primarily to be appropriate in evaluating digitalization
projects and not necessarily projects in general, such as projects only related to e.g. lean
production or sheer automation projects.

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1.5 Outline of Thesis
This thesis consists of ten chapters, which are briefly presented below.
1. Introduction: This chapter gives the background information to the research area and
creates an understanding of the problem. The purpose of the study is explained, and
research questions defined followed by a presentation of delimitations and the outline of
the thesis.
2. Method: This chapter presents the methodological approach and method chosen. A
conceptual visualization of the research process is given in order to make sense of the
logics and connections of different parts. An exposition of how data is collected and
utilized is provided as well.
3. Literature Review: This chapter consists of a literature review comprising relevant
knowledge for the subject of the thesis. Necessary concepts are defined and the background
to digitalization and its origin is given. Furthermore, basics of the steel industry are
explained and useful frameworks for the investment analysis are presented.
4. Effects: This chapter explains the approach from which effects of digitalization are
identified and describes the underlying frameworks. Potential effects are identified in the
existing literature based on the identified approach.
5. Investment Model: This chapter contains a presentation of how the investment model
is developed based on three analyses; LoA Analysis, Quantitative Analysis and Qualitative
Analysis. The model is built based on findings from the literature review together with
insights from the case study company, a European special steels producer.
6. Application of Investment Model: This chapter is directly referring to the case study
conducted at a European special steels company, aiming to answer the research questions
of this study. One specific potential investment is considered, and all data presented in this
section is collected at the case study company. Moreover, a detailed explanation on how it
is supposed to be used is given.

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7. Results: This chapter provides a presentation of the results from all three different
analyses of the investment model. The main results are shown in terms of NPV, IRR, ROI,
PB, SoPI and qualitative indexes. Results from sensitivity analyses are also presented.
8. Analysis of Results: This chapter is an overall analysis of the results in chapter 7, with
theoretical findings in the literature review as a starting point. Results are being
triangulated in order to broaden the understanding of their implications.
9. Discussion: This chapter contains a discussion and argumentation of the research
method used in this thesis. Furthermore, the reliability, validity and generalizability of the
model, as well as the thesis in general is discussed.
10. Conclusion: This chapter answers the stated research questions of the thesis and
explains how answers were arrived at. It also provides a summary of the main findings on
a higher level as well as a recommendation for producing companies and suggestions for
future research.

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2 Method
This chapter presents the methodological approach and method chosen. A conceptual
visualization of the research process is given in order to make sense of the logics and
connections of different parts. An exposition of how data is collected and utilized is
provided as well.
2.1 Research Design
This research is primarily descriptive in essence, as it attempts to “determine, describe or
identify what is” rather than why something is or how it came to be (Ethridge, 2004). We
aim to collect data and information that enables a better and more complete description
about the impacts of digitalization projects. Descriptive research is effective for analyzing
non-quantified topics and issues, and it also gives opportunity for integrating qualitative
and quantitative methods of data collection, where case-studies are one commonly used
data collection method. Furthermore, a deductive approach is taken for conducting this
descriptive research, meaning that reasoning goes from the general to the particular. Using
a deductive approach is advantageous for explaining causal relationships between concepts
and variables as well as for measuring concepts quantitively. Due to the nature of the
chosen field of study, this approach is considered suitable for appropriately address the
stated purpose and research questions. (Ethridge, 2004; Fox, 2007)
2.2 Research Method
The research method can be seen as a systematic roadmap to how research is planned to
be conducted. The project will be conducted based on a mixed method, where the aim is
to combine a qualitative single case study with quantitative findings in the literature to
fulfill the stated purpose. Data will be collected using both existing literature as well as the
single case study to iteratively develop a quantitative investment model for evaluation of
digitalization projects.
The first part of the study consists of a qualitative pre-study where information and data
will be collected by conducting a literature review. This literature review will consist of
three main parts; defining relevant concepts and their origins, explaining the steel industry
and identifying successfully used frameworks for evaluation of projects. Areas of our

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particular interest are for instance digitalization, digitization, digital transforms,
automation and steel industry production processes. In addition, existing literature will be
examined in order to identify potential effects of digitalization.
The aim of the single case study is twofold; firstly, one aim is to identify additional factors
to include in the model that were not covered by the literature, by observing the production
line. Secondly, primary data will be provided by the company in the case study, which will
be used for verification of the investment model and for applying the model on a real case
in a specific subprocess in production.
The merged data collection from the literature review and case study will form the
foundation of our study and the quantitative investment model. After identifying crucial
economic consequences of digitalization, the investment model will be built in the software
Excel. The outcome of the model when applied in the case study situation shall be carefully
examined, and a sensitivity analysis will be established. Results will be compared with the
literature and analyzed so that useful insight and conclusions can be drawn. Study of
literature and model construction will be an iterative process where all our findings should
be anchored in the literature and not only based on hypotheses from the case study
company. A conceptual overview of the research process can be found in figure 1 below.
Figure 1 Research Process
2.3 Data Collection
The collection of data will be derived from two channels; secondary data from existing
literature and primary data from the case study company. Primary data will be collected
by conducting field studies and by having continuous meetings, mainly with the system
development manager at the case study company who is highly involved in the company’s
ongoing digital transformation. In addition, a few semi-structured interviews with people
working with topics that are relevant for fulfilling the goal of this thesis may be conducted,
for instance in order to collect necessary initial input data to the investment model.

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3 Literature Review
This chapter consists of a literature review comprising relevant knowledge for the subject
of the thesis. Necessary concepts are defined and a background to digitalization and its
origin is given. Furthermore, basics of the steel industry are explained and useful
frameworks for the investment analysis are presented.
3.1 Digital Definitions
Digitization, digitalization and digital transformation are closely related concepts and often
interchanged in a way that shortchange the power and importance of digital transformation.
The definition of these digital concepts is scattered and diffuse. These words are
wrongfully used as synonyms in everyday language and depending on whom you ask the
answer of the definitions will vary. Most people are confident when speaking about
digitization and digitalization since the notations are frequently used in both the academic
world and everyday life. However, the close association is triggering confusion and not
even the researchers agree upon a standardized definition. Thus, the unclear definition
could be a smaller contributing factor to why many companies struggle to see the potential
and benefits the transformations really brings. The truth is that neither of the three terms
are synonyms, but indeed very closely related.
3.1.1 Digitization
Most people agree upon the definition of digitization established by the Oxford English
Dictionary (OED) and the straightforward definition is “…the conversion of analogue data
(esp. in later use images, video, and text) into digital form”. (Oxford English Dictionary,
2016). The process of digitizing could for an example be the conversion of handwritten
papers to digital documents or conversion of LP and VHS to Spotify and Netflix. In other
words, digitization could also be defined as “the ability to turn existing products or services
into digital variants, and thus offer advantages over tangible products” (Stolterman & Fors,
2004). The last definition is closer related to digitalization since the conversion of a good
or service to a digital variant may be argued to change the whole business model for some
companies e.g. Netflix, HBO etc. However, the aim of a digitization project is rarely to
change the value proposition or the business model in order to create new revenue streams
and it does not include the organizational transformation needed to adopt to the new
digitized solution.

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3.1.2 Digitalization
The OED states that digitalization is “the adoption or increase in use of digital or computer
technology by an organization, industry, country, etc.” (Oxford English Dictionary, 2016).
Digitalization it is not only the digital technology in itself, where information is
represented in bits, it is “the use of digital technologies in order to change a business model
and to provide new revenue and value producing opportunities.” (Bloomberg, 2018). The
core of digitalization also includes the digital skills and reorganization needed to
implement a new digital solution. Digitization is a prerequisite for digitalization and plays
a key role in such processes. For an example, the conversion from manual manufacturing
to smart manufacturing is a digitalization process where the employees need to change
from working with physical equipment to managing a computer program and handle new
problems like cybersecurity and transparency.
3.1.3 Digital Transformation
Digital transformation is far beyond digitization and digitalization. According to
Stolterman and Fors (2004) digital transformation is “…the changes that the digital
technology causes or influences in all aspects of human life.” (Björkdahl et al., 2018).
Another literature states that digital transformation refers to “the customer-driven strategic
business transformation that requires cross-cutting organizational change as well as the
implementation of digital technologies” and cannot be implemented as a project. A digital
transformation often includes several digitalization projects at the same time. (Bloomberg,
2018) The organization should thrive to restructure the whole organization in order to more
effectively benefit from data, create new values and finally acquire some of the economic
value that it has created (Fasth et al., 2008). Only when the norm is adjusted to the new
digital technologies and work ethics, the transformation is considered complete.
3.2 Automation and Digitalization
Just as with digitalization, automation is another concept that have been given several
definitions over the years. Another word that is often used when it comes to automation is
robotization, which in this report will be used synonymously. Cambridge University
Dictionary defines automation as “the use of machines and computers that can operate
without needing human control” (Cambridge University Press, 2020). However, the
definition by Fasth et al. (2008) can be found more universal, defining automation as “a

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technology by which a process or procedure is accomplished without human assistance”.
This definition allows not only machines and computers to be a part of automation, but
also communication systems and other digital systems that help reduce the need of human
assistance in a process. Consequently, digitalization is an important tool in order to
increase the level of automation in production systems
Due to the definition, automation is not only about transforming manual processes to
automatic ones but also about transforming them into completely autonomous systems
with no need of human assistance, which is what defines a 100 % automatic system or
process. However, the main purpose with automation is to achieve increased system
efficiency, in that regard 100 % automation is not always the best solution. The aim is to
target most appropriate level of automation in each manufacturing situation, rather than
the highest level possible, as a certain mix of machines and human interaction may be the
more efficient solution. (Ten & St, 2015; Tihinen et al., 2017) It may sound surprising that
the level of automation can be “too high”. In fact, excessive levels of automation may
result in weak system performance, (Endsley and Kiris 1995; Parasuraman et al. 2000) as
a result of too complex processes. Complex processes are often more vulnerable to
disturbances, which might decrease the overall production efficiency (Ylipää 2000). It may
also be that production tasks are too unstructured to be fully automized. On the other hand,
if the level of automation is too low production efficiency is not maximized. A low level
of automation could also cause working injuries and sick leaves.
An arrangement where devices and components communicate through a continuous flow
of information is commonly called Machine-to-machine (M2M) interaction, which is
appropriate when tasks benefit from automation. Furthermore, in cases where higher levels
of automations are inappropriate and human interaction is preferable, the arrangement is
called Human-to-Machine (H2M) collaboration. In addition, research efforts are invested
in so-called Machine-to-Human (M2H) communication or “collaborative robotics”. Here,
complex and unstructed manufacturing tasks are performed in collaboration between
advanced specially designed robots and humans. The goal with these highly advanced
technologies is to enable automation for tasks that earlier was preferred to be performed
totally manual. (Rojko, 2017)

12
A commonly used framework for measuring the level of automation is the LoA framework,
which is described in more detail in section 3.5.1. The LoA framework evaluates the level
of automation based on two grounds; one mechanical and one informational, where the
informational part is closely related to digitalization.
3.3 History of Industrial Revolution
To create a better understanding of the concept of digitalization and its impacts it is
important to derive all the way back to its origin. The source of the contemporary concept
can be derived to centuries ago and started with the first industrial revolution. Some basic
components of digital transformation are machinery, electricity, automation and
knowledge. The process from manual manufacturing by manpower to smart mass
production executed by smart machines, operating using own mental power is over two
and a half decade long. The industrial revolution did not only change how companies
produced goods, how people lived and how people defined political issues, it basically
changed the whole world. (Rojko, 2017)
The definition of industrial revolution can be divided into two parts. First, industrial
revolution incudes a large collection of transformations with origin in radical
technological innovations. Second, it infers organizational reforms changing
manufacturing industries, leading to widely established innovations changing the economy
at large. (Gassmann et al., 2014)
The first industrial revolution developed in Britain during late 17th
century, followed by
western Europe and United States. Eventually, places such as Russia, Japan and southern
Europe unfolded the concept of industrialization. It is indeed difficult to determine an exact
year when the different industrial waves bursted out, since industrialization occurred
during different times at various places. What could be done is to identify when the
concepts developed and started to become more widespread and in the figure 2, an
overview of all industrial revolutions can be found.
Industry 1.0 is characterized by the implementation of new power sources in the production
processes. Power by humans and animals was substituted by machines driven by fossil
fuels. This resulted in increased human organization, management and coordination that
had never been considered necessary before. The main innovations of this era were steam

13
power and weaving looms driven by power. The steam engine was constructed to extract
energy from heated coal in order to create steam and the power looms did no long need
human assistance as the foot pedals were replaced. The revolution enabled more efficient
manufacturing, but also brought groups of people together and created sense of solidarity.
The steam power discovery was followed by electricity and factory production in late 18th
century, which was the key invention of the second revolution. (Henriette et al., 2015)
The third industrial revolution, also the so-called digital revolution took place a century
after the second and most producing companies could now benefit from mass production,
line production and the importance of automation became more essential. (Tihinen et al.,
2017) During this paradigm Information Technology (IT) started booming and analogue
technology was transformed to digital. Central innovations as integrated circuit chips,
computers, microprocessors, cellular phones and internet transformed the traditional
production and created a foundation for future digitalization. (Rojko, 2017) Industry 3.0
allows flexible production, higher variety of products and programmable machines,
however flexible production in terms of quantity was still a limitation. (Rojko, 2017)
Today the western countries just entered the Fourth Industrial Revolution that originally
emerged in Germany and was provoked by the fast growth of Information and
Communication Technologies (ICT). Central to this era is smart automation of cyber-
physical systems leading to decentralization within the organization and more advanced
data connection systems, which in turn enables higher flexibility within mass custom
production and in production quantity.
Figure 2 History of Industrialization

14
3.3.1 Industry 4.0
Industry 4.0 differ considerably from previous industrial happenings in the history. It is
not just another disruptive technology or yet another industrial revolution. The fourth
industrial revolution is a thrive to change into something unknown and implies using
Industry 4.0 strategy to sustain competitive in the market. The revolution was announced
prior to its implementation and not after it was fully established, which is one main
difference to previous industrial revolutions. (Rojko, 2017).
As mentioned, the fourth industrial revolution was triggered by the digitalization upswing
and development of ICT, but also saturation of the market, which forced the emergence of
new solutions. Production cost have been diminished by lean production and concepts of
just-in-time production and even more by outsourcing production to developing countries
offering lower work cost. (Björkdahl et al., 2018) The new paradigm with robotic, digital
and automatic technologies allows lower production cost in developed countries such as
Sweden and not only in low cost countries. (Rojko, 2017) The main idea is to seize the
potential of new technological concepts such as internet, IoT, integration of technical and
business processes, digital mapping and smart manufacturing, to minimize costs. (Bossen
& Ingemansson, 2016)
However, there are difficulties to identify potential impacts of Industry 4.0 and the
implementation of new technology in the early process. The benefits from industrialization
and digitalization may be recognized centuries after its implementation and some
intermediate steps in the process are required in order to enable later innovations. It is
possible that some steps in the transformation process are nonprofitable at first, even if the
whole solution in the end is a positive investment. A bottle neck in industrial transforms
are to identify the financial gains and the economic impacts, since it takes time to realize
profits from over-time projects contributing to many soft term consequences.
3.4 Steel Industry
The iron and steel industry enable the development of several other industries; heavy
engineering, energy and construction industry (World Steel Association, 2019) and plays
a key role in the global economy. There are over six million people working within the
industry and every two job in the steel sector create 13 more jobs throughout its supply
chain. In 2018 more than 1808 million tons of crude steel were produced, where China as

15
the largest actor on the market alone stood for more than 50 % of the total steel output. A
common global challenge is the large CO2 emissions the production entails. On average
every ton of produced steel yields 1.83 tons of CO2 emissions. (World Steel Association,
2019). The steel industry is a subindustry of the manufacturing industry which in turn is a
subindustry of a larger process industry, as shown in figure 3. The manufacturing industry
covers all manufacturers producing products by converting raw materials or commodities,
often in large scale, for example textiles, machines, equipment etc. While processing is a
broader term and could be defined as series of mechanical or chemical operations to change
or preserve something. Food is for example, processed and not manufactured.
Figure 3 Industry Chart
Steel in particular is manufactured using an alloy of iron and carbon, which sometimes
also includes other alloying elements in order to obtain different characteristics. It is used
in buildings, infrastructures, automobiles, machines etc. Some advantages of steel are, it is
possible to mold it plastically in both cold and hot conditions, harden it in multiple ways,
use alloying elements in order to change the properties of the steel and recycle most of the
materials. There exist three typical variations of steel; carbon steel, low alloy steel and
high alloy steel. Each type of steel holds different characteristics and are used for different
purposes. Furthermore, the variation of steels can be categorized as either commercial
steels with plain carbon and no alloys or special steels produced for special purposes with
different alloys.

16
3.4.1 Production Process
Today there mainly exists two different ways to produce steel and the process varies by
the raw materials and the furnaces process. The traditional way to produce steel is to use
iron ore and a blast furnace. However, today’s technology also allows us to reuse scrap
steel. When using scrap steel in the production process, electric arc furnaces are used
instead, where electricity is forced through an arc enforcing desired result and temperature.
Both methods can be described by the modern steel making process, which can be divided
into six steps and in a primary and secondary steel making phase. Please find illustration
of both methods in figure 4 below.
The first step is iron making where iron ore is reduced using coke and coal in a blast furnace
with high temperature, this way molten iron is produced. At this stage there are still many
impurities in the molten iron, so a smaller amount of scrap steel is infused. In the primary
steel making phase, oxygen is forced into an LD-converter, causing a temperature rise to
1700 Celsius degrees (World Coal Association, 2019), which reduce the carbon impurities
by 90 % and the molten iron is transformed to molten steel. (Melfab Engineering, 2017)
This process in particular gives rise to a high amount of carbon dioxide emissions. (SSAB,
2020) When only using scrap, the two first stages will be reduced by an electric arc furnace,
since the scrap steel already holds some of the desired characteristics. Following step is
the secondary steel making where more specific properties of the steel is determined, in a
so-called ladle, by de-oxidation, alloy addition (boron, chromium, molybdenum etc.) and
other operations ensuring the exact quality. (Wikipedia, 2020) Next in the casting, the
molten steel is tapped into cooling molds, drawn out and finally cut into desired length,
before completely cooled. When it is fully cooled it is transported for primary forging,
where the casts are formed in a hot rolling process. Here, small defects can be corrected,
and the optimal quality is ensured. Sometimes a secondary forming is necessary and
operations like coating, thermal treating, pressing etc. is performed in order to get the
correct shape and finish.

17
Figure 4 Steel Production Process
In this paper, the main focus will lie on potential digitalization projects in the last steps of
the steelmaking process, i.e. continuous casting, rolling and coating.
3.3.2 Current State
Even though the steel industry, as a part of the process industry, lies far behind the
automotive and traditional manufacturing industry when it comes to digitalization, they
see high potentials with future transformation projects (Björkdahl et al., 2018). The process
industry has in general more strict manufacturing processes and products with less
flexibility- Therefore, the current focus is to digitalize the value chain rather than the
product itself. Research believe that more focus on surveillance, control and optimization
of value chain can result in higher resource efficiency in energy, environment, transport
and raw material management. The Swedish steel industry is currently focusing on higher
value-added products (Björkdahl et al., 2018) where they compete with production
efficiency and capacity. Thus, the greatest driving force in the steel industry is internal cost
saving and the goal is to reach a more even production flow with higher automation levels
through digitalization. Even though the investments are extensive, many companies have
a positive believe that these investments are profitable and look forward to implementing
concepts of smart manufacturing such as auto corrections and Machine to Machine
communication (M2M) (Murri et al., 2019).
Today the steel industry is in general very energy intensive. However, the European steel
industry is characterized by modern energy and emission efficient plants and make fast

18
progress towards a carbon dioxide free production (Bossen & Ingemansson, 2016). With
Big Data analysis the steel industry can expect a more energy efficient production with
only small efforts (Björkdahl et al., 2018). Currently, most actors on the steel market have
a connected melting process where they can collect measure points such as temperature.
Some also take measurements for quality and productivity related factors in order to
understand the relation between the production process and material characteristics, and
thereby developing products with higher quality. Another company highlights the
importance of the interface between the raw data and the user and most companies collect
large amounts of data but does not utilize it in a user-friendly way. One example of such
user-friendly interface is a mobile app that shows the current states of different furnaces.
(Murri et al., 2019)
Downstream production areas such as rolling and coating are the processes most affected
by digitalization and Industry 4.0 (Neef et al. 2018). The technical barriers are considered
less problematic than the organizational issues. As a conclusion, the main challenges are
legacy equipment, long payback time, data security and uncertainty about impacts on jobs.
Another challenge is the aging of workforce where many of the existing employees
possesses great industry knowledge, but on the other hand lack digital knowledge like
programming skills. (Gassmann et al., 2014) The resistance to change, learning and
collaborate is giving the companies a hard time to get through the digital transformation
without replacing parts of the staff. In a modern rolling production, using cameras and
other digital solutions as decision support, the employees are younger and hold both
computer and multilanguage skills. Meanwhile the traditional rolling production facility
consist of higher average age of employees where every individual possesses skills that
are harder to pass forward onto new employees.

19
3.5 Assessment Methods
Most companies have a large number of potential projects competing to be implemented.
Project proposals usually grows from multiple levels; top management, head of
departments and people working on the floor all possesses creative ideas about how to
improve the business. In order to make well-grounded decisions about which of all projects
to initiate, they need to be evaluated on a structured basis. As this report aims to take both
quantitative and qualitative effects into account, the investment model consequently needs
to consist of two main analyses; one quantitative and one qualitative analysis.
The quantitative analysis includes aspects that could be described in monetary terms while
the qualitative analysis includes more vague aspects that are more difficult or even
impossible to explain in monetary terms. In general, a variety of both monetary and
nonmonetary objectives may influence a decision, which is the reason why qualitative
analyses are usually developed side by side with economic costs and benefits analysis to
include both aspects. As this thesis consider digitalization projects specifically, it is in
addition interesting to evaluate the change in level of automation, see section 3.2 for
explanation of how automation and digitalization are related. Theories building the
foundation of the quantitative and qualitative analysis as well as how level of automation
can be measured, will be explained in the sections below. These theories form the basis for
the investment model developed in this thesis.
3.5.1 LoA Framework
One common framework for evaluating the level of automation in manufacturing
processes is the Level of Automation (LoA) framework that was developed in the
DYNAMO project between 2004 and 2007, carried out in association with Chalmers
University of Technology, Jönköping School of Engineering, and IVF Industrial Research
Corporation. The LoA framework is a tool to measure and get an overview of the level of
automation and current information flows in production systems. It is built on a concept
assuming that tasks in manufacturing include both mechanical and cognitive activities. The
mechanical activity refers to the physical part of the task and are represented by the
Mechanical LoA, while the cognitive activity refers to the data and information exchange
which is represented by the Information LoA. The reference scale for different LoAs is
ranging from 1 – 7, corresponding to different levels of automation ranging from totally

20
manual to totally automatic. An overview of the LoA reference scale is shown in table 1
below. (Granell et al., 2007)
Table 1 Levels of Automation Reference Scale
To enable better understanding of the different levels in the reference scale, a short
explanation of each level will be given. Starting with Mechanical LoA, level 1 suggests
for tasks to be “totally manual”, meaning that it is performed entirely by man-force. For
instance, this level could apply to manual lifts in production. The second level, level 2,
refers to “static hand tool” which for example could be about using a screwdriver to tighten
a screw. Level 3, “flexible hand tool” would instead be the level of automation if a wrench
was used for this matter, as it can be set in different ways and thereby perform a variety of
operations. Next level, level 4, says “automatic hand tool” and if following the same
example as for previous levels, this level suggests using an electric screwdriver to complete
the task. Another example of level 4 would be usage of a crane. For level 1 – 3, the work
has been performed manually by man-force but with more or less helpful and flexible tools.
From level 4, tasks are supported by some sort of automation, meaning the task no longer
need manpower to execute the main task. Level 4 refers to manual work performed by
using automatic tools, e.g. usage of an electric screwdriver. Level 5 refers to “static
workstation”, which implies usage of static machines constructed for one single operation.
For instance, a lathe or an automatic crane. The sixth level, “flexible workstation”, applies
to when a flexible machine is used, with the ability to perform a variety of tasks. To
exemplify, this level includes machines that could produce products with different lengths

21
or thicknesses. To reach level 7, a totally automatic machine is used to perform a task, that
automatically adjusts its settings depending on the situation. AI, M2M and big data are
inevitable technologies that need to be considered, in order to reach LOA above 6.
Continuing with Information LoA, the first level “totally manual” applies to when the
person performing a task finds their own way of working without any informational
exchange. In other words, when there are no instructions available for how a task should
be performed. One example of this level is when the quality of a painted sheet of steel is
inspected with a person’s eyes only, without any specified routines for how it should be
assessed. Moving on, level 2 is when information is used in a decision giving matter, where
the person performing a task receives suggestions on the order of actions. The
informational exchange focuses more on mediating what should be done rather than how.
One example of this level is when employees conduct their work based on a working order
that suggests them what to do. The third level, “teaching”, is when the worker receives
instructions for how a task should be performed, for example by checklists or manuals.
Next level, level 4, is explained as “questioning” and can be considered the first level of
human-machine interaction. This level applies to when a system or machine generates
questions in order to ensure that correct settings are selected. For instance, it could be that
an employee changing the settings in order to produce another product type, whereupon
the machine asks “do you really want to change from X to Y?” before resetting production
settings from X to Y. Level 5 refers to “supervising”, referring to all kinds of alarm systems
and other control systems that calls for workers attention if an abnormal situation arises.
The sixth level is when the technology is “interventional” and takes its own command if
necessary. An example could be using sensors for automatic control and adjustment of a
task. The highest level, level 7, is reached when a system is totally automatic with no need
of human interaction.
On a higher level, there are two main steps when using the LoA framework. The first step
is to measure Mechanical LoA and Information LoA for different tasks in production in
order to define the current state of automation. The measurement process for determining
levels of Mechanical LoA and Information LoA for a specific task in production will not
be considered in this paper. Please find the report “Measuring and analysing Levels of
Automation in an assembly system” by Fasth et al. (2008), which gives a more detailed
explanation about how measurements should be done.

22
The second step is to assess relevant minimum and maximum values of LoA for each
operation. By determining relevant values, an area of automation potential can be defined,
to which observed LoAs from on-site measurements should be compared (Björkdahl et al.,
2018). Example of such area could be found in the Mechanical-Information-LoA diagram
in figure 5, where the vertical and horizontal lines correspond to relevant minimum and
maximum values for Mechanical and Information LoA respectively and the black spot
represents the observed value. The defined area forms a square, which are called “Square
of Possible Improvements” (SoPI) and sets the boundaries for possible automation
improvements, with regards to a company’s requirements. SoPI can indicate how to take
advantage of the automation potential and help assessing the current state with regards to
its future potential.
Figure 5 Mechanical-Information-LoA Diagram Showing SoPI
3.5.2 Discounted Cash Flow
An economic valuation of an investment is the analytical process of determining its current
or expected worth. There are various methods for doing so, where each may result in
different valuations. Some methods include looking at past and similar investments to
estimate an appropriate value. However, since digitalization projects in general have little
or very limited historical data to rely on for comparison with future projects, the discounted
cash flow (DCF) method is considered most suitable for the case of this report and form
the basis of the investment model that is aimed to be developed.

23
The discounted cash flow (DCF) method is a commonly used valuation method used for
valuating a company, a project or an asset, that is suitable for both financial investments
as well as for industry investments. This method takes the time-value of money in account
and is therefore appropriate for any situation where money is spent in the present with
expectations of receiving money in the future. The valuation is based on finding the present
value of the expected future cash flows of an investment, which is done by using a discount
rate. When conducting a DCF analysis the investor must estimate future cash flows and an
appropriate discount rate. Please find equation (1) for DCF calculations where DCF =
discounted cash flow, CF = cash flow and r = discount rate. (Chen 2020a)
!"# =
"#!
(1 + ()!
+
"#"
(1 + ()"
+ ⋯ +
"##
(1 + ()#
(1)
The value of an appropriate discount rate can vary depending on the situation but needs to
be sufficient enough to cover the required rate of return of an investment, when taking risk
and time-value of money in consideration. One discount rate that is commonly used by
companies is the weighted average cost of capital (WACC). WACC is the overall required
return of a firm, calculated by its cost of capital proportionally weighted between the two
categories equity and debt. However, any discount rate could be used in the DCF analysis,
as long as it is an appropriate reflection of the required rate of return (RRR). (Chen 2020a)
Based on the DCF method, different perspectives can be used for comparing investments
with each other as well as for deciding which ones to pursue. Some commonly used
analyzes are net present value, internal rate of return, payback period and return on
investment which will all be given further explanations in the below sections.
Net Present Value
The general perception is that assessing an investment based on its net present value (NPV)
is very effective when it comes to evaluating projects as it takes the time-value of money
as well as risk in consideration NPV is calculated by summarizing the discounted future
cash flows, which is the present value of future cash flows, and subtracting the initial
investment cost. If the present value of cash flows is equal to or exceeds the initial
investment cost, the investment should be considered. In other words, a positive NPV

24
indicates for the investment to be profitable, while a negative NPV indicates for it to result
in net loss. The main drawback of NPV analysis is the uncertainty of estimations about
future events that may not be reliable, e.g. expected future cash flows. Also, it is most
suitable for evaluating one single project or for comparison of similar projects. Therefore,
this analysis is not appropriate for comparing investments with large differences in terms
of e.g. lifespan and initial investment cost. Please find equation (2) for NPV calculations
where NPV = net present value, CF = cash flow, r = discount rate and C$ = initial
investment cost. (Kenton, 2019)
-./ =
"#!
(1 + ()!
+
"#"
(1 + ()"
+ ⋯ +
"##
(1 + ()#
− "$ (2)
Internal Rate of Return
Internal Rate of Return (IRR) is the discount rate contributing to a net present value, of an
investment, equal zero. Because of the nature of the formula, IRR cannot be calculated
analytically and must instead be calculated by using software programmed for this matter.
IRR refers to the minimum required rate of growth an investment needs to generate, in
order to be a net positive investment. The internal rate of return rule states that if IRR is
greater than the minimum required rate of return, the investment should be carried through
and vice versa. This analysis can be used for comparing all kinds of projects with each
other. When comparing projects based on IRR analysis, projects with the highest
difference between IRR and RRR should be pursued. However, IRR can be misleading if
used alone and is therefore recommended to use as a supplement to for instance NPV.
While NPV analysis indicates the amount of value an investment creates to the company,
IRR analysis indicates how fast the value is earned. Please find equation (3) for IRR
calculations where NPV = net present value, CF = cash flow, r = internal rate of return and
C$= initial investment cost. (Hayes, 2019)
0 = -./ =
"#!
(1 + ()!
+
"#"
(1 + ()"
+ ⋯ +
"##
(1 + ()#
− "$ (3)
Payback Period
Payback period (PB) analysis calculates how long time it takes for an investment to be
paid back. In other words, the payback period is the amount of time it takes for an

25
investment to reach its breakeven. This is calculated by dividing the initial investment cost
with the average annual cash flow generated from the project, without discounting cash
flows to their present value. PB analysis is widely used mainly because of its simplicity.
Consequently, the simplicity of the method also makes it the least accurate (Kenton, 2019).
The main reason for this is that PB does not take the time-value of money or risk in
consideration (Wilkinson, 2013). In general, shorter payback periods indicates for more
attractive investments and vice versa. lease find equation (4) for PB calculations where CF
= annual cash flow and C$= initial investment cost.
.4 =
"$
"#
(4)
Return on Investment
Return on investment (ROI) measures the efficiency of an investment and is usually
presented as a percentage. It is calculated by subtracting the current value of an investment
with the initial investment cost and dividing the difference with the initial investment cost.
A positive ROI indicates for a profitable investment and a negative ROI a loss. The higher
the ROI, the more attractive the investment opportunity. This analysis is suitable for
comparing a variety of project with each other. Please find equation (5) for ROI
calculations where NPV = net present value, CF = cash flow, r = internal rate of return and
C$= initial investment cost. (Chen, 2020b)
678 =
"#!
(1 + ()! +
"#"
(1 + ()" + ⋯ +
"##
(1 + ()# − "$
"$
(5)
3.5.3 Multicriteria Analysis
Most individuals are familiar with quantitative techniques for valuations but are less
familiar with qualitative techniques. A qualitative valuation of a project aims to address
qualitative aspects by assessing qualitative factors relevant for the implementation of it. A
commonly used method for assessing qualitative aspects is conducting a Multicriteria
Analysis (MCA). MCA is a description for any structured approach used to evaluate
different options based on their nonmonetary impact, allowing for decision makers to
include a full range of for example social, environmental and technical perspectives when

26
making a decision. It has its origins in decision theory and has been successfully used in
various fields (Rosén et al., 2009).
The basic concept of MCA is to assess how well different alternatives fulfill one or more
desired objectives, which are described as a number of criterions identified for the analysis.
Alternatives are evaluated based on to what level they fulfill the criterions and summarized
in order to enable an overall judgement. Some examples of MCA methods are multi-
attribute utility methods, analytical hierarchy process (AHP), outranking, non-
compensatory methods, linear additive methods and fuzzy set theory. The qualitative
analysis in the investment model will be based on a linear additive method, which is maybe
the most widely used MCA method (Communitites and Local Government, 2009). Linear
additive analyses mean that each criterion is weighted and graded in order to calculate a
final score in terms of a weighted sum. On a higher level, conducting a linear additive
MCA could be explained by the following five steps;
1. Identify criterions from which alternatives will be assessed
2. Assign weights to each criterion
3. Assign scores to each criterion for alternatives
4. Calculate a weighted sum of the total score
5. Conduct sensitivity analysis
All identified criterions should be independent of each other. If there are high dependency
among two criterions, they run the risk of giving some aspects greater impact than others
because of double counting their contribution in the analysis. Alternatives are judged based
on the criterions through a scoring system. One difficulty with using this method is
deciding how to set weights on criterions as no general rules exist for this judgement,
which is therefore highly subjective and dependent on the stakeholder interest. Because of
the uncertainty of the determined weights for each criterion, a sensitivity analysis should
be conducted.
The main advantages of conducting an MCA is that qualitative aspects can be considered
in a comparable and structured way, adding further support and transparency to the

27
decision-making process. It is also a flexible method where criterions are not locked
forever but can be changed for evaluation of different alternatives. However, criterions
have to be consistent to allow for comparison between project. In other words, they have
to be assessed from the same criterions in order to be comparable. A risk of using the MCA
methods is that results might be interpreted as scientific, while the outcome in fact is highly
subjective. Different variations of MCA methods can also give different results, which
further may lead to uncertainty among decision-makers about which method is best for a
particular case. Another important thing to keep in mind when developing an MCA
framework and identifying assessment criterions is to make sure the requirement for time
and manpower resources for the analysis are reasonable (DETR, 2000), as the level of
complexity can be adjusted depending on how criterions are selected. Conducting an
analysis with a large number of complex criterions will generate a more detailed decision
support but also consume more resources, while a smaller number of less complex
criterions will generate a simpler decision support but require less resources.

28
4 Effects of Digitalization
This chapter explains the approach from which effects of digitalization are identified and
describes the underlying frameworks. Potential effects are identified in the existing
literature based on the defined approach.
4.1 Approach
In previous studies, a number of general conclusions have been drawn about digitalization
within manufacturing companies. As mentioned earlier in this report, studying the existing
literature shows that effects of digitalization have been identified, but in rather vague or
loose terms without considering quantitative aspects. However, it is interesting for the
scope of this project to analyze what those effects are and how they could impact a
company. The existing literature have been analyzed from an Internal Efficiency
perspective, with regards to digitalization at Process level, only including effects that lie
within the area of a business internal functioning.
Tihinen et al., (2017) identify four levels where digitalization could be implemented;
Process level, Organization level, Business Domain level and Society level, see illustration
in figure 6. Digitalization at Process level is defined as “the adoption of digital tools and
streamlining processes by reducing manual steps” and is directly connected to the
manufacturing stage of a firm. At Organization level, digitalization is about “offering new
services and discarding obsolete practices and offering existing services in new ways”,
having more focus on how new services can be developed. The definition for digitalization
at Business Domain Level is when it is “changing roles and value chains in ecosystems”,
focusing on the interplay between actors in the value chain. Lastly, Society level is when
digitalization changes social structures. This report focuses on digital implementations at
Process level, centering around the steel manufacturing processes. Digitalization at other
levels will not be considered explicitly as they lie outside the scope of this project.

29
Figure 6 Levels of Digitalization
Tihinen et al., (2017) also state that the effects of digitalization within a firm can be studied
from different viewpoints, namely; Internal Efficiency, External Opportunities and
Disruptive Change. Only by studying digitalization from all three viewpoints, one could
fully understand the whole picture of how digitalization affects a business, see figure 7.
Internal Efficiency is about the “improved way of working via digital means and re-
planning internal processes”, focusing on effects within the internal functioning of a
business, keeping the external processes unchanged. Thus, these impacts are affecting how
things are being done rather than what are being done. External Opportunities include “new
business opportunities in existing business domain”, i.e. the emergence of new services,
customers etc. as a result of digitalization. Here, changes in the value offer of a business is
considered. Lastly, Disruptive Change covers changes from digitalization that causes
completely new business roles compared to earlier ones, meaning that the current business
of a company may become obsolete. In this report, effects of digitalization are primarily
studied from an Internal Efficiency perspective, marked green in figure 7.

30
Figure 7 Viewpoints for Analyzing Digitalization Impact
All effects identified in the literature can be categorized in two main categories;
quantitative, qualitative. Quantitative effects can easily be derived into monetary terms and
contribute to a direct economic impact, in terms of cost savings. Whereas qualitative
effects mainly contribute to an indirect economic impact and are almost impossible to
explain in monetary terms at first sight. Worth mentioning, is that both quantitative and
qualitative effects have an economic impact, however the difficulty to attribute to cost
savings and monetary savings vary at large. Both categories can possibly be quantified in
either monetary or nonmonetary terms. For example, the qualitative effect “work
satisfaction” can be quantified in terms of number of sick leaves, however it is difficult to
see the exact economic outcome.
Furthermore, under each category all identified effects are either seen as positive or
negative. Positive effects include effects from digitalization in production which result in
a positive economic impact for a company, mainly by increasing process efficiency and
thereby decreasing costs per unit. Increased process efficiency is mentioned as the main
aggregated effect from digitalization in almost all papers addressing the topic of
digitalization at process level, for instance in papers by Björkdahl et al. (2018), Goldfarb
& Tucker (2019) and Murri et al. (2019), among others. Negative effects include effects
resulting in negative economic impact for a company, often referring to additional costs.
An illustration of the classification of effects with respect to their category and positive or
negative economic impact is to be found in figure 8 below.

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Figure 8 Classification of Effects
Effects have been identified from the approach that each effect should stand for a distinct
result in production. Yet, some effects have correlations, even if this fact is attempted to
be minimized. When studying the literature, it appeared some papers were referring to the
same consequences but with different words. In these cases, a joint definition or naming
of the effect was decided upon and used in this report, with references to all papers where
the implication of the effect was mentioned. For instance, one effect defined in this report
is named “less production losses”. One paper mention “fewer deviations” as an effect of
digitalization, which means the same thing as “less production losses”. Therefore, “fewer
deviations” has not been defined as an effect of its own in this work and included in the
effect “less production losses”.
4.2 Quantitative Effects
Followed by the definition mention above, this section addresses all the identified
quantitative effects in the literature and gives a more specific explanation of each. In some
cases, examples will be given. However, the identified potential quantitative effects in
terms of percentage can be found in section 4.2.1. Moreover, all positive effects are
assigned with (+), while the negative effects are identified with (-). A summary of all
quantitative effects is presented in figure 9 in the end of this section.
(+) Increased Productivity
Productivity is a common measure for how much value is created per unit input factor, for
instance how many tons of steel is produced per hour. Productivity can increase as a result
from implementing automation and digitalization projects (Herzog et al., 2018). By using

32
digital tools, more advanced production planning is enabled, which in turn can increase
the availability of a production plant and improve capacity use (Björkdahl et al., 2018). An
optimized production flow allows for current production volumes to increase. When
production volumes increase, the production cost per unit decreases as fixed costs in
production are distributed on more units.
(+) Less Production Losses
As a result of adopting digital tools, production losses can decrease on account of more
stable production with fewer deviations (Herzog et al., 2018). One reason why production
may become more stable is because digitalization often reduces the human factor.
Production losses are costly, and digital tools can help detect deviations at an early stage,
which helps preventing from further refining a product that is already outside the quality
reference range. Also, there may be a chance to fix a deviation if it is detected in time. This
way production losses can be reduced, and cost savings achieved.
(+) Shorter Downtime
Production downtime refers to the period of time when production is shut down without
producing any goods or performing any value adding tasks. Downtime can be categorized
into two main categories; planned downtime and unplanned downtime. Planned downtime
is the time scheduled for continuous maintenance during which a system cannot be used
for normal production. This time is mainly used to ensure reliable production and avoid
sudden disruptions. Unplanned downtime is the opposite of planned downtime, referring
to the amount of time production is offline due to unexpected events, such as for instance
power outages and breakdowns.
Digitalization has the ability to limit the amount of unplanned downtime and optimize the
amount of planned downtime. (Murri et al., 2019) The limitation and optimization of
downtime can be achieved by using predictive maintenance, which is further explained
under the effect “More efficient maintenance work” later in this section. Downtime can
also be reduced by digital machines being able to either configure themselves or at least
being configured more efficiently due to digital systems (Björkdahl et al., 2018). In
general, unplanned downtime is more costly than planned downtime.

33
(+) Less Misconfigurations of Machines
The occurrence of machine misconfigurations can be limited or even eliminated as a
consequence of digital machines reducing the human factor in the configuration process,
which in turn may reduce losses from manual misconfigurations. Misconfigurations of
machines lead to production losses due to incorrect production, which as stated earlier are
a heavy cost factor in most industries. Misconfigurations also contribute to longer
downtimes as the machines will need to be reconfigured. Digitalization enables for
machines to configure themselves (Rüßmann et al., 2015).
(+) More Efficient Maintenance Work
Minimizing maintenance costs is a big challenge for many industries (Murri et al., 2019)
Maintenance work can be managed more efficiently mainly through the digital concept
predictive maintenance, which is based on remote monitoring of equipment (Herzog et al.,
2018). Better accessibility and quality of production and order data can help optimizing
the scheduling of maintenance work, both in terms of time and frequency. Predictive
maintenance can decrease planned downtime by optimizing continuous maintenance. For
instance, the risk of turning off a furnace due to maintenance work just before an important
order will be reduced. By optimizing continuous maintenance, maintenance done due to
safety reasons only to assure reliability can be minimized as well and instead be performed
when necessary (Arens, 2019). Unplanned downtime may decrease as mechanical devices
can be repaired or replaced proactively in advance instead of after it has broken.
(+) Higher Quality
Higher quality refers to higher product quality enabled by higher production quality. By
implementing reproducible procedures, contributing to less manual steps, decreased
number of deviations and reduced production losses, the quality can be improved (Bossen
& Ingemansson, 2016; Herzog et al., 2018). Higher quality implies increased revenue,
since higher quality products can be sold at a higher price. The economic impact is not
always obvious, but as the quality improves, the company could also face less customer
service matters and complaints, indicating less administrative costs for handling
dissatisfied customers. (Vernersson et al., 2015)

34
(+) Less Low-skilled Jobs
One economic benefit from automation of repetitive processes are less low-skills jobs. In
this context low-skilled jobs are not equivalent to low-paid jobs but refers to monotone
jobs with routine tasks. Typical low- skilled activities in industry include manual operation
of specialized machine tools, short-cycle machine feeding, repetitive packaging tasks and
monotonous monitoring tasks. At first, implementation of autonomous system contributes
to decreased low-skilled jobs as the human workers are replaced by robotics. Implying a
decrease in labor cost, which can directly be translated to a cost saving. The fear of the so-
called technological unemployment was already prominent in the early 18th
century but
has been proven to be unjustified and wrong (Brånbry, 2016). Even if it seems like low-
skilled jobs are being replaced, there is proof of new job paths emerging; upgrading low-
skilled jobs and new digital low-skilled jobs, . For example, Henning Kagermann (2017)
says that “in the future, workers will be employed less as “machine operators” and more
“in the role of the experienced expert, decision- maker and coordinators”. (Hirsch-
kreinsen, 2017)
(+) Reduced Raw Material Consumption
Another advantage of digitalization is more efficient use of resources, contributing to less
consumption of raw materials. As a result of new digital systems, fewer deviations are to
be expected, meaning more no unnecessary loss of raw material. (Herzog et al., 2018;
Rojko, 2017)
(+) More Efficient Energy Use
Energy can be used more efficiently if production is optimized through digitalization
(Herzog et al., 2018; Murri et al., 2019). By using energy more efficiently, CO2-emissions
are reduced, leading to a greener production. Considering todays’ increasing
environmental awareness, this is an important factor for companies to keep a competitive
market position. Furthermore, electricity costs can be reduced when utilizing new energy
friendly technologies . Bossen & Ingemansson (2016) claim that small adjustments would
lead to significant energy savings for the mining and steel industry.
(-) New jobs
At the same time as low-skilled jobs disappear, new jobs emerge. Automation and
digitalization can create new jobs, particularly within IT and data science (BCG, 2015)

35
(Rojko, 2017a). The growing use of connectivity and software to collect data and manage
production flow will increase the demand for employees with new skills. Furthermore, the
European Centre for the Development of Vocational Training (CEDEFOP), expects there
will be over 151 million job openings between 2016 - 2030, with 91 % being created due
to the replacement needs and the remaining 9 % due to new job openings (Panorama, 2018)
New jobs flourish the societies and economies, but for the single company, it is considered
as another labor cost. Consequently, digital systems might bring new expenditures in terms
of new jobs.
(-) Disturbance in Production
There is a risk that the production will experience some turbulence in the transition towards
a digital transformation (Murri et al., 2019). Disturbances might lead to downtimes and
deviations, which is a factor that should be considered when adopting new digital
technologies. The aim should be to maintain a stable production during the transition but
can be difficult depending on the situation. However, production should be kept as stable
as possible at all times. If implementing a new system means stagnant production for
several days or even weeks, the alternative cost should be taken in consideration.
Figure 9 Summary of Quantitative Effects

36
4.2.1 Quantified Quantitative Impacts
In the following section, all quantified findings in the literature, stated in a percentage
improvement, are presented. As stated, the number of studies considering quantitative
aspects of digitalization projects are few. However, there are some papers giving
indications for how big the effects could potentially be, which will be addressed below. A
compiled version can be found in table 2, at the end of this section.
- According to Bauernhansl et al., (2016), production costs could decrease by 10 -
30 % as a result from adopting Industry 4.0.
- Tihinen et al., (2017) claims that for a typical automation/IT system, only 20 - 40
% of the total investment is spent on purchasing the system. The other 60 - 80 %
are additional costs arising from maintaining and adjusting the system during its
lifespan, so called upgrade services, which becomes an additional expenditure.
- In a report from McKinsey (2016), it is claimed that predictive maintenance can
help reducing maintenance costs by 10 – 40 % and 10 – 20 % of waste. Operating
costs are also estimated to be reduced by 2 – 10 %. Planned downtime is expected
to be optimized, and unplanned downtime limited with an estimated reduction by
2 – 10 %.
- In another report from Boston Consulting Group (2015), a quantitative analysis of
the impacts from Industry 4.0 in German manufacturing companies was carried
out. The study showed that productivity improvements on conversion costs will
range from 15 – 25 %. Conversion costs include direct labor and overhead expenses
arising due to transformation of raw materials into finished products (Horton,
2019), excluding material costs. When material costs are included, the
improvement instead corresponds to 5 – 8 %. It is predicted that Industrial-
components manufacturers will achieve the highest productivity improvements,
around 20 – 30 %. This report also expects component manufacturers to reduce
labor costs, operating costs and overhead costs by 30 % over five to ten years.
(Rüßmann et al., 2015)

37
Table 2 Summary of Quantified Quantitative Impacts
4.3 Qualitative Effects
As mentioned above, qualitative effects are judged to have an economic impact on a high
level but are somewhat more difficult to quantify than the quantitative effects. This section
follows the same structure as previous section and all positive effects are assigned with
(+), while the negative effects are identified with (-).
(+) Shorter Time-to-market
The time required for a product development process, from product idea to finished
product, is often referred to as time-to-market (TTM). Shorter TTM is an effect from more
efficient internal development cycles (Bossen & Ingemansson, 2016) and reduced lead
times (Murri et al., 2019). This helps a company faster responding to dynamic market
demands and change in customer requirements.
(+) Increased Flexibility
Increased flexibility is mentioned as an important effect of digitalization by many authors;
Murri et al. (2019), Herzog et al. (2018), ESTEP (2017), Bossen & Ingemansson (2016),
Rojko (2017), among others. Flexibility can improve by self-organizing cyber physical
production systems allowing for flexible mass custom production and flexibility in
production quantity (Rojko, 2017), making production of small lot sizes economically

38
defensible. Higher flexibility in production can shorten delivery times for specific orders
as it becomes easier to reprioritize in production. Short delivery times is a rapidly growing
customer requirement and is one important competitive advantage for manufacturing
companies (Murri et al., 2019; Rüßmann et al., 2015). Increased flexibility also makes
production of smaller lot sizes economically defensible.
(+) Increased Traceability
Increased traceability is another potential benefit which can be achieved by connecting
ingoing raw materials with products as well as tracing customer orders in the production
flow. In general, manufacturing companies see great benefits with increased traceability.
(Björkdahl et al., 2018). One benefit could be improved customer service by better quality
of, and access to, production data.
(+) Customized Goods
Customizing goods can become economically defensible as a result of digital systems,
allowing for production of smaller lot sizes (Murri et al., 2019). Offering customized
products may help companies target new customers. Shorter TTM is also an enabling
factor for customized goods.
(+) Better Work Satisfaction
Employee work satisfaction can increase through automation of routine tasks, by giving
employees the possibility to develop new skills, focus on more value adding tasks, flourish
their creativity, and last but not least work more time efficiently. (Murri et al., 2019; Ten
& St, 2015). Work satisfaction is also positively impacted by a more friendly and flexible
work environment, which also can be achieved by new digital solutions. (Murri et al.,
2019; Rojko, 2017). For example, as software like CAD becomes more sophisticated, a
production quality engineer can design complex products, test its functionalities and life
cycle, in different digital environment without having to set a foot on the production floor.
(+) Improved Health and Safety of Workforce
In order to maintain the trust between the employees and employers, it is important for the
employers to provide a safe and healthy work environment. Companies violating human
rights or contributing to unethical work conditions can face legal punishments, but
moreover high pressure from social media leading to decreased market shares. By

39
implementing collaborative robotics and other digital technologies the health and safety of
a workforce can improve, since the human involvement in dangerous environments
reduces. (Murri et al., 2019).
(-) Re-skilling of current employees
When upgrading the production using new digital technologies, such as sensors to collect
deeper and useful insights in the production, skills of current employees needs to be either
upgraded or replaced. Therefore, it is important to have workers with transferable and
flexible skillsets to stay competitive. Low-skilled jobs are slowly being replaced by
autonomous machines and, while there will be over 1 750 000 job openings for ICT
professionals during the period 2016-2030. Even if the production companies no longer
need an operator to drive a static machine, someone needs to manage; data security,
software upgrades and how to best visualize and make use of the data. As a conclusion,
current workforce will need new competencies in the transition towards a more digital
production process. The re-skilling process may require additional resources for
developing education programs, manuals, etc. The re-skilling process of current employees
is not only costly, but also time consuming. (Murri et al., 2019)
Figure 10 Summary of Qualitative Effects

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