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

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III

Effects of Digitalization in Steel Industry

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

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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

Effects of Digitalization in Steel Industry

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 6.3 Quantitative Analysis ................................................................................................. 59 6.4 Qualitative Analysis ................................................................................................... 65

7 Results ...................................................................................................................... 67 7.1 LoA Analysis ............................................................................................................... 67 7.2 Quantitative Analysis ................................................................................................. 69

7.2.1 Sensitivity Analysis ............................................................................................................... 72 7.3 Qualitative Analysis ................................................................................................... 73

7.3.1 Sensitivity Analysis ............................................................................................................... 74 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 10.2 Answer of Research Question 2 ................................................................................ 79 10.3 Answer of Research Question 3 ................................................................................ 80 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.

9

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.

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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.

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(+) 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)

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(+) 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

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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)

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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

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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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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.

5.1 Conceptual Overview

An investment model was developed based on a combination of existing literature within

relevant areas and the contextual situation at the case study company. The foundation and

overall structure of the model were mainly derived through existing literature. However,

in order to quantify key components and industry specific operations the model was

validated by further insights from a delaminated production section in the steel

manufacturing process. The model aims to evaluate digitalization projects based on three

different analyses; LoA analysis, Quantitative analysis and Qualitative analysis.

The purpose of the LoA analysis is to identify areas with high investment potential based

on current and potential levels of Information LoA and Mechanical LoA. The Quantitative

analysis is a monetary valuation of projects based on the DCF method and cost savings

enabled from change in the five cost driving factors Maintenance, Availability, Personnel,

Quality and Downtime. The qualitative analysis is a nonmonetary valuation based on a

multicriteria approach, taking the many soft term effects from digitalizing into account.

The qualitative analysis is based on the assessment criterions Flexibility, Traceability,

Work Satisfaction, Health & Safety and Re-skilling. A conceptual overview of the

investment model can be found in figure 11. Sensitivity analyses are established for the

quantitative and qualitative analyses in order address the uncertainty in input data. They

generate an evaluation of how sensitive results are to uncertainties in inputs.

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Figure 11 Conceptual Overview of Investment Model

5.2 LoA Analysis

There are two main features of the LoA analysis in the model; firstly, to map the current

state of automation in order to identify subprocesses with high investment potential and

secondly, to evaluate the potential change in LoA by going through with an investment.

By conducting a thorough mapping of the current state in a delimited production section it

is possible the identify a specific area of the production that lags behind the rest with

respect to digitalization and automatization and therefore needs to be examined more

carefully. Examples of production sections at the case study company are for instance

rolling or coating. Each production section consists of a number of subprocesses that needs

to be identified. At the case study company, a subprocess usually refers to one single

operation, for instance a crane or a furnace and is easily defined by the company.

Required input data from the company to conduct the LoA analysis is relevant minimum

and maximum values of LoA as well as current LoA values from on-site measurements for

each subprocess within a production section. Based on the data collection, a visualization

of SoPI can be created, as explained in section 3.5.1. The visualizations for distinct

subprocesses constitute a tool to help the company identify areas with high automation

potential interesting for initiating digitalization projects. For example, a subprocess with

its current LoA close to the maximum values of Mechanical and Information LoA should

be interpreted as less interesting compared to a subprocess with its current LoA far from

the maximum values. The analysis can also help identifying subprocesses that urgently

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should be considered for digitalization projects if its current LoA value is located close to,

or below its minimum values.

Except from mapping current state of automation, another feature with the analysis is to

visualize the future potential state of LoA as a result of a specific investment. When an

investment is being evaluated, this analysis considers the effect on LoA after the

investment. The user shall determine an investment’s change on Mechanical and

Information LoA, which can be visualized in the SoPI square and easily be compared with

its current state by plotting both the values. In this way, the potential change on LoA

through an investment could be graphically evaluated. LoA values will be maximized

when the potential state lies in the upper right corner of the SoPI square, and a significant

graphical movement in LoA towards that corner can be seen as one contributing factor in

the decision about whether or not to accept a specific project.

Furthermore, a percentage for the level of automation is calculated by the sum of the

current Mechanical and Information LoAs divided by 14 (the maximal sum). A relevant

percentage is also calculated, with the only difference that the sum is instead divided by

the sum of relevant maximum LoA values, which may be lower than 14. The difference

between the sum of relevant maximum LoAs and the sum of current LoAs corresponds to

the investment potential.

5.3 Quantitative Analysis

The quantitative analysis of the model aims to answer whether a specific investment is

profitable or not and to enable economical comparison between different projects. It is

based on a DCF valuation and will evaluate the investment opportunity in terms of NPV,

IRR, PB and ROI which are explained in the literature review, see section 3.5.2. For the

DCF valuation, annual cost savings achieved from an investment are seen as positive

expected cash flows. Investments are the evaluated based on the discounted cost savings

and their initial investment cost.

Five main factors contributing to cost savings have been identified based on a combination

of the literature review and the contextual situation at the steel company. Quantitative

effects from digitalization identified in the literature review have been considered. These

43

effects have been clustered together and customized to make logical sense, due to how cost

savings can be achieved in the steel industry based on the MECE (Mutually Exclusive

Collectively Exhaustive) principle (Minto, 2010). This principle means every factor should

be distinctly different and altogether make a complete whole.

The factors include all identified positive and negative quantifiable effects from the

literature review except Less Misconfigurations of Machines. The reason for this is that

misconfigurations of machines appeared to be a non-existing issue within the steel industry

and was therefore excluded in the model. The matching between findings from the

literature review with requirements of the steel industry was mainly done with help from

interviews conducted with people at the steel company. The five factors can be seen as the

main areas of economic improvements resulting from steel companies implementing

digitalization projects in production, and are identified as;

• Maintenance

• Productivity

• Personnel

• Quality

• Downtime

Cost savings per factor is calculated by determining its current costs and potential costs

after the investment. The difference between the current state and the potential state

constitutes for the potential cost saving. All input values estimated for the potential state

after an investment are given three different cases; base case, best case and worst case.

This apply to all input values that require estimation of future values after implementing

the investment. Consequently, the DCF analysis will give three different outcomes

depending on which of the cases that is considered.

Next step was to break down each factor into its value drivers and identify which costs

belong to which key factor, which was realized by very supportive coworkers possessing

in-depth industry specific knowledge. The top management provided conceptual ideas and

general information about the production process, while the financial division supported

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with figures and specific costs referred to the production process. An explanation of how

each factor is broken down to costs and how current and potential states are calculated are

explained below.

5.3.1 Initial Investment Data

All investment models require initial input data. Many future calculations are dependent

on these initial input values and are therefore playing a fundamental role in the investment

analysis. For example, initial investment cost, time horizon etc. are crucial factors to

generate reliable and accurate results. Except for these factors, the investment model asks

for data like; utilized production hours, production volume, replacement time, replacement

costs, upgrade service costs and replacement time. Lastly, the user is asked to give the

discount rate for the project, which can affect the outcome of the analysis considerably.

The first input is the initial investment cost, which varies depending on the characteristics

of the project. The investment cost for a strategic project is straight forward. As strategic

investments are completely new solutions implemented as an expansion to existing

solutions, the entire initial investment should be accounted for in the calculations. The

purpose of these investment is usually to generate higher production efficiency or create

new revenue streams. On the contrary, if current equipment becomes obsolete and needs

replacement, it can either be replaced by the exact same equipment or a new upgraded

equipment using new digital technologies. Hence, the initial investment for replacement

projects, with new digital solutions, should for most parts be lower than strategic projects.

In this case, only the difference between the cost for new upgraded equipment and the cost

for new equipment with old solution, should be accounted for in the initial investment cost.

In the model Initial investment cost is divided in three categories; initial investment cost

(strategic), initial investment cost (replacement) and initial investment cost, where the

replacement part accounts for the cost from a simple replacement with no upgraded

technologies, while the strategic part includes cost related to an investment in a new

upgraded equipment. Ultimately, all calculation in the model are based on the initial

investment cost, which is the difference between the other two categories according to the

description above.

Second input value is lifetime, referring to the number of years the new investment is

expected to generate cash flows. To continue, the third value asked for is, utilized

45

production hours, meaning number of hours the production is running on a yearly basis.

Upgrade service costs is next input value and accounts for continuous costs associated with

the equipment or software, i.e. license cost for software. Furthermore, it is vital to know

the replacement time or replacement cost, which is the time/cost for implementing the new

equipment. During this time, the production cannot proceed and results in a alternative cost

affecting the investment calculation negatively. Lastly, production volume on a yearly

basis and the discount rate should be inserted and thereby complete the initial investment

data. Please find illustration in section 6.3, figure 12.

5.3.2 Cost saving factors

Maintenance

The first cost saving factor is Maintenance. This factor includes the identified effect More

Efficient Maintenance Work from the literature review. At the case study company, costs

for maintenance is a heavy post where small improvements could result in high financial

gains. Maintenance includes expenses for repairs, replacements and continuous

maintenance. The company divides their maintenance work in three different categories;

Internal Maintenance, Additional Internal Maintenance and External Maintenance.

Internal Maintenance includes material costs for materials with regards to repairs and

replacements performed by the company’s own personnel. However, personnel costs and

alternative costs in terms of lost production values are not included in this category. The

reason for this is that personnel costs are covered by Additional Internal Maintenance and

lost production is accounted for in the cost saving factor Downtime, which will be

described later. Cost savings due to Internal Maintenance are calculated by knowing the

current annual material cost for maintenance as well as the size of the savings that could

be achieved due to the investment, expressed in monetary terms. Internal Maintenance

could for instance decrease if old machinery is exchanged with new one which does not

have as many breakdowns, and therefore not as many replacements of devices. The

material costs for replacements that can be eliminated as a result of the new digital solution

shall be seen as the potential cost saving.

Additional Internal Maintenance includes the cost for own personnel working extra hours

due to maintenance reasons. This category does not cover maintenance performed during

46

normal working hours. The cost for Additional Internal Maintenance is calculated by

knowing the cost for an extra working hour multiplied by the total number of extra working

hours for internal maintenance during a year. Savings can be calculated by estimating how

much the annual number of extra working hours for internal maintenance may decrease.

For instance, the number of extra working hours may decrease as a result from less

breakdowns when new machinery is implemented.

Lastly, External Maintenance includes maintenance work performed by external actors.

The cost for external maintenance includes costs for both material and personnel. In

general, external maintenance is more expensive than internal maintenance due to higher

personnel costs. Invoices from performed external maintenance presents a total invoice

value without separating material and personnel costs. By estimating how much the yearly

external maintenance cost will decrease in SEK due to an investment, savings can be

calculated. For instance, there might be one sort of breakdown that is usually fixed by

external players that may be performed less frequently or not at all as a consequence of the

investment.

The annual maintenance savings are calculated by summarizing the yearly savings for

Internal Maintenance, Additional Internal Maintenance and External Maintenance. The

yearly savings for each category is calculated by subtracting the potential cost from current

cost, as explained.

Productivity

Productivity is the second cost saving factor and includes the effects Increased

Productivity and More Efficient Energy Use from the literature review. Productivity

possibly allows for production volumes to increase if later production flow has enough

capacity. However, if this is achieved, the consequence of increased production volume is

twofold.

Firstly, energy for driving the machines can be used more efficiently. At the case study

company, gas, oil and electricity are the energy sources driving the production.

Consequently, fixed operation costs can be distributed on more units and the cost per unit

will decrease as a result of increased availability. Digitalization projects can for instance

increase availability by implementing optimization programs to better utilize capacity. One

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example would be to invest in an optimization program to optimize the filling in an oven,

which would increase the degree of filling which is the same as increasing availability in

this specific case. By knowing the current operation cost and production volume, the

current cost per ton can be calculated. The increased production volume can be calculated

by estimating how much availability will increase from the digitalization project. Dividing

the fixed operation costs with the new production volume will give a potential cost per ton.

Cost savings per ton is calculated as the difference between current cost per ton and

potential cost per ton. However, this will not affect the annual availability savings since

the fixed operation costs for the company will be the same after an investment.

Secondly, the production will create more value during a year due to increased

productivity. The increased value of steels produced is seen as an additional income, e.g.

a positive cash flow, in the model. By estimating the increase in production flow and

knowing the value for each produced ton, the annual value of additional steels produced

can be calculated. The increased production volume multiplied with the value of one

produced ton equals the yearly availability income and is considered a positive cash flow

in the analysis.

Personnel

The third cost saving factor is Personnel, which is measured in number of FTEs, which as

mentioned stands for full-time equivalent. The number of FTEs an operation requires refers

to the number of full-time employments needed. 1 FTE is equivalent to one full-time

worker while 0.5 FTE is equivalent to one worker with a part-time employment of 50 %.

FTE includes the positive effect Less Low-skilled Jobs and the negative effect New jobs.

When an investment is being considered, the change in required personnel for the

subprocess should be estimated. A digitalization project might lead to a reduced number

of FTEs for one task but may create a new job that requires personnel. Therefore, what is

interesting is the net change in FTE after considering both reduction and creation of

working tasks. Historically, reducing the number of FTEs in production has been the main

initiative for increasing the level of automation at the case study company.

Savings due to FTE reduction are calculated by determining the total personnel costs in

the current state and potential state by multiplying the cost per FTE with the number of

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FTEs for current and potential state respectively. The difference between current state and

potential state after an investment corresponds to the yearly cost saving.

Quality

Quality is the fourth factor contributing to cost savings and is in this study referring to

production quality. This factor includes the positive effect Less Production Losses and

Reduced Raw Material Consumption from the literature review. Reduced production

losses are a possible result of digitalization enabling for smoother production with fewer

deviations. In other words, if deviations decrease; deviation costs will go down and

production quality increase.

Deviations are more or less costly depending on their nature. There are two kinds of

deviations at the case study company; jettison deviations and reprocess deviations. Jettison

deviations are the most expensive ones and refers to deviations where things in production

have gone completely wrong and the whole unit needs to be discarded. The cost for jettison

deviations is the cost of production until jettisoned, minus the scrap value. In other words,

this cost equals the accumulated value created by all earlier value adding processes in the

production flow, except the value that that will remain when scrapped. Reprocess

deviations on the other hand, are not as expensive as jettison deviations and refers to

deviations where production have gone wrong but can be restored by reprocessing the unit.

For each subprocess, there are a limited number of possible reprocesses, i.e. painting. The

cost for reprocessing only includes cost for the reprocess operation and do not take

alternative costs, like taking time and capacity from original production, in consideration.

The cost per ton for jettison deviations as well as for reprocess deviations are known by

the company and also how many tons on an average each deviation holds. By estimating

how much deviations could decrease if a digitalization project is initiated, current and

potential deviation costs can be calculated. Quality savings are calculated as the difference

between current deviation costs and potential deviation costs.

Downtime

The fifth and final factor identified to generate cost savings is Downtime, simply including

the positive effect Shorter Downtimes. There are three different kinds of downtimes at the

company; planned downtime, unplanned downtime (normal) and unplanned downtime

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(bottleneck). Planned downtime refers to downtime due to scheduled maintenance work.

Unplanned downtime (normal) is caused by deviations that have no noteworthy effect on

the remaining production flow, while unplanned downtime (bottleneck) is caused by

deviations that affect later production flow. The cost for Unplanned Downtime (normal)

does not include alternative costs from lost production later in the production flow.

However, alternative costs from lost production in the remaining production flow are

included if the activity represents a bottleneck. The literature review states that unplanned

downtime is more costly than planned downtime. This applies to the case study situation

as well; the standard hourly cost per downtime is identical in both cases, but the number

of downtime hours varies. Consequently, the total cost for unplanned downtime is higher

than for planned downtime.

By knowing the cost per hour for the different categories of downtime, and the yearly

average hours for each one, current downtime costs can be calculated. Potential state is

calculated by estimating how much each downtime category could decrease due to the

investment.

5.3.3 Discount rate

As described in the literature review, discount rate is a manual input by the investor,

usually represented by the RRR and WACC. For digitalization projects the discount rate

is subjective and reflects the specific company’s RRR, commonly predetermined by the

top management team. As many know, DCF models are very sensitive to the chosen

discount rate. Thus, the RRR in the investment model will be separated into low, middle

and high, in order to detect how the result varies depending on RRR. The user chooses a

relevant RRR as input for the middle case, then the high and low case are directly

calculated as the middle case, plus and minus one percent respectively.

The case study company distinguishes between replacement projects utilizing new digital

technologies and new digital strategic investments. A normal replacement project of

obsolete equipment does not require any return, as it only replaces existing equipment and

performs the exact same operations. In comparison, a new digital strategic investment has

a fixed RRR of 14 %. Consequently, the RRR of replacement investments with new digital

technologies and solutions are usually somewhere between 0 % and 14 %.

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5.3.4 Sensitivity Analysis

In order to identify how sensitive the economic KPIs NPV, IRR, ROI and PB are to the

variation of input cases in the quantitative analysis combined with different variations of

discount rate, a sensitivity analysis has been conducted. We aim to examine how the

uncertainty in the output can be allocated to two different sources of input. If a small

variation in discount rate or the chosen case contribute to big fluctuations in output,

impacting the final decision making, then the investment model is highly sensitive and

should be used with caution. In the case where all variations of inputs give the same output,

the economic impact of the considered project is more certain. In the other cases where all

the different variations of input leads to mixed decision-making incentives, the precaution

principle should be considered.

Using the sensitivity analysis, one can test the robustness of the result, get better

understanding of the correlation between input and output, and easily identify errors in the

model. Based on these insights, the model can be simplified by removing redundant parts

of the model structure once the user identified the inputs that have no effect. Moreover,

input values that create significant volatility in output should be carefully analyzed in order

to improve the robustness of the model. This framework should be used as an indication.

However, it is up to the user how big impact the risk from the mathematical model should

have on the final decision making, contra the qualitative effects and other risks associated

with the project.

This sensitivity analysis is based on the simple model called one-at-the-time (OAT), where

one factor at the time is varied and analyzed while holding the rest of the factors constant.

In this specific model there are only two different factors considered; case and discount

rate, as mentioned above. A basic assumption in this model is that each of the parameters

should be somewhat uncorrelated. The advantage of OAT is that all changes in output can

be ascribed to variation in one specific factor.

The sensitivity analysis is divided into four identical matrixes where all KPIs are

represented in one matrix each. The vertical axis in the matrixes represents the first factor

“case” which in turn is represented by three variations; base, best and worst. The different

cases can be chosen in the quantitative analysis, where base case represents the most

expected scenario and correspond to basic input values for all cost drivers identified under

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each cost saving factor; maintenance, availability, FTE, quality and downtime. As the

assumption may vary, a minimal and a maximal value for each entry should be estimated.

The best case represents the highest (max) potential value possible for each cost driver and

reflects an optimistic belief of project outcome. Lastly, the worst case is based on the

lowest (min) potential value possible for each cost driver and reflects a precautious

approach. On the horizontal axis three different variations of discount rate (WACC);

middle, high and low, represented in numbers can be found. The middle case is based on

the actual discount rate given by the case study company, which is adjusted by plus and

minus one percent for respective case. This shows how sensitive the outputs are to changes

in discount rate. An illustration of the sensitivity analysis can be found in section 7.2.1

under results.

Furthermore, the analysis is complemented with a summary showing the average, median

and standard deviation for each KPI. By logical reasoning, the median will always be the

base case combined with the middle discount rate, which is considered as the most likely

outcome. A smaller standard deviation shows lower volatility in output based on the

different variations of input and a specific decision should not depend too much on

different assumptions made in the model. Therefore, lower standard deviation implies a

more reliable outcome.

5.4 Qualitative Analysis

The qualitative analysis aims to evaluate the positive qualitative contribution to the

company as a consequence of implementing a digitalization project. It is built based on the

qualitative effects identified from the literature review, which are potential effects from

digitalization that does not explicitly refer to specific costs and are thereby difficult to

quantify in monetary terms. Instead of deriving an economic outcome of these effects, the

qualitative contribution is concretized through an MCA approach, where a linear additive

method is chosen to form the basis of the analysis. Some modifications to the “general”

linear method have been done due to the fact that this model aims to evaluate different

kinds of project and not only several alternatives for solving one single problem. Therefore,

the five steps defined in the literature review have been modified and the method used in

this model instead contains of seven steps, where step number two “decide relevance for

each criterion” and step number 6 “identify and calculate indexes” is added as a

52

supplement in order to enable a more nuanced evaluation. This modification is inspired by

others work; indexes are for example calculated in the works by DETR (2000) and Rosén

et al. (2009) among many others. Furthermore, a relevance function as well as indexes

were used in the work by the Swedish Havs-och vattenmyndigheten (2016).

1. Identify criterions from which alternatives will be assessed

2. Decide relevance for each criterion

3. Assign weights to each criterion

4. Assign scores to each criterion for alternatives

5. Calculate a weighted sum of the total score

6. Identify and calculate indexes

7. Conduct sensitivity analysis

Step 1: Identify criterions from which alternatives will be assessed

Criterions should be selected so that they are appropriate for assessing qualitative impacts

from digitalizing. Criterions for this model have been defined based on the literature

review and are therefore considered as appropriate criterions as they are claimed by authors

to be potential effects which digitalization is more or less able to fulfill. Furthermore, they

are identified to be either positive (P) or negative (N).

The identified qualitative effects in the literature review are Shorter Time-to-market,

Increased Flexibility, Increased Traceability, Customized Goods, Better Work

Satisfaction, Improved Health and Safety of Workforce and Re-skilling of Current

Employees, where all effects are positive except the last one. In order to assure

independency among the identified effect, we decided to cluster Shorter Time-to-market

and Customized Goods into the effect Increased Flexibility. The reason for doing so is that

increased flexibility in production helps shortening the time-to-market and enables for

easier customization of goods. Another reason was due to the contextual situation at the

case study company; it is easier for decision-makers to estimate the effect in terms of

flexibility rather than the other two. In this way, conducting the analysis will be less

resource consuming. The final criterions for the analysis were defined as;

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• Flexibility (P)

• Traceability (P)

• Work Satisfaction (P)

• Health & Safety (P)

• Re-skilling of Employees (N)

Step 2: Decide relevance for each criterion

As a variety of digitalization project are aimed to be evaluated through this analysis, a

relevance function was added. When analysing a specific effect, relevance should be set to

either “1” or “0” depending on whether or not each criterion is relevant to it. A criterion is

relevant if the project in consideration have an impact on the specific effect. For example,

if the project of analysis will increase Flexibility relevance is set to “1” and vice versa. The

scale for relevance is binary and consequently includes 1 and 0.

Step 3: Assign weights to each criterion

Weighting criterions is about determining how importance each criterion is to the project

in consideration. This is done in order to adjust the final score for each criterion and thereby

judging the relative importance of their scales. Assigning scores to each criterion, step 4,

is about measuring performance while this step, assigning weights, is about determining

the value or importance of that performance. Thus, this is a fundamental step of the MCA

analysis as it will significantly affect the final outcome of the analysis.

The weighting process in this model is recommended to be done in line with the SWING

method of weighting, which is commonly used by most proponents of MCA to elicit

weights (DETR, 2000). The SWING method basically suggests the decision-maker to

decide the criterion with the biggest “swing” at first, meaning deciding what criterion that

is of highest importance to the project. This criterion should be assigned the highest value

of the weighting scale. When assigning weights to the remaining criterions, they should be

Relevance Scale 1: This specific qualitative effect is directly impacted by the project in consideration 0: This specific qualitative effect is not impacted by the project in consideration

54

compared to the most important criterion as a reference. As the number of criterions in this

model are rather limited, the stakeholders involved in the decision making should be able

to agree upon weights without any significant difficulties. If complication regarding

assigning weights would occur, one solution is that all stakeholders suggests weights for

each criterion and the average or median value can be used for analysis.

As stated in the literature review, assigning weights is highly subjective. The scale for

weights is discrete and ranges from 1 – 5, depending on the level of importance a criterion

has to the project in consideration.

Step 4: Assign scores to each criterion

Each criterion is to be assigned a score that expresses the value associated with the

consequence a project has on the particular criterion. If the weight is an expression for the

“value of impact” the score is an expression for the “strength of impact”. The score

estimates the size of impact the project in consideration has on the specific criterion.

In our analysis, the positive criterions, that have been identified from the positive

qualitative effects in the literature review, are scored on a discrete scale ranging from 1 –

5 depending on what level of impact the project will have on the specific criterion. For the

criterion that have been identified from the negative qualitative effect in the literature

review, the scoring scale goes from -1 to -5. This separation is done due to their positive

and negative contributions to the total impact of initiating a project.

Weighting Scale 1: This specific qualitative effect has very low importance to the project in consideration 2: This specific qualitative effect has low importance to the project in consideration 3: This specific qualitative effect has moderate importance to the project in consideration 4: This specific qualitative effect has high importance to the project in consideration 5: This specific qualitative effect has very high importance to the project in consideration

Scoring Scale (P) 1: The project has very small impact on the specific qualitative effect 2: The project has small impact on the specific qualitative effect 3: The project has moderate impact on the specific qualitative effect 4: The project has high impact on the specific qualitative effect 5: The project has very high impact on the specific qualitative effect

55

Step 5: Calculate a weighted sum of the total score

When criterions are identified and relevance, weights and scores are assigned for the

project in consideration, the next step is to calculate a weighted sum of the total score. The

total score for each criterion is calculated by multiplying relevance, weight and score. If a

criterion is considered irrelevant for the project, relevance will be set to “0” and the total

score for that criterion will equal 0. Provided that relevance for a criterion is set to “1”, the

total score for that criterion could be either negative or positive depending on the nature of

the criterion and thereby which scoring scale that is being used.

The weighted sum for the project is calculated by summarizing the total scores for all

criterions. The formula for how to calculate the weighted sum, i.e. total score :%&%, is shown

in equation (6) where 6# = relevance, ;# = weight for criterion n, !! = score for criterion

n, n = criterion. With the scoring and weighting scales in this model, the minimum and

maximum values of the weighted sum is -25 and 100 respectively.

:%&% = < 6# ∗ ;# ∗'

#(!:#(6)

Step 6: Identify and calculate indexes

In order to be able to compare the total qualitative score between different projects and

draw reasonable conclusions, one index and one relative index is calculated. To obtain the

index, the total score :%&% from previous step is divided by the sum of the maximal total

outcome. The index is an indication of how well an investment meets the theoretical

potential positive contribution to the company. It is automatically calculated in the Excel

model as a weighted average over the maximum total score, where the weight on each

criterion is equal and independent of the relevance. Meaning, even if only four out of the

five criterions are relevant, the denominator in the index calculation is still based on the

Scoring Scale (N) -1: The project has very small impact on the specific qualitative effect -2: The project has small impact on the specific qualitative effect -3: The project has moderate impact on the specific qualitative effect -4: The project has high impact on the specific qualitative effect -5: The project has very high impact on the specific qualitative effect

56

maximum total score from all five criterions. This makes sense, since the index should be

lower for a project with only four relevant criterions than a project with five relevant

criterions assigned with the same scores for each respective criterion. Currently, there are

four positive criterions identified with a maximal score of 25 each, meaning that the total

maximum score for all criterions equals 100 (4*25). The maximal score is achieved when

the negative criterion has no relevance and does not affect the total score. A presentation

of how index 8 is calculated is shown in equation (7), where :%&% = total score, ")*+ =

maximal score per criterion and -, = # positive criterions.

8 = :%&%")*+ ∗ -,

(7)

As the analysis aims to address the positive contribution to a company due to an

investment, the total score is defined to never equal below zero. Consequently, if the

weighted score for the negative criterion exceed the score of positive criterions, the total

score will equal zero. Further, the total score ranges between 0 and 100, implying the

index will have an output range between 0 % and 100 %. An index of 0 % could imply

that the total qualitative effect from this project is a liability, whereas an index above 0 %

indicates that this project generates a positive qualitative outcome and can be viewed as an

asset.

In the model, a relative index is also calculated where the only difference is the weighting.

The weight for normal index is fixed and independent of relevance, however the relative

index is dependent on the relevance for each criterion. For an example, if three out of four

positive criterions are relevant, the denominator in the relative index will be reactive and

only take three criterions in consideration, meaning the score will be divided by 75 (3*25)

instead of 100. The relative index cannot be compared between different projects since the

number of criterions for this index is not constant. It is only useful to evaluate one single

project. Equation (8) presents how relative index 8- is calculated, where :%&% = total score,

")*+ = maximal score per criterion and -. = # relevant positive criterions.

8- =:%&%

")*+ ∗ -.(8)

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The qualitative outcomes are found to be rather difficult to translate into exact numerical

values, however intuitively it makes sense for these qualitative factors to have an indirect

economic impact and need to be taken in consideration when evaluating a new potential

project. It is up to the user how much the qualitative effects should weight in comparison

to the quantitative analysis.

Step 7: Conduct sensitivity analysis

Last but not least, the user should estimate the uncertainty of the point assessments made.

To do so, a scale was developed where the uncertainty assessment ranges from 1 to 3, see

table below. This uncertainty assessment appears as a drop-down list in the valuation

model and is a manual input made by the user. For an example the “Health & Safety”

metric in the qualitative analysis may be quantifiable in terms of number of injuries, as

well as “work satisfaction” may be decided by number of sick leaves or resignations.

Therefore, the uncertainty value for these criterions are likely to be lower. On the other

hand, “traceability” is much more difficult to quantify and should imply a higher

uncertainty level. As a conclusion, a point assessment with any quantitative base, even if

it is not quantification in economic terms, has lower uncertainty.

Sensitivity Scale 1: The uncertainty of grade input value is low, meaning the input is true to the real situation 2: The uncertainty of grade input value is moderate, meaning the input is somewhat true to the real situation 3: The uncertainty of grade input value is high, meaning the input may be true to the real situation

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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.

6.1 Project “Smart Crane”

To make sense of exactly how large the economic impact from different digitalization

projects in the steel industry is, contextual values were gathered based on the project in

consideration in a delimited production area. One potential digitalization project currently

in consideration is a smart crane used for insertion of raw steel between inventory and

oven. Currently, the company is using a manual crane to execute this operation. However,

this crane has become obsolete and needs to be replaced in the near future. Therefore, this

is classified as a replacement investment and not a strategic investment. Strategic

investments are usually more expensive and has higher required rate of return than

replacement investments. Example of a strategic investment could be an investment in a

new sensor system to better keep track of the products throughout the production flow.

This is not an existing system and would imply a new investment made for strategic

reasons. In this case, the smart crane would replace an old obsolete manual crane, in need

of replacement. So, the investment decision the company is facing is whether they should

buy a new manual crane or invest in a new, a bit more expensive, smart crane.

This investment model is developed in the Microsoft software Excel. Note that all beige

cells in the visualization are inputs and all white cells contains formulas or important

information and should therefore never be edited by the user.

6.2 LoA Analysis

The chosen production section consists of ten different subprocesses; “Takkran N2”,

“Inlägg”, “Ugn N2”, “Diregerare”, “Ugn N4”, “QT”, “Småkranar”, “Takkran H4”,

“Linjen” and “Märkning”. Current LoA values were determined for all subprocesses

through on-site measurements prior to the study of this report and were provided by the

59

case study company. Laster, relevant min and max LoA values were determined for each

subprocess during this project.

For the subprocess in consideration, “Takkran”, Mechanical LoA equals 4, which also is

the lowest Mechanical LoA that could possibly be accepted in this activity. Level 4 is the

lowest level due to the heavy weight of steels being lifted, which simply could not be

performed alone by man-force. The optimal level of mechanical automation is 7, as the

company would benefit from having a totally automatic machine for this activity. If the

company choose to invest in a new manual crane instead of the automatic smart crane,

LoA levels will remain the same. However, if the current crane is replaced with the

automatic crane the Mechanical LoA will raise to 6. The automatic crane is a flexible

machine, able to operate at different speeds etc.

Currently, Information LoA in “Takkran” is at level 2. The person running the crane

receives instructions about the order in which steel sheets should be moved. Level 2 is also

considered the minimum level for this task and level 7 is set as the optimal level. Investing

in the smart crane will bring up Information LoA to 5. The automatic crane will not be able

to handle errors itself, however the machine will communicate errors through alarm

systems.

6.3 Quantitative Analysis

As described, the potential Mechanical and Information LoA are measured as 6 and 5,

respectively. The investment cost for the smart crane amounts up to 23 MSEK. However,

the actual investment for the digital solution, represented by “initial investment cost” in

figure 12, is the difference between the price of a new manual crane, 20 MSEK and the

smart crane, which equals to 3 MSEK. As the old crane needs to be replaced, the expense

for at least one manual crane is inevitable to keep the production going. The estimated

lifetime for the smart crane is 10 years, yielding potential cost savings for at least 10 years.

At the case study company, they currently utilize 5988 hours (250 days) of production

every year. During this time, 276 250 tons of steel is produced, and the time needed for

changing the old crane to a smart crane is 288 hours (12 days). These 288 hours do not

affect downtime in the model, since the company has a planned production stoppage of

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672 hours on a yearly basis, during which the change of equipment can occur. To continue,

this particular investment has no upgrade service costs, that needs to be taken in

consideration.

Figure 12 Overview of Initial Investment Data

Implementation of new smart crane would result in less downtime, caused by breakdowns,

as the human factor will more or less be eliminated. An automatic smart crane is

programmed to drive as optimally as possible and drives more smoothly than any human.

Heavy breaking and fast acceleration wears more on the machine, resulting in higher

internal maintenance cost, in terms of more frequent replacement of breaks and faster

consumption of machine oil.

On the other hand, additional internal maintenance cost will rise, at least in the beginning,

since the new solution is more complex, implying more extensive troubleshooting. In

addition, this cost should decrease as a result of less breakdowns, leading to less need of

extra personnel. Furthermore, the external maintenance cost should be slightly decreased

as less breakdowns occur. All aspects of maintenance should be affected by the new

investment, but as seen in figure 14 below, the maintenance savings are equal to 0. This is

a result of lack of data. To compute the economic impact on maintenance savings, the

company should possess data information on how much maintenance expenses each

category amounts up to. The company currently only keep track of “important”

maintenance expenses, which means large expenses having a direct impact on the

company’s overall cash flow.

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Figure 13 Overview of Maintenance Savings

Intuitively, when replacing a manual crane by a smart crane, the production efficiency

would improve. This is true only if, the rest of the production flow has no capacity

limitation. For this specific case, it is not true since the production capacity in this

production process is limited by the capacity in the oven placed after the crane in the

production flow. The oven is using 100 % of its current capacity, meaning the productivity

in the process cannot be improved even if the crane can operate at a higher speed. If the

capacity in the oven was to be improved, tremendous productivity savings could be

achieved, since the new crane can work more effectively if needed. The average value of

one ton of steel is 12 000 SEK. In the case where higher production volume can be reached,

the company would generate higher revenue and increase their profit margins as the costs

are unchanged.

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Figure 14 Overview of Productivity Savings

The most obvious and maybe largest saving potential can be found in the Personnel

savings. Currently there are five full time employees operating the manual crane. With a

smart crane replacement, these five workers would no longer be needed as drivers.

Although, the new crane is not totally autonomous and needs supervision in case of errors.

A Common error is for example, when the plates are too thin and the magnet on the crane

too strong, several plates are incorrectly lifted at once. This implicates wrong treatment

further down the process, contributing to jettisons or retreatments. To cope with this

problem, a worker needs to manually press a button and demagnetize the crane. Therefore,

the most likely scenario is that half of an FTE would be offered to stay, be re-skilled, and

work in a control room; thereby changes their daily working task.

As mentioned, all potential estimations are given three cases; base, best and worst.

Previously, the base case has been explained, while the best case suggests none of these 5

workers are necessary to keep, meaning current supervisors in the control rooms have

capacity to oversee another subprocess. In contrast, the worst case implies one FTE is

needed to oversee the new smart crane, resulting in less FTE savings. Based on the most

likely situation, the potential Personnel savings would amount up to 2 835 000 SEK per

year and the company face a cost saving of 90 % compared to before, as seen in figure 16.

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Figure 15 Overview of Personnel Savings

In figure 17 the calculations for quality savings can be found, showing that the number of

jettison and reprocessed deviations will stay constant after this investment. The truth

behind this result is, insufficient data. The company in question, is in need of extensive

changes when it comes to data collection. Today, the company gathers data of deviation in

only one categorization, type of deviation. Information about number of repainted and

reground sheets are available. However, there is no knowledge about where in the

production process these deviations emerged. As well as, only large deviations are tracked

and the alternative cost for reprocessed steel sheets are not accounted for in any calculation.

To summarize, the economic impact on quality savings is difficult to quantify due to

incomplete data, even if the smart crane may result in fewer deviation, since it drives softer

and more smoothly.

Figure 16 Overview of Quality Savings

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Regarding downtime savings once again, two different scenarios should be considered. In

figure 18, calculation for downtime savings can be found for the scenario of today, where

the production capacity is limited by the next production step. The potential cost saving

equals 0, even if today’s knowledge indicates less planned and unplanned downtimes. All

breaks and restroom visits are times so the oven always can operate at 100 %, meaning

there is a backlog sufficient enough to cover smaller downtimes in the crane. In the other

scenario, where there is unlimited capacity in remaining production process, unplanned

downtime will decrease, in terms of restroom visits and shortage of personnel, contributing

to a cost saving. Furthermore, unplanned downtime could potentially decrease as less

breakdowns eventuates. As recognized in the figure below, unplanned downtime has two

categorizations; bottleneck and normal. This part is developed after case study company’s

preferences as they have production processes where the unplanned downtimes causes

production failure further down the production stream, leading to higher costs.

Figure 17 Overview of Downtime Savings

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6.4 Qualitative Analysis

An overview of the qualitative analysis of investing in an automatic crane compared to a

new manual crane is presented in table 3 at the end of this section. If a new manual crane

is considered, the net qualitative impact would equal zero as it is no different from current

situation.

Work satisfaction is considered a relevant criterion for the investment in consideration.

Referring to the Quantitative Analysis, the best-case scenario is when a 100% reduction of

FTEs can be achieved, and this criterion would not have to be accounted for . However, as

there is a risk that the activity will still require 0.5 – 1 FTE after the investment, the

criterion is seen as relevant. The weight is set to 1 since whether or not work satisfaction

in this subprocess will be affected, by investing in the automatic crane, is of very low

importance. However, if work satisfaction will be affected, the impact is considered to be

moderate and grade is set to 3. The reason for this is that the person that may remain for

this activity will be digitally controlling the smart crane from a control room together with

other employees, instead of manually running it. In general, working with others improves

work satisfaction as the level of human interaction increases. On the other hand, the person

running the manual crane could experience higher stimulation from practical work than

from sitting in front of a screen. Because of the uncertainty in whether or not this activity

will continue to require personnel, as well as the uncertainty about how work satisfaction

will be affected depending on individual preferences, the sensitivity of inputs is set to 3.

Criterion 2, “Health &Safety”, is also relevant for the smart crane. The risk for injuries

will significantly decrease due to two main reasons. First, the risk for accidentally running

into something or someone will be eliminated when the crane is automatic. Second, an

automatic crane requires that the area for its operation is enclosed. Health and Safety is

highly prioritized at the company; therefore, weight is set to 4. The impact will be very

high because of stated reasons and grade is set to 5. As it is known these safety precautions

will be taken, the sensitivity is low and set to 1.

The relevance of Flexibility is set to 0, since the considered project has no impact on this

criterion.

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Re-skilling of Employees, the only negative criterion in the calculation, is relevant. The

automatic crane is a completely new technology at the company and will require education

of personnel in order to run the production, i.e. how it is controlled from the control room

and how to handle errors. Education is crucial for the implementation; therefore, weight is

set to 4. However, new knowledge required is considered to be small and grade is set to -

1. Still it is difficult to appropriately estimating how smooth an implementation of new

technology will actually be, which is the reason sensitivity is set to 2.

The last criterion, “Traceability”, is relevant and significantly affected. In the ongoing

digital transformation, it is of high importance for the company to improve traceability as

it constitutes a prerequisite for smart manufacturing. Consequently, weight is set to 5. The

automatic crane will scan the serial number of steel sheets in order to know how to move

it, and it will also register which serial number is lifted at a certain date and time. The

impact on traceability is very high and grade is set to 5. Sensitivity is set to 1 because of

the low uncertainty in input values.

Table 3 Overview of Qualitative Analysis

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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.

7.1 LoA Analysis

This section shows the results from the mapping of the production section as well as the

assessment of the specific project. Starting with the mapping, the average current

Mechanical LoA and Information LoA for the entire production section equals 4.6 and 3.2

respectively. Mechanical LoA ranges from 4 – 5, while Information LoA ranges from 2 –

5, where Mechanical LoA exceeds Information LoA in all subprocesses except one, where

they are equal. The average minimum value for Mechanical LoA is 4.3, which is higher

than the average minimum for Information LoA of 1.9.

This current state of LoA, in which where mechanical levels tend to be higher than

cognitive levels, can be explained by the chronological order in the history of the industrial

revolution. As mechanical solutions were implemented earlier in history than digital

solutions, companies have had more time to improve and develop the mechanical parts of

production systems compared to the cognitive parts. The average relevant maximum LoA

for Information is 7, while the average relevant maximum Mechanical LoA is below 7,

since some activities would not benefit from possessing more advanced features than for

instance level 5 or 6. An overview of the LoA mapping and the percentual levels of LoA,

Relative LoA and Potential is presented in table 4 below.

Table 4 LoA Mapping

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A visualization of the level of automation in relation to its potential for each subprocess

and accumulated for the entire production section is presented in figure 19. It shows that

the subprocesses “Takkran N2”, “QT” and “ “Småkranar” are the subprocess with the

highest digitalization potential, while “Ugn N2” is the most digitalized subprocess. In total,

the production section reached an automation level of 58 %, implying it contains great

potential for future digitalization investments.

Figure 18 LoA Chart over Investment Potential

Continuing with the assessment of the specific investment, figure 20 shows SoPI results

referring to investing in the automatic crane. Currently, “Takkran N2” is at the bottom left

corner of the square, holding the lowest acceptable levels of automation. The investment

would entail a significant improvement; Mechanical LoA would increase by two levels

and Information LoA by three levels. Expressed in percentages, the level of automation

would increase from 43 % to 79 %. The LoA improvement can be considered an incentive

for investing.

69

Figure 19 SoPI Results

7.2 Quantitative Analysis

The quantitative analysis shows a large positive outcome for the NPV. From the literature

review, it is known that projects with an NPV greater than 0 should be considered a good

investment opportunity but needs to be complemented with other analyses. This result is

confirmed by the IRR, PB and ROI. Since the annual saving obtained from this investment

is around 2.8 MSEK and the initial cost for the digital expansion is 3 MSEK, the PB

becomes only 1.1 years. Hence, this project hits break-even after only 1.1 years, without

considering the time-value of money. In this case, the PB is short, meaning in one year the

money will no longer be tied-up in this investment and can carry new investment

opportunities. Similarly, an IRR at 71 % is significantly higher than the require rate of

return of 14 %, implying this is a good investment.

Last but not least, to reinforce the hypothesis of a positive investment the ROI was

determined. As visualized in the table below, the ROI are close to 400 %, meaning a value

4 times larger than the initial investment can been realized through this investment. To

obtain a better feel of an ROI of 400 %, it can be compared to the return from an investment

in a risk-free asset, for example 10 years treasury bond rate, with an average around 2 SEK

70

over time. After comparison it can be concluded that project is a great investment, from an

economic standpoint.

Table 5 Quantitative KPIs Results

The only cost saving factor that could be quantified in the case study, based on the current

conditions, is Personnel. The current cost for five operators is 3.15 MSEK, and if applying

the base case where 0.5 FTE will remain in operation, the cost for personnel will be

equivalent to 0.3 MSEK. Please find the illustration of the different in figure 21. To find

the saving per factor please see figure 22 and figure 23 shows how the savings are

distributed among the different factors.

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Figure 20 Saving Potential

Figure 21 Savings Per Factor

Figure 22 Savings Pie Chart

72

7.2.1 Sensitivity Analysis

To simulate different possible outcomes of the quantitative result, different discount rates

are compared with different cases of input data. There are three possible cases and three

variations of discount rates, giving 9 possible outcomes for each KPI, illustrated in next

figure. The best outcome is generated by the combination; best case and lowest discount

rate, which makes sense as the best case generates the highest saving potential and the

lowest discount rate generate highest discounted value possible. On the contrary, the worst

case against the highest discount rate results in the worst outcome, with the same

reasoning. From conducting this sensitivity analysis one can conclude that all KPIs are

more sensitive to movements in the chosen case than fluctuation in discount rate. In the

figure below, please find all the different outcomes.

Figure 23 Quantitative Sensitivity Analysis Result

Per definition of median, it is reasonable that the base case combined with middle discount

rate gives the median. The median in this case should be interpreted as the most likely or

most common outcome. The median and average lies within the same range, indicating no

extreme outcomes for any of the combination. The standard deviation is quite small in all

cases, indicating the result is somewhat stable and does not deviate from the mean too

much. Lower volatility implies a more reliable result and in this case the result is

unanimous and indicates positive investment opportunity.

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Table 6 Sensitivity Analysis Summary

Moreover a tornado diagram has been established to acknowledge how different KPIs

varies when the discount rate changes by plus and minus one percent, respectively. The

diagram, in figure 24, indicates that one percent change in discount rate implies 5 percent

change on NPV and ROI. However, the same change in discount rate only generates 2

percent change on IRR. Moreover, it is reasonable that PB remains unaffected by the

change in discount rate, as the metric does not take any time value into consideration. As

a conclusion, NPV and ROI is more sensitive to discount rate changes, while IRR is less

sensitive.

Figure 24 Discount Rate Tornado Diagram

7.3 Qualitative Analysis

Table 7 shows the results of the qualitative analysis. The total score for the investment

equals 39 out of 100, resulting in an index of 39 %. This can be interpreted as that the total

qualitative contribution is 39 % out of the maximal possible contribution of a digitalization

project. Not many digitalization projects succeed to impact all criterions, which is the

74

reason why an index of 39 % can be interpreted as a high index. The relative index equals

52 %, which is higher than index since only 3 out of 4 positive criterions were relevant.

This result shows how well the investment fulfills the criterions it aims to, or is able to,

have an impact on.

Table 7 Qualitative Analysis Results

7.3.1 Sensitivity Analysis

The sensitivity analysis of the Qualitative Analysis is illustrated in figure 25. The

illustration is clearly showing that input values for Work Satisfaction and Re-skilling of

Employees hold the highest uncertainties. However, since both the index for Work

Satisfaction as well as for Re-skilling of Employees is low, the deflection from the

uncertainties will affect the analysis to very little extent. Note that index for Re-skilling of

Employees is presented on the negative y-axis, since it represents a negative assessment

criterion. As the criterions with the highest indexes are the least sensitive, the overall

sensitivity of the analysis is low, and the qualitative results can be considered accurate.

Figure 25 Qualitative Sensitivity Analysis Results

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8 Analysis of Results

To begin with, it is of importance to enlighten that the results of this thesis presented in

chapter 7 are somewhat subjective, since they are all based on estimations and assumptions

made by the user of the model. However, the sensitivity analyses showed low sensitivity

in outputs for the specific project the model was applied to. In general, the non-monetary

results of the LoA Analysis and Qualitative Analysis could be examined with less caution

than the Quantitative Analysis.

Firstly, the investment “smart crane” considered in this thesis showed to be a positive

investment from all three analyses. However, the timing of the project determined whether

or not it would be considered a replacement investment or a strategic investment, which in

the end has great significance to the quantitative results. If the timing would have been

different, and the current crane was still able to function as normal, the project would

instead have been considered a strategic investment. The main differences would be the

initial investment cost which would equal 23 MSEK instead of 3 MSEK, and also the

discount rate for DCF calculations which would be set to 14 % for the entire investment.

When applying the quantitative model on the same case but considering the project a

strategic one, all the economic KPIs are instead extremely negative and it would be very

difficult to argue for implementation. Moreover, the importance of timing when

considering digitalization is therefore crucial in economic investment analyses.

Secondly, the results presented in section 7.2 are limited since some impacts on the cost

saving factors Maintenance, Productivity, FTE, Quality and Downtime were not taken into

account due to lack of data or difficulties in estimating potential state. Due to the lacking

data collection, marginal effects are not able to be accounted for in the analysis. One

important insight from this thesis is that not enough data is collected at the case study

company, which we believe applies to many other manufacturing companies as well. Thus,

this constitutes one factor obstructing solid analyses of economic impacts within the area

of digitalization at process level.

In addition, evaluating the actual effects of digitalization is limited by how other operations

in the production flow are functioning. In the case of the smart crane, production volumes

can increase if productivity improve in remaining production flow as well. However, these

76

effects can only be achieved if other projects at other subprocesses or production sections

are implemented as well. This is one of the main complexities with digitalization projects;

the positive impacts can be fully achieved only when the entire production chain is

digitalized, and a digital transformation attained.

Lastly, in the project of consideration, five out of five identified cost saving factors were

estimated by the case study company to be theoretically impacted. Due to this fact, it can

be interpreted that the effects that are argued for in the literature seem present the actual

effects in a real manufacturing situation. FTE was the only cost saving factor this study

was able to quantify in monetary terms, which would decrease by 100 % in the best case

and by 80 % in the worst case. The literature review showed one quantified quantitative

effect was decreased conversion costs by 15 – 25 %, see section 4.2.1. Conversion costs

include direct labor and overhead expenses, excluding material costs. In this thesis, costs

for direct labor are the same as FTE costs. However, as no results about change in overhead

expenses were derived in this study, comparison with quantified effects in the literature

review was not possible.

77

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.

9.1 Discussion of Method

An observation when studying the existing literature addressing effects of digitalization

was that many articles contained a low level of transparency and replicability. The impacts

identified were sometimes from different perspectives or completely correlated, meaning

the researcher uses two different words to explain the same effect. The chosen

methodology allows the reader to transparently understand how effects of digitalization

are addressed in this thesis, i.e. at Process Level from an Internal Efficiency perspective.

A limitation of this study is in fact the chosen research methodology where a single case

study is conducted. Therefore, the results and conclusions of this paper should be used

with caution in other situations where circumstances are different.

Another limitation was the current situation; with the pandemic of Covid-19, meetings,

field trips and presentation were cancelled. Among those, planned visits in production were

cancelled where knowledge and input data were supposed to be collected. Due to the

circumstances, discussions with the supervisor at the case study company became even

more important in order to collect the required information.

9.2 Reliability & Validity

As mentioned in Analysis of Results, chapter 7, the case study showed that theoretically,

5 out of 5 cost saving factors included in the quantitative analysis were affected by the

digitalization project in consideration. This can be seen as a validating factor for cost

saving factors being identified in a rigorous way.

As this study aims to address effects of digitalization at Process level from an Internal

Efficiency perspective, there may be consequences outside the scope that potentially could

affect the economic or qualitative outcome.

78

As this study aim to develop a quantitative investment model based on data collection from

literature reviews and a single case study, it is important to keep in mind that the results

are depend on assumptions and estimations made by the user of the model which may

reduce the level of reliability. As shown in results, small adjustments to the discount rate

result in large fluctuations of different KPIs.

As described in the problematization, the number of legitimate sources providing insightful

and rigorous result of the economic aspect of digitalization in steel industry or

digitalization in general, are limited. As a result, it was difficult to collect and find data

from published literature, leading to lower validity. During this study we mainly focused

on reliable sources like textbooks, published articles with large amount of citations,

academic journals and reports published by larger organizations or well-known

consultancy firms, with minor compromises. These minor compromises combined with

limited number of primary sources lower the generalizability.

9.3 Generalizability

We believe the structure of the investment model is developed to be appropriate for use at

several manufacturing industries and not only the case study company, since the structures

are mainly derived from findings in the literature. The LoA analysis should be applicable

to other situations without any modifications. However, the Quantitative Analysis may

need to be modified in terms of how each cost saving factor is calculated, while the cost

saving factors themselves (Maintenance, Productivity, Personnel, Quality and Downtime)

are more general. Regarding the Qualitative Analysis, it could definitely be applied to other

cases, there may be other quantitative effects important for a company that should be taken

into account if the contextual situation were different, and new criterions might need to be

added to the analysis.

79

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.

10.1 Answer of Research Question 1

The first research question was formulated as “What are the potential impacts of

digitalization in a delimited steel production section?”, which this thesis managed to

answer in a transparent way. The potential impacts of digitalization can be categorized into

different sorts of effects; quantitative or qualitative. Depending on their economic impact,

they can also be identified as either positive or negative. Digitalization is closely related to

automation and another effect from digitalizing in productions is an increased level of

automation. In total, eleven quantitative effects and seven qualitative effects were

identified, whereof ten out of eleven quantitative effects were relevant at the delimited

steel production section. The relevant quantitative effects can be clustered into five cost

saving factors; Maintenance, Productivity, Personnel, Quality and Downtime, while the

qualitative effects can be summarized with five qualitative criterions; Flexibility (P),

Traceability (P), Traceability (P), Work Satisfaction (P), Health & Safety (P) and Re-

skilling of Employees (N).

10.2 Answer of Research Question 2

Research question number two asked; “How can potential impacts from digitalization

projects be quantified?”. The investment model developed in this thesis provides a three-

part support for decision making regarding digitalization projects. The first analysis, LoA

Analysis, is a quantification of how digitalization may increase the level of automation in

production. Quantitative effects are quantified by the quantitative model, which is based

on a DCF valuation, quantifying the effects in terms of cost savings, NPV, IRR, PB and

ROI. Qualitative effects can be quantified by conducting a MCA based on the five

qualitative criterions and thereby calculating score, index and relative index.

80

10.3 Answer of Research Question 3

The last research question was stated as “What potential cost savings can be expected from

digitalization projects?”. Section 4.2.1 “Quantified Quantitative Impacts” presents what

the existing literature is claiming regarding how different costs in production may be

affected through digitalization. Based on the single case study conducted in this work, cost

savings can be calculated based on the factors Maintenance, Productivity, Personnel,

Quality and Downtime, as mentioned in in the answer of the first research question. The

results of this work showed that a steel producer could expect yearly cost savings of nearly

3 MSEK by investing in a smart crane, only taking the change in personnel costs into

account.

10.4 General Conclusion

Due to the findings of this thesis, digitalization has a significant impact on producing

industries. However, these impacts are sometimes difficult to quantify, mainly because of

poor data collection, unstructured data and issues related to estimating the extent of change.

The results of the single case study imply for the largest impacts to include reduce

personnel costs, increased health and safety at the workplace and increased traceability in

production. Digitalization projects are net positive investments if the timing is right, i.e. if

they are implemented at the end of the lifespan of current technology and therefore can be

considered a replacement project, only requiring a certain return on the accessible cost for

the digital solution compared to the cost of current technology. However, digitalization

can become a vicious circle if only monetary valuations are taken into consideration for

decision makings. Effects can reach its full potential only when production efficiency is

optimized in the whole production line, meaning initiation of projects may be necessary in

order to enable synergies that will later affect the economic result. Therefore, there may

be incentives to initiate a digitalization project even if the quantitative analysis is currently

showing negative results on KPIs.

81

10.5 Recommendation & Future Research

Throughout this study several issues regarding concretizing and quantifying economic

effects of digitalization in steel industry have arisen. A considerable insight from this study

is the lack of data collection within the area of “quantitative effects of digitalization”.

Therefore, future research should bring more attention to structuring interviews and collect

more information on how industries work with digitalization and what effects that has been

identified in historical projects. A fundamental prerequisite for researchers to better collect

data on how companies work with digitalization, is diversified and structured data

collection at each individual company. Without necessary measurements on the production

it is impossible to see the full economic potential of digitalization. Therefore, companies

need to invest in new digital systems, such as sensor systems, allowing more intensive data

collection and hire knowledgeable workers whom can make sense of the data. Collected

data needs to be flexible and categorized on several factors in the entire production. These

are large investment for companies and unfortunately without the data it is impossible to

analyze the economic benefit from digitalization.

Due to the time limitation it was not possible to apply the model to several projects or

account for the affect extension projects brings. As mentioned before, large economic

benefits are often realized by several digitalization projects combined and not each one

separately. Therefore, another interesting research area is to examine the economic benefit

when accounting for different extension projects.

As addressed in the report, timing is an important factor for new digitalization investments.

However, not all digitalization projects are replacement projects and some strategic

projects are vital to the future digitalization progress at the companies. As for now in 2020,

digitalization seems inevitable and most companies are aware of the cost of not following

this trend. So once again, maybe the question is not if it is economic beneficial to

implement new digital technologies, but rather what is to be lost in not doing so.

82

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Smart crane Takkran N2 Saturday, 9 May 2020

Quantitative AnalysisCase chosen 1Base case 1Best case 2Worst case 3

SAVINGS CALCULATION

Maintenance Unit Current Potential

Internal maintenance cost SEK/year 0 0- Improvement SEK 0 - base case 0 - best case 0 - worst case 0

Additional internal maintenance cost SEK/hour 0 0Additional internal maintenance hour/year 0 0- Improvement hours 0 - base case 0 - best case 0 - worst case 0

External maintenance cost SEK/year 0 0- Improvement SEK 0 - base case 0 - best case 0 - worst case 0

Total maintenance cost SEK/year 0 0Maintenance cost per tonnes SEK/tonnes 0,0 0,0

Maintenance savings SEK/year 0- Savings in percentage 0%Savings per tonnes SEK/tonnes 0,0

Productivity Unit Current Potential

Operation cost SEK/year 0 0Production volume tonnes/year 276 250 276 250Cost per tonnes SEK 0,0 0,0Capacity use 100% 100% - base case 100% - best case 100% - worst case 100%Total productivity income 0,0 0,0

Productivity Income SEK/year 0- Savings in percentage 0%Savings per tonnes SEK/tonnes 0,0

Personnel Unit Current Potential

Cost per FTE SEK/year 630 000 630 000#FTE 5,0 0,5 - base case 0,5 - best case 0,0 - worst case 1,0Total peronnel cost SEK/year 3 150 000 315 000Personnel cost per tonnes SEK/tonnes 11,4 1,1

Personnel savings SEK/year 2 835 000- Savings in percentage 90%Savings per tonnes SEK/tonnes 10,3

Quality Unit Current Potential

Jettison deviations tonnes/year 276 276- Improvement (%) tonnes/year 0% - base case 0% - best case 0% - worst case 0%

Cost per deviation SEK/tonnes 9 800 9 800

Reprocessed deviations tonnes/year 0 0- Improvement (%) tonnes/year 0% - base case 0% - best case 0% - worst case 0%Cost per deviation SEK/tonnes 180 180

Total deviation cost SEK/year 0 0Deviation cost per tonnes SEK/tonnes 0,0 0,0

Quality savings SEK/year 0- Savings in percentage 0%Savings per tonnes SEK/tonnes 0,0

Downtime Unit Current Potential

Planned downtime hours/year 615 615- Improvement (%) hours/year 0% - base case 0% - best case 0% - worst case 0%Cost per downtime SEK/hour 553 607 553 607

Unplanned downtime (bottleneck) hours/year 0 0- Improvement (%) hours/year 0% - base case 0% - best case 0% - worst case 0%Cost per bottleneck SEK/hour 0 0

Unplanned downtime (normal) hours/year 2 160 2 160- Improvement (%) hours/year 0% - base case 0% - best case 0% - worst case 0%Cost per normal SEK/hour 553 607 553 607

Total downtime cost SEK/year 0 0Downtime cost per tonnes SEK/tonnes 0,0 0,0

Downtime savings SEK/year 0- Savings in percentage SEK/tonnes 0%Savings per tonnes 0,0

TOTAL SAVINGS SEK/year 2 835 000 - base case 2 835 000 - best case 3 150 000 - worst case 2 520 000- Savings in percentage 90,0%

Savings per tonnes SEK/tonnes 10,3 - base case 10,3 - best case 11,4 - worst case 9,1

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