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AND THE MAIN FIELD OF STUDY INDUSTRIAL MANAGEMENT, SECOND CYCLE, 30 CREDITS STOCKHOLM SWEDEN 2020,

Driving Autonomous Heavy Vehicles into the Future

A Business Model Perspective

GABRIEL KITZLER ANNA SAIBEL

KTH ROYAL INSTITUTE OF TECHNOLOGY

SCHOOL OF INDUSTRIAL ENGINEERING AND MANAGEMENT

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Driving Autonomous Heavy Vehicles into the Future

A Business Model Perspective

by

Gabriel Kitzler Anna Saibel

Master of Science Thesis TRITA-ITM-EX 2020:330 KTH Industrial Engineering and Management

Industrial Management

SE-100 44 STOCKHOLM

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Driving Autonomous Heavy Vehicles into the Future

Ett affärsmodellsperspektiv

av

Gabriel Kitzler Anna Saibel

Examensarbete TRITA-ITM-EX 2020:330 KTH Industriell teknik och management

Industriell ekonomi och organisation

SE-100 44 STOCKHOLM

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Master of Science Thesis TRITA-ITM-EX 2020:330

Driving Autonomous Heavy Vehicles into the Future – A Business Model Perspective

Gabriel Kitzler Anna Saibel

Approved

2020-06-09

Examiner

Lars Uppvall

Supervisor

Matti Kaulio

Commissioner

Scania CV AB

Contact person

Rodrigo Caetano Abstract

In light of the many environmental challenges that the world currently faces, new sustainable solutions are called for. The concept of autonomous heavy vehicles (AVs) is considered to be one of the next megatrends within transportation and this technology shift is predicted to improve safety and logistics as well as to cut driver costs and reduce CO2-emissions. However, from a company's perspective, technology shifts are not without risks as technical disruptions can cause core competencies to become obsolete and radical technology innovation can be fatal to a company that does not innovate its business models simultaneously. Due to the complexity and novelty of the AV technology, business model innovation within the field has been lagging behind and there is an area of uncertainty regarding how a future business model for AVs could be formulated In order to investigate potential business models for AV applications, this study has been carried out as an exploratory case study of two industry specific applications for goods transports within confined areas at the heavy vehicle manufacturer Scania in Södertälje, Sweden. The Business Model Canvas tool developed by Osterwalder and Pigneur (2010) has been used to map the business models of these two cases with the purpose of combining them into a general model.

Furthermore, four important capabilities at the company have been identified and determined as to whether they qualify as core competencies based on the criteria presented by Prahalad and Hamel (1990) and then discussed in relation to how they can be leveraged in a future business model.

The findings of this study help to formulate a business model perspective for future AV goods transport applications that consists of a service-based model characterised by a focus on collaboration and value co-creation, an adaptable level of integration with the customers' systems, transfer of ownership of products to the manufacturer and a value-driven source of differentiation.

Lastly, the study concludes that Lean production and modularity are two existing core competencies of Scania that could be leveraged dynamically in a future business model connected to this technology shift.

Keywords: Autonomous Heavy Vehicles, Technology Shifts, Business Model Innovation,

Business Model Canvas, Core Competencies, Servitisation

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Examensarbete TRITA-ITM-EX 2020:330

Driving Autonomous Heavy Vehicles into the Future – Ett affärsmodellsperspektiv

Gabriel Kitzler Anna Saibel

Godkänt

2020-06-09

Examinator

Lars Uppvall

Handledare

Matti Kaulio

Uppdragsgivare

Scania CV AB

Kontaktperson

Rodrigo Caetano Sammanfattning

Mot bakgrunden av de många miljömässiga utmaningar som världen står inför idag krävs nya hållbara lösningar. Konceptet självkörande tunga fordon (eng. autonomous heavy vehicle - AV) anses vara en av de nästa megatrenderna inom transportindustrin och detta teknikskifte förutspås förbättra säkerhet och logistiksystem samt sänka förarkostnader och minska koldioxidutsläpp.

Från ett företags perspektiv är teknikförändringar dock inte utan risker då tekniska disruptioner kan göra kärnkompetenser föråldrade och radikal teknisk innovation rentav kan innebära en dödsdom för ett företag som inte simultant innoverar sina affärsmodeller. Till följd av teknikens komplexitet och låga mognadsgrad har affärsmodellsinnovation inom fältet hamnat efter och det finns ett område av osäkerhet gällande hur en framtida affärsmodell för självkörande fordon skulle kunna formuleras.

I syfte att undersöka potentiella affärsmodeller för AV-applikationer har denna studie genomförts som en utforskande fallstudie av två industrispecifika applikationer för godstransporter inom avgränsade områden hos lastbilstillverkaren Scania i Södertälje, Sverige. Verktyget Business Model Canvas, utvecklat av Osterwalder och Pigneur (2010), har använts för att kartlägga affärsmodellerna för dessa två applikationer i syfte att kombinera dem till en generell modell.

Vidare har fyra viktiga kapabiliteter i företaget identifierats och fastställts huruvida de kvalificerar som kärnkompetenser baserat på kriterierna som presenteras av Prahalad och Hamel (1990) och sedan diskuterats i relation till hur de kan utnyttjas i en framtida affärsmodell.

Resultaten av denna studie hjälper till att formulera ett affärsmodellsperspektiv för framtida AV- godsapplikationer som innebär en servicebaserad modell kännetecknad av ett fokus på samarbete och värdesamskapande, en anpassningsbar integration till kundernas system, överföring av ägandeskap av produkter till tillverkaren och en värdedriven differentiering. Slutligen dras slutsatsen att Lean produktion och modularitet är två befintliga kärnkompetenser hos Scania som skulle kunna utnyttjas dynamiskt i en framtida affärsmodell kopplat till detta teknikskifte.

Nyckelord: Självkörande tunga fordon, Teknikskiften, Affärsmodellsinnovation, Business Model

Canvas, Kärnkompetenser, Tjänstefiering

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1 Introduction 1

1.1 Background . . . 1

1.2 Problem Statement . . . 3

1.3 Purpose . . . 4

1.4 Research Questions . . . 4

1.5 Delimitations . . . 4

1.6 Thesis Sponsor . . . 5

1.7 Disposition of Report . . . 5

2 Frame of Reference 7 2.1 Surviving Technology Shifts . . . 7

2.2 Business Model Innovation . . . 8

2.2.1 Business Model Ambiguity . . . 8

2.2.2 The Business Model Canvas . . . 9

2.3 Competitive Advantage, Capabilities and Core Competencies . . . 11

2.3.1 Identifying Core Competencies . . . 11

2.3.2 The Risk of Core Rigidities . . . 12

2.3.3 Dynamic Capabilities . . . 13

2.3.4 Critical Competency . . . 13

2.4 Collaboration and Value Co-creation . . . 14

2.5 Servitisation . . . 15

2.5.1 Downstream Vertical Integration . . . 16

2.6 Summary of Frame of Reference . . . 17

3 Methodology 18 3.1 Choice of Methodological Approach . . . 18

3.2 Research Design . . . 19

3.2.1 Initial Phase . . . 19

3.2.2 Main Phase . . . 21

3.2.3 Final Phase . . . 22

3.3 Frame of Reference . . . 22

3.4 Data Collection . . . 23

3.4.1 Interview Methodology . . . 23

3.4.2 Observations . . . 23

3.4.3 Methodology for Data Analysis . . . 24

3.5 Limitations . . . 24

3.6 Validity and Reliability . . . 25

3.7 Ethical Considerations . . . 26

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4.2 Harbour Case Background . . . 30

5 Findings and Analysis 33 5.1 Mining Case . . . 33

5.1.1 Mining Development Phase - Business Model Canvas . . . 33

5.1.2 Mining Commercial Phase - Business Model Canvas . . . 36

5.2 Harbour Case . . . 38

5.2.1 Harbour Development Phase - Business Model Canvas . . . 38

5.2.2 Harbour Commercial Phase - Business Model Canvas . . . 40

5.3 Identified Capabilities . . . 42

6 Discussion 45 6.1 Comparison between Mining and Harbour Applications . . . 45

6.2 Future Core Competencies . . . 48

6.2.1 Core Competencies in the Business Model . . . 50

6.2.2 Dynamic Capabilities, Core Rigidities and Critical Competency . . . 51

6.3 A Future Business Model Perspective . . . 52

6.3.1 A Service-based Business Model . . . 52

6.3.2 An Alternative Business Model . . . 55

6.3.3 Sustainability . . . 56

7 Conclusion 58 7.1 Conclusions . . . 58

7.2 Implications . . . 59

7.2.1 Industrial Implications . . . 59

7.2.2 Academic Implications . . . 60

7.3 Suggestions for Further Studies . . . 60

References 60

Appendix A SAE Levels of Automation Classification 66

Appendix B Exploratory Interview Guide 67

Appendix C Identified Capabilities at Scania 69

Appendix D Case Study Interview Guide 70

Appendix E Final BMC for a General Model 71

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Figure 1.1: Major technology shifts a↵ecting the transportation industry (Scania, 2020a) 2 Figure 1.2: Illustration of a hub and hub2hub transportation system (Scania, 2020a) . 2 Figure 1.3: Illustration of how the business opportunities could scale over time for heavy

Autonomous Vehicles (Scania, 2020a) . . . 3

Figure 2.1: The Business Model Canvas (BMC) tool, adapted from Osterwalder and Pigneur (2010) . . . 9

Figure 2.2: An illustration of core competencies seen as the roots of a tree structure (MBA, 2020) . . . 12

Figure 2.3: A visual representation of the competency hierarchy, adapted from Srivas- tava (2005) and Hamel (1994) . . . 13

Figure 2.4: A framework for managing the critical competency of a firm (Srivastava, 2005) . . . 14

Figure 2.5: An illustration of Party Logistics provider levels within the goods trans- portation industry (Scania, 2019b) . . . 16

Figure 3.1: An overview of the workflow of the study . . . 19

Figure 4.1: An illustration of two phases within the mining initiative . . . 28

Figure 4.2: Considered business model alternatives for the Mining Case . . . 29

Figure 4.3: An overview of a typical seaside operational flow within a harbour . . . 31

Figure 4.4: An illustration of two independent phases within the harbour initiative . . 31

Figure 5.1: The Mining Case - A BMC illustration for the Development Phase . . . . 33

Figure 5.2: The Mining Case - A BMC illustration for the Commercial Phase . . . 36

Figure 5.3: The Harbour Case - A BMC illustration for the Development Phase . . . . 38

Figure 5.4: The Harbour Case - A BMC illustration for the Commercial Phase . . . . 40

Figure 5.5: Frequency of highlighted capabilities from the exploratory interviews . . . 42

Figure 6.1: A General Hub Application - An Aggregated BMC illustration for a Com- mercial Phase . . . 46

Figure 6.2: An illustration of how the core competencies Modularity and Lean produc- tion can be integrated into the Key Resources and Value Proposition blocks 51 Figure A.1: SAE Levels of Automation Classification (SAE, 2018) . . . 66

Figure E.1: Final BMC for a general business model perspective . . . 71

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Table 3.1: Exploratory interviews at the department of Autonomous Solutions at Scania 20 Table 3.2: Case study interviews involving a Mining Case and a Harbour Case . . . . 22 Table 4.1: Division of responsibilities between Scania and the Mining Company . . . . 28 Table 6.1: A table presenting the identified capabilities, selected focused capabilities

and qualified core competencies that can be used by Scania to gain a com- petitive advantage in an autonomous future . . . 50 Table C.1: An overview of the frequency of highlighted capabilities and potential core

competencies from the exploratory interviews . . . 69

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ACE Automated, Connected and Electric AGV Automated Guided Vehicle

API Application Programming Interface ATS Autonomous Transport Solutions AV Autonomous Vehicle

BMC Business Model Canvas CaaS Capacity as a Service CO2 Carbon dioxide

GDP Gross Domestic Product IT Information Technology

KTH Kungliga Tekniska H¨ogskolan (eng. Royal Institute of Technology) LaaS Logistics as a Service

MaaS Mobility as a Service ODD Operational Design Domain OEM Original Equipment Manufacturer PL Party Logistics

R&D Research and Development SAE Society of Automotive Engineers TaaS Transport as a Service

VaaS Vehicle as a Service

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This study has been conducted as a master thesis project during the spring of 2020 on behalf of the Swedish heavy vehicle manufacturer Scania. The thesis is the final part of a Master of Science degree for two students at the department of Industrial Engineering and Management at KTH, Royal Institute of Technology.

There are many individuals who are owed gratitude for their knowledge, guidance and feedback that made this thesis project possible, especially our supervisor at Scania, Rodrigo Caetano. We would also like to thank the whole department of Autonomous Solutions at Scania for giving us the opportunity to take part in their ongoing projects and making us feel welcome in the team.

Furthermore, we would like to thank our supervisor at KTH, Matti Kaulio, Head of department of Industrial Engineering and Management, for his support and guidance during this semester despite the special circumstances of the Covid-19 pandemic. We would also like to thank Rami Darwish at the Integrated Transport Research Lab for providing valuable insights and perspectives.

We also want to thank our fellow students at the department of Industrial Engineering and Man- agement at KTH for inspiring discussions and valuable feedback during seminar sessions, and also family for proof-reading and giving suggestions for improvements.

Anna Saibel and Gabriel Kitzler Stockholm, June 2020

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Introduction

This chapter introduces the topic of the thesis and sets both the theoretical and practical context background for the studied phenomenon. The problem statement is introduced along with the pur- pose and the research questions that the thesis aims to answer. Furthermore, the delimitations of the study are discussed, the thesis sponsor is presented and finally a brief disposition of the report is given.

1.1 Background

Since the invention of the wheel over 3000 years ago, the mechanised way of transporting goods and people has shaped the world we live in today. Since the industrialisation and the introduction of the combustion engine, the global Gross Domestic Product (GDP) curve has increased exponen- tially (see Figure 1.1). Unfortunately, the carbon dioxide (CO2) curve has followed the same trend and the heavy transport industry calls for sustainable solutions (Scania, 2020a). The concept of Autonomous Vehicles (AVs) is considered one of the next megatrends within transportation (Kuh- nert et al., 2017). An autonomous vehicle is defined as a vehicle that can interpret and adapt to their surrounding through a combination of sensor tools and artificial intelligence to solve certain predetermined tasks (Taeihagh and Lim, 2019). According to Litman (2020) AVs are predicted to improve safety, eco-driving, city space utilisation, traffic optimisation and cut driver costs, which are incentives that push this imminent technology shift to the close horizon. Combined with other megatrends such as electrification and connectivity, the future Automated, Connected and Electric (ACE) vehicles have the potential of breaking the CO2 curve, while maintaining the rising GDP levels. However, from a company’s perspective, technology shifts are not without risks as technical disruptions can cause core competencies to become obsolete and radical innovation can be fatal to a company that does not innovate its business models simultaneously (Tongur and Engwall, 2014).

Traditionally, Original Equipment Manufacturers (OEMs) have focused on producing tangible goods and providing customers with services, such as repair & maintenance (Lay, 2014). To- day however, OEMs increasingly adapt their existing business models to a more service-oriented approach, either in order to add value to existing products or to create completely new value propo- sitions. This servitisation concept has paved the way for new service-based business models within the transport industry, where vehicles are converted from physical products into a service, often referred to as Mobility as a Service (MaaS) (Mulley et al., 2018). Within the industry of heavy vehicle manufacturers, related service-based model concepts defined as Vehicle as a Service (VaaS) and Transport as a Service (TaaS) are used to illustrate di↵erent pathways for the next generation

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Figure 1.1: Major technology shifts a↵ecting the transportation industry (Scania, 2020a)

of business models. TaaS is commonly used when referring to transportation of goods (e.g. trucks), while MaaS is used when discussing transportation of people (e.g. ride sharing). These two main pillars, transportation of goods versus people, can be divided further into two sub-fields of so-called hub and hub2hub solutions (see Figure 1.2), where hub implies transportation within a confined area, (e.g. a harbour, mining or airport area) and hub2hub includes transportation between hubs.

Figure 1.2: Illustration of a hub and hub2hub transportation system (Scania, 2020a)

The hub and hub2hub operational concepts are important stepping stones in the transition to- wards full mobility, where most land based road transports are replaced with AVs. An illustration of the business opportunity volumes and scalability over time can be seen in Figure 1.3. Since transportation of goods is less complicated than transportation of people, the confined hub goods transport operation phase can be viewed as the first important transition phase in the AV tech- nology shift. Although the business opportunities and profitability might be higher in the future more scalable full autonomous mobility phase, the first transitional hub goods transport operation phase is crucial in gaining a commercial foothold in the autonomous industry and advancing the technology for all heavy commercial autonomous vehicle manufacturers (Scania, 2020a).

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Figure 1.3: Illustration of how the business opportunities could scale over time for heavy Au- tonomous Vehicles (Scania, 2020a)

Within the automotive industry the Swedish heavy commercial vehicle manufacturer Scania is in the forefront regarding innovative vehicle solutions and strives towards a safe, sustainable and fossil- free environment (Scania, 2019a). Within Scania, the ACE concept is well-established and used in their work towards meeting the aforementioned goals (Scania, 2020a). Autonomous Transport Solutions (ATS) are developed by Scania in close cooperation with leading technology suppliers and academic institutions and at this moment AVs can operate in controlled industrial settings whereas deployment on public roads, which is a more complex environment, will be available in a not too distant future. Automation in itself is only one part of the ATS as it also encompasses handling logistics, the assignment of tasks to vehicles and information-sharing between vehicles and infrastructure, which opens up new business opportunities for heavy commercial vehicle man- ufacturers (Scania, 2019a). Two of Scania’s ongoing projects within confined hub goods transports is a mining and a harbour application (Scania, 2019a; Aulbur et al., 2020).

Today, Scania is known for their premium customised cabins and advanced combustion engines.

However, the AV technology shift will inevitably a↵ect heavy commercial vehicle manufacturers’

capabilities to di↵erentiate themselves within their value proposition with core competencies such as driver comfort diminishing as physical drivers become obsolete. In addition, with electrification removing the combustion engine, heavy commercial vehicle manufacturers will need to identify and utilise their other strengths and existing core competencies that can still be used in an autonomous future (Prahalad and Hamel, 1990). Setting aside technical difficulties there are many dilemmas surrounding the implementation of driverless vehicles that, to only mention a few of them, involve policy and legislation as well as infrastructure and consumer acceptance, which complicates the development of business models in a technological shift to AVs (Contissa et al., 2017; Threlfall, 2019). For many OEMs, Scania included, the emphasis has been on developing the autonomous technology (Fagnant and Kockelman, 2015). This technology focus seems to have caused business model innovation within the area to be lagging behind, as is often the case in the early phases of technology shifts (Tongur and Engwall, 2014; Kaulio et al., 2017).

1.2 Problem Statement

Due to the complexity and many uncertainties in the shift to autonomous vehicles there is a lack of empirics regarding business models in this area, where most previous studies have focused on the autonomous technology itself. Business model innovation in the industry has up until now been mostly based on trial and error and the number of commercial business cases has been limited.

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Specifically, there is a lack of a consolidated business model for general applications. Also, there is a great uncertainty as to how AV manufacturers will be able to di↵erentiate themselves and leverage their strengths when unique core competencies become obsolete and the technology becomes more standardised in an autonomous future.

1.3 Purpose

The purpose of this study is to investigate potential business models for AV applications for goods transports within confined areas, eventually developing a consolidated general business model based on a Mining Case and a Harbour Case. Additionally, the purpose is to identify core competencies that can be used by AV manufacturers in an autonomous future. Finally, the aim is that this busi- ness model perspective, together with the identified core competencies, can be used as a general application model for OEMs within the transportation industry when initiating new projects for goods transport within hubs.

The study provides a theoretical contribution to Business Model Innovation literature in the context of technology shifts (e.g. Tongur and Engwall, 2014; Kaulio et al., 2017). Also, the study has an empirical contribution surrounding the early transition phase in the shift to AVs and how core competencies can be leveraged in a business model. Furthermore, the research has a methodological and practical contribution in the sense of using the Business Model Canvas (BMC) as a management tool for comparing and aggregating models of di↵erent cases.

1.4 Research Questions

The aim of the thesis will be fulfilled through answering the following main research question:

MRQ: How can a business model be formulated for a general application within goods transports of confined areas for a heavy vehicle manufacturer in an autonomous future?

The main research question will be answered through the following two sub-questions:

– RQ1: How can a business model for a mining case and a harbour case be described using the Business Model Canvas tool and what are the similarities and di↵erences between their components?

– RQ2: Which strengths and capabilities qualify as core competencies in an autonomous future for a heavy commercial vehicle manufacturer and how can they be leveraged in their business model?

1.5 Delimitations

This thesis is delimited to case studies of the Swedish heavy commercial vehicle manufacturer Scania. According to (Aulbur et al., 2020) it has been shown that viable business models can already be built around the so-called Level 4 in accordance to the Society of Automotive Engineers (SAE) index, meaning that full automation will not be required (See Appendix A for SAE levels of automation classification). This makes autonomous driving an imminent development in the truck industry and this study will focus on exploring business models concerning driverless AVs of Level 4 and above. The term driverless being in the sense that no driver is needed inside the vehicle, which means that the vehicle does not necessarily need to be fully autonomous, but could

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be partly supervised or controlled remotely.

The focus within the report will be on the autonomous part of future vehicles, however, it is assumed that future commercial AVs will also be electrified as this technology seems to mature faster (Scania, 2020a). Along the same line, connectivity is assumed to be a natural part of the technology in this study as all Scania’s vehicles are already connected.

The study is further delimited to goods transportation as opposed to people transportation (e.g.

buses) and more specifically goods transportation within confined areas, in particular within mining and harbour operations. This is motivated by the fact that autonomous transportation of inanimate objects within confined areas, so-called hub operations, is more defined regarding legislation as well as technology.

1.6 Thesis Sponsor

The thesis is sponsored by the heavy commercial vehicle manufacturer Scania, a company estab- lished 1891 in Sweden, with headquarters in S¨odert¨alje. The company is part of TRATON GROUP, which is an umbrella brand for Scania, MAN, Volkswagen Caminh˜oes e ˆOnibus and RIO. Scania’s new generation of trucks has won most of the industry tests for the fantastic comfort, best driving characteristics and the record low fuel consumption (Scania, 2020b). With its 51,000 employees in about 100 countries the company is the market leader in the development of sustainable transport solutions. (Scania, 2019c).

1.7 Disposition of Report

The thesis is outlined in accordance to the following structure:

Chapter 1 introduces the topic of the thesis and sets both the theoretical and practical context background for the studied phenomenon. The problem statement is introduced along with the purpose and the research questions that the thesis aims to answer. Furthermore, the delimitations of the study are discussed, the thesis sponsor is presented and finally a brief disposition of the report is given.

Chapter 2 will serve as a frame of reference for the study. It follows primarily literature regarding strategic management of technological innovation and the resource-based view of the firm: more specifically technology shifts, business models, core competencies and capabilities as well as value co-creation and servitisation. The purpose of the chapter is twofold: first to provide a knowledge base, by which this study can be related and discussed, and secondly to form a lens from which background the authors have viewed this master thesis project.

Chapter 3 describes the methodology used in this study including the choice of methodological approach, the research design, as well as how the frame of reference and data collection was con- ducted and analysed. Finally, limitations, validity and reliability as well as ethical considerations are presented.

Chapter 4 describes the setting of two cases and is primarily based on data collected from the Case Study Interviews (see Appendix D for the interview guide). It covers the background of a

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Mining Case and a Harbour Case in order to give the reader more substance before going into extensive details of the Business Model Canvases in the next chapter.

Chapter 5 lays out the findings and consequent analysis of the study. It starts o↵ with presenting the data gathered from the two cases using the Business Model Canvas tool to map their business models in both a development phase and a commercial phase. Di↵erences between the phases are highlighted in italics in the commercial phase canvases. Finally, the four most frequently empha- sised capabilities that were identified are introduced.

Chapter 6 discusses the empirical findings and analysis in the previous chapter in relation to the Frame of Reference, in order to answer the research questions. Firstly, an aggregated business model derived from both business cases is formed and discussed in terms of similarities and di↵er- ences. Secondly, the identified capabilities in the previous chapter are determined as to whether they qualify as core competencies and discussed in terms of how they can be manifested and lever- aged in the business model. Thirdly, a business model perspective synthesising the main aspects of the aforementioned discussions is presented, which could be used in an autonomous future for other goods applications within confined areas. This is then briefly compared to an alternative perspective based on the Swedish start-up Einride. Lastly, long-term implications on sustainability are discussed.

Chapter 7 concludes the study by providing an answer to the main research question and dis- cussing implications regarding future business models for goods transports in confined areas as well as core competencies in the future. Finally, suggestions for further studies are presented.

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Frame of Reference

This chapter will serve as a frame of reference for the study. It follows primarily literature regard- ing strategic management of technological innovation and the resource-based view of the firm: more specifically technology shifts, business models, core competencies and capabilities as well as value co-creation and servitisation. The purpose of the chapter is twofold: first to provide a knowledge base, by which this study can be related and discussed, and secondly to form a lens from which background the authors have viewed this master thesis project.

2.1 Surviving Technology Shifts

According to Utterback (1994), ”innovation in industry is a process that involves an enormous amount of uncertainty, human creativity, and chance.” A firm’s survival through technology shifts is dependent on many factors but a common denominator of historical survivalists is the abil- ity of organisational ambidexterity (O’Reilly and Tushman, 2013). Organisational ambidexterity refers to the ability of a company to both exploit their existing competencies and incrementally improve upon them while at the same time explore new areas and technologies. In this way it is possible to simultaneously conduct both incremental and radical innovation through practicing several contradictory structures, processes and cultures within the same company. At the start of implementation, ambidexterity is typically inefficient since it requires doubling e↵orts and the consumption of resources for the parallel innovation processes. Tongur and Engwall (2014) high- light that technology shifts can be fatal to manufacturing companies not only due to technical innovation problems but also due to inertia within business models and business model innova- tion. Technology shifts are difficult to master, and a mix of innovation within both technology and business models and not just one of the fields is necessary to survive (Tongur and Engwall, 2014). Furthermore, the ability of organisational double ambidexterity has been identified as a crucial survival skill when it comes to technology shifts (Kaulio et al., 2017). That is, the ability to exploit and explore both technology and business model innovation simultaneously to counter the many uncertainties that are involved in the emergence of new technology in the face of technology shift. If a company is only changing the technological components of a system and not their busi- ness model, they may fail to capture the value of the technology itself (Blomkvist and Johansson, 2016).

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2.2 Business Model Innovation

According to Teece (2010), all firms make use of a business model, either implicitly or explicitly.

A business model describes the design or structure for the value that a company creates, delivers and captures. More concretely, business models define how a company delivers value to customers, motivates customers to pay for value and converts payments to revenue (Teece, 2010; Osterwalder and Pigneur, 2010). This reflects the classical questions that are usually posed within manage- ment: what customers want, how they want it, how the company can be organised to best meet these needs, get paid for it and make profits. Teece (2010) highlights three particular barriers and thresholds that obstruct replication of other companies’ business models. Firstly, the implemen- tation of a business model can involve complicated systems, processes and resources that are hard to achieve. Secondly, the model can be too obscure and hard for competitors to imitate. Thirdly, already established companies can be too tied down by their own existing business models to even consider imitating a new one, which does not stop new and smaller actors from copying them. In essence, business models are rarely obvious or clear in new business environments and technologies.

Furthermore, they often develop over time, which makes the ability to learn and adapt crucial for companies.

2.2.1 Business Model Ambiguity

The overall concept of a business model has been well debated with some scholars highlighting that the connections between customer needs and company capabilities to meet those needs enable a system perspective on how value is created, while others criticise it for being vague and ambiguous (Chesbrough and Rosenbloom, 2002). Some scholars refer to the term business model similar to a term of art; it may be recognisable by most people, however its true nature is vaguely defined (Lewis, 2000). It was not until the late 1990s that the term received increased attention amongst academics in an attempt to analyse the value creation in the new kind of web-based companies that appeared with the emergence of Internet (Zott et al., 2000).

During the last century, companies have been busy trying to understand how to perform their business and manage their operations, generating countless of new management theories (Drucker, 1994). However, in a world where the only constant is change, these theories are not enough ac- cording to Drucker (1994), since they are based on assumptions of a static environment. When the customer, the market, or even society is changing, there is a need to also question what to do.

This implies that these assumptions have to be continuously questioned in order to stay relevant and to understand who the customer is and what the customer values. Magretta (2002) claims that a good business model should not only answer those two questions when defining the value proposition but also to help understand how to generate money from that o↵ering, thus identify how to capture value.

Demil and Lecocq (2010) suggest that there are two ways of looking at a business model, either with a static or a transformational approach. The former implies that it is simply a blueprint, an instruction on how a business should structure itself and generate revenue, whereas the trans- formational approach is rather a tool to address change internally as well as externally. Further, Demil and Lecocq (2010) suggest that by adopting a transformational approach it allows for a more adaptable business model, which is more resilient to change.

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2.2.2 The Business Model Canvas

The Business Model Canvas tool (see Figure 2.1) o↵ers a framework for structuring a business model in a more tangible manner with nine building blocks forming a scaled-down illustration of how a company works (Osterwalder and Pigneur, 2010). In its fundamental state, a business model can be divided into three main aspects: value proposition, value creation and value capture (Tongur and Engwall, 2014) and the Business Model Canvas can be used as a hands-on tool for outlining a company’s business model components that constitute these three aspects co(Osterwalder and Pigneur, 2010). Furthermore, the tool can assist firms in aligning their activities by illustrating potential trade-o↵s. These components or building blocks are key partners, key activities, key re- sources, value proposition, customer relationships, distribution channels, customer segments, cost structure and revenue streams (see Figure 2.1).

Figure 2.1: The Business Model Canvas (BMC) tool, adapted from Osterwalder and Pigneur (2010)

Value Proposition

The value proposition is at the heart of the business model canvas framework and can essentially be seen as the solution a company o↵ers to a customer’s problem and the manner in which a com- pany di↵erentiates themselves on the market, i.e. why customers would choose one company over another (Johnson et al., 2008; Osterwalder and Pigneur, 2010). The proposed value is commonly provided in the form of a product or service or a combination of the two.

Key Partners

Osterwalder and Pigneur (2010) refer to key partners as more than strategic alliances between non-competitors. They also include the supplier and buyer partner relationship as well as joint ventures and collaborations which enable reliable resource access and value creation in the form of complementing competence sharing.

Key Activities

Key Activities are closely linked to Key Resources as they together comprise what is necessary to create significant value. In manufacturing companies, key activities are often related to manufac- turing, designing, research and development and the actual delivery of products (Osterwalder and

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Pigneur, 2010; Johnson et al., 2008). In a service-based business model, key activities are more likely related to tailoring and delivering solutions to problems of individual customers.

Key Resources

Key Resources may consist not only of physical assets but all resources that contribute to a com- pany’s competitive advantage such as intellectual properties, knowledge based, business know-how, financial and human resources (Johnson et al., 2008). Combined with key activities, these two com- ponents may constitute the primary source of value creation (Osterwalder and Pigneur, 2010).

Customer Relationships

Customer relationships may not only a↵ect how a value proposition is perceived but also be used to co-create value with customers (Osterwalder and Pigneur, 2010), for instance by inviting cus- tomers to upload content on streaming sites or to write reviews that assist other customers in their choices. This component also includes value creation in the form of more personal customer assistance and creating loyalty in their customer segments (Kindstr¨om, 2008).

Distribution Channels

Distribution channels refer to how a company makes potential customers aware of their services and products, as well as making them available for purchase. This can be done through indirect partner channels such as retailers or through in-house sales forces, which may a↵ect how the cus- tomer perceives the value proposition (Osterwalder and Pigneur, 2010).

Customer Segments

When forming a business model, it is fundamental to identify whose needs the value proposition should address, that is, who the customer actually is (Osterwalder and Pigneur, 2010). This is commonly done by categorising customers with similar demands and formulate value propositions on the premises of these segments. The value propositions may di↵er between di↵erent customer segments and it is necessary for a firm to understand them in order to make informed decisions on which segments to pursue and distribute resources accordingly.

Cost Structure

When building cost structures Johnson et al. (2008) propose that companies should start with estimating the cost of delivering their value proposition and then set prices depending on desired margins. Osterwalder and Pigneur (2010) emphasise that despite the fact that costs should always be minimised in any business model, it is advisable to separate cost-driven business models from those that are value-driven. The former focus on delivering low price value propositions for markets and customers that are price sensitive and the latter focus on premium value propositions and high value creation, such as premium branding of customised cabins for heavy vehicles. The approaches may vary, but in one way or another, they share a focus on value.

Revenue Streams

Revenue streams address how a company acquires or captures an appropriate share of the value it creates and o↵ers to its customer in order to generate revenue (Johnson et al., 2008). Revenue streams may vary between di↵erent customer segments with di↵erent pricing mechanisms and profit formulas (Osterwalder and Pigneur, 2010).

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2.3 Competitive Advantage, Capabilities and Core Compe- tencies

Traditionally, competitive forces theory asserts that, in order to gain competitive advantage, a firm must exploit the forces driving the market dynamics (Hafeez et al., 2002). A company could for example identify external threats and opportunities by using Porter’s five forces model. Prahalad and Hamel (1990) proposed a di↵erent approach that generated substantial interest in the notion of core competencies and capabilities, which helped popularize a new school of economic thought called The Resource-based View of the Firm. While the traditional approach could be considered an outside-in process, where the company starts with external analysis and then performs internal analysis, the process advocated by Prahalad and Hamel (1990) is an inside-out approach (Javidan, 1998) that suggests that companies need to fully understand their core competencies and capabil- ities in order to successfully exploit their resources and gain competitive advantage.

Many scholars advocate that core competencies and capabilities can be the trump cards of a firm’s competitive advantage in their business model (Hafeez et al., 2002; Yang, 2015; Agha et al., 2012).

However, if a company is too dependent on only a few competencies and is unable to adapt as they become obsolete in a technology shift, they can instead become its downfall (Schilling, 2012;

Prahalad and Hamel, 1990). In the existing literature on strategy and resource-based theory the two terms competencies and capabilities are frequently intermingled (Burgelman et al., 2001;

G¨okkaya and ¨Ozba˘g, 2015). While several authors have attempted to distinguish such terms as for example core competencies, distinctive competencies, and core capabilities, these e↵orts have sometimes created more confusion than clarity (Hitt and Ireland, 2001). For instance, Prahalad and Hamel (1990) use the term core competency to refer to a harmonised combination of multiple skills and resources that distinguish a firm in the marketplace. They further use the term capabilities to distinguish more elemental skills, such as advertising or logistics management, which might contribute to or qualify as a core competency. By contrast, other authors have argued that core competencies are more elemental technological or production skills, while capabilities are more broadly based and may encompass the firm’s entire value chain (Stalk et al., 1992). This confusion is not very surprising given the near semantic equivalence of the terms competence and capability (Schilling, 2012). Many dictionaries define both in terms of abilities, and some definitions use competence in their definitions of capabilities and vice versa. In this frame of reference however, the definition of capabilities and core competencies provided by Prahalad and Hamel (1990) will be used.

2.3.1 Identifying Core Competencies

Prahalad and Hamel (1990) further define core competencies as the collective learning of an organi- sation on how to coordinate diverse production skills and integrate multiple streams of technologies.

Core competencies are essentially firm-specific accumulations of expertise resulting from previous investments and from learning by doing. As an illustration, a diverse company can be viewed as a tree, where core competencies resemble the root system that provides nourishment, sustenance and stability, while trunk and major limbs are core products and leaves and fruits are end products (see Figure 2.2). Core competencies can for example help companies to produce market leading products at a lower cost and with a higher production rate than competitors. Studies have shown that companies that are aware of their development of capabilities and core competencies have been successful on the market (Schilling, 2012). Identifying core competencies can even be crucial to surviving technology shifts as it helps firms to better understand and express their value propo- sitions in emerging markets (Gallon et al., 1995). By viewing the business as a portfolio of core

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competencies, managers are better able to focus on value creation and meaningful new business development, rather than cost cutting or opportunistic expansion (Prahalad and Hamel, 1990).

Figure 2.2: An illustration of core competencies seen as the roots of a tree structure (MBA, 2020) Prahalad and Hamel (1990) o↵er the following criteria or tests to identify if a capability is in fact constituting a core competency of a firm:

A unique signature in the organisation

The capability is a significant source of competitive di↵erentiation and provides a unique signature to the organisation (Prahalad and Hamel, 1990). It makes a significant contribution to the value a customer perceives in the end product.

Covers more than one business

The capability transcends a single business and covers a range of businesses, both current and new. For example, a company like Honda’s core competence in engines enables the company to be successful in businesses as diverse as automobiles to lawn mowers, generators and motorcycles (Schilling, 2012).

Hard to imitate

The capability is difficult for competitors to imitate. In general, competencies that arise from the complex harmonisation of multiple technologies will be difficult to imitate. The competence may have taken years or even decades to build and the combination of resources and embedded skills will be difficult for other firms to acquire or duplicate.

According to Prahalad and Hamel (1990) few firms are likely to be leaders in more than five or six core competencies. If a company has compiled a list of 20 to 30 capabilities, it probably has not yet identified its true core competencies.

2.3.2 The Risk of Core Rigidities

Leonard-Barton (1992) notes that sometimes the very things that a firm excels at can enslave it, making the firm rigid and overly committed to inappropriate skills and resources. Incentive systems may evolve favouring activities that reinforce a certain core competency and the organisational culture may reward employees who are most closely connected to core competencies with higher status with better access to other organisational resources. While these systems and norms can

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prove beneficial in reinforcing and leveraging the firm’s existing core competencies, they can also inhibit the development of new core competencies. For example, a firm’s emphasis on a scientific discipline that is central to its core competency, such as the combustion engine, can make the firm less attractive to individuals from other disciplines. Rewards for engaging in existing core competency activities can discourage employees from pursuing more exploratory activities. Finally, knowledge accumulation tends to be very path dependent. Firms that have sets of well-developed knowledge along a particular trajectory may find it very hard to assimilate or utilise knowledge that appears unrelated to that trajectory, potentially limiting the firm’s flexibility (Dosi, 1988;

Tripsas and Gavetti, 2000).

2.3.3 Dynamic Capabilities

In fast-changing markets, or in the face of technology shifts, it can be extremely useful for a firm to develop or acquire a core competency or capability that is able to respond to change (Schilling, 2012). Whereas in the model of Prahalad and Hamel (1990), core competencies and capabilities relate to sets of specific core products, it is also possible for a firm to develop capabilities that are not specific to any set of technologies or products, but rather to a set of abilities that enable it to quickly reconfigure its organisational structure and routines in response to new opportunities (King and Tucci, 2002; Eisenhardt and Martin, 2000). Such competencies are termed Dynamic Capabilities.

Dynamic Capabilities enable firms to quickly adapt to emerging markets or major technological discontinuities. A firm can for example manage its relationships with alliance partners not as individual relationships focused on particular projects, but rather as an integrative and flexible system of capabilities that extends the firm’s boundaries (Bartlett and Nanda, 1990).

2.3.4 Critical Competency

Hamel (1994) further expands on the hierarchy and di↵erences between core competencies, capa- bilities, and constituent skills (see Figure 2.3).

Figure 2.3: A visual representation of the competency hierarchy, adapted from Srivastava (2005) and Hamel (1994)

The author states that the distinction between the various levels of competencies is more a matter of convenience but that the understanding of hierarchy of competencies is essential. The all- encompassing competency that stands above all the others is the Critical Competency. Hamel (1994) defines the Critical Competency as the ability itself of a firm to successfully identify, nur- ture, develop, upgrade and deploy its hierarchy of competencies to attain sustainable competitive advantage. Srivastava (2005) concludes that, to gain sustainable competitive advantage, managers

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should invest time, e↵ort, and resources in developing their Critical Competency. The first step towards developing this ability is to understand that such competencies exist and realize that they make a di↵erence to the competitive advantage of a firm. The Critical Competency can therefore be equated to the skill of operationalising and managing the core competencies and capabilities for the benefit of the firm. Srivastava (2005) proposes a holistic framework for helping companies with their Critical Competency management (see Figure 2.4).

Figure 2.4: A framework for managing the critical competency of a firm (Srivastava, 2005)

This proposed Critical Competency framework shows that the possession of core competencies will not result in a competitive advantage by itself. Instead, companies need to work continuously by reviewing the resources in their competencies pool and hunt for relevant competencies. These competencies are then identified and enlightened as core competencies using di↵erent methodologies such as the already presented criteria proposed by Prahalad and Hamel (1990). The competencies further need to be deployed in the organisation and the company should focus on developing or acquiring core competencies as well as continuously work on nurturing, upgrading or even abandoning them in relation to changing internal and external environments, such as a technology shift (Srivastava, 2005). Srivastava (2005) and Hamel (1994) state that the concept of Critical Competency is the most important resource a firm should possess for sustainable competitive advantage as it represents the skill of identifying, managing and leveraging their other resources.

2.4 Collaboration and Value Co-creation

As the focus on operational performance increases, business-to-business-partnerships are gaining rising attention in management and in academic research. To pose an example, researchers claim that Toyota’s success in the automotive industry is due to its flexible and entrepreneurial collabo- rations and the fact that their collaborative suppliers have shared path-breaking technologies that helped to leverage value chain efficiency (Dyer and Hatch, 2004; Dyer and Singh, 1998).

There are many benefits with external collaborations. If successful, collaborations with other com-

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panies make it possible to co-create high quality products, attract the most valuable customers and reach extraordinary profits by combining perspectives, knowledge and skills of di↵erent partners (Ploetner and Ehret, 2006). Further, while remaining two independent companies, partnerships enable companies to grow core competencies and bundle them to competitive customer solutions.

Butler et al. (2011) highlight that partnership can be a mechanism for dealing with ”mutual pain”

as well as ”mutual gain”, where mutual trust is the main mediator.

A vertical partnership is defined as a specific type of relationship between a customer and a supplier that is ”based on mutual dependency and trust, where both parties are committed to collaboration in a non-competitive environment beyond a sequence of buying-selling transactions” (Ploetner and Ehret, 2006). An atmosphere of mutual trust, strong experience in conflict resolution and empathetic comprehension is key and there should be an understanding of the fact that the success of each firm is dependent on the other firm (Anderson and Narus, 1990). These relationships are particularly useful in the global environment to penetrate new markets (Srivastava, 2005). Ploetner and Ehret (2006) points out that vertical partnership with efficient communication and flexibility is specifically suitable in a turbulent environment under constant flux, such as in the in early phases of a technology shift.

2.5 Servitisation

New technologies and open global trading systems contribute to a wide variety of choices for clients and the development of the world economy has changed the traditional balance between customer and supplier (Teece, 2010). Varying customer requirements may be measured, and supply options are more transparent. This increases requirements on businesses to be even more client-centered. A good business model provides value propositions that are convincing to clients, achieves favorable cost and risk structures and enables a considerable value absorption of the business. According to Teece (2010), clients do not just demand products - they want solutions for their needs. For exam- ple, they are increasingly interested in transport solutions rather than trucks as physical products.

Alongside with this change of environment, incumbent firms are at risk of failure since they are generally inferior at allocating enough resources to technologies that originally are not applicable in their core market (Christensen and Bower, 1996).

The term servitisation refers to the transition from selling physical products to a more service- based business model and was initially coined by Vandermerewe and Rada (1988). Since then, this concept has been studied by numerous authors as a competitive strategy within di↵erent industries (e.g. Wise and Baumgartner, 1999; Oliva and Kallenberg, 2003) and there is an increasing interest for the topic since transitioning towards servitisation implies creating value-adding capabilities and exploiting higher value business activities for traditional OEMs (Roy et al., 2009). The product and service definitions are according to Roy et al. (2009) intertwined and are both important when discussing the term servitisation. However, the concept of servitisation is not universally applicable and to be both e↵ective and efficient OEMs need to configure their business to support their new value o↵ering as well as understand what the o↵ering implies for their customers (Oliva and Kallen- berg, 2003). When implemented successfully there are several benefits with integrating services into core products. Firstly, there are economic arguments as services generally provide a higher margin and is a more stable source of revenue. Secondly, customers are demanding more services since the importance of flexibility and specialisation is increasing. Thirdly, there is a competitive argument since services are harder to imitate (Oliva and Kallenberg, 2003).

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More recently, the discussion about servitisation within the transport industry has led to the emerging concept of Mobility as a Service (MaaS), where essentially the means of conveyance is seen not as a physical asset to purchase but as a service available on demand (Mulley et al., 2018).

There exist several service models particularly connected to heavy vehicle manufacturers where Vehicle as a Service (VaaS) is the least complex, where the customer determines the demand and the manufacturer covers the need by providing heavy vehicles. Transport as a Service (TaaS) is another model that includes the execution of the actual transport operations and dispatch of missions. Additionally, Logistics as a Service (LaaS) is a more developed service model where, in addition to providing the vehicle and executing missions, the OEM also provides the logistics planning.

2.5.1 Downstream Vertical Integration

As part of a servitisation transition some companies choose to vertically integrate their supply chains downstream in order to grow or sometimes as pure means of survival (Guan and Rehme, 2012). For manufacturing firms, downstream vertical integration plays an important role as it can help to secure distribution channels of their products, especially in markets with increased uncertainties (Rangan et al., 1993). Furthermore, it can be the key to controlling cost reductions and efficiency gains in the supply chain (Frohlich and Westbrook, 2001) and even to generate new large revenue sources (Wise and Baumgartner, 1999). According to Wise and Baumgartner (1999) manufacturing firms need to expand their focus from operational excellence to customer allegiance in order to capture value downstream. Guan and Rehme (2012) highlight that vertical downstream integration has the potential of transforming a manufacturer into a strategic partner that provide integrated solutions based on the customer’s needs.

Today a heavy vehicle manufacturer typically provides vehicles as products for transport and logistics operator customers (Lay, 2014). When looking downstream on the supply chain of a heavy vehicle manufacturer there are di↵erent layers of logistics providers divided into so-called Party Logistics provider Levels (PL-levels), where the first party logistics providers are the cargo owners (manufacturers, retailers, resource producers), the second logistics providers are the carriers and drivers (transportation), while the third and fourth party logistics providers are involved with logistics planning (logistics providers, consultants) (see Figure 2.5).

Figure 2.5: An illustration of Party Logistics provider levels within the goods transportation industry (Scania, 2019b)

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2.6 Summary of Frame of Reference

To summarise and synthesise the Frame of Reference, theory regarding technology shifts, business model innovation, core competencies and capabilities as well as concepts of value co-creation and servitisation has been presented to form a knowledge base and background lens for the rest of the study.

The concept of Autonomous Vehicles indisputably implies a major technology shift connected to many uncertainties and risks. It is important to understand how companies have managed to survive previous shifts by innovating not only their technology but also their business models si- multaneously both radically and incrementally, an ability known as double ambidexterity. This forms a background lens and motivates the study’s focus on business models concerning AVs and not the technology itself.

This leads to the focus on business model innovation, where theory is presented on what business models fundamentally are and the ambiguity that is often connected to them. To counter this am- biguity a hands-on tool in the form of the Business Model Canvas is presented. This tool captures the three parts of a business model: the value creation, value proposition and value capture in nine building blocks, which are defined and elaborated. This tool will be used for the data collection and analysis of this study, which is described in more detail in the methodology chapter.

The concept of core competencies and capabilities are defined and it is explained how they can be identified and used to express a company’s strengths and be utilised in their business models.

Furthermore, it is covered how a firm needs to be careful not to depend too much on certain core competencies so that they do not become core rigidities. Additionaly, the term dynamic capability is introduced and used to describe capabilities that cover more than one business, are more adapt- able and can be used to respond to change such as a rapid technology shift. Lastly, the concept of a so-called critical competency as a company’s skill of operationalising and continously working with its pool of core competencies and capabilities is defined. These references will be used in the study to identify capabilities at Scania as well as to validate if they meet the criteria of being core competencies.

When innovating within new technologies some capabilities may be outside a firm’s boundaries, wherefore it might be suitable to collaborate with other actors. The benefits of collaborations are presented together with a partnership approach that might be convenient in early phases of technology shifts.

Lastly, a section regarding servitisation is presented; a concept that elaborates on how OEMs can undergo a transition from selling physical products to more service-based business models. It is explained how some companies choose to vertically integrate downstream in their value chains.

This will be used to discuss how heavy vehicle manufacturers could develop their business models in an autonomous future.

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Methodology

This chapter describes the methodology used in this study including the choice of methodological approach, the research design, as well as how the frame of reference and data collection was con- ducted and analysed. Finally, limitations, validity and reliability as well as ethical considerations are presented.

3.1 Choice of Methodological Approach

The purpose of this study was to investigate potential business models for AV applications for goods transports within confined areas, eventually developing a consolidated general business model based on a Mining Case and a Harbour Case. Additionally, core competencies that can be used by Sca- nia in an autonomous future were to be identified. Finally, the aim was that this business model perspective, together with the identified core competencies, could be used as a general application model for OEMs within the transportation industry when initiating new projects for goods trans- port within hubs.

Since the studied phenomenon of autonomous technology at the time was novel and in the early stages of a technology shift, the study was conducted as a qualitative exploratory case study. The method suits critical, early phases of real-life contexts connected to management theories, when key variables and their relationships are explored (Gibbert et al., 2008; Yin, 2014; Eisenhardt, 1989). Another choice for methodological approach that was considered was a quantitative study.

However, this was deemed unsuitable as there were very little empirics from previous studies within the field and limited quantitative data to collect.

Throughout the project the researchers explored the heavy commercial truck manufacturer Scania and two of their business cases with an abductive approach, which is a non-linear process that alternates between empirical observations and theory. This allows a mixture between deductive and inductive approaches and expands knowledge building in an exploratory case study (Dubois and Gadde, 2002).

Scania was considered to be a suitable company for conducting case studies at since they are not only industry leading, but also have several collaborations with academia and other firms, which brings them to the forefront of new technologies such as ATS and connectivity (Scania, 2019a).

The studied area is complex and required a lot of data, which could be collected by informants and other sources at the department for Autonomous Solutions at Scania due to their close connection

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to the AV projects. At the time of the study the company was, among others, working with a Mining Case and a Harbour Case, which were investigated in this report. These projects were in di↵erent stages of development phases, which were believed to advance autonomous solutions for goods transports in confined areas. Based on data collected through interviews they were analysed and mapped using the Business Model Canvas tool from both a Development Phase and a future Commercial Phase perspective. These two Commercial Phase models were then compared and aggregated into a single Aggregated Business Model that could be generalised and cover similar applications and customer segments. Furthermore, data regarding strengths and capabilities that constitute potential core competencies in an autonomous future could be collected at the company.

3.2 Research Design

The problem formulation was not initially stated by the thesis sponsor, but rather developed by the researchers after initial exploration of the company and business cases. The research questions and the purpose were continuously revised.

This exploratory case study was divided into three phases: an Initial Phase, a Main Phase and a Final Phase (see Figure 3.1). All three phases followed an abductive approach and the actual writing process of the thesis was of an iterative nature, following the proposed prototypisation method of Blomkvist and Hallin (2014).

Figure 3.1: An overview of the workflow of the study

3.2.1 Initial Phase

In the Initial phase the researchers participated in several presentations and seminars at Scania covering the studied cases and Scania’s strategic roadmap for the future. These seminars included a so-called Pathfinder presentation discussing a study that sought to find out which decisions to make and which paths to pursue regarding the future of autonomous solutions and related business models at Scania. Another presentation called Autonomous Strategy 2025 outlined the strategy of the company up until 2025. The researchers were also engaged in an Autonomous Introduction Training held at Scania Academy covering the fundamentals of the technology and business op- portunities of AVs. The main purpose of participating in the presentations and seminars was to become acquainted with the concept of autonomous technology and future business models.

The presentations and seminars were followed by twelve semi-structured interviews conducted with the department responsible for developing future business models for Autonomous Solutions at Scania, (see Table 3.1). These interviews covered the context of autonomous solutions: specifically, what the interview subjects thought about the future of the industry, how the autonomous solution

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is predicted to be sold as well as what Scania’s strengths and capabilities could be in an autonomous future (see Appendix B for the Exploratory Interview Guide). In addition, the most important ongoing projects within autonomous technology at the department were covered, where some interviewees focused more on public transport and some more on goods transport. However, all of them were knowledgeable and had di↵erent perspectives on the future of autonomous solutions, on Scania and on the heavy vehicle industry in general. The interviewees were considered suitable for the initial interview sessions as the scope of the study was not yet completely delimited. The interviews were mainly used to become acquainted with the topic, collect valuable data on Scania’s strengths and capabilities and also to identify the two business cases that were further investigated within this project.

Table 3.1: Exploratory interviews at the department of Autonomous Solutions at Scania

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3.2.2 Main Phase

Within the main phase the context around the two identified business cases, a Mining Case and a Harbour Case, and their surrounding business models were investigated during four structured interview sessions with four key individuals at Scania who were deeply involved in either of the cases (see Table 3.2 interview A-D). In the first part of the interview sessions the interviewees were asked to deliver extensive background information about the cases. This was later supplemented with information in documents and material available through a shared database that could be used to independently access and cross-check data. Secondly, the business models of the cases were mapped using the Business Model Canvas tool, both with a Development Phase and a fully Commercial Phase in mind. The interview subjects were asked questions based on the nine build- ing blocks of the Business Model Canvas, which were shown to the interview subjects on a large whiteboard (see Appendix D for the Case Study Interview Guide). The answers to the questions were during the interviews summarised under each block on the whiteboard and the result was further transferred to a digital database for future analysis. The interview subjects that were the first ones to be interviewed within each case, namely interviewee A and B, initiated the interviews with empty canvases, while interviewee C and D were given the former completed canvases as a reference to start with. Within each block, interviewee C and D validated the existing elements, modified them or presented new data.

After conducting the four interviews, the elements were analysed by the researchers and all of the blocks for each separate case were iterated once again based on the collected data. This consolida- tion resulted in one canvas for each business case respectively. Furthermore, the canvas elements were divided and modified with regards to whether they were part of a Development Phase of the project or a fully Commercial Phase. Hence, the Findings and Analysis chapter contains two ver- sions of the canvases for each business case, a total of four canvases, representing the two di↵erent phases of the projects.

In order to receive a customer perspective on the Mining Case a fifth more unstructured interview was conducted with a former employee of the customer company, referred to as the Mining Com- pany. The interview subject had previously worked for the Mining Company but now worked as a consultant safety manager for Scania (see Table 3.2 Interview E). Because of his involvement within the mining project from both the customer’s side and Scania, this interviewee was considered to be suitable to collect data from as he could provide di↵erent perspectives. The interview session was arranged in a similar manner as Interview A-D, that is initially the interviewee was asked to deliver some background information about the Mining Case from the Mining Company’s point of view. Thereafter the Business Model Canvases for the Development Phase and the Commercial Phase within mining were introduced and each element was outlined for the interviewee, where the person validated the information, modified it or presented new elements. The main purpose with this interview was to receive validation from a customer perspective with an emphasis on value proposition and customer relationships, which were considered most relevant to discuss from this perspective. Furthermore, the interview gave important insights about Scania’s strengths and capabilities as they are perceived from a customer.

References

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