Driving Autonomous Heavy Vehicles Into the Future a Business Model Perspective

Home , Scania

DEGREE PROJECT IN THE FIELD OF TECHNOLOGY INDUSTRIAL ENGINEERING AND MANAGEMENT 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

Gabriel Kitzler Anna Saibel

Master of Science Thesis TRITA-ITM-EX 2020:330 KTH Industrial Engineering and Management Industrial Management SE-100 44 STOCKHOLM

Driving Autonomous Heavy Vehicles into the Future Ett affärsmodellsperspektiv

Gabriel Kitzler Anna Saibel

Examensarbete TRITA-ITM-EX 2020:330 KTH Industriell teknik och management Industriell ekonomi och organisation SE-100 44 STOCKHOLM

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 Examiner Supervisor 2020-06-09 Lars Uppvall Matti Kaulio Commissioner Contact person Scania CV AB 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

Examensarbete TRITA-ITM-EX 2020:330

Driving Autonomous Heavy Vehicles into the Future – Ett affärsmodellsperspektiv

Gabriel Kitzler Anna Saibel Godkänt Examinator Handledare 2020-06-09 Lars Uppvall Matti Kaulio Uppdragsgivare Kontaktperson Scania CV AB 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 Table of Contents

1 Introduction 1 1.1 Background...... 1 1.2 ProblemStatement...... 3 1.3 Purpose...... 4 1.4 ResearchQuestions...... 4 1.5 Delimitations ...... 4 1.6 ThesisSponsor ...... 5 1.7 Disposition of Report ...... 5

2 Frame of Reference 7 2.1 SurvivingTechnologyShifts ...... 7 2.2 BusinessModelInnovation...... 8 2.2.1 BusinessModelAmbiguity ...... 8 2.2.2 TheBusinessModelCanvas...... 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 ResearchDesign ...... 19 3.2.1 Initial Phase ...... 19 3.2.2 Main Phase ...... 21 3.2.3 Final Phase ...... 22 3.3 FrameofReference...... 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 4 Case Description 27 4.1 Mining Case Background ...... 27 4.2 Harbour Case Background ...... 30

5 Findings and Analysis 33 5.1 MiningCase...... 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 IdentiﬁedCapabilities ...... 42

6 Discussion 45 6.1 Comparison between Mining and Harbour Applications ...... 45 6.2 FutureCoreCompetencies...... 48 6.2.1 Core Competencies in the Business Model ...... 50 6.2.2 Dynamic Capabilities, Core Rigidities and Critical Competency ...... 51 6.3 AFutureBusinessModelPerspective...... 52 6.3.1 AService-basedBusinessModel ...... 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

AppendixDCaseStudyInterviewGuide 70

Appendix E Final BMC for a General Model 71 List of Figures

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 transportation 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- mercialPhase ...... 46 Figure 6.2: An illustration of how the core competencies Modularity and Lean production 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 List of Tables

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 competitive 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 List of Abbreviations

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 Acknowledgement

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 Chapter 1

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 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.

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, trac 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 propositions. 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

1 Driving Autonomous Heavy Vehicles into the Future - A Business Model Perspective

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 towards 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 technology 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).

Chapter 1 Page 2 Driving Autonomous Heavy Vehicles into the Future - A Business Model Perspective

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 manufacturers (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 diculties 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.

Chapter 1 Page 3 Driving Autonomous Heavy Vehicles into the Future - A Business Model Perspective

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 business 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

Chapter 1 Page 4 Driving Autonomous Heavy Vehicles into the Future - A Business Model Perspective 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 established 1891 in Sweden, with headquarters in Södertälje. The company is part of TRATON GROUP, which is an umbrella brand for Scania, MAN, Volkswagen Caminhões e Onibusˆ 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 conducted 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 emphasised 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↵erences. 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 leveraged 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 discussing 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.

Chapter 1 Page 6 Chapter 2

Frame of Reference

This chapter 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.

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 ability 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 inecient since it requires doubling e↵orts and the consumption of resources for the parallel innovation processes. Tongur and Engwall (2014) highlight 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 innovation. Technology shifts are dicult 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 business model, they may fail to capture the value of the technology itself (Blomkvist and Johansson, 2016).

7 Driving Autonomous Heavy Vehicles into the Future - A Business Model Perspective

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 management: 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 implementation 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 according 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 transformational 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.

Chapter 2 Page 8 Driving Autonomous Heavy Vehicles into the Future - A Business Model Perspective

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 resources, 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 company 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 manufacturing, 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 company’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 components 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 customers 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 customer 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,aﬁrm 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 capabilities 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; Gökkaya 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 organisation 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 propositions in emerging markets (Gallon et al., 1995). By viewing the business as a portfolio of core

Chapter 2 Page 11 Driving Autonomous Heavy Vehicles into the Future - A Business Model Perspective 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 dicult for competitors to imitate. In general, competencies that arise from the complex harmonisation of multiple technologies will be dicult to imitate. The competence may have taken years or even decades to build and the combination of resources and embedded skills will be dicult 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

Chapter 2 Page 12 Driving Autonomous Heavy Vehicles into the Future - A Business Model Perspective 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, capabilities, 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, nurture, develop, upgrade and deploy its hierarchy of competencies to attain sustainable competitive advantage. Srivastava (2005) concludes that, to gain sustainable competitive advantage, managers

Chapter 2 Page 13 Driving Autonomous Heavy Vehicles into the Future - A Business Model Perspective 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 collaborations and the fact that their collaborative suppliers have shared path-breaking technologies that helped to leverage value chain eciency (Dyer and Hatch, 2004; Dyer and Singh, 1998).

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

Chapter 2 Page 14 Driving Autonomous Heavy Vehicles into the Future - A Business Model Perspective 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 ecient 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 example, 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 ecient 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).

Chapter 2 Page 15 Driving Autonomous Heavy Vehicles into the Future - A Business Model Perspective

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 eciency 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 simultaneously 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 ambiguity 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 adaptable 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.

Chapter 2 Page 17 Chapter 3

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 conducted 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 transport 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

18 Driving Autonomous Heavy Vehicles into the Future - A Business Model Perspective 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 opportunities 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

Chapter 3 Page 19 Driving Autonomous Heavy Vehicles into the Future - A Business Model Perspective 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 building 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 versions 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.

Chapter 3 Page 21 Driving Autonomous Heavy Vehicles into the Future - A Business Model Perspective

Table 3.2: Case study interviews involving a Mining Case and a Harbour Case

3.2.3 Final Phase

In a ﬁnal phase the data collected was analysed further and discussed in relation to theory and the research questions. Based on the business model canvases of the commercial phases of the two cases an Aggregated Business Model Canvas was created including the di↵erences and similarities of the two projects. For clarity, the elements presented within this Aggregated Business Model Canvas were highlighted di↵erently depending on their type and origin.

From data collected through interviews in the Initial Phase of the study, nine capabilities or strengths of Scania that could be used in an autonomous future were identified. The four strengths that were most frequently highlighted by the interview subjects were selected as focused capabilities. These were later analysed and discussed in relation to the definitions of a core competency based on the three criteria presented by Prahalad and Hamel (1990) to determine whether they qualified as core competencies. This followed the framework of Srivastava (2005), where access was made to the company’s competency pool and relevant competencies that could be identified and enlightened as core competencies were ”hunted” down (see figure 2.4). Furthermore, the four focused capabilities were discussed in relation to theory on dynamic capabilities and core rigidities. They were then integrated and expressed in the Aggregated Business Model Canvas as part of answering the research questions.

A discussion was presented regarding the analysed data in relation to servitisation concepts and service-based models. Scania’s business model perspective was then compared to an alternative model based on the small start-up company Einride. The information regarding Einride was based on an interview with their CEO in an article from 2019 and material gathered from the company’s website. This served the purpose of anchoring the study in correlation to another perspective, without conducting a full case study of another company, which was outside the scope and resources of this study.

3.3 Frame of Reference

The purpose of the frame of reference was twofold, ﬁrst to provide a knowledge base upon which this study could be related and discussed, and secondly to form a background lens through which

Chapter 3 Page 22 Driving Autonomous Heavy Vehicles into the Future - A Business Model Perspective the authors viewed the master thesis project. The chapter also acted as a complement for the primary sources, as it could provide a theoretical foundation for the collected empirical data.

Apart from covering basic definitions of autonomous vehicles, several topics were reviewed: primarily scientific articles, books and reports surrounding strategic management of technological innovation and more specifically technology shifts, business models, core competencies and capabilities. In addition, articles related to servitisation, downstream vertical integration and service- based models were also reviewed.

Literature was continuously revised throughout the study and was hence not limited to a speciﬁc phase. As the maturity of the study increased, new topics were assessed to be of interest, whereby literature on new topics was iteratively reviewed and added to the report. The main source of the material for the frame of reference was collected through KTH Primo, which is a search tool provided by KTH Royal Institute of Technology, where the majority of the accessible journals have been peer-reviewed. Google Scholar was also used, especially for the literature connected to servitisation. In the process of researching articles, references with frequently cited authors were primarily used.

3.4 Data Collection

The nature of a case study brings the possibility to use di↵erent types of collection methods, which is desirable since a combination of several methods may produce richer data and more accurate results through data triangulation (Yin, 2014).

3.4.1 Interview Methodology

Conducting interviews is a good choice of method when the goal is to receive a broad insight into a complex problem as well as to collect perspectives and opinions from various actors (Blomkvist and Hallin, 2014; Yin, 2014). To form suitable interview guides, ﬁndings in the literature were reviewed and supervisors at Scania and Royal Institute of Technology (KTH) were consulted. Primary source data collection was mainly obtained through conducting interviews with key individuals at the department for Autonomous Solutions at Scania. These interview subjects were picked on the criteria of having a background within engineering and/or business. Furthermore, they had all some years of working experience within the industry of heavy commercial vehicles and automation.

The interviews were held in person with both researchers present, audio was recorded after approval from the informants and notes were taken by the researcher who was not leading the interview session. The material was stored digitally in a database and organised in a systematic way so that both researchers had access to the data. The researchers were also granted access to a large number of internal documents related to the two business cases, which were used as a complement to the interviews for the problematisation, case descriptions, the ﬁndings and analysis chapter and the discussion.

3.4.2 Observations

Participatory observations constituted a part of the data collection, where the researchers were part of a team at the department for Autonomous Solutions at Scania. The authors had the opportunity to work together with other employees in an open-plan landscape oce. Furthermore,

Chapter 3 Page 23 Driving Autonomous Heavy Vehicles into the Future - A Business Model Perspective the researchers participated in weekly meetings and had the possibility to discuss and validate the project with di↵erent team members continuously.

3.4.3 Methodology for Data Analysis

Since the business case studies were conducted internally within Scania, they were analysed using a within-case approach that Collis and Hussey (2014) describe. This approach allows the researcher to become fully acquainted with the material as it provides a deep insight into patterns (Collis and Hussey, 2014).

Furthermore, the data analysis followed the steps of a qualitative data analysis in accordance to Miles and Huberman (1994), which implies the steps of data reduction, data display and verifica- tion. Data reduction implies simplification, selection and focus of the data, which is convenient when the researchers have access to a large amount of data (Collis and Hussey, 2014). Data reduction has been made when analysing audio, interview notes and the Business Model Canvas mapping by rewriting them thematically. Data display regards presenting the data in an understandable way, in this case in the form of business model canvases, which were then verified by the interview subjects.

The Business Model Canvas framework developed by Osterwalder and Pigneur (2010) was used for collecting, structuring and displaying case data (see Figure 2.1). Furthermore, the framework was used for analysis and comparison when creating an Aggregated Business Model Canvas in order to distinguish the shared and separate business components of the Mining and Harbour Case applications. The tool was found suitable to use for this purpose since it o↵ers a hands-on framework for structuring a business model in a more tangible manner as the nine building blocks can be used for outlining a company’s business activities (Osterwalder and Pigneur, 2010).

3.5 Limitations

For the sake of transparency, limitations of the study have been identiﬁed. Since the case study was conducted within one single company, it is not unlikely that data gathered from internal interview subjects may contain bias to some extent. To counter this, perspectives from the customer side in each case as well as other actors were considered. One perspective from a former experienced employee in one of the cases, speciﬁcally the Mining company, was collected and provided the researchers valuable insights. Also, the researchers aimed to conduct a similar unstructured interview with an individual connected to the customer side of the Harbour Case. However, this was not possible since the harbour business case at that point was in a phase where all parameters were not settled yet and there was a wish not to interfere in the customer relationship.

Time is a factor that undoubtedly limits every master thesis. In the spring of 2020 when this project was conducted the Covid-19 pandemic outbreak was a↵ecting the global industries in ways never seen before. However, it did not cause major limitations to this study as most of the data had already been collected at the time of the pandemic outbreak in Sweden, and the thesis work could hence be eciently continued remotely. However, it did limit the observations part of the study as the researchers could no longer access facilities and oces of the thesis sponsor and the other team members of the Autonomous Solutions department were all very restricted in their working hours.

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3.6 Validity and Reliability

Many di↵erent methods may be used in order to assess the quality and rigour of a case study research paper. One elaborated and commonly used method within the positivist tradition consists of four di↵erent criteria: internal validity, construct validity, external validity and reliability (Gibbert et al., 2008; Yin, 2014; Campbell, 1975; Campbell and Stanley, 1963). This section aims to explore what the four criteria implies together with a discussion about how well this study meets the criteria.

Internal Validity Internal validity describes the logic and the causal relationship between variables that have been studied and the results (Yin, 2014; Gibbert et al., 2008), hence it relates to the reasoning of the data analysis phase. Two measures have been taken to enhance internal validity. Firstly, a clear research framework was formulated, which demonstrates the variables that leads to certain outcomes. Secondly, several interview subjects, observations as well as a customer perspective were added to the research in order to verify the ﬁndings. This was a careful attempt to create data triangulation. A third measure suggested by Gibbert et al. (2008) is to match the results within the study with either predicted empirical results or results from a previous study, which was not possible to do because of the novelty of the studied phenomenon.

Construct Validity Construct validity refers to the extent to which the study actually investigates the matter that it claims to investigate (Gibbert et al., 2008). For example, if the research has been a↵ected by subjective judgement from the researchers during the data collection phase the observation of re- ality could be inaccurate. Two measures have been taken to achieve construct validity. Firstly, an attempt has been made to create a clear chain of evidence so that other researchers are able to reconstruct the same procedure from research questions to conclusions with the same results. Secondly, as already mentioned, the authors have adopted di↵erent data collection strategies and used di↵erent data sources in order to view the phenomenon from di↵erent angles.

External Validity External validity has to do with the degree of generalisability, or to which extent the results of the study can be expected in other contexts as well (Gibbert et al., 2008). To achieve external validity, theories must be shown to account for phenomena within multiple settings. According to Yin (2014) statistical generalisability is impossible to achieve even within multiple case studies as the data sample will realistically always be of insucient size. However, analytical generalisability, which refers to generalisability from empirical observations to theory, is conceivable. According to Eisenhardt (1989) a cross-case analysis that contains four to ten cases is a requirement for achiev- ing analytical generalisability. Due to the low number of comparable cases this study does not meet this requirement. However, the case design and the extensive review of the contexts enable further studies by other researchers to supplement the study with comparable cases in the future.

Reliability Reliability refers to the extent to which other researchers would reach the same conclusions if they would repeat the study, where transparency and replicability are being measured (Yin, 2014; Gibbert et al., 2008). Transparency has been enhanced through careful documentation and research procedures, which has been clearly stated within the Research Design of this chapter. There is a detailed outline of the methods used, all of the data collection sources have been presented and interview guides have been used and reported. Replication has been accomplished by storing all of

Chapter 3 Page 25 Driving Autonomous Heavy Vehicles into the Future - A Business Model Perspective the material concerning the study in a case study database including notes, documents, interview guides and recordings, organised in a suitable manner for future retrieval.

3.7 Ethical Considerations

Throughout the design and execution of the study, attention has been paid to ethical considerations in accordance to the Swedish Research Council, SRC (2017). The interviewees were informed about the purpose of the the study, the interview session and how the data will be handled. The participation in the interviews was voluntary and permission for audio recording and collecting data was granted to the researchers in the beginning of every interview session. Content that was based on interviews, more specifically the case descriptions, findings and analysis as well as the discussion chapter, were sent to the interview subjects and responsible managers for review, whereby inaccuracies and misinterpretations were corrected. Confidential information was separated in two versions of this report, one for academic purpose and one to be presented at Scania. The data was only used for the stated purpose in the paper and information granted in trust has not been spread to unauthorised individuals. The collected data was archived for continued preservation so that the research results could be checked and verified upon request after publication.

Precaution has been taken to protect the privacy and conﬁdentiality of the companies involved and the research subjects’ personal information to minimise the impact of the study on their physical, mental and social integrity. Interview subjects have been anonymised within the report only with their background and/or role as sole distinctions. When handling the business cases, consideration has been taken not to interfere in the customer relationship when not suitable. Also, the name of the clients, speciﬁc geographical location, detailed operations and other information that could distinguish them has upon request by the thesis sponsor been actively avoided in the report.

Chapter 3 Page 26 Chapter 4

Case Description

This chapter 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 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.

4.1 Mining Case Background

The mining industry has been a long standing important customer segment for Scania (Interview A). The value proposition has historically been to deliver tailor-made solutions and robust, reliable products that cater to mining-specific demands with safety and cost-eciency as the main focus (Interview C). The industry-specific division at Scania, Scania Mining has a global presence and is a well-known supplier in the industry. Many mining operations are remotely located and connected to high risk and safety concerns for drivers and personnel, which pushes salaries and driver costs to sometimes extreme levels (Interview A). Many of the largest mining companies in the world have been using autonomous solutions for their equipment and vehicles to some degree for many years to counter these factors. Mining operations are in their nature based in confined areas, which makes a mining operation an ideal business case for Scania’s autonomous solutions department.

A few years back Scania initiated an autonomous truck project together with one of the biggest mining companies in the world, based in one of their smaller mining operations (Interview A). The Mining Company agreed to give Scania testing opportunities for future autonomous business collaboration on a route transporting material from an extraction point to an o↵-loading point. In an initial development phase, one autonomous prototype vehicle was deployed and tested with safety drivers in close collaboration with the customer in order to build a foundation for a future commercial phase. In this future commercial phase, scaleable ﬂeets of vehicles could perform within several operations transporting di↵erent types of materials. An illustration of these two phases, a Development and a Commercial Phase, can be seen in Figure 4.1.

27 Driving Autonomous Heavy Vehicles into the Future - A Business Model Perspective

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

Mining companies typically use very large haul trucks weighing as much as 450 tonnes in order to be able to transport as much material divided between as few human drivers as possible. With the driver out of the picture, a window is opened for Scania’s comparatively small autonomous trucks with a payload capacity of only 40-45 tonnes, as small trucks have many advantages over large ones (Interview A). These advantages are part of Scania’s value proposition to the Mining Company and are more elaborated in the Findings and Analysis section in Chapter 5.

In the development phase, Scania and the Mining Company are collaborating closely together and the division of contributions of the operation so far is presented in Table 4.1. Within this Devel- opment Phase the Mining Company has agreed to provide fuel and planning for when and where supplies and material are to be transported, whereas Scania has been responsible for providing the physical vehicles and repair & maintenance of them (Interview A). When more vehicles are involved in the future, the Mining Company might contribute with more safety drivers and Scania with fleet management. In this Development Phase Scania and the Mining Company have been collaborating in a partnership approach with the aim of both advancing and testing the technology, while sharing costs and benefits as well as developing a viable business model together. The contribution of both companies are expected to result in 1) a deployable solution that creates value for the Mining Company, 2) a field demonstration of Scania products as well as a commercially ready product and 3) a pathway to Scania’s future commercial sales (Interview A).

Table 4.1: Division of responsibilities between Scania and the Mining Company

Chapter 4 Page 28 Driving Autonomous Heavy Vehicles into the Future - A Business Model Perspective

Four alternative business model prototypes have been discussed among both companies (see Figure 4.2) (Interview A). These included a Truck Purchase model where the Mining Company would purchase and take ownership of the vehicles and then pay for fuel, operation costs as well as vehicle maintenance. This is similar to how heavy truck OEMs operate currently with the distinction that Scania would be responsible for maintenance and ownership of the autonomous software system, which the Mining Company could dispatch missions to the vehicles through. Interestingly, the ownership of the autonomous software system is considered necessary to remain with Scania in all proposed business models (Interview A). This is mainly due to legislation and liability issues, and according to analysts at Scania there is a consensus within the truck manufacturing industry that if something would go wrong with an AV in the future, e.g an accident, the manufacturer of the vehicle would be held accountable (Interview A). In the remaining prototype models, Truck Lease, Technology Lease and Transport Service Scania would keep ownership of the physical vehicles, moving towards a service-based model. In the Truck Lease model the Mining Company would essentially lease the trucks and pay for their maintenance, whereas in the Technology Lease and Transport Service models Scania would take full responsibility for the technology and the overall solution. In the Transport Service model Scania would receive payment per transported material and in the Technology Lease model the Mining Company would pay for availability of vehicle capacity and the overall solution on for example a monthly or annual basis. In all prototype models the Mining Company would be responsible for fuel and control of the actual operations.

Figure 4.2: Considered business model alternatives for the Mining Case

Chapter 4 Page 29 Driving Autonomous Heavy Vehicles into the Future - A Business Model Perspective

4.2 Harbour Case Background

Over the last decades advancements within automation technology have introduced smarter and safer ways of controlling harbours or ports (Interview B, see Table 3.2 in Chapter 3). Due to the increase of maritime trades and global automation initiatives there has been an increase in the number of so-called smart ports. Today most global ports have integration of automated technology to some extent, which has been used to develop intelligent solutions for ecient control of trac and trade ﬂows in the harbour, increasing port capacity and overall port eciency.

Currently, Scania is within the initial stages of a pilot project in one of the biggest ports (Inter- view B, Interview D). The port in question is an important logistics hub for the entire world with advanced terminals loading and o↵-loading various types of goods each day. The company that owns the port, a partly governmentally owned so-called Port Authority, is industry leading with advanced logistics system platforms and investments in a number of other ports around the world. Scania considers the project with this harbour as an opportunity to work with an experienced partner to develop autonomous transport solutions within conﬁned port areas and aims to demon- strate that they are forerunners within the ﬁeld of AVs (Interview B).

The operations within the harbour is divided between the Port Authority, Terminal Operators and Transport Operators (Interview B, Interview D). The surrounding harbour area and infrastructure belongs to the Port Authority, while the infrastructure within terminals, where cargo ships dock, load and unload, is operated and owned by several global companies, so-called Terminal Operators. The Port Authority charge payment from the Terminal Operators for having their operations based at the harbour and for integrating their flows with the Port Authority’s transport system. The Port Authority is in charge of the operations outside the terminals as well as the connecting flows between the terminals, while the Terminal Operators monitor their own internal flows. Currently Transport Operators are contracted to transport containers between terminals and other transfer points using manual trucks.

Overall the degree of automation is high within this harbour, where many parts of the handling is fully automated, while other parts of the process flow are still operated manually (Interview B, Interview D). An illustration of a typical seaside flow at a fully automated terminal can be seen in Figure 4.3. Autonomous quay cranes collect containers from docked ships and transfer them to so-called Automated Guided Vehicles (AGVs). Thereafter automated AGVs use tracks on the ground to transfer the containers to a container stack. The rest of the flow within the terminal, including stacking cranes, horisontal transport and gate process is also automated. However, the trucks transporting containers between terminals and other transfer points are working manually. These manual trucks are adapted to be longer than regular vehicles and transport up to five containers in order to be financially feasible with a driver, causing the trucks to be neither flexible nor fast (Interview B). This limits the logistics process of the whole harbour and therefore the Port Authority, which operates the central logistics platform, seeks to automate this important step of the land side flow. To this end they have built a large internal transport infrastructure that links the terminals and other transfer points together. Following a feasibility study, the port has decided that autonomous trucks will be the most cost ecient transport method for this part of the flow (Interview B).

Chapter 4 Page 30 Driving Autonomous Heavy Vehicles into the Future - A Business Model Perspective

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

Scania is one of the OEM’s that potentially could take part in the provision of this autonomous transport service. Within a development phase of the project the Port Authority aims to develop their abilities to dispatch and monitor an autonomous vehicle, which Scania has been given the opportunity to be part of. With this pilot project Scania aims to develop their autonomous competence; both regarding the maturity within their technology but also within operation performance (Interview B, Interview D). Further, Scania is hoping that this pilot project will prepare them in the case that they receive a contract for a commercial transport service within the port. An illustration of the phases of the project may be seen in Figure 4.4.

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

Within the development phase the intention is to create a pilot in a confined testing area where the level of complexity is reduced (Interview B, Interview D). A large focus will be on defining the Operational Design Domain (ODD) for the autonomous driving system, which describes the specific conditions under which an autonomous vehicle is intended to operate. Further, emphasis will be on synchronising the di↵erent systems, ODD-scenarios and developing communication standards. The project requires close collaboration between Scania and the port to integrate systems and optimise logistic flows. As a part of this, Scania will launch vehicles optimised for container transport that are fitted with Scania’s fully autonomous driving platform (Interview B). Furthermore, Scania will integrate their software systems and communication interface with the overall port operating system that belongs to the Port Authority as well as develop an Application Programming Interface (API) to synchronise o↵-board functionality. Finally, a staged deployment of on-board vehicle technology will occur to test management of container movements within the confined area. Initially, the autom ation process will be supported with manual control and later on with a safety driver until the autonomous deployment is complete.

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The Port Authority is planning to run a future large scale commercial operation in the port within the aforementioned internal transport ﬂow between terminals (Interview B, Interview D). This particular route is predicted to optimise the ﬂow within the entire port and contribute to greater eciency by reducing time and cost for all parties involved (Interview B). A good example of this is that ships will be able to make fewer stops since the solution will make it possible to load and unload their cargo at one single location and distribute the goods further to other places without having to dock elsewhere within the harbour, as is the case today (Interview B). A challenge in this scenario would be that the Port Authority’s overall port operating system and the terminal operators’ systems with di↵erent interfaces and software will have to communicate with the autonomous solution through a generic communication interface. An important task in this potential commercial phase would then be to understand how the operator’s systems work and how their interfaces can be integrated with the OEM’s solutions.

If the Harbour Case would enter a future commercial phase, there could be several payment models to consider. One alternative for the Harbour Case could be Technology Lease,wherepaymentis made based on the total vehicle capacity that is provided. Another possible payment model could be Transport Service, where payment is made per a unit of transported goods. However, regardless of how the payment model might develop the aim would be to eventually ﬁnd a business model that balances risk and reward for both parties, allowing for shared investment and returns, creating a win-win scenario (Interview B, Interview D).

Chapter 4 Page 32 Chapter 5

Findings and Analysis

This chapter 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 emphasised capabilities that were identiﬁed are introduced.

5.1 Mining Case

Below is a visual representation of the Business Model Canvas (BMC) of the development phase of a Mining Case (see Figure 5.1), whereby each of the nine blocks are described thoroughly.

5.1.1 Mining Development Phase - Business Model Canvas

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

33 Driving Autonomous Heavy Vehicles into the Future - A Business Model Perspective

Key Partners There are a few di↵erent key partners to consider in the development phase of Scania’s mining project. Firstly, they share a partnership approach together with the Mining Company itself, collaborating and sharing beneﬁts until operations and technology have matured and a commercial phase can be entered (Interview A, Interview C). The Mining Company contributes with resources such as the controlled test site where they operate, truck facilities ranging from washing and refuelling stations to an autonomous software platform. Secondly, they are also using local universities as partners, which allows them testing and study opportunities in exchange for their knowledge that can help ﬁll in white spaces in Research and Development (R&D). In addition, a third-party software company is partnered with in order to help integrate Scania’s systems with the Mining Company’s platform. Another partner is a Telematics Company that provides connectivity, which is a necessity for communication between and to the AVs. Lastly, Scania collaborates with di↵erent standardisation organisations in order to ease integration and lower the costs when integrating di↵erent ATS modules with third-party systems from di↵erent suppliers.

Key Activities Key activities include truck prototyping as well as standardisation and integration of Scania’s software systems (Interview A). Also, testing and deployment on-site in the confined testing area are essential activities together with competence building within both Scania and the Mining Com- pany. Furthermore, background R&D for the project constitutes daily work activities in Södertälje and covers software and hardware development. Regular repair and maintenance of the vehicles and their sensors, which involves keeping track of the system health as well as refuelling and cleaning are also key activities together with the actual operation of transporting material.

Key Resources The key resources that Scania takes advantage of in this phase are their autonomous competence and R&D department combined with the very large monetary funding that comes with being an incumbent ﬁrm investing heavily in autonomous technology (Interview A, Interview C). Further- more, they have resources in the form of already deployed local operations teams and competence surrounding the mining industry in their well-established Scania Mining division.

Cost Structure The main costs for Scania in this phase include hardware manufacturing as prototypes are very expensive to produce or purchase without the beneﬁts of economies of scale that comes in a commercial phase (Interview A). Moreover, repair and maintenance and software R&D are other regular costs. During development the test vehicle needs to send large amounts of data in real time to be analysed by the R&D department, which constitutes a high cost for large cloud server usage. In addition, salaries for Scania employees placed locally and other local teams together with the deployment of new vehicles and systems are other large costs for Scania in this phase.

Value Proposition In their value proposition, Scania has emphasised the value that lies within small autonomous trucks compared to the very large haul trucks typically used by mining companies (Interview A). These include lower unit costs as smaller trucks are less expensive to produce than large haul trucks. Furthermore, smaller sizes combined with Scania’s well-known modularity concept also implicate higher ﬂexibility, which refers to the fact that these trucks can more easily be adapted to di↵erent mining operations and conditions. Smaller trucks implicate increased safety that comes with reduced weight and better maneuverability. It is also assumed that smaller vehicles will have a faster decarbonisation development as electriﬁcation of normal on-road-trucks is predicted to

Chapter 5 Page 34 Driving Autonomous Heavy Vehicles into the Future - A Business Model Perspective develop faster than mining industry vehicles. This is something Scania can take advantage of when using their modularatiy concepts to transfer batteries and motors to their mining adapted trucks. Another advantage is that smaller vehicles can drive, manually or autonomously, on a wider range of infrastructure since large mining haul trucks have heavy tyres that are by legislation and regulations not allowed to utilise normal roads. Moreover, smaller vehicles are considered to be more resilient in their operation; if one vehicle needs to be repaired that specific unit might only constitute 2 percent of a fleet compared to a large haul truck that might constitute 10 percent. On the same note, smaller vehicles have a higher fleet flexibility and scalability as it is easier to incrementally increase a fleet of small vehicles by single units instead of investing heavily in another large haul truck. Lastly, smaller trucks are considered to have a higher technology innovation rate as the technology involving them matures faster than heavy haul trucks (Interview A).

Apart from the advantages of the AVs themselves, modularity and safety are also important parts of Scania’s value proposition in all their business cases (Interview A). Furthermore, user friendliness and partnership model approach in itself are factors that are expressed as value proposed to the Mining Company. Finally, ease of system integration has been a crucial selling point for Scania as the Mining Company wants to be able to integrate their existing systems without having to for instance add extra control monitors or other equipment (Interview E).

Customer Relationships During the development phase the customer relationship between Scania and the Mining Company is characterised by development collaboration and co-creation of value (Interview A, Interview C). The Mining Company has been a long-standing customer and partner to Scania and according to Interview C, their history together has created a bond of loyalty and mutual trust that are key ingredients in a long lasting business relationship.

Distribution Channels Scania has a well-established global distribution network already in place that they use to both distribute vehicles and spare parts as well as to make customers aware of their value proposition (Interview A). In this case Scania’s Business Model developer team in Sweden are directly involved with the Mining Company together with the local Scania Mining subdivision from a commercial perspective. As one of the industry leaders this collaboration with the Mining Company could lead to valuable word-of-mouth marketing according to Interview A and Interview C.

Customer Segments According to Interview C there are subﬁelds within the mining industry to consider in a future commercial phase. However, within this development phase the customer segment in this application is limited to the mining industry itself.

Revenue Streams In the development phase the Mining Company and Scania are collaborating and sharing beneﬁts, while discussing monetary payment models are for future commercial set-ups (Interview A, Inter- view C). As such, this phase includes revenue streams in the form of non-monetary value of R&D, testing opportunities and competence building.

Chapter 5 Page 35 Driving Autonomous Heavy Vehicles into the Future - A Business Model Perspective

5.1.2 Mining Commercial Phase - Business Model Canvas

Below is a visual representation of the predicted Business Model Canvas of a potential future commercial phase of the Mining Case, where di↵erences from the development phase are highlighted in italics (see Figure 5.2). Thereafter, each of the nine blocks are described thoroughly.

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

Key Partners Once the Mining Case matures and enters a commercial phase the partnership approach that ex- isted between the Mining Company and Scania will transition into a more traditional customer and supplier relationship. Hence, the Mining Company is no longer part of this block, whereas a third-party software company and a telematics company are predicted to remain important key partners (Interview A).

Key Activities In the commercial phase, key activities will include updating and maintaining software and repairing the physical vehicles. The main operations will be to transport di↵erent types of material in several mining operations and most likely include other mining side activities such as service vehicles in the form of for example water trucks and blasting trucks (Interview C).

Key Resources Key resources in this phase are predicted to include a more matured autonomous competence and a more experienced R&D department, local operations team and Scania Mining division. In this commercial phase Scania is no longer prototyping single vehicles but instead mass-producing AVs and can therefore take full advantage of their economies of scale and Lean production system (In- terview A, Interview C).

Cost Structure The main costs are predicted to be for hardware manufacturing, repair and maintenance with the addition of software maintenance as well as salaries for local teams. A cost that is expected to de-

Chapter 5 Page 36 Driving Autonomous Heavy Vehicles into the Future - A Business Model Perspective crease substantially and is therefore not included in this block of the commercial phase is the cost of cloud servers. The reason for this is that Scania will no longer have to send as large amounts of real- time data from their vehicles, as was the case in the development phase (Interview A, Interview C).

Value Proposition In the commercial phase Scania’s value proposition will still consist of the many beneﬁts of small autonomous trucks compared to large haul trucks (Interview A). Additionally, safety and user friendliness are still considered to be important elements (Interview E). However, added in this phase is the value expressed as reliability assuming that Scania is able to prove themselves that they are capable of delivering safe and reliable solutions on time. Furthermore, an ecient customer support is predicted to be part of a future value proposition together with optimised ﬂeet management when there are several vehicles involved in the operations. Although autonomous vehicles are predicted to become increasingly standardised (Interview A, Interview C), modularity is still a concept that is considered to be part of the value proposition in the Mining Case as it allows the customer to easily adapt the vehicle for di↵erent operations.

Customer Relationships For the customer relationship, value co-creation and mutual trust are still important factors. In the future, Scania and the Mining Company are predicted to develop more of a traditional customer and technology provider nature where loyalty is highly valued.

Distribution Channels In the commercial phase Scania will still draw advantages from their already established global distribution network. At this point they will have a supplier and technology provider role to an industry leading mining company, which by word-of-mouth will make other customers aware of the value they bring (Interview A, Interview C).

Customer Segments Although the customer segment will technically be the mining industry as a whole it is predicted that Scania’s AVs will be involved in several di↵erent sub segments within the industry in a future commercial phase.

Revenue Streams The revenue streams in the commercial phase could consist of di↵erent payment models. One alternative could be payment for Technology Lease; that is to say the mining company is paying for the capacity of the vehicles and essentially lease the technology without transferring ownership (Interview A). Another alternative could be Transport Service where payment is made per transported ton material.

Chapter 5 Page 37 Driving Autonomous Heavy Vehicles into the Future - A Business Model Perspective

5.2 Harbour Case

Below is a visual representation of the predicted Business Model Canvas of the development phase of a Harbour Case (see Figure 5.3), whereby each of the nine blocks are described thoroughly.

5.2.1 Harbour Development Phase - Business Model Canvas

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

Key Partners Within the development phase of Scania’s harbour project they will have a partnership with the Port Authority as well as standardisation organisations (Interview B, Interview D). The Port Au- thority is a company partly owned by the local government that is responsible for the harbour area and the general infrastructure outside the terminals. Scania will share risks and beneﬁts together with the Port Authority that will provide a physical infrastructure, a controlled test site, some developers, autonomous expertise and an overall port operation software platform (Interview B, Interview D). Furthermore, Scania will collaborate with di↵erent standardisation organisations to ease integration and lower the costs when integrating di↵erent ATS modules with third-party systems from di↵erent suppliers.

Key Activities There are a few key activities connected to this project within a development phase. Among many things they will involve truck prototyping, standardisation and integration of Scania’s and the Port Authority’s software platforms (Interview B, interview D). In addition, testing and deployment will be carried out within the test site, which includes implementation of o↵-board functionality such as control towers. Also, competence building within the companies will be crucial activities and background R&D will constitute the daily software and hardware development. The test site operation itself will imply repair and maintenance of the vehicle and sensors, which involves keeping track of the system health as well as refuelling and cleaning. Furthermore, the actual transportation of empty containers within the test area constitutes a key activity.

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Key Resources There are many key resources that Scania could take advantage of in this phase. They have an extensive autonomous competence, a well-developed R&D department and as an incumbent ﬁrm, Scania has large monetary funding that can be invested in the development of new technology. In addition, they will probably have a local operations team from Scania present in the harbour area, including test drivers and personnel, who will be in charge of repair and maintenance (Interview B).

Cost Structure In the development phase there will be several cost drivers to consider. First, in the production of few prototype vehicles, hardware manufacturing will be a big expense since Scania will not be able to take advantage of economies of scale. Second, repair and maintenance of the testing vehicle will probably need to be included in Scania’s cost structure as they are predicted to keep ownership of the vehicle (Interview D). In addition, other costs are software R&D and cloud service resources, where the latter is due to the fact that large amount of real-time data will be sent from the vehicle to the R&D department for analysis (Interview B). Other large expenses are salaries for Scania employees and local teams present in the harbour area as well as connectivity in order to operate the AV.

Value Proposition The autonomous transport system that the Port Authority intends to establish is in many ways superior compared to the manual system that is presently operated. Firstly, due to the fact that drivers are removed the autonomous system is cost-reduced. This is a parameter of huge importance for the Port Authority since it is partly owned by the government (Interview B). Also, the system will allow a seamless, ﬂexible and ecient operation customised to the speciﬁc area.

Apart from the value proposition derived from the transport system being autonomous, there are other aspects that could express value. For example, safety and user friendliness are parameters that are heavily focused on within Scania (Interview B, Interview D). Also, the partnership model approach with close collaboration between the customer and supplier is considered part of the value proposition (Interview B). In addition, elements such as ease of integration will be value proposed to the Port Authority.

Customer Relationships The relationship between Scania and the Port Authority is predicted to have a few interesting characteristics. In this phase it will supposedly be characterised by development collaboration in the sense that the companies will be co-creating value through researching and testing together as well as sharing intellectual property, building a relationship on mutual trust. However, the fact that the Port Authority is governmentally owned and that there is tax money involved could cause other observers, such as local media, to scrutinise projects of this kind (Interview B, Interview D). This public expense parameter further entails an internal scrutiny, where quantitative performance measurements are being applied objectively when assessing Scania, in order to quality assure them.

Distribution Channels Scania has access to distribution channels that are used to distribute physical vehicles and spare parts, and also to spread awareness about their value proposition. Firstly, they have a well- established global distribution network that can be used for both purposes. Secondly, the Port Authority is one of the largest and most well-developed companies of its kind and since they are industry leaders other Port Authorities may keep their eyes on this collaboration (Interview B, Interview D). In other words, the fact that this harbour collaborates with Scania puts a quality

Chapter 5 Page 39 Driving Autonomous Heavy Vehicles into the Future - A Business Model Perspective stamp on the OEM. Another aspect of this word-of-mouth marketing is that the Terminal Operators are global companies who have their own networks, which causes the word to spread to other ports.

Customer Segments Within the development phase the customer segment is broadly limited to the harbour industry and the operation will constitute solely of transportation of empty containers at a testing area within the harbour (Interview B, Interview D).

Revenue Streams Within the development phase the Port Authority and Scania will with their collaboration share risks and beneﬁts with the non-monetary value of R&D and competence building in return (Inter- view B).

5.2.2 Harbour Commercial Phase - Business Model Canvas

Below is a visual representation of the predicted Business Model Canvas of a potential future commercial phase of the Harbour Case, where di↵erences from the development phase are highlighted in italics (see Figure 5.4). Thereafter, each of the nine blocks are described thoroughly.

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

Key Partners In the event of a future commercial phase the key partners constellation might change. Firstly, the Port Authority’s role as a key partner could diminish as the companies’ relationship presumably transitions towards being of a customer-supplier nature (Interview B, Interview D). On the contrary, as the operation moves from a testing area to become involved within the terminals’ ﬂows, the Terminal Operators will presumably become new key partners. Since the terminals have a varying degree of automation and trac ﬂows, Scania will have to learn from these operators and cooperate with them in order to integrate software and hardware interfaces. In addition, Scania will need to partner with a Telematics Company that could provide them with connectivity for communication between and to the AVs.

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Key Activities Within a commercial phase, key activities are predicted to transition into updating and maintaining software as well as repairing and maintaining hardware. Furthermore, the main operation could be to transport customer goods containers on a route that extends between the terminals, rail, road and sea within the harbour (Interview B). This has to be done in an ecient way that matches the ﬂows of the di↵erent terminals.

Key Resources The autonomous competence that will be present in the development phase is predicted to become more mature in a commercial phase. Also, the more experienced R&D department and local operations team will remain as key resources. Due to the fact that Scania in a commercial phase will be able to mass produce AVs instead of prototyping single vehicles, they could take advantage of their economies of scale and Lean production system (Interview B, Interview D).

Cost Structure As in the development phase, the main costs are predicted to include hardware manufacturing, repair and maintenance of the vehicles as well as software maintenance. In addition, salaries for the local teams and connectivity from the Telematics Company are predicted to remain signiﬁcant costs. However, the expense for cloud servers is expected to decrease considerably due to the fact that large amounts of real-time data will no longer need to be sent to Scania from the vehicles (Interview D).

Value Proposition In a commercial phase Scania’s value proposition will still consist of the advantages of an autonomous transport system, possibly even more reinforced by the newly emerged partnership between Scania and the Terminal Operators, as the system will be able to reduce the number of loading points and increase the capacity for transporting containers. Also, the parameters of safety and user friendliness are predicted to remain. Another element that is added in the commercial phase is the value expressed as reliability, assuming that Scania could prove that they can deliver reliable solutions on time. Also, Scania will o↵er customer support as well as optimised ﬂeet management as the number of vehicles increases (Interview B, Interview D). Although it is believed that autonomous vehicles will be more standardised, modularity is considered to be part of the value proposition in a commercial phase as it enables adaption for potential side operations.

Customer Relationships In a commercial phase, value co-creation and mutual trust are still predicted to be important factors. Furthermore, since the Port Authority is a partly governmentally owned company where tax money is involved, Scania will probably continue to be assessed on objective quantitative performance measurements to assure that they deliver what is promised (Interview B). Finally, the relationship between Scania and the Port Authority is predicted to shift towards a more traditional customer and technology provider relationship (Interview B, Interview D).

Distribution Channels In a commercial phase, distribution channels in the form of global distribution networks and partnerships with global Terminal Operators will probably be of even more importance than before (Interview B). Furthermore, Scania would take the role as a supplier and technology provider to an industry leader, which by word-of-mouth could raise the awareness among other potential customers.

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Customer Segments The customer segment within a commercial phase will remain within confined harbours and ports. However, since automation and autonomous technology is profitable to invest in mainly within cyclic internal flows with many repetitive tasks, the customer segment will be limited to such harbours and fields (Interview B, Interview D). This is opposed to flexible solutions where the flow or type of transport di↵ers from time to time. However, the size of the harbour and the fleet may be of less importance - as long as the utilisation rate is high, the business may be profitable.

Revenue Streams The revenue streams in a commercial phase could consist of di↵erent payment models. One alternative is Technology Lease, which refers to payment for vehicle capacity and technology without transferring vehicle ownership to the customer (Interview B). Another alternative payment model could be Transport Service, which could refer to charging the Port Authority per transported container (Interview B, Interview D).

5.3 Identiﬁed Capabilities

Twelve subjects from the Autonomous Solutions department at Scania were interviewed in this study. An open question regarding what the interviewees believed will be Scania’s strengths in the future was raised (see question 11 in the Exploratory Interview Guide, Appendix B). From this data, nine capabilities were identiﬁed, whereas four of them were mentioned most frequently and will be the focus within this study. These capabilities were: Reliability, Modularity, Lean production and Safety (see Figure 5.5 below. A more detailed table can be viewed in Appendix C.)

Figure 5.5: Frequency of highlighted capabilities from the exploratory interviews

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Reliability Reliability is a strength highlighted as an important feature in the autonomous future by most interview subjects (9 out of 12, see Figure 5.5) and is considered tightly connected to the premium brand of Scania. Reliability in the sense that everything is delivered on time and that the vehicles have a high uptime will be important selling points in an autonomous future (Interview vi; Inter- view x; Interview xi). The autonomous solution in itself creates a predictability and reliability that a human driver cannot reach (Interview ix), and in a growing autonomous world Scania will have to keep what is promised and o↵er reliable products that work eciently (Interview viii; Interview xi). Furthermore, Interview iv emphasised that many customer relationships are built on loyalty and therefore ”continuous the aspect of trust and reliability in customer relationships and customer support will be an important strength in the future”. Interview x adds that reliability in activities such as repair, maintenance and customer support will be extremely valuable when building and retaining long-lasting customer relationships.

Modularity Eight interview subjects highlighted modularity as an important strength for Scania. The modularity concept allows the company to modify and exchange parts of their products easily and o↵er highly customised vehicles. Interviewee iii pointed out that modularity has always been a strength of Scania and will most probably be of use in an autonomous world even when there is no driver. Also, Interviewee iv emphasised that in the future Scania will need to have a modular system that is scalable and can fulﬁl certain cases and meet di↵erent customer needs. Interviewee ii describes how future concepts are planned to have generic powertrains and convertible bodies that can be used for di↵erent activities.

When discussing modularity, the question arose whether the capability will be of as great importance in the future as it has been historically. In an autonomous future it is assumed that many vehicles will be much more standardised and there will be no need for driver centred custom cabins where driver comfort is important (Interview ix). Currently purchases of premium products are often emotionally based from the customer perspective but according to Interview ix sales are speculated to be of a more objective nature in the future. Furthermore, according to Interview v and Interview vi much of the physical modularity in the production will disappear with the transition to more service-based business models, since clients will care less about the details and more about the actual transportation of goods from one destination to another. In addition, Interviewee ix points out that a fleet of interchangeable autonomous standardised trucks will allow a higher degree of flexibility, making physical modularity less significant in the future.

However, the modular concept can be applicable to software production just as well as hardware production. Scania is not primarily an Information Technology (IT) company and will need a strategy to optimise the software ﬂows as well (Interview x). Interviewee ii expanded on this, claiming that modularity can be a strength within software development, where di↵erent layers of o↵-board technology can be modularised to be easily adapted to every customer’s di↵erent needs. Ease of integration and understanding the customer needs are of great importance since Scania’s customers typically already have many di↵erent systems in place that have to be integrated (Interview ii, Interview xi), something that modularity can favour. Also, other interviewees highlighted the importance of developing a generic modular platform that can be quickly adapted with interfaces that suit speciﬁc customer needs (Interview i, Interview xi). Furthermore, Interviewee i claimed that software modularity can be a capability that could make Scania a premium brand in the future.

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Lean Production Another highlighted strength of Scania is their Lean production system that six of the interview subjects mentioned. Scania is considered to have practiced Lean long before the term was coined (Interview ii) and today Scania has a production and logistics system that ”few can beat” (Inter- view xii). According to Interview vi, if the value of autonomous vehicles shifts towards a more cost-driven model they can take advantage of Scania’s strong tradition of Lean and ecient production as key resources. Furthermore, once Scania enters a more mature commercial phase and starts to scale their production, the advantages of economies of scale and Lean production will be even more apparent. Interview ix pointed out ”once we start to scale we need to make the production as ecient as possible. And that is where Lean comes in”. Also, Interviewee xi claimed that in an autonomous world that ”moves fast” it will be crucial to think in the terms of Lean even more than today to make the production more ecient and ﬂexible.

Safety When asking the interview subjects what they think Scania’s strengths will be in an autonomous future one of the ﬁrst things that was mentioned by ﬁve of the subjects was safety. The autonomous system will imply less congestion on the roads, which will lead to a safer transport system (Inter- view viii), which is in line with Scania’s overall safety thinking. Safety has always been paramount for Scania and according to Interviewee ii ”Scania is only going to deploy when we are 100 percent sure everything is safe. This is part of the culture at Scania.” Furthermore, Interviewee i and Interviewee ii attributed safety as part of their premium brand in an autonomous future, pointing out that when premium cabins are no longer a selling point, ”we will still be a premium brand when it comes to safety and reliability” (Interview i).

Apart from safety, several interview subjects highlighted cyber security as part of their safety concerns (e.g. Interview v, Interview vii); ”When all autonomous vehicles are connected, cyber security will be of utmost importance //...// and we will have to keep all of the infrastructure con- stantly up to date” (Interview v). Many of Scania’s customers, for example the Mining Company, also express cyber security as a paramount aspect when they request services from their suppliers (Interview v).

Chapter 5 Page 44 Chapter 6

Discussion

This chapter 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 dif- ferences. 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 leveraged 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.

6.1 Comparison between Mining and Harbour Applications

A total of four Business Model Canvases have been created for the two studied cases, displaying the development phases and the predicted commercial phases. These canvases may be viewed in the previous Findings and Analysis chapter (see Figure 5.1, Figure 5.2, Figure 5.3 and Figure 5.4). Based on the commercial phase canvases an Aggregated Business Model Canvas has been formed including the predicted similarities and di↵erences between the two (see Figure 6.1 below). For clarity, the elements presented within this Aggregated Business Model Canvas are highlighted di↵erently depending on their type and origin. Elements that were present in both the Mining and the Harbour Case are included but not highlighted, while those that were only present in one of them are marked in italics. Some similar elements have been modified as a more general concept to fit a general application. Finally, elements that have been identified as capabilities, with potential of being future core competencies of the company, are highlighted in bold. This will help to answer the second half of the first research question:

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

Similarities and Di↵erences As seen in the aggregated canvas below there are many similar elements when comparing the studied business cases, but also a few di↵erences (see Figure 6.1). The similarities form a basis for a general business model for future goods applications within hubs, while the di↵erences provide additional elements that could be valuable in a general application. All elements are further dis-

45 Driving Autonomous Heavy Vehicles into the Future - A Business Model Perspective cussed within their building blocks below.

Figure 6.1: A General Hub Application - An Aggregated BMC illustration for a Commercial Phase

Key Partners It is evident from both case studies that there is a need for strong partnerships in an autonomous future. For instance, unless Scania becomes a producer of connectivity itself, a telematics company will have to be one of their key partners. Also, in both cases some sort of local integration partner exists. However, these partners have slightly di↵erent functions when comparing the di↵erent cases. In the Mining Case a local third-party software company is partnered with to actively help with integration and maintenance of systems and interfaces, whereas in the Harbour Case the Terminal Operators are predicted to be more passively available to cooperate with Scania in order to integrate their software and hardware interfaces for compatibility. Based on this, in a general application some form of local integration partner, that can assist with integration of software, will most likely be present.

Key Activities In both cases key activities in a commercial phase are predicted to include updating and maintaining software as well as repairing physical vehicles, which includes keeping track of the system health, maintenance of the hardware as well as refuelling and cleaning. Furthermore, the main operational key activity is to transport material, which could be of di↵erent kind depending on the customer segment. Also, di↵erent side operations could be developed to some extent, as is the case within the Mining application but not seen within the Harbour Case yet.

Key Resources Key resources that are present in both case canvases are matured in-house autonomous competence, an experienced R&D department and a local operations team, as well as the advantage of economies of scale and Scania’s ecient Lean production system. The Lean production system has been identiﬁed as a valuable capability and potential core competency that could be utilised in any application. On the other hand, whether Scania will have speciﬁc application divisions present in each industry seems to be dependent on the size and complexity of it. Certain applications

Chapter 6 Page 46 Driving Autonomous Heavy Vehicles into the Future - A Business Model Perspective might grow and Scania could thereafter develop specialised divisions, as is the case today with Scania Mining. In a general application, however, some extent of expertise for each application is considered a necessary resource (Interview ii).

Cost Structure In both cases main costs are predicted to derive from hardware manufacturing as well as hardware and software repair and maintenance, which will have to be part of Scania’s responsibilities in a future service-based model (Interview viii). These elements are fundamental for a heavy autonomous vehicle manufacturer and will hence be present in any case application’s cost structure. Additionally, from the analysis of the case canvases it is apparent that there will be a need for some sort of local team in the area, whose salaries are also a cost factor. However, it is not completely clear which actor should take on the cost of connectivity, since there is a di↵erence between the studied cases, and therefore it might be a cost factor within a general application.

Value Proposition Scania’s general value proposition is predicted to be based on the ﬁndings and analysis connected to safety and reliability for the physical trucks, including risk mitigation and to deliver what is promised on time. In addition, an ecient customer support, ﬂeet management service as well as user friendliness and modularity are factors that are meant to ease integration and use of the products and services for di↵erent customers. Thus, they are predicted to be an important part of the value proposition. Safety, reliability and modularity span multiple industries and are highlighted as focused capabilities and potential core competencies for Scania.

The value proposition might be slightly di↵erent dependent on the industry and application; for the Harbour Case the autonomous transport system in itself that is cost-reduced, seamless, ecient and flexible constitutes part of the value proposition (the elements are described in more detail under Figure 5.4 in Chapter 5). In the Mining Case, however, apart from the transport system being autonomous the value also lies within the truck size and compatibility, which implies a lower unit cost, higher flexibility, increased safety, faster electrification, less infrastructure limitations, increased operation resiliency, fleet scalability and a higher technology innovation rate (the elements are described in more detail under Figure 5.2 in Chapter 5). In a general application the autonomous transport system in itself will likely be the more driving part of the value proposition, while truck size and compatibility could still play an important role depending on the industry.

Customer Relationships The customer and technology provider relationship is predicted to be characterised by value co- creation, mutual trust and loyalty within both the Mining and Harbour applications, which will probably be signiﬁcant within a general application as well. However, the fact that Scania’s customer in the Harbour Case is governmentally owned causes an internal scrutiny, where objective quantitative performance measurements are being applied when assessing Scania in order to quality assure them. This is, however, not the case with the Mining Company, which means this element is situational. However, to some extent performance measurements will most likely be present independent of the ownership form of the customer, and therefore this element is included in the canvas for a general application.

Distribution Channels In order to distribute vehicles and spare parts as well as to spread awareness of their value proposition, Scania has an advantage of being a supplier to industry leading companies together with having established a global distribution network that by word-of-mouth can raise awareness among

Chapter 6 Page 47 Driving Autonomous Heavy Vehicles into the Future - A Business Model Perspective other potential customers. This is evident in both studied cases and will most likely be crucial parts of this block for a general application as well.

Customer Segments In the Aggregated Business Model Canvas the customer segments expand to general applications within hub operations similar to the studied mining and harbour industries.

Revenue Streams From both cases it would seem that Scania is going to keep ownership of their autonomous vehicles and provide them as a service, which aligns with the servitisation concept, where OEMs are moving from the traditional selling of physical products towards providing services (Vandermerewe and Rada, 1988) as new revenue streams. In both cases di↵erent payment models are discussed, such as Technology Lease that implies payment for available vehicle capacity and Transport Service with payment per transported material.

6.2 Future Core Competencies

The focused capabilities identiﬁed in Chapter 5 will be discussed in relation to whether they qualify as core competencies at Scania and how they can be leveraged in their business model in an autonomous future. The purpose is to answer and discuss the second research question:

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?

As seen in the Frame of Reference, Prahalad and Hamel (1990) o↵er the following criteria in order to qualify a company’s capability as a core competencies: 1) the capability is a unique signature in the organisation, 2) it covers more than one business and 3) it is hard to imitate.

Reliability The reliability capability was pointed out as a feature closely connected to the premium brand of Scania and is believed to be a unique signature in the organisation (e.g. Interview iii, Interview x). This capability spans over several businesses as it includes delivery on time, reliable products with high uptime as well as an ecient customer service and activities such as repair and maintenance. The question remains if it is a capability that is hard to imitate and thus qualiﬁes as a core competency. Arguably, reliability is a broad term that could be attributed to several di↵erent factors and other capabilities, and it is dicult to state how hard it would be for a competitor to replicate it. Scania has a well-established global distribution network with presence and retailers in most countries, a threshold that is not that easy to cross for any competitor. However, it is reasonable to consider that many incumbent OEMs in the heavy vehicle manufacturing industry have access to similar networks, and in essence reliability might not be a core competency by deﬁnition, but rather a strength that stems from multiple capabilities that Scania possesses.

Modularity Another capability that Scania is known for is the modularity concept. Scania has always prided themselves on their modular vehicles, which has allowed them to be able to switch components easily while keeping the same core products. The concept of having highly customisable products that can be tailored to every customer’s speciﬁc needs, taste or demands over all of their businesses has long been a unique signature of the organisation. From the data collected in interviews it is

Chapter 6 Page 48 Driving Autonomous Heavy Vehicles into the Future - A Business Model Perspective apparent that this capability’s survival in an autonomous future has been questioned due to the increased level of standardisation that is predicted following partly the removal of physical drivers. However, the concept of modular design and thinking is predicted to survive if not to the same extent in physical manufacturing, which will still be important to tailor di↵erent needs in various settings, but perhaps also in the new form of software modularity.

Since Scania is not inherently a software manufacturer, software development capabilities need to be built and/or acquired. The modularity capability could allow them to more easily integrate these new acquired skills in their existing platforms. Furthermore, the modularity capability can help Scania to build modular software stacks that can be tailored to di↵erent customer’s needs and levels of integration. Modularity clearly covers more than one business and is a unique signature for the organisation, but in order to qualify as a core competency it needs to be hard to imitate. Modularity design and thinking could not only be connected to a strong culture and competence pool built over many years, but also be tied to many intellectual properties and patents. This creates a barrier that can be hard for competitors to imitate, making modularity a capability that could pass as a core competency for Scania in an autonomous future.

Lean Production Lean production is a definitive part of Scania’s culture and the company is often used as a prime example of a Lean organisation, making it a unique signature of the brand (Miina, 2012). Scania has developed their own Lean philosophy, known as Scania Production System (SPS) and it is part of the company culture. Considering that lower costs most likely will be a strong competitive advantage in an autonomous future, Lean production can make a significant contribution to the value a customer perceives in the end product in this form. Furthermore, Scania’s mining division has developed a service framework called Scania Site Optimisation, which o↵ers guidance with implementing Lean for their customers within mining operations (Winblad, 2016). This means that e↵ectively Scania can use their Lean capability and expertise as a service for their customers and not only in their own production system. Lean production certainly covers more than one business as it a↵ects all manufacturing within Scania and can also be used as a complementary service, which fulfils the second criteria of spanning multiple businesses. In order for Lean production to qualify as a core competency it also needs to pass the third criteria; it has to be dicult for competitors to imitate. Studies show that one of the greatest challenges when implementing Lean production is people management and to immerse Lean thinking into the organisational culture (Bakke and Johansen, 2019), making it a capability that is hard to imitate as the level of Lean at Scania has taken decades to build. Lean production seems to pass all three of the criteria presented by Prahalad and Hamel (1990) and thus constitutes a core competency in an autonomous future.

Safety Safety is another capability that interviewees considered to always have been a part of Scania’s culture. Extensive testing and making sure all products and systems are one hundred percent safe is something that could slow down certain processes, yet it is according to all interview subjects a top priority for Scania. Another aspect of safety, that seems to become increasingly important in an autonomous future, is cyber security. This would require software capabilities that can possibly build on the safety thinking that is already present in the organisation. Safety, much like reliability, plays a part in all of Scania’s businesses and thus this capability, perhaps isolated to a range of safety thinking and testing processes, pass the ﬁrst two criteria of constituting a core competency. However, it might be hard to motivate that this capability is dicult for competitors to imitate. The fact that all OEMs could say that they prioritise safety and are most likely regulated by safety and industry standards causes this capability to be an important asset for Scania but dicult to

Chapter 6 Page 49 Driving Autonomous Heavy Vehicles into the Future - A Business Model Perspective defend as a core competency by deﬁnition. Nevertheless, it can still be used as a way of expressing value in Scania’s business models and thus it remains an important selling point in an autonomous future.

6.2.1 Core Competencies in the Business Model

Out of the nine identiﬁed capabilities and the four focused capabilities discussed, two of them seem to qualify as core competencies, namely Modularity and Lean production (see Table 6.1). The other two, Reliability and Safety, are less tangible capabilities that appear to stem from multiple factors and are less dicult to imitate by competitors. Nevertheless, all four focused capabilities are valuable assets to Scania and can be expressed in a future autonomous vehicle business model and help them gain competitive advantage.

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 competitive advantage in an autonomous future

As seen in the studied cases, the core competency Modularity can be expressed in Scania’s Value Proposition both referring to software and hardware modularity as it o↵ers customers ease of integration and vehicle adaptability. In addition, it can be reasoned that modularity thinking could be seen as a part of Scania’s Key Resources since the concept could potentially make it easier for Scania to develop, acquire and integrate other necessary skills and capabilities, such as software development (see Figure 6.2).

The core competency Lean production could be considered an important Key Resource in Sca- nia’s business model, as is evident in the studied cases. In addition, it could be utilised as a complementary service to Scania’s customers and, similar to modularity, be expressed in their Value Proposition (see Figure 6.2).

The capabilities Reliability and Safety could both be expressed in the Value Proposition of a general business model, as they have historically been connected to the premium brand of Scania and could still be in an autonomous future according to the data analysed in this study.

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Figure 6.2: An illustration of how the core competencies Modularity and Lean production can be integrated into the Key Resources and Value Proposition blocks

6.2.2 Dynamic Capabilities, Core Rigidities and Critical Competency

A capability can be classified as either a Dynamic Capability or a Core Rigidity, as defined by Schilling (2012) and Leonard-Barton (1992). The focused capabilities in this study could constitute Dynamic Capabilities, as they are not specific to any set of technologies or products. They are rather connected to a set of abilities and seem to be able to survive technology shifts and be utilised in new settings in Scania’s business model. This type of Dynamic Capabilities that are able to respond to change are perhaps the most valuable strengths and should be focused on to prepare for this technology shift. On the other hand, core competencies that are connected to a specific set of technologies, like the combustion engine and the premium cabin, are at risk of becoming Core Rigidities that are unable to respond to change and even become obsolete after the shift. According to Leonard-Barton (1992) it can be dangerous to have an organisational culture that overly rewards employees who are most closely connected to a specific core competency as it inhibits the development and nurturing of other more dynamic ones, such as Reliability, Modularity, Lean production and Safety. Finally, as suggested by Hamel (1994) and Srivastava (2005), the concept of a Critical Competency, that is the skill of managing and continuously developing, nurturing or abandoning core competencies in relation to changing external and internal environments, seems to be crucial. Scania undoubtedly possesses many resources and capabilities, and in order to gain competitive advantage they need to nurture those that can still be used in an autonomous future, e.g. Lean production and modularity, develop new ones that will be needed, e.g. software development as well as abandon those that will become obsolete, e.g. the combustion engine.

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6.3 A Future Business Model Perspective

6.3.1 A Service-based Business Model

When discussing the future of an autonomous heavy vehicle manufacturer from a business model perspective it is evident both from the interviews and the case studies that Scania moves towards a more service-based model aligning with the servitisation concept introduced in the frame of reference chapter. This servitisation driving force seems to not only stem from gaining competitive advantage and creating additional revenue streams, as Vandermerewe and Rada (1988) traditionally explains the concept, but to also derive from legislative and accountability concerns regarding the ownership and operational control of the AVs. Most interviewees claim that there is a consensus in the industry that if an accident occurs involving an AV, then the manufacturer will be held accountable. This essentially gives an AV manufacturer incentives to remain in control of both their vehicles and software (e.g Interview xi). This transfer of ownership could mean that the company shows a tendency of moving downstream in its value chain, to some extent replacing actors that typically procure heavy vehicles and operate them today. This means that AV manufacturers could potentially reach a second Party Logistics Provider layer in an autonomous future (see Figure 2.5 in Chapter 2).

The service level, where vehicles are not sold as physical products but instead provided as a service, is summarised within the industry with the term Vehicle as a Service (VaaS). Many of the interviewees claim that Scania could eventually even be involved in logistics planning (LaaS) and ﬂeet management, replacing a third- and fourth- Party Logistics provider, which could create additional services and value in their business model. However, from the case studies it would seem that the service model of a general application within hubs will, at least in the beginning, be more of a vehicle capacity provider nature, which could even be called Capacity as a Service (CaaS). In both studied cases the customers seem inclined to remain in control of their own logistics planning and operations. However, in the long-term perspective this could change.

The overall concept of a future service-based model that is presented above is characterised by four main attributes: collaboration and co-creation, di↵erent levels of service integration, ownership and accountability, and a value-driven source of di↵erentiation.

Collaboration and Co-creation Scania is an incumbent ﬁrm with large ﬁnancial resources, which enables it to invest in new technologies and to enter new markets. The company has an extensive global distribution network as well as a mostly in-house production at their disposal, enabling them to manage their business mostly on their own (Interview i). However, from the case studies and the interviews it can be seen that Scania probably will to a larger extent need to rely on partnerships and collaborations to complement their own capabilities, technologies and components that may extend the company’s boundaries. According to Utterback (1994) close collaboration with other actors when innovating within new technologies may be absolutely necessary.

There are di↵erent types of collaboration forms that Scania could take advantage of. Firstly, in order to complement missing capabilities they could, at least in a development phase, collaborate horizontally with third party companies such as local integration partners, providers of software and telematics. In that way they could, while remaining independent, grow capabilities needed and bundle them together into customer solutions. Secondly, Scania may advantageously enter extensive non-competitive vertical collaborations with their clients in development phases. In these

Chapter 6 Page 52 Driving Autonomous Heavy Vehicles into the Future - A Business Model Perspective scenarios they need to put emphasis on building trust in their customer relationship. This is clearly seen in both cases, where Scania and the customers are closely collaborating and co-creating value within the development phases. This stage seems to be necessary while negotiating, testing, integrating technology and ﬁguring out unknown factors in new applications and industries. As the technology and business models mature the development phases will most likely become shorter, until new projects can enter commercial phases sooner. However, the capability to realize and retain vertical collaborations might be a factor that could di↵erentiate Scania from other OEMs in an autonomous future and allow them to gain crucial footholds in this early stage of the technology shift.

Di↵erent Levels of Service Integration From the data collected in the case study and interviews it would seem that the level of service integration with the customer could di↵er between applications. This level refers to how much the manufacturer will be involved in operations and logistics planning. It is reasonable to assume that this would depend on factors such as customer needs, the customer’s own capabilities as well as the industry and market. In some cases, Scania’s service as an AV provider could constitute only a smaller piece of the whole system, whereas in others they could be involved in the larger picture, moving towards providing TaaS or even LaaS. One of the interviewees mentioned that ”It is important that we gain a foothold in di↵erent applications, even if it’s just a small part of it. We need to be adaptable with our business model and have a cross-functional understanding for di↵erent levels of operations” (Interview v). Among other things, this would mean that the AV provider’s software platforms need to be ﬂexible to di↵erent levels of integration and customer needs, which is where the core competency of modularity in terms of software could be used to design such adaptable systems.

A question raised is what will happen to second PL-companies, that operate heavy vehicles currently, when the driver is no longer needed. In some cases an AV provider like Scania might replace smaller transport operators and carriers completely, e↵ectively integrating vertically downstream in their value chain. In other applications, depending on the level of integration, the customer might be in charge of operations and send missions to the vehicles via an integrated software platform as seen in the Mining and Harbour cases, which would still remove the need for carriers and drivers. On the other hand, one could assume that it will not be realistic to change a whole manual vehicle ﬂeet to autonomous vehicles overnight. Instead, there could be a dynamic and gradual shift where some parts of a ﬂeet become autonomous and traditional manual operators are still involved. Additionally, another perspective is that these operators have capabilities and experience that a traditional heavy vehicle manufacturer might lack. These skills could be taken advantage of in an autonomous system and, instead of completely replacing them, the transport operators could instead become partners who continue to operate the vehicles remotely rather than in the driver’s seat.

Depending on the level of service integration, it would seem that OEMs like Scania face a transfor- mation apart from the actual technology shift, where they will need to understand ﬂows of goods and customer needs on a new level. Where traditionally customers would specify what kind of vehicles they need, in the future Scania might take the role of identifying the customer’s transport needs for them. To do this they will probably need to develop new capabilities within logistics that might not be possessed by a traditional OEM. This competency will most likely need to be developed, acquired or complemented with partnerships and collaborations, or perhaps an existing core competency of Lean production could be used to minimise waste and optimise logistics planning.

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Ownership and Accountability When discussing future service-based models it is relevant to reflect on the aspects of ownership and accountability as these may have a large impact. Several interviewees pointed out that there is a consensus within the industry that the manufacturer will be held accountable in the event of an accident or any other incident when there is no physical driver involved. This pushes the OEMs to remain in control of their AVs and this transfer of ownership from the customer to the provider could have several implications. Repair and maintenance of physical vehicles has historically constituted a very important revenue stream for Scania. However, when ownership and responsibility for the condition of the vehicles remain with the manufacturer it seems more likely that these activities will shift into a cost as seen in the Aggregated Business Model Canvas (see Figure 6.1). Thus, Scania will have to find new revenue streams to compensate with, such as additional services. Another consequence for the manufacturer could be the increased cost of capital and the risk of having unused capacity and fleets standing still. These risks might have to be mitigated with some kind of financial lease solutions, perhaps similar to how flight companies lease their planes.

Although the manufacturer would keep ownership of their vehicles, as discussed before, the level of integration within each application may vary and the customer might choose to be in charge of the full operations. However, due to liability and risk-sharing concerns the manufacturer will probably want to keep control of the communication with the vehicles and let whoever operates them send their missions and dispatches through their channels. Furthermore, it is reasonable that any telematics company partner that provides connectivity will only act as a data transfer provider and the manufacturer will keep control of the encrypted data. One could assume that the level of control that the manufacturer wants to maintain will be higher in the beginning of the technology shift but, as legislation and technology matures, this might change. The realistic scenario could be that manufacturers adapt their approach to di↵erent industries, customers and models but retain ownership of the physical vehicles and related software, and instead provide a service-based solution.

A Cost-driven versus Value-driven Model A discussion regarding the value proposition in a service-based model is whether it would be cost- driven, focusing on delivering services and solutions at the lowest price, or value-driven, focusing on high quality and premium services. On one hand, as the autonomous technology matures and standardisation increases together with less advanced electric engines, the heavy vehicle manufacturer sector could experience a squeeze where barriers for new entrants are lowered and prices could be pushed down. When customers are searching for capacity and transport solutions rather than quality customised cabins, lower costs may drive the business model to a larger extent. In this scenario, Scania can take advantage of their core competency Lean production and their economies of scale to minimize waste and keep costs and prices low. However, Scania has always been a premium brand and, according to many of the interview subjects, they will need to continue to transfer this into delivering premium services that customers are willing to pay higher prices for (e.g. Interview i, Interview v, Interview vii). This would suggest a service-based model that is still value-driven with concepts such as safety and reliability being important parts of the value proposition, as seen in the case studies. According to Osterwalder and Pigneur (2010) these two approaches may vary, but in one way or another they still share a focus on value. Perhaps the most realistic scenario is that Scania will have to adapt their approach depending on the price sensitivity of di↵erent markets as well as the maturity of the technology shift. Regardless, they should strive to keep their premium brand intact in order to di↵erentiate themselves.

Chapter 6 Page 54 Driving Autonomous Heavy Vehicles into the Future - A Business Model Perspective

6.3.2 An Alternative Business Model

In this study, the focus has been on the heavy vehicle manufacturer Scania. However, there are currently numerous smaller start-ups developing autonomous vehicle technology in the world, as is often the case in the early phases of a megatrend technology shift (Gartner., 2020). A well-known fact within management and strategy theory is that smaller start-ups face many di↵erent challenges compared to incumbent ﬁrms but also have many advantages such as less rigidness and improved innovation capabilities (Besanko, 2013; Utterback, 1994). This makes it relevant to assume that a start-up working with autonomous vehicles and goods transports might develop di↵erent business model strategies. Therefore, an alternative business model perspective based on the Swedish startup Einride will be presented and discussed in relation to the perspective based on Scania.

Einride The start-up Einride was founded by a former director of manufacturing engineering assembly at Volvo GTO Powertrain in Sweden in 2016 and currently has approximately 60 employees (Bottone, 2019). They deﬁne themselves as ”the intelligent transportation company” with their autonomous trucks making the transportation of goods more intelligent, emission-free, safe, cost-ecient, and ultimately more sustainable. Their self-driving vehicles are fully electriﬁed and coordinated via intelligent routing software, which integrates customers with trac data along with other func- tionalities in order to optimise delivery time, battery life and energy consumption.

At present, the start-up has two self-driving vehicles: the T/Pod and the T/Log (Einride., 2020). The former is their first autonomous truck and is aimed at road transportation of a variety of goods, while the latter is an adaptation specifically designed for the transportation of logs within forestry. They have no driver’s cabin but can be remotely controlled by a human operator where needed. No driver’s cabin and no driver mean a smaller vehicle, which increases loading capacity, provides greater flexibility, increases safety, lowers operating costs and optimises energy consumption. This means that for example the T-log is able to run solely on batteries.

According to their CEO in an interview from 2019, Einride stands out from other companies in the self-driving business primarily because of the way they make transport more sustainable (Bottone, 2019). He states that it is quite possible to perfectly retroﬁt a conventional diesel truck with self- driving technology and remote control, and that there are several companies looking into solutions like this. He continues with stating that it is possible to create a good business case out of this, however, he claims that it will not reduce carbon dioxide emissions. On the contrary, according to him a case like this is likely to increase emissions due to transports being made cheaper. Thus, self-driving technology without electriﬁcation could turn out to be a curse for the environment (Bottone, 2019).

The CEO of Einride further explains that they, as of 2019, have several large American companies as customers, and during 2020 they will establish a foothold in the USA and run a few pilots there. Operations in Sweden will also be expanded, and they will begin industrialising production of their vehicles. Furthermore, in the future they will have hundreds of vehicles operating in both America and in Europe. They are planning to build a complete system together with their partners with 5G connectivity and charging infrastructure to support their operations. He ﬁnishes with ”This will be the future of transportation, perfectly scalable, and we’ll already have put a dent in those emission curves” (Bottone, 2019).

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Einride versus Scania Without conducting a full case study of Einride, it would seem that they have constructed their business model similar to Scania with some sort of servitisation concept and strong partnerships. However, it would seem that they have put a lot of emphasis on including sustainability in their value propositions as a means of di↵erentiating themselves from their competitors. This sustainability aspect, with electriﬁcation breaking the CO2-curve, could be a part of Scania’s value proposition as well but has not been emphasised in the data collected within the delimitation of goods transports within hubs in this study. This could be due to the fact that Scania is still using their highly advanced combustion engine today, although electriﬁed autonomous vehicles is a part of their long-term plan (Interview i, Interview ii, Interview ix).

In 2019 Einride was given permission by the Swedish Transport Agency to expand a pilot for the Swedish transport operator DB Schenker to a public road in central Sweden, where DB Schenker would monitor and operate the vehicles remotely. This partnership model, with a second Party Logistics Provider like DB Schenker still operating the driverless vehicles, could indicate that Einride’s model is less inclined to replace traditional carriers and instead provide AVs to them who then drive them remotely. However, it should be noted that this might be part of their pilot and testing strategy as, compared to Scania, their limited resources and funding might a↵ect their short-term strategies. As stated by several interviewees in this study, no one really knows what the constellation within the Party Logistics Layers (see Figure 2.5 in Chapter 2) will be in the future and, as with many aspects in this technology shift, it is a matter of trial and error.

6.3.3 Sustainability

When discussing business models for autonomous technology for goods transports, especially combined with electriﬁcation, there are several long-term implications on sustainability. Some of these aspects will be discussed from the three perspectives of people, planet and proﬁt.

People From a people, or a societal, perspective autonomous technology could enable safer and more ef- ﬁcient transport solutions as well as reduce congestion on the roads. In practice, this could imply shorter and less expensive shipping for consumers ordering products online. It could, on the other hand, increase consumption and the amount of goods that are being transported.

The transport system today is extremely centred around the driver with the whole infrastructure adapted to the driver’s limits, such as legislated resting times (Interview xi, Interview v, Interview vii). The heavy vehicle driver and carrier industry has historically not been entirely healthy when it comes to working environment and driver conditions (Interview viii). In an industry where the margins are very low, drivers are usually pushed to their limits and legislation is not always followed. However, this whole system and infrastructure would completely change when the heavy vehicles become autonomous. One could point out that autonomous vehicles would implicate that drivers and carriers could lose their jobs, however, new working opportunities will most likely arise as there are new needs for remote controllers, maintenance crews and other related services.

Planet From a planet perspective autonomous technology, especially when combined with electriﬁcation, could have a major impact on sustainability and make a dent in the rising CO2-curves (Scania, 2020a). Due to driver limitations the speed of heavy goods transports on the roads are usually very high, a↵ecting both safety and CO2-emissions. However, due to the same limitations, including

Chapter 6 Page 56 Driving Autonomous Heavy Vehicles into the Future - A Business Model Perspective legislated resting times and other factors, the average speed of a heavy goods transport from start to ﬁnal destination is rather low. With the driver removed from the picture, the vehicles could instead consistently maintain the same low average speed without stopping, thus reducing total CO2-emissions (Interview ix).

More ecient transport systems could infer less expensive shipping, which could a↵ect consumer behaviour negatively. That is to say, when transports are less expensive, consumers might be inclined to order more, increasing overall transportation and CO2-emissions. However, with electrification and more ecient autonomous logistics systems, the environmental impact of this behaviour could potentially be countered and mitigated. Apart from the autonomous and electrification technology themselves there will probably be many challenges, where whole logistics flows and infrastructure will need to be changed from a driver centred flow into an autonomous one. This could take many years, especially if one considers legislation and other regulations that will need to be developed and implemented.

Profit A business model where the driver is removed and AVs are sold as a service could have many impacts on the profit side of sustainability, both for the customer and the provider. Today the driver stands for a considerable part of the costs and undoubtedly its removal could increase profits for cargo owners considerably. However, it should be noted that, before the technology has matured, the costs for an AV could outweigh the costs of a normal vehicle, together with a physical driver, and the change will realistically happen gradually until a sustainable profit could be ensured.

On the supplier side, a manufacturer of AVs will most likely face many challenges in this technology shift regarding profitability. Increased standardisation and lower barriers of entry due to less advanced electric engines could increase competitiveness, make it harder to di↵erentiate on the market and potentially push prices and profits down. Furthermore, if the ownership of the vehicles remains with the manufacturer due to legislation, accountability and risk-sharing concerns, historically important revenue streams such as repair and maintenance could shift into costs for the provider. In order to generate sustainable profits, a manufacturer will likely need to find new ways to di↵erentiate itself and develop new revenue streams, such as with premium services or leveraged capabilities.

Chapter 6 Page 57 Chapter 7

Conclusion

This chapter concludes the study by providing an answer to the main research question and discussing 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.

7.1 Conclusions

The concept of Autonomous Vehicles is considered to be one of the next imminent megatrends within transportation. The technology is predicted to improve safety, logistics, cut driver costs and reduce CO2-emissions. Due to the complexity, novelty and many uncertainties in this technology shift there is a lack of empirics regarding business models in this area, where most previous studies have focused on the autonomous technology itself. Furthermore, there is a great uncertainty as to how an AV manufacturer will be able to di↵erentiate itself and leverage its capabilities when unique core competencies become obsolete and the technology becomes more standardised in an autonomous future. Business model innovation in the industry has until now been mostly based on trial and error and the number of commercial business cases has been limited. Speciﬁcally, there has been a lack of a consolidated business model that can be used for general applications. The early stages of this technology shift are seen within goods transports in conﬁned areas and the researchers have had the opportunity to explore two such business cases at the Swedish heavy vehicle manufacturer Scania.

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 leveraged by Scania in an autonomous future were identified. Finally, the aim for this business model perspective, together with the identified core competencies, was that they in the future can be used as a general model for OEMs within the transportation industry when initiating new projects for goods transport within hubs. This is summarised by the main research question, which will be answered in the following section.

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

A business model for a general application was formulated by aggregating two models based on two separate cases using the Business Model Canvas tool (see Appendix E for the ﬁnal version of this

58 Driving Autonomous Heavy Vehicles into the Future - A Business Model Perspective

Aggregated Business Model Canvas). In this model, four valuable capabilities that can be leveraged in an autonomous future were identiﬁed: safety, reliability, Lean production and modularity,where the latter two were deemed to qualify as core competencies that are unique signatures of the organisation, span over multiple businesses and are dicult for competitors to imitate. Together these components and the nine building blocks in the Aggregated Business Model Canvas formulate a business model perspective that is service-based and 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 di↵erentiation.

7.2 Implications

7.2.1 Industrial Implications

From an industrial perspective, there are several implications that can be drawn from this study regarding the autonomous future of a heavy vehicle manufacturer. From the analysis of the research it would seem that manufacturers share a consensus that they will keep ownership of their physical autonomous vehicles, due to accountability and risk-sharing concerns, and instead supply for example technology lease services or transport solutions to their customers. This would imply that the industry needs to adapt to a new service-based model, where costs for repair and maintenance as well as other capital costs are transferred from the customer to the manufacturer. Additionally, in order to generate new revenue streams and capture more value, AV manufacturers will need to consider new related services, such as providing logistics solutions and handling operations.

Furthermore, as the customised driver’s cabin is removed, the advanced combustion engine is replaced with an electric motor as well as fleet flexibility and standardisation become increasingly important in the business model, barriers of entry could be reduced. This would increase competitiveness as well as e↵ectively causing important core competencies to become obsolete. This calls for new means of value-driven di↵erentiation and manufacturers will need to focus on dynamic capabilities and core competencies that can respond to change and span over multiple businesses. Two capabilities that a traditional heavy vehicle manufacturer might lack are software development and logistics planning. These strengths will most likely need to be built, acquired or complemented with partnerships. However, in this study it is discussed that the existing core competencies of Lean production could be utilised in optimising logistics planning and modularity thinking could help with the integration of newly acquired software capabilities. Overall, it could be beneficial for all OEMs in the industry to focus on reinforcing the so called critical competency, that is the skill of managing and operationalising a company’s capabilities and core competencies by nurturing existing ones, developing new ones and abandoning those that risk becoming rigid and obsolete.

In the early stages of this technology shift, before maturation, it will be crucial to gain footholds in important customer segments. This could be done by collaborating and co-creating value together with future customers in so-called development phases, as seen in the analysed cases. OEMs will need to leverage such vertical collaborations but also horizontal partnerships in order to compensate their own capabilities and strengths. Additionally, the industry needs to be prepared to ﬂexibly adapt to di↵erent levels of integration in di↵erent segments and applications. Finally, there is an implication that the Business Model Canvas can be used as a management tool in order not only to map business models and integrate core competencies but also to compare and aggregate di↵erent application models in an ecient manner.

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7.2.2 Academic Implications

From an academic viewpoint, the results of this study have a two main implications. Firstly, the study indicates that the Business Model Canvas tool can be used to not only map a business model, but to also aggregate two di↵erent models into a more general perspective. Secondly, it shows how core competency theory can be connected to business model innovation in a tangible manner.

7.3 Suggestions for Further Studies

This study shed light on a topic that currently has an enormous gap in the literature due to the novelty of the autonomous technology. It focused on business model innovation in the setting of two di↵erent industry cases regarding goods transports in conﬁned areas. In addition, it gave insights into how a future service-based business model could be formulated combined with aspects on core competencies that could be leveraged in an autonomous future. Below are suggestions for future studies where empirics and conclusions from this study could be used and further expanded upon.

The access to business analogous cases was limited at the time of the study. Hence, it would be convenient to conduct additional studies of more case initiatives within other industries in order to validate the business model produced in this study and further expand the general canvas.

This study has focused on goods transportation within conﬁned areas, so-called hubs. As the technology and market matures, it would be interesting to conduct similar case studies expanding to hub-to-hub applications or even full mobility on public roads. Also, it would be interesting to study public transportation as opposed to goods transportation.

Another interesting future study could be to further investigate the four dynamic capabilities dis- covered to be valuable in an autonomous future. When combined, they may reinforce the e↵ect of each other, consequently creating synergy e↵ects. If this is the case, these capabilities could be even more valuable for an OEM.

Finally, the discussion of this study touches on an alternative business model approach by a start- up company. Further studies could more extensively compare incumbent ﬁrms with new entrants in a deeper case study, as their approaches may vary signiﬁcantly.

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Chapter Page 65 Appendix A

SAE Levels of Automation Classiﬁcation

To provide structure and international standardisation as well as a recognisable language surrounding the technology, AVs have been classified into five di↵erent levels of automation by SAE International, also known as Society of Automotive Engineers, a globally active professional as- sociation and standard developing organisation for engineering professionals in various industries. An overview of these SAE-levels and their definitions can be found in Figure A.1 below.

Figure A.1: SAE Levels of Automation Classiﬁcation (SAE, 2018)

66 Appendix B

Exploratory Interview Guide

Personliga fr˚agor/Personal questions:

1. Vad jobbar du med just nu p˚aScania?/What do you work with at Scania?

2. Hur l¨ange har du jobbat p˚aScania?/For how long have you been working at Scania?

3. Vad har du jobbat med tidigare?/What have you been working with previously?

4. Vad har du förutbildningsbakgrund som ärrelevant förditt jobb?/What’s your educational background that is relevant for your job?

Oppna¨ fr˚agor/Open questions:

5. Vad tror du autonoma fordon innebärförframtiden närdet kommer till transport av varor/människor?/What do you think autonomous vehicles will imply for the future when it comes to goods/people transport?

6. Vilket ser du som den st¨orsta utmaningen med autonoma fordon? (Tex. poli- cyer och lagstiftning, teknologi och innovation, infrastruktur och konsumenters acceptans)/What do you see as the biggest challenge with autonomous vehicles (e.g. policy and legislation, technology and innovation, infrastructure, consumer acceptance).

7. Hur tror du att Scania kommer s¨alja fullt autonoma fordon i framtiden (niv˚a fem)?/In what way do you think Scania will be selling fully autonomous vehicles in the future (level 5)?

8. Kommer Scania sälja tjänster och helhetslösningar eller fordon som produkter tror du? Hur kommer uppdelningen se ut? Hur ser du p˚arisken att binda kapital i fordonet?/Will Scania provide vehicles as a service and full solutions rather than vehicles as products? How will the distribution be like? What do you think about the risk in keeping ownership of the vehicles?

9. Vilka av Scanias autonoma projekt ärdu involverad i? Ge gärna lite bakgrund- information kring dessa. Vad ärdin syn p˚adessa projekt?/Which of Scania’s autonomous projects are you involved in? Please give some background information on these. What is your view of these projects?

10. Vad tror du man kan dra f¨orl¨ardomar fr˚andet dessa projekt?/What learning outcomes do you think that there will be from these projects?

67 Driving Autonomous Heavy Vehicles into the Future - A Business Model Perspective

11. Vad ärScanias styrkor idag jämfört med konkurrenterna och hur tror du att de kommer att utvecklas p˚asikt?/What are Scania’s strengths today compared to competitors and how do you think they will evolve in the long run?

Chapter B Page 68 Appendix C

Identiﬁed Capabilities at Scania

Table C.1: An overview of the frequency of highlighted capabilities and potential core competencies from the exploratory interviews

69 Appendix D

Case Study Interview Guide

Case background:

1. Berätta gärna s˚auttömmande om bakgrunden till caset som du kan/Please provide us an as exhaustive background information to the case as you can.

Business Model Canvas:

Betr¨a↵ande detta business case:/In regards to this business case:

2. Vilka skulle du sägaärScanias nyckelpartners i det härcaset?/What would you say are Scania’s key partners in this case?

3. Vilka ¨arde viktigaste aktiviteterna?/What are the main or key activities?

4. Vilka ¨arnyckelresurserna?/Which would be the key resources?

5. Vilka skulle du sägaär de största kostnadsdrivarna?/What would you say are the main cost drivers?

6. P˚avilket sätt uttrycker Scania sitt värde förkunden? Hur di↵erentierar sig Scania gentemot andra företag? Varförväljer kunder just Scania?/In what way does Scania express their value to the customer? How does Scania di↵erentiate themselves compared to other companies? Why do customers choose Scania?

7. Kan du beskriva egenskaperna hos relationen mellan dig och kunden?/Could you describe the characteristics of the relationship between you and the customer?

8. Hur görScania sina kunder medvetna om sitt värdeerbjudande och hur dis- tribuerar de sina tjänster i detta case?/How does Scania make their customers aware of their value proposition and how do they distribute their services in this case?

9. Vilka ¨arde olika kundsegmenten?/What are the di↵erent customer segments?

10. P˚avilket sätt f˚arScania sin intäkt fördetta case?/In what way does Scania get revenue in this case?

70 Appendix E

Final BMC for a General Model

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