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IN THE FIELD OF TECHNOLOGYDEGREE PROJECT INDUSTRIAL ENGINEERING AND MANAGEMENTAND THE MAIN FIELD OF STUDYINDUSTRIAL MANAGEMENT,SECOND CYCLE, 30 CREDITS
, STOCKHOLM SWEDEN 2020
Driving Autonomous Heavy Vehicles into the FutureA Business Model Perspective
GABRIEL KITZLER
ANNA SAIBEL
KTH ROYAL INSTITUTE OF TECHNOLOGYSCHOOL OF INDUSTRIAL ENGINEERING AND MANAGEMENT
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Driving Autonomous Heavy Vehicles into the Future
A Business Model Perspective
by
Gabriel Kitzler Anna Saibel
Master of Science Thesis TRITA-ITM-EX 2020:330 KTH Industrial Engineering and Management
Industrial Management SE-100 44 STOCKHOLM
Driving Autonomous Heavy Vehicles into the Future
Ett affärsmodellsperspektiv
av
Gabriel Kitzler Anna Saibel
Examensarbete TRITA-ITM-EX 2020:330 KTH Industriell teknik och management
Industriell ekonomi och organisation SE-100 44 STOCKHOLM
Master of Science Thesis TRITA-ITM-EX 2020:330
Driving Autonomous Heavy Vehicles into the Future – A Business Model Perspective
Gabriel Kitzler
Anna Saibel Approved
2020-06-09 Examiner
Lars Uppvall Supervisor
Matti Kaulio Commissioner
Scania CV AB Contact person
Rodrigo Caetano
Abstract
In light of the many environmental challenges that the world currently faces, new sustainable solutions are called for. The concept of autonomous heavy vehicles (AVs) is considered to be one of the next megatrends within transportation and this technology shift is predicted to improve safety and logistics as well as to cut driver costs and reduce CO2-emissions. However, from a company's perspective, technology shifts are not without risks as technical disruptions can cause core competencies to become obsolete and radical technology innovation can be fatal to a company that does not innovate its business models simultaneously. Due to the complexity and novelty of the AV technology, business model innovation within the field has been lagging behind and there is an area of uncertainty regarding how a future business model for AVs could be formulated
In order to investigate potential business models for AV applications, this study has been carried out as an exploratory case study of two industry specific applications for goods transports within confined areas at the heavy vehicle manufacturer Scania in Södertälje, Sweden. The Business Model Canvas tool developed by Osterwalder and Pigneur (2010) has been used to map the business models of these two cases with the purpose of combining them into a general model. Furthermore, four important capabilities at the company have been identified and determined as to whether they qualify as core competencies based on the criteria presented by Prahalad and Hamel (1990) and then discussed in relation to how they can be leveraged in a future business model.
The findings of this study help to formulate a business model perspective for future AV goods transport applications that consists of a service-based model characterised by a focus on collaboration and value co-creation, an adaptable level of integration with the customers' systems, transfer of ownership of products to the manufacturer and a value-driven source of differentiation. Lastly, the study concludes that Lean production and modularity are two existing core competencies of Scania that could be leveraged dynamically in a future business model connected to this technology shift.
Keywords: Autonomous Heavy Vehicles, Technology Shifts, Business Model Innovation, Business Model Canvas, Core Competencies, Servitisation
Examensarbete TRITA-ITM-EX 2020:330
Driving Autonomous Heavy Vehicles into the Future – Ett affärsmodellsperspektiv
Gabriel Kitzler
Anna Saibel Godkänt
2020-06-09
Examinator
Lars Uppvall Handledare
Matti Kaulio Uppdragsgivare
Scania CV AB Kontaktperson
Rodrigo Caetano
Sammanfattning
Mot bakgrunden av de många miljömässiga utmaningar som världen står inför idag krävs nya hållbara lösningar. Konceptet självkörande tunga fordon (eng. autonomous heavy vehicle - AV) anses vara en av de nästa megatrenderna inom transportindustrin och detta teknikskifte förutspås förbättra säkerhet och logistiksystem samt sänka förarkostnader och minska koldioxidutsläpp. Från ett företags perspektiv är teknikförändringar dock inte utan risker då tekniska disruptioner kan göra kärnkompetenser föråldrade och radikal teknisk innovation rentav kan innebära en dödsdom för ett företag som inte simultant innoverar sina affärsmodeller. Till följd av teknikens komplexitet och låga mognadsgrad har affärsmodellsinnovation inom fältet hamnat efter och det finns ett område av osäkerhet gällande hur en framtida affärsmodell för självkörande fordon skulle kunna formuleras.
I syfte att undersöka potentiella affärsmodeller för AV-applikationer har denna studie genomförts som en utforskande fallstudie av två industrispecifika applikationer för godstransporter inom avgränsade områden hos lastbilstillverkaren Scania i Södertälje, Sverige. Verktyget Business Model Canvas, utvecklat av Osterwalder och Pigneur (2010), har använts för att kartlägga affärsmodellerna för dessa två applikationer i syfte att kombinera dem till en generell modell. Vidare har fyra viktiga kapabiliteter i företaget identifierats och fastställts huruvida de kvalificerar som kärnkompetenser baserat på kriterierna som presenteras av Prahalad och Hamel (1990) och sedan diskuterats i relation till hur de kan utnyttjas i en framtida affärsmodell.
Resultaten av denna studie hjälper till att formulera ett affärsmodellsperspektiv för framtida AV-godsapplikationer som innebär en servicebaserad modell kännetecknad av ett fokus på samarbete och värdesamskapande, en anpassningsbar integration till kundernas system, överföring av ägandeskap av produkter till tillverkaren och en värdedriven differentiering. Slutligen dras slutsatsen att Lean produktion och modularitet är två befintliga kärnkompetenser hos Scania som skulle kunna utnyttjas dynamiskt i en framtida affärsmodell kopplat till detta teknikskifte.
Nyckelord: Självkörande tunga fordon, Teknikskiften, Affärsmodellsinnovation, Business Model Canvas, Kärnkompetenser, Tjänstefiering
Table of Contents
1 Introduction 1
1.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Problem Statement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.3 Purpose . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
1.4 Research Questions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
1.5 Delimitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
1.6 Thesis Sponsor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
1.7 Disposition of Report . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
2 Frame of Reference 7
2.1 Surviving Technology Shifts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
2.2 Business Model Innovation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
2.2.1 Business Model Ambiguity . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
2.2.2 The Business Model Canvas . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
2.3 Competitive Advantage, Capabilities and Core Competencies . . . . . . . . . . . . 11
2.3.1 Identifying Core Competencies . . . . . . . . . . . . . . . . . . . . . . . . . 11
2.3.2 The Risk of Core Rigidities . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
2.3.3 Dynamic Capabilities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
2.3.4 Critical Competency . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
2.4 Collaboration and Value Co-creation . . . . . . . . . . . . . . . . . . . . . . . . . . 14
2.5 Servitisation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
2.5.1 Downstream Vertical Integration . . . . . . . . . . . . . . . . . . . . . . . . 16
2.6 Summary of Frame of Reference . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
3 Methodology 18
3.1 Choice of Methodological Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
3.2 Research Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
3.2.1 Initial Phase . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
3.2.2 Main Phase . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
3.2.3 Final Phase . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
3.3 Frame of Reference . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
3.4 Data Collection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
3.4.1 Interview Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
3.4.2 Observations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
3.4.3 Methodology for Data Analysis . . . . . . . . . . . . . . . . . . . . . . . . . 24
3.5 Limitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
3.6 Validity and Reliability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
3.7 Ethical Considerations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
4 Case Description 27
4.1 Mining Case Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
4.2 Harbour Case Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
5 Findings and Analysis 33
5.1 Mining Case . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
5.1.1 Mining Development Phase - Business Model Canvas . . . . . . . . . . . . . 33
5.1.2 Mining Commercial Phase - Business Model Canvas . . . . . . . . . . . . . 36
5.2 Harbour Case . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
5.2.1 Harbour Development Phase - Business Model Canvas . . . . . . . . . . . . 38
5.2.2 Harbour Commercial Phase - Business Model Canvas . . . . . . . . . . . . 40
5.3 Identified Capabilities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
6 Discussion 45
6.1 Comparison between Mining and Harbour Applications . . . . . . . . . . . . . . . 45
6.2 Future Core Competencies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
6.2.1 Core Competencies in the Business Model . . . . . . . . . . . . . . . . . . . 50
6.2.2 Dynamic Capabilities, Core Rigidities and Critical Competency . . . . . . . 51
6.3 A Future Business Model Perspective . . . . . . . . . . . . . . . . . . . . . . . . . . 52
6.3.1 A Service-based Business Model . . . . . . . . . . . . . . . . . . . . . . . . 52
6.3.2 An Alternative Business Model . . . . . . . . . . . . . . . . . . . . . . . . . 55
6.3.3 Sustainability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56
7 Conclusion 58
7.1 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58
7.2 Implications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59
7.2.1 Industrial Implications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59
7.2.2 Academic Implications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60
7.3 Suggestions for Further Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60
References 60
Appendix A SAE Levels of Automation Classification 66
Appendix B Exploratory Interview Guide 67
Appendix C Identified Capabilities at Scania 69
Appendix D Case Study Interview Guide 70
Appendix E Final BMC for a General Model 71
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 trans-
portation industry (Scania, 2019b) . . . . . . . . . . . . . . . . . . . . . . . 16
Figure 3.1: An overview of the workflow of the study . . . . . . . . . . . . . . . . . . . 19
Figure 4.1: An illustration of two phases within the mining initiative . . . . . . . . . . 28
Figure 4.2: Considered business model alternatives for the Mining Case . . . . . . . . 29
Figure 4.3: An overview of a typical seaside operational flow within a harbour . . . . . 31
Figure 4.4: An illustration of two independent phases within the harbour initiative . . 31
Figure 5.1: The Mining Case - A BMC illustration for the Development Phase . . . . 33
Figure 5.2: The Mining Case - A BMC illustration for the Commercial Phase . . . . . 36
Figure 5.3: The Harbour Case - A BMC illustration for the Development Phase . . . . 38
Figure 5.4: The Harbour Case - A BMC illustration for the Commercial Phase . . . . 40
Figure 5.5: Frequency of highlighted capabilities from the exploratory interviews . . . 42
Figure 6.1: A General Hub Application - An Aggregated BMC illustration for a Com-
mercial Phase . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46
Figure 6.2: An illustration of how the core competencies Modularity and Lean produc-
tion can be integrated into the Key Resources and Value Proposition blocks 51
Figure A.1: SAE Levels of Automation Classification (SAE, 2018) . . . . . . . . . . . . 66
Figure E.1: Final BMC for a general business model perspective . . . . . . . . . . . . . 71
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 com-
petitive advantage in an autonomous future . . . . . . . . . . . . . . . . . . 50
Table C.1: An overview of the frequency of highlighted capabilities and potential core
competencies from the exploratory interviews . . . . . . . . . . . . . . . . . 69
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 Hogskolan (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 pur-
pose and the research questions that the thesis aims to answer. Furthermore, the delimitations of
the study are discussed, the thesis sponsor is presented and finally a brief disposition of the report
is given.
1.1 Background
Since the invention of the wheel over 3000 years ago, the mechanised way of transporting goods
and people has shaped the world we live in today. Since the industrialisation and the introduction
of the combustion engine, the global Gross Domestic Product (GDP) curve has increased exponen-
tially (see Figure 1.1). Unfortunately, the carbon dioxide (CO2) curve has followed the same trend
and the heavy transport industry calls for sustainable solutions (Scania, 2020a). The concept of
Autonomous Vehicles (AVs) is considered one of the next megatrends within transportation (Kuh-
nert et al., 2017). An autonomous vehicle is defined as a vehicle that can interpret and adapt to
their surrounding through a combination of sensor tools and artificial intelligence to solve certain
predetermined tasks (Taeihagh and Lim, 2019). According to Litman (2020) AVs are predicted to
improve safety, eco-driving, city space utilisation, tra�c optimisation and cut driver costs, which
are incentives that push this imminent technology shift to the close horizon. Combined with other
megatrends such as electrification and connectivity, the future Automated, Connected and Electric
(ACE) vehicles have the potential of breaking the CO2 curve, while maintaining the rising GDP
levels. However, from a company’s perspective, technology shifts are not without risks as technical
disruptions can cause core competencies to become obsolete and radical innovation can be fatal to
a company that does not innovate its business models simultaneously (Tongur and Engwall, 2014).
Traditionally, Original Equipment Manufacturers (OEMs) have focused on producing tangible
goods and providing customers with services, such as repair & maintenance (Lay, 2014). To-
day however, OEMs increasingly adapt their existing business models to a more service-oriented
approach, either in order to add value to existing products or to create completely new value propo-
sitions. This servitisation concept has paved the way for new service-based business models within
the transport industry, where vehicles are converted from physical products into a service, often
referred to as Mobility as a Service (MaaS) (Mulley et al., 2018). Within the industry of heavy
vehicle manufacturers, related service-based model concepts defined as Vehicle as a Service (VaaS)
and Transport as a Service (TaaS) are used to illustrate di↵erent pathways for the next generation
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 to-
wards full mobility, where most land based road transports are replaced with AVs. An illustration
of the business opportunity volumes and scalability over time can be seen in Figure 1.3. Since
transportation of goods is less complicated than transportation of people, the confined hub goods
transport operation phase can be viewed as the first important transition phase in the AV tech-
nology shift. Although the business opportunities and profitability might be higher in the future
more scalable full autonomous mobility phase, the first transitional hub goods transport operation
phase is crucial in gaining a commercial foothold in the autonomous industry and advancing the
technology for all heavy commercial autonomous vehicle manufacturers (Scania, 2020a).
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 man-
ufacturers (Scania, 2019a). Two of Scania’s ongoing projects within confined hub goods transports
is a mining and a harbour application (Scania, 2019a; Aulbur et al., 2020).
Today, Scania is known for their premium customised cabins and advanced combustion engines.
However, the AV technology shift will inevitably a↵ect heavy commercial vehicle manufacturers’
capabilities to di↵erentiate themselves within their value proposition with core competencies such
as driver comfort diminishing as physical drivers become obsolete. In addition, with electrification
removing the combustion engine, heavy commercial vehicle manufacturers will need to identify and
utilise their other strengths and existing core competencies that can still be used in an autonomous
future (Prahalad and Hamel, 1990). Setting aside technical di�culties there are many dilemmas
surrounding the implementation of driverless vehicles that, to only mention a few of them, involve
policy and legislation as well as infrastructure and consumer acceptance, which complicates the
development of business models in a technological shift to AVs (Contissa et al., 2017; Threlfall,
2019). For many OEMs, Scania included, the emphasis has been on developing the autonomous
technology (Fagnant and Kockelman, 2015). This technology focus seems to have caused business
model innovation within the area to be lagging behind, as is often the case in the early phases of
technology shifts (Tongur and Engwall, 2014; Kaulio et al., 2017).
1.2 Problem Statement
Due to the complexity and many uncertainties in the shift to autonomous vehicles there is a lack of
empirics regarding business models in this area, where most previous studies have focused on the
autonomous technology itself. Business model innovation in the industry has up until now been
mostly based on trial and error and the number of commercial business cases has been limited.
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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 busi-
ness model perspective, together with the identified core competencies, can be used as a general
application model for OEMs within the transportation industry when initiating new projects for
goods transport within hubs.
The study provides a theoretical contribution to Business Model Innovation literature in the context
of technology shifts (e.g. Tongur and Engwall, 2014; Kaulio et al., 2017). Also, the study has an
empirical contribution surrounding the early transition phase in the shift to AVs and how core
competencies can be leveraged in a business model. Furthermore, the research has a methodological
and practical contribution in the sense of using the Business Model Canvas (BMC) as a management
tool for comparing and aggregating models of di↵erent cases.
1.4 Research Questions
The aim of the thesis will be fulfilled through answering the following main research question:
MRQ: How can a business model be formulated for a general application within goods transports
of confined areas for a heavy vehicle manufacturer in an autonomous future?
The main research question will be answered through the following two sub-questions:
– RQ1: How can a business model for a mining case and a harbour case be described using
the Business Model Canvas tool and what are the similarities and di↵erences between their
components?
– RQ2: Which strengths and capabilities qualify as core competencies in an autonomous future
for a heavy commercial vehicle manufacturer and how can they be leveraged in their business
model?
1.5 Delimitations
This thesis is delimited to case studies of the Swedish heavy commercial vehicle manufacturer
Scania. According to (Aulbur et al., 2020) it has been shown that viable business models can
already be built around the so-called Level 4 in accordance to the Society of Automotive Engineers
(SAE) index, meaning that full automation will not be required (See Appendix A for SAE levels
of automation classification). This makes autonomous driving an imminent development in the
truck industry and this study will focus on exploring business models concerning driverless AVs
of Level 4 and above. The term driverless being in the sense that no driver is needed inside the
vehicle, which means that the vehicle does not necessarily need to be fully autonomous, but could
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 estab-
lished 1891 in Sweden, with headquarters in Sodertalje. The company is part of TRATON GROUP,
which is an umbrella brand for Scania, MAN, Volkswagen Caminhoes e Onibus and RIO. Scania’s
new generation of trucks has won most of the industry tests for the fantastic comfort, best driving
characteristics and the record low fuel consumption (Scania, 2020b). With its 51,000 employees in
about 100 countries the company is the market leader in the development of sustainable transport
solutions. (Scania, 2019c).
1.7 Disposition of Report
The thesis is outlined in accordance to the following structure:
Chapter 1 introduces the topic of the thesis and sets both the theoretical and practical context
background for the studied phenomenon. The problem statement is introduced along with the
purpose and the research questions that the thesis aims to answer. Furthermore, the delimitations
of the study are discussed, the thesis sponsor is presented and finally a brief disposition of the
report is given.
Chapter 2 will serve as a frame of reference for the study. It follows primarily literature regarding
strategic management of technological innovation and the resource-based view of the firm: more
specifically technology shifts, business models, core competencies and capabilities as well as value
co-creation and servitisation. The purpose of the chapter is twofold: first to provide a knowledge
base, by which this study can be related and discussed, and secondly to form a lens from which
background the authors have viewed this master thesis project.
Chapter 3 describes the methodology used in this study including the choice of methodological
approach, the research design, as well as how the frame of reference and data collection was con-
ducted and analysed. Finally, limitations, validity and reliability as well as ethical considerations
are presented.
Chapter 4 describes the setting of two cases and is primarily based on data collected from the
Case Study Interviews (see Appendix D for the interview guide). It covers the background of a
Chapter 1 Page 5
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Mining Case and a Harbour Case in order to give the reader more substance before going into
extensive details of the Business Model Canvases in the next chapter.
Chapter 5 lays out the findings and consequent analysis of the study. It starts o↵ with presenting
the data gathered from the two cases using the Business Model Canvas tool to map their business
models in both a development phase and a commercial phase. Di↵erences between the phases are
highlighted in italics in the commercial phase canvases. Finally, the four most frequently empha-
sised capabilities that were identified are introduced.
Chapter 6 discusses the empirical findings and analysis in the previous chapter in relation to the
Frame of Reference, in order to answer the research questions. Firstly, an aggregated business
model derived from both business cases is formed and discussed in terms of similarities and di↵er-
ences. Secondly, the identified capabilities in the previous chapter are determined as to whether
they qualify as core competencies and discussed in terms of how they can be manifested and lever-
aged in the business model. Thirdly, a business model perspective synthesising the main aspects
of the aforementioned discussions is presented, which could be used in an autonomous future for
other goods applications within confined areas. This is then briefly compared to an alternative
perspective based on the Swedish start-up Einride. Lastly, long-term implications on sustainability
are discussed.
Chapter 7 concludes the study by providing an answer to the main research question and dis-
cussing implications regarding future business models for goods transports in confined areas as
well as core competencies in the future. Finally, suggestions for further studies are presented.
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 regard-
ing strategic management of technological innovation and the resource-based view of the firm: more
specifically technology shifts, business models, core competencies and capabilities as well as value
co-creation and servitisation. The purpose of the chapter is twofold: first to provide a knowledge
base, by which this study can be related and discussed, and secondly to form a lens from which
background the authors have viewed this master thesis project.
2.1 Surviving Technology Shifts
According to Utterback (1994), ”innovation in industry is a process that involves an enormous
amount of uncertainty, human creativity, and chance.” A firm’s survival through technology shifts
is dependent on many factors but a common denominator of historical survivalists is the abil-
ity of organisational ambidexterity (O’Reilly and Tushman, 2013). Organisational ambidexterity
refers to the ability of a company to both exploit their existing competencies and incrementally
improve upon them while at the same time explore new areas and technologies. In this way it
is possible to simultaneously conduct both incremental and radical innovation through practicing
several contradictory structures, processes and cultures within the same company. At the start
of implementation, ambidexterity is typically ine�cient since it requires doubling e↵orts and the
consumption of resources for the parallel innovation processes. Tongur and Engwall (2014) high-
light that technology shifts can be fatal to manufacturing companies not only due to technical
innovation problems but also due to inertia within business models and business model innova-
tion. Technology shifts are di�cult to master, and a mix of innovation within both technology
and business models and not just one of the fields is necessary to survive (Tongur and Engwall,
2014). Furthermore, the ability of organisational double ambidexterity has been identified as a
crucial survival skill when it comes to technology shifts (Kaulio et al., 2017). That is, the ability to
exploit and explore both technology and business model innovation simultaneously to counter the
many uncertainties that are involved in the emergence of new technology in the face of technology
shift. If a company is only changing the technological components of a system and not their busi-
ness model, they may fail to capture the value of the technology itself (Blomkvist and Johansson,
2016).
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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 manage-
ment: what customers want, how they want it, how the company can be organised to best meet
these needs, get paid for it and make profits. Teece (2010) highlights three particular barriers and
thresholds that obstruct replication of other companies’ business models. Firstly, the implemen-
tation of a business model can involve complicated systems, processes and resources that are hard
to achieve. Secondly, the model can be too obscure and hard for competitors to imitate. Thirdly,
already established companies can be too tied down by their own existing business models to even
consider imitating a new one, which does not stop new and smaller actors from copying them. In
essence, business models are rarely obvious or clear in new business environments and technologies.
Furthermore, they often develop over time, which makes the ability to learn and adapt crucial for
companies.
2.2.1 Business Model Ambiguity
The overall concept of a business model has been well debated with some scholars highlighting that
the connections between customer needs and company capabilities to meet those needs enable a
system perspective on how value is created, while others criticise it for being vague and ambiguous
(Chesbrough and Rosenbloom, 2002). Some scholars refer to the term business model similar to
a term of art; it may be recognisable by most people, however its true nature is vaguely defined
(Lewis, 2000). It was not until the late 1990s that the term received increased attention amongst
academics in an attempt to analyse the value creation in the new kind of web-based companies
that appeared with the emergence of Internet (Zott et al., 2000).
During the last century, companies have been busy trying to understand how to perform their
business and manage their operations, generating countless of new management theories (Drucker,
1994). However, in a world where the only constant is change, these theories are not enough ac-
cording to Drucker (1994), since they are based on assumptions of a static environment. When
the customer, the market, or even society is changing, there is a need to also question what to do.
This implies that these assumptions have to be continuously questioned in order to stay relevant
and to understand who the customer is and what the customer values. Magretta (2002) claims
that a good business model should not only answer those two questions when defining the value
proposition but also to help understand how to generate money from that o↵ering, thus identify
how to capture value.
Demil and Lecocq (2010) suggest that there are two ways of looking at a business model, either
with a static or a transformational approach. The former implies that it is simply a blueprint,
an instruction on how a business should structure itself and generate revenue, whereas the trans-
formational approach is rather a tool to address change internally as well as externally. Further,
Demil and Lecocq (2010) suggest that by adopting a transformational approach it allows for a
more adaptable business model, which is more resilient to change.
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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 re-
sources, value proposition, customer relationships, distribution channels, customer segments, cost
structure and revenue streams (see Figure 2.1).
Figure 2.1: The Business Model Canvas (BMC) tool, adapted from Osterwalder and Pigneur (2010)
Value Proposition
The value proposition is at the heart of the business model canvas framework and can essentially
be seen as the solution a company o↵ers to a customer’s problem and the manner in which a com-
pany di↵erentiates themselves on the market, i.e. why customers would choose one company over
another (Johnson et al., 2008; Osterwalder and Pigneur, 2010). The proposed value is commonly
provided in the form of a product or service or a combination of the two.
Key Partners
Osterwalder and Pigneur (2010) refer to key partners as more than strategic alliances between
non-competitors. They also include the supplier and buyer partner relationship as well as joint
ventures and collaborations which enable reliable resource access and value creation in the form of
complementing competence sharing.
Key Activities
Key Activities are closely linked to Key Resources as they together comprise what is necessary to
create significant value. In manufacturing companies, key activities are often related to manufac-
turing, designing, research and development and the actual delivery of products (Osterwalder and
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Driving Autonomous Heavy Vehicles into the Future - A Business Model Perspective
Pigneur, 2010; Johnson et al., 2008). In a service-based business model, key activities are more
likely related to tailoring and delivering solutions to problems of individual customers.
Key Resources
Key Resources may consist not only of physical assets but all resources that contribute to a com-
pany’s competitive advantage such as intellectual properties, knowledge based, business know-how,
financial and human resources (Johnson et al., 2008). Combined with key activities, these two com-
ponents may constitute the primary source of value creation (Osterwalder and Pigneur, 2010).
Customer Relationships
Customer relationships may not only a↵ect how a value proposition is perceived but also be used
to co-create value with customers (Osterwalder and Pigneur, 2010), for instance by inviting cus-
tomers to upload content on streaming sites or to write reviews that assist other customers in
their choices. This component also includes value creation in the form of more personal customer
assistance and creating loyalty in their customer segments (Kindstrom, 2008).
Distribution Channels
Distribution channels refer to how a company makes potential customers aware of their services
and products, as well as making them available for purchase. This can be done through indirect
partner channels such as retailers or through in-house sales forces, which may a↵ect how the cus-
tomer perceives the value proposition (Osterwalder and Pigneur, 2010).
Customer Segments
When forming a business model, it is fundamental to identify whose needs the value proposition
should address, that is, who the customer actually is (Osterwalder and Pigneur, 2010). This is
commonly done by categorising customers with similar demands and formulate value propositions
on the premises of these segments. The value propositions may di↵er between di↵erent customer
segments and it is necessary for a firm to understand them in order to make informed decisions on
which segments to pursue and distribute resources accordingly.
Cost Structure
When building cost structures Johnson et al. (2008) propose that companies should start with
estimating the cost of delivering their value proposition and then set prices depending on desired
margins. Osterwalder and Pigneur (2010) emphasise that despite the fact that costs should always
be minimised in any business model, it is advisable to separate cost-driven business models from
those that are value-driven. The former focus on delivering low price value propositions for markets
and customers that are price sensitive and the latter focus on premium value propositions and high
value creation, such as premium branding of customised cabins for heavy vehicles. The approaches
may vary, but in one way or another, they share a focus on value.
Revenue Streams
Revenue streams address how a company acquires or captures an appropriate share of the value
it creates and o↵ers to its customer in order to generate revenue (Johnson et al., 2008). Revenue
streams may vary between di↵erent customer segments with di↵erent pricing mechanisms and
profit formulas (Osterwalder and Pigneur, 2010).
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2.3 Competitive Advantage, Capabilities and Core Compe-
tencies
Traditionally, competitive forces theory asserts that, in order to gain competitive advantage, a firm
must exploit the forces driving the market dynamics (Hafeez et al., 2002). A company could for
example identify external threats and opportunities by using Porter’s five forces model. Prahalad
and Hamel (1990) proposed a di↵erent approach that generated substantial interest in the notion
of core competencies and capabilities, which helped popularize a new school of economic thought
called The Resource-based View of the Firm. While the traditional approach could be considered
an outside-in process, where the company starts with external analysis and then performs internal
analysis, the process advocated by Prahalad and Hamel (1990) is an inside-out approach (Javidan,
1998) that suggests that companies need to fully understand their core competencies and capabil-
ities in order to successfully exploit their resources and gain competitive advantage.
Many scholars advocate that core competencies and capabilities can be the trump cards of a firm’s
competitive advantage in their business model (Hafeez et al., 2002; Yang, 2015; Agha et al., 2012).
However, if a company is too dependent on only a few competencies and is unable to adapt as
they become obsolete in a technology shift, they can instead become its downfall (Schilling, 2012;
Prahalad and Hamel, 1990). In the existing literature on strategy and resource-based theory
the two terms competencies and capabilities are frequently intermingled (Burgelman et al., 2001;
Gokkaya and Ozbag, 2015). While several authors have attempted to distinguish such terms as
for example core competencies, distinctive competencies, and core capabilities, these e↵orts have
sometimes created more confusion than clarity (Hitt and Ireland, 2001). For instance, Prahalad and
Hamel (1990) use the term core competency to refer to a harmonised combination of multiple skills
and resources that distinguish a firm in the marketplace. They further use the term capabilities
to distinguish more elemental skills, such as advertising or logistics management, which might
contribute to or qualify as a core competency. By contrast, other authors have argued that core
competencies are more elemental technological or production skills, while capabilities are more
broadly based and may encompass the firm’s entire value chain (Stalk et al., 1992). This confusion
is not very surprising given the near semantic equivalence of the terms competence and capability
(Schilling, 2012). Many dictionaries define both in terms of abilities, and some definitions use
competence in their definitions of capabilities and vice versa. In this frame of reference however,
the definition of capabilities and core competencies provided by Prahalad and Hamel (1990) will
be used.
2.3.1 Identifying Core Competencies
Prahalad and Hamel (1990) further define core competencies as the collective learning of an organi-
sation on how to coordinate diverse production skills and integrate multiple streams of technologies.
Core competencies are essentially firm-specific accumulations of expertise resulting from previous
investments and from learning by doing. As an illustration, a diverse company can be viewed as
a tree, where core competencies resemble the root system that provides nourishment, sustenance
and stability, while trunk and major limbs are core products and leaves and fruits are end products
(see Figure 2.2). Core competencies can for example help companies to produce market leading
products at a lower cost and with a higher production rate than competitors. Studies have shown
that companies that are aware of their development of capabilities and core competencies have
been successful on the market (Schilling, 2012). Identifying core competencies can even be crucial
to surviving technology shifts as it helps firms to better understand and express their value propo-
sitions in emerging markets (Gallon et al., 1995). By viewing the business as a portfolio of core
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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 di�cult for competitors to imitate. In general, competencies that arise from the
complex harmonisation of multiple technologies will be di�cult to imitate. The competence may
have taken years or even decades to build and the combination of resources and embedded skills
will be di�cult for other firms to acquire or duplicate.
According to Prahalad and Hamel (1990) few firms are likely to be leaders in more than five or six
core competencies. If a company has compiled a list of 20 to 30 capabilities, it probably has not
yet identified its true core competencies.
2.3.2 The Risk of Core Rigidities
Leonard-Barton (1992) notes that sometimes the very things that a firm excels at can enslave it,
making the firm rigid and overly committed to inappropriate skills and resources. Incentive systems
may evolve favouring activities that reinforce a certain core competency and the organisational
culture may reward employees who are most closely connected to core competencies with higher
status with better access to other organisational resources. While these systems and norms can
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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, capa-
bilities, and constituent skills (see Figure 2.3).
Figure 2.3: A visual representation of the competency hierarchy, adapted from Srivastava (2005)and Hamel (1994)
The author states that the distinction between the various levels of competencies is more a matter
of convenience but that the understanding of hierarchy of competencies is essential. The all-
encompassing competency that stands above all the others is the Critical Competency. Hamel
(1994) defines the Critical Competency as the ability itself of a firm to successfully identify, nur-
ture, develop, upgrade and deploy its hierarchy of competencies to attain sustainable competitive
advantage. Srivastava (2005) concludes that, to gain sustainable competitive advantage, managers
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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 collabo-
rations and the fact that their collaborative suppliers have shared path-breaking technologies that
helped to leverage value chain e�ciency (Dyer and Hatch, 2004; Dyer and Singh, 1998).
There are many benefits with external collaborations. If successful, collaborations with other com-
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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 e�cient communication and flexibility is
specifically suitable in a turbulent environment under constant flux, such as in the in early phases
of a technology shift.
2.5 Servitisation
New technologies and open global trading systems contribute to a wide variety of choices for clients
and the development of the world economy has changed the traditional balance between customer
and supplier (Teece, 2010). Varying customer requirements may be measured, and supply options
are more transparent. This increases requirements on businesses to be even more client-centered. A
good business model provides value propositions that are convincing to clients, achieves favorable
cost and risk structures and enables a considerable value absorption of the business. According to
Teece (2010), clients do not just demand products - they want solutions for their needs. For exam-
ple, they are increasingly interested in transport solutions rather than trucks as physical products.
Alongside with this change of environment, incumbent firms are at risk of failure since they are
generally inferior at allocating enough resources to technologies that originally are not applicable
in their core market (Christensen and Bower, 1996).
The term servitisation refers to the transition from selling physical products to a more service-
based business model and was initially coined by Vandermerewe and Rada (1988). Since then, this
concept has been studied by numerous authors as a competitive strategy within di↵erent industries
(e.g. Wise and Baumgartner, 1999; Oliva and Kallenberg, 2003) and there is an increasing interest
for the topic since transitioning towards servitisation implies creating value-adding capabilities and
exploiting higher value business activities for traditional OEMs (Roy et al., 2009). The product
and service definitions are according to Roy et al. (2009) intertwined and are both important when
discussing the term servitisation. However, the concept of servitisation is not universally applicable
and to be both e↵ective and e�cient OEMs need to configure their business to support their new
value o↵ering as well as understand what the o↵ering implies for their customers (Oliva and Kallen-
berg, 2003). When implemented successfully there are several benefits with integrating services
into core products. Firstly, there are economic arguments as services generally provide a higher
margin and is a more stable source of revenue. Secondly, customers are demanding more services
since the importance of flexibility and specialisation is increasing. Thirdly, there is a competitive
argument since services are harder to imitate (Oliva and Kallenberg, 2003).
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More recently, the discussion about servitisation within the transport industry has led to the
emerging concept of Mobility as a Service (MaaS), where essentially the means of conveyance is
seen not as a physical asset to purchase but as a service available on demand (Mulley et al., 2018).
There exist several service models particularly connected to heavy vehicle manufacturers where
Vehicle as a Service (VaaS) is the least complex, where the customer determines the demand and
the manufacturer covers the need by providing heavy vehicles. Transport as a Service (TaaS)
is another model that includes the execution of the actual transport operations and dispatch of
missions. Additionally, Logistics as a Service (LaaS) is a more developed service model where,
in addition to providing the vehicle and executing missions, the OEM also provides the logistics
planning.
2.5.1 Downstream Vertical Integration
As part of a servitisation transition some companies choose to vertically integrate their supply
chains downstream in order to grow or sometimes as pure means of survival (Guan and Rehme,
2012). For manufacturing firms, downstream vertical integration plays an important role as it
can help to secure distribution channels of their products, especially in markets with increased
uncertainties (Rangan et al., 1993). Furthermore, it can be the key to controlling cost reductions
and e�ciency 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 transportationindustry (Scania, 2019b)
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2.6 Summary of Frame of Reference
To summarise and synthesise the Frame of Reference, theory regarding technology shifts, business
model innovation, core competencies and capabilities as well as concepts of value co-creation and
servitisation has been presented to form a knowledge base and background lens for the rest of the
study.
The concept of Autonomous Vehicles indisputably implies a major technology shift connected to
many uncertainties and risks. It is important to understand how companies have managed to
survive previous shifts by innovating not only their technology but also their business models si-
multaneously both radically and incrementally, an ability known as double ambidexterity. This
forms a background lens and motivates the study’s focus on business models concerning AVs and
not the technology itself.
This leads to the focus on business model innovation, where theory is presented on what business
models fundamentally are and the ambiguity that is often connected to them. To counter this am-
biguity a hands-on tool in the form of the Business Model Canvas is presented. This tool captures
the three parts of a business model: the value creation, value proposition and value capture in nine
building blocks, which are defined and elaborated. This tool will be used for the data collection
and analysis of this study, which is described in more detail in the methodology chapter.
The concept of core competencies and capabilities are defined and it is explained how they can
be identified and used to express a company’s strengths and be utilised in their business models.
Furthermore, it is covered how a firm needs to be careful not to depend too much on certain core
competencies so that they do not become core rigidities. Additionaly, the term dynamic capability
is introduced and used to describe capabilities that cover more than one business, are more adapt-
able and can be used to respond to change such as a rapid technology shift. Lastly, the concept
of a so-called critical competency as a company’s skill of operationalising and continously working
with its pool of core competencies and capabilities is defined. These references will be used in the
study to identify capabilities at Scania as well as to validate if they meet the criteria of being core
competencies.
When innovating within new technologies some capabilities may be outside a firm’s boundaries,
wherefore it might be suitable to collaborate with other actors. The benefits of collaborations
are presented together with a partnership approach that might be convenient in early phases of
technology shifts.
Lastly, a section regarding servitisation is presented; a concept that elaborates on how OEMs can
undergo a transition from selling physical products to more service-based business models. It is
explained how some companies choose to vertically integrate downstream in their value chains.
This will be used to discuss how heavy vehicle manufacturers could develop their business models
in an autonomous future.
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 con-
ducted and analysed. Finally, limitations, validity and reliability as well as ethical considerations
are presented.
3.1 Choice of Methodological Approach
The purpose of this study was to investigate potential business models for AV applications for goods
transports within confined areas, eventually developing a consolidated general business model based
on a Mining Case and a Harbour Case. Additionally, core competencies that can be used by Sca-
nia in an autonomous future were to be identified. Finally, the aim was that this business model
perspective, together with the identified core competencies, could be used as a general application
model for OEMs within the transportation industry when initiating new projects for goods trans-
port within hubs.
Since the studied phenomenon of autonomous technology at the time was novel and in the early
stages of a technology shift, the study was conducted as a qualitative exploratory case study. The
method suits critical, early phases of real-life contexts connected to management theories, when
key variables and their relationships are explored (Gibbert et al., 2008; Yin, 2014; Eisenhardt,
1989). Another choice for methodological approach that was considered was a quantitative study.
However, this was deemed unsuitable as there were very little empirics from previous studies within
the field and limited quantitative data to collect.
Throughout the project the researchers explored the heavy commercial truck manufacturer Scania
and two of their business cases with an abductive approach, which is a non-linear process that
alternates between empirical observations and theory. This allows a mixture between deductive
and inductive approaches and expands knowledge building in an exploratory case study (Dubois
and Gadde, 2002).
Scania was considered to be a suitable company for conducting case studies at since they are not
only industry leading, but also have several collaborations with academia and other firms, which
brings them to the forefront of new technologies such as ATS and connectivity (Scania, 2019a).
The studied area is complex and required a lot of data, which could be collected by informants and
other sources at the department for Autonomous Solutions at Scania due to their close connection
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 op-
portunities of AVs. The main purpose of participating in the presentations and seminars was to
become acquainted with the concept of autonomous technology and future business models.
The presentations and seminars were followed by twelve semi-structured interviews conducted with
the department responsible for developing future business models for Autonomous Solutions at
Scania, (see Table 3.1). These interviews covered the context of autonomous solutions: specifically,
what the interview subjects thought about the future of the industry, how the autonomous solution
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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Driving Autonomous Heavy Vehicles into the Future - A Business Model Perspective
3.2.2 Main Phase
Within the main phase the context around the two identified business cases, a Mining Case and
a Harbour Case, and their surrounding business models were investigated during four structured
interview sessions with four key individuals at Scania who were deeply involved in either of the
cases (see Table 3.2 interview A-D). In the first part of the interview sessions the interviewees were
asked to deliver extensive background information about the cases. This was later supplemented
with information in documents and material available through a shared database that could be
used to independently access and cross-check data. Secondly, the business models of the cases
were mapped using the Business Model Canvas tool, both with a Development Phase and a fully
Commercial Phase in mind. The interview subjects were asked questions based on the nine build-
ing blocks of the Business Model Canvas, which were shown to the interview subjects on a large
whiteboard (see Appendix D for the Case Study Interview Guide). The answers to the questions
were during the interviews summarised under each block on the whiteboard and the result was
further transferred to a digital database for future analysis. The interview subjects that were the
first ones to be interviewed within each case, namely interviewee A and B, initiated the interviews
with empty canvases, while interviewee C and D were given the former completed canvases as a
reference to start with. Within each block, interviewee C and D validated the existing elements,
modified them or presented new data.
After conducting the four interviews, the elements were analysed by the researchers and all of the
blocks for each separate case were iterated once again based on the collected data. This consolida-
tion resulted in one canvas for each business case respectively. Furthermore, the canvas elements
were divided and modified with regards to whether they were part of a Development Phase of the
project or a fully Commercial Phase. Hence, the Findings and Analysis chapter contains two ver-
sions of the canvases for each business case, a total of four canvases, representing the two di↵erent
phases of the projects.
In order to receive a customer perspective on the Mining Case a fifth more unstructured interview
was conducted with a former employee of the customer company, referred to as the Mining Com-
pany. The interview subject had previously worked for the Mining Company but now worked as a
consultant safety manager for Scania (see Table 3.2 Interview E). Because of his involvement within
the mining project from both the customer’s side and Scania, this interviewee was considered to
be suitable to collect data from as he could provide di↵erent perspectives. The interview session
was arranged in a similar manner as Interview A-D, that is initially the interviewee was asked to
deliver some background information about the Mining Case from the Mining Company’s point of
view. Thereafter the Business Model Canvases for the Development Phase and the Commercial
Phase within mining were introduced and each element was outlined for the interviewee, where
the person validated the information, modified it or presented new elements. The main purpose
with this interview was to receive validation from a customer perspective with an emphasis on
value proposition and customer relationships, which were considered most relevant to discuss from
this perspective. Furthermore, the interview gave important insights about Scania’s strengths and
capabilities as they are perceived from a customer.
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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 capabil-
ities. 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 rigidi-
ties. 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: pri-
marily scientific articles, books and reports surrounding strategic management of technological
innovation and more specifically technology shifts, business models, core competencies and capa-
bilities. 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 in-
dividuals 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 o�ce. 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 reduc-
tion 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 inter-
view 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 experi-
enced 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 e�ciently continued remotely. However, it did limit the observations part of the
study as the researchers could no longer access facilities and o�ces 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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Driving Autonomous Heavy Vehicles into the Future - A Business Model Perspective
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 con-
sists 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 out-
comes. 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 insu�cient 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 considera-
tions 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 dis-
cussion 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-e�ciency 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 technol-
ogy, 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
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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
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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 technol-
ogy to some extent, which has been used to develop intelligent solutions for e�cient control of
tra�c and trade flows in the harbour, increasing port capacity and overall port e�ciency.
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 con-
tainers 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 e�cient transport method for this part of the flow
(Interview B).
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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 compe-
tence; 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, empha-
sis 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
e�ciency 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 au-
tonomous 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, where payment is
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).
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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 empha-
sised 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
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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, col-
laborating 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 refu-
elling 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↵er-
ent 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 soft-
ware 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 Sodertalje
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 clean-
ing 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 com-
mercial 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
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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 friendli-
ness 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 applica-
tion 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.
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5.1.2 Mining Commercial Phase - Business Model Canvas
Below is a visual representation of the predicted Business Model Canvas of a potential future com-
mercial 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 re-
pairing 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-
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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 e�cient cus-
tomer 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 owner-
ship (Interview A). Another alternative could be Transport Service where payment is made per
transported ton material.
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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 back-
ground 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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Driving Autonomous Heavy Vehicles into the Future - A Business Model Perspective
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 impor-
tance for the Port Authority since it is partly owned by the government (Interview B). Also, the
system will allow a seamless, flexible and e�cient 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
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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 com-
mercial 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 con-
trary, 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 tra�c 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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Driving Autonomous Heavy Vehicles into the Future - A Business Model Perspective
Key Activities
Within a commercial phase, key activities are predicted to transition into updating and main-
taining 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 e�cient 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 op-
erations 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 au-
tonomous 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 com-
mercial 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 per-
formance 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 part-
nerships 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 cus-
tomers.
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Driving Autonomous Heavy Vehicles into the Future - A Business Model Perspective
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 har-
bours 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 alter-
native 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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Driving Autonomous Heavy Vehicles into the Future - A Business Model Perspective
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 e�ciently (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 modu-
larity 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 impor-
tance 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 transi-
tion 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 strat-
egy 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 cus-
tomers 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 e�cient pro-
duction 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 e�cient 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 e�cient 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 lever-
aged in the business model. Thirdly, a business model perspective synthesising the main aspects
of the aforementioned discussions is presented, which could be used in an autonomous future for
other goods applications within confined areas. This is then briefly compared to an alternative per-
spective 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 stud-
ied 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-
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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 part-
ner 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 main-
taining 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 compe-
tence, an experienced R&D department and a local operations team, as well as the advantage of
economies of scale and Scania’s e�cient 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
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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 au-
tonomous 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 e�cient customer support, fleet management service as well as
user friendliness and modularity are factors that are meant to ease integration and use of the prod-
ucts 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, e�cient
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 ele-
ments 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 proposi-
tion, 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 cus-
tomer 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 propo-
sition, 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
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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 qual-
ify 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 e�cient 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 com-
petency. Arguably, reliability is a broad term that could be attributed to several di↵erent factors
and other capabilities, and it is di�cult 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
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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 im-
plementing 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 di�cult 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 di�cult 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 di�cult to
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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 fac-
tors and are less di�cult 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 qualifiedcore competencies that can be used by Scania to gain a competitive advantage in an autonomousfuture
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 in-
tegration 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 beintegrated 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 ref-
erence chapter. This servitisation driving force seems to not only stem from gaining competitive
advantage and creating additional revenue streams, as Vandermerewe and Rada (1988) tradition-
ally 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 con-
sensus 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 manu-
facturers 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 inter-
viewees 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 tech-
nologies 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 ex-
tensive non-competitive vertical collaborations with their clients in development phases. In these
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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, inte-
grating 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 cur-
rently, 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 plat-
form 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 devel-
oped, 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 con-
stituted 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 manufac-
turer 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 sce-
nario, 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.
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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 busi-
ness 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-e�cient, and
ultimately more sustainable. Their self-driving vehicles are fully electrified and coordinated via
intelligent routing software, which integrates customers with tra�c 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 consump-
tion. 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 sus-
tainability 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 com-
bined 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 fol-
lowed. 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 e�cient transport systems could infer less expensive shipping, which could a↵ect consumer be-
haviour 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 e�cient 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 ma-
tured, 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 technol-
ogy 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, his-
torically 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 dis-
cussing implications regarding future business models for goods transports in confined areas as well
as core competencies in the future. Finally, suggestions for further studies are presented.
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 tech-
nology 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 perspec-
tive, 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 di�cult 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 re-
garding 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 phys-
ical 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 re-
placed 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 compet-
itiveness 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 nurtur-
ing 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 e�cient manner.
Chapter 7 Page 59
Driving Autonomous Heavy Vehicles into the Future - A Business Model Perspective
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 in-
sights 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.
Chapter 7 Page 60
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Appendix A
SAE Levels of Automation
Classification
To provide structure and international standardisation as well as a recognisable language sur-
rounding 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 fragor/Personal questions:
1. Vad jobbar du med just nu pa Scania?/What do you work with at Scania?
2. Hur lange har du jobbat pa Scania?/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 for utbildningsbakgrund som ar relevant for ditt jobb?/What’s your
educational background that is relevant for your job?
Oppna fragor/Open questions:
5. Vad tror du autonoma fordon innebar for framtiden nar det kommer till transport
av varor/manniskor?/What do you think autonomous vehicles will imply for the future
when it comes to goods/people transport?
6. Vilket ser du som den storsta 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 salja fullt autonoma fordon i framtiden (niva
fem)?/In what way do you think Scania will be selling fully autonomous vehicles in the
future (level 5)?
8. Kommer Scania salja tjanster och helhetslosningar eller fordon som produkter
tror du? Hur kommer uppdelningen se ut? Hur ser du pa risken 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 ar du involverad i? Ge garna lite bakgrund-
information kring dessa. Vad ar din syn pa dessa projekt?/Which of Scania’s au-
tonomous 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 for lardomar fran det 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 ar Scanias styrkor idag jamfort med konkurrenterna och hur tror du att de
kommer att utvecklas pa sikt?/What are Scania’s strengths today compared to competi-
tors 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 competenciesfrom the exploratory interviews
69
Appendix D
Case Study Interview Guide
Case background:
1. Beratta garna sa uttommande om bakgrunden till caset som du kan/Please provide
us an as exhaustive background information to the case as you can.
Business Model Canvas:
Betra↵ande detta business case:/In regards to this business case:
2. Vilka skulle du saga ar Scanias nyckelpartners i det har caset?/What would you
say are Scania’s key partners in this case?
3. Vilka ar de viktigaste aktiviteterna?/What are the main or key activities?
4. Vilka ar nyckelresurserna?/Which would be the key resources?
5. Vilka skulle du saga ar de storsta kostnadsdrivarna?/What would you say are the
main cost drivers?
6. Pa vilket satt uttrycker Scania sitt varde for kunden? Hur di↵erentierar sig
Scania gentemot andra foretag? Varfor valjer 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 gor Scania sina kunder medvetna om sitt vardeerbjudande och hur dis-
tribuerar de sina tjanster 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 ar de olika kundsegmenten?/What are the di↵erent customer segments?
10. Pa vilket satt far Scania sin intakt for detta 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
71