THE ROLE OF AUTONOMOUS
TRANSPORTATION TECHNOLOGIES ON
LOGISTICS MANAGEMENT IN THE UK
FASHION INDUSTRY
By
Sarah AlRamadhan
Student ID: 170181271
Supervised by
Yasmine Sabri
A Dissertation
Submitted to the faculty of the School of Engineering and Applied Science
Aston University
In Partial Fulfilment of the Requirements
For the Degree of Bachelors of Logistics with Supply Chain Management
April 2021
Abstract
Employment of autonomous transportation means in the fashion industry's supply chains is
slowly increasing. It seems to be just a question of time before they are used throughout the
whole industry. The underlying research investigates the advantages and challenges of UK
fashion industry participants utilising autonomous vehicles in their logistics operations, including
whether it is a financially sound decision. To achieve this broader goal, clear research objectives
have been defined, including examining the positive effects and possible challenges of digital
transportation impacting supply chain management. Additionally, proposing recommendations of
how the fashion industry can overcome the challenges and benefit from digital transportation
opportunities. Secondary data of reliable literature and primary data collected through a
questionnaire from 12 respondents have been collected and compared to achieve these
objectives. To evaluate the impact of the potential positive and negative effects and conclude
with recommendations for the UK fashion industry participants thinking of employing
autonomous vehicles in their logistics operations. They can use the research findings and
recommendations to ensure they can benefit from the positive effects and avoid or manage any
of the challenges they may face.
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Table of contents
1
2
INTRODUCTION.................................................................................................................4
1.1
RESEARCH BACKGROUND...........................................................................................................4
1.2
RESEARCH AIM............................................................................................................................6
1.3
RESEARCH OBJECTIVES..............................................................................................................6
LITERATURE REVIEW.....................................................................................................7
2.1
2.1.1
Travel time..............................................................................................................................................7
2.1.2
Reduction in human error.......................................................................................................................7
2.1.3
Reduction in Cost....................................................................................................................................8
2.1.4
Improved safety......................................................................................................................................8
2.1.5
The societal perception of AV and the opportunity for businesses to position as Highly Sustainable. .9
2.2
3
POSITIVE IMPLICATIONS OF AV.................................................................................................7
CHALLENGES OF IMPLANTING AUTONOMOUS TRANSPORTATION TECHNOLOGY.................10
2.2.1
Higher costs..........................................................................................................................................10
2.2.2
Legislation – liability issues..................................................................................................................10
2.2.3
International conventions and law........................................................................................................11
2.2.4
National law..........................................................................................................................................12
2.2.5
Ethical pressures...................................................................................................................................12
RESEARCH METHODOLOGY.......................................................................................14
3.1
RESEARCH DESIGN....................................................................................................................14
3.2
RESEARCH ONION.....................................................................................................................15
3.3
RESEARCH PHILOSOPHY...........................................................................................................16
3.4
RESEARCH APPROACH...............................................................................................................16
3.5
RESEARCH STRATEGY...............................................................................................................17
3.6
RESEARCH CHOICE....................................................................................................................17
3.7
TIME HORIZON...........................................................................................................................17
3.8
TECHNIQUES AND PROCEDURE.................................................................................................18
3.8.1
Population and Sampling......................................................................................................................18
3.8.2
Data collection......................................................................................................................................18
3.8.3
Question formation...............................................................................................................................18
3.9
DATA ANALYSIS.........................................................................................................................20
2
3.10
4
5
ETHICAL CONSIDERATION........................................................................................................20
FINDINGS AND DISCUSSION.........................................................................................21
4.1
CLOSED-ENDED QUESTIONS......................................................................................................21
4.2
OPEN-ENDED QUESTIONS:.........................................................................................................29
CONCLUSION....................................................................................................................31
5.1
ATTAINMENT OF RESEARCH OBJECTIVES..............................................................................31
5.2
RESEARCH IMPLICATIONS........................................................................................................34
5.3
RESEARCH LIMITATIONS..........................................................................................................34
5.4
FUTURE RESEARCH RECOMMENDATIONS...............................................................................34
REFERENCES............................................................................................................................35
APPENDIX...................................................................................................................................41
Progress Logbook..................................................................................................................41
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1 Introduction
1.1 Research background
Over the last decade, the fashion industry has developed and changed drastically. Today,
fashion has become a highly complex system. Fast fashion, particularly, utilises an elaborate
structure of “supply chain management, merchandising techniques, and retail technology” (Choo
et al. 2012, p. 243). Most of the time, fast fashion businesses are vertically integrated, which
increases flexibility and information sharing (Birtwistle, Siddiqui & Fiorito 2003).
Clark (2008) explains that the effect of technology on “just in time” manufacturing has
enabled faster retail turnover (p. 428). Therefore, fast fashion places emphasis on speed and
quantity while offering the latest trends at affordable prices (Choo et al. 2012; Sheridan et al.
2006). Designers have to incorporate consumer preferences in an attempt to satisfy their needs,
thus encouraging consumption (Bhardwaj & Fairhurst 2010). With the increasing access to
fashion shows and their dissemination in magazines and on the web, retailers such as Zara,
H&M, Mango, and Top Shop are able to incorporate runway designs into their products, offering
lower quality and prices (Barnes & Lea-Greenwood 2006). Using this real-time data to
understand fashion-conscious consumer needs, designers can better orientate their products and
lower the risks associated with unpredictability (Jackson 2001). Due to these fast fashion trends,
the fashion industry participants are striving to attain the highest efficiency. This demand for
efficiency has increased the need for efficient transportation, which has led to the incorporation
of autonomous technology for transportation in fashion supply chains.
The society of autonomous engineering (SAE) has defined autonomous vehicles as “An
autonomous car is a vehicle capable of sensing its environment and operating without human
involvement. A human passenger is not required to take control of the vehicle at any time, nor is
a human passenger required to be present in the vehicle at all. An autonomous car can go
anywhere a traditional car goes and do everything that an experienced human driver does”.
By implementing autonomous vehicle use in fashion supply chains, certain implications arise.
Before examining the positive and negative implications of autonomous transportation for
fashion brands, it is essential to learn how this technology has emerged over the years and how it
became viable to be used in the business world. Vehicle automation is not at all a recently made
progress, as its first envisioning dates back to the externally controlled phantom cars of the
1920s. A primitive version of the autonomous vehicle (AV) concept was illustrated at the 1939
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New York World’s Fair, where designer Norman Bel Geddes incorporated them in his exhibit
Futurama. Futurama was a visionary concept, which targeted the facilitation of people and goods
movement across the country, enabled by the automated “Magic Motorways” and suburb-based
spatial developments. Magic Motorways featured trench-like lanes, accompanied by
electromagnetic trails, for the vehicles to keep their lanes, while vehicles were embedding
railway signalling systems and electronic speed controls (Geddes 1940). Serious research on
driverless technology began at the 6’s (Beiker 2012), and the first projects involving truly selfdriving vehicles were conducted for the US Defence Advanced Research Projects Agency
(DARPA) by Carnegie Mellon University’s (CMU) Navlab and the University of Michigan in
1984, as well as by a partnership of Mercedes-Benz and Bundeswehr University Munich in 1987.
First coast-to-coast driverless car trip – “No Hands Across America” - was also implemented by
CMU’s Navlab in 1995, where 98,2% of 2,849 miles from Pittsburgh to San Diego were
completed autonomously at an average speed of 102,3 km/h (CMU 2017)
The diffusion of autonomous technology in the market has already taken place by enriching
conventional cars with Advanced Driving Assistance Systems (ADAS). The purpose of those
kinds of equipment is to execute some parts of the driving task, supporting the driver’s decisionmaking process and preventing human fault. ADAS include, among others, the Adaptive Cruise
Control (ACC) (car moves at a fixed speed set by the driver), Lane Keeping Assist (LKA),
collision avoidance systems, automated parking equipment and blind-spot detection. These
driver’s assistance means have gained growing popularity since their inauguration two or more
decades ago (Toyota Global 2012).
Moreover, another wide-scale project with AV is Google Waymo, which was introduced in
the US in 2009 and has already accomplished 3 million self-driven miles (Waymo website
2017). Another milestone in establishing driverless mobilities was the introduction of Tesla’s
autopilot in October 2015 (National Highway Traffic Safety Administration of the US 2017).
This equipment, as stated in the National Highway Traffic Safety Administration of the US
(NHTSA) (2017), allows the car to drive at a fixed speed in a standardised environment, such as
a motorway, almost without the driver’s input (NHTSA 2017; Automotive News, 2015). Today’s
most common autonomous transportation technology is platooning, which involves using a lead
truck driven by a human followed by 2-4 automated vehicles that follow the lead vehicle (Turri,
Besselink & Johansson 2016). In addition to platooning, another autonomous transportation
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technology mentioned by Sivak and Schoettle (2015) is Safe Road rains. The use of this
technology began in 2009. Under this method, a convoy of 3 automated vehicles is led by a truck
driven by a human, while another one follows it at the end. Moreover, some other vital
technologies which have digitalised transportation include the 5G mobile network that facilitates
vehicle-to-vehicle communication.
Autopilot is another necessary form of automation of vehicles that has two components
Traffic-Aware Cruise Control (TACC) and Autosteer. TACC keeps the car’s speed at a fixed
rate, chosen by the driver, as the conventional cruise control, but also has sensors to identify
proceeding and following cars. This is in order to make driving on a motorway more convenient
by reducing the need for the driver to turn the software off every time a slower vehicle is in front
of the car. Autosteer goes one step ahead and identifies cars and other moving objects in the
adjacent lanes so that the car can change lanes without input from the driver. The equipment
mentioned above is still not fully developed; this is why it can work only under standardised
conditions, such as in motorways, where it can better understand the driving environment than in
cities. Furthermore, it is crucial to know when using this equipment that the driver should have
their hands on the steering wheel, pay attention to the driving task and be ready to take over
control at any time in the journey. Moreover, the driver can be held equally responsible for the
car’s movement as if it was a fully manual car (NHTSA 2017).
There is a massive amount of literature available for examining the impact of autonomous
transportation. However, most of these studies have focused on evaluating its impact on societies
and the environment. This creates some gaps in the literature, as there are very few studies
concentrating on the impact of autonomous technologies on businesses’ logistics management.
As autonomous technology is gaining adoption in the fashion industry due to fast fashion trends
requiring efficient deliveries, this research aims to evaluate the positive and negative
implications of autonomous technologies for logistics management operations of fashion
industry participants.
1.2 Research aim
To incorporate both primary and secondary data findings to evaluate both positive and
negative effects of autonomous transportation on logistics management in the UK’s fashion
industry.
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1.3 Research objectives
• To examine the positive effects of digital transportation on the logistics management
function in the fashion industry in the UK.
• Reveal possible challenges of digital transportation, which can negatively impact supply
chain management.
• To propose recommendations of how the fashion industry can overcome the challenges and
benefit from the opportunities offered by digital transportation.
2 Literature review
2.1 Positive implications of AV
2.1.1
Travel time
Some scholars who have focused on elaborating the positive implications of autonomous
vehicles have claimed that AV will not need driver's input, while at the same time, and will rely
solely on their embedded smart technologies. So, it will be possible to enhance the usefulness
and productivity of travel time by enabling a vast number of possibilities for leisure,
communication or distance working (Kyed 2017; Lange 2017). Moreover, converting the car
from a private driving machine to a "self-moving" space will allow vehicles to operate without a
driver, by eliminating the human factor, thus eliminating the stops taken by the driver in long
trips for rest and allowing the vehicle to finish the trip in considerably faster which consequently
reducing lead and delivery times, benefiting the business. A synopsis of all the above could be
that automation is expected to increase mobility, improve road safety and facilitate sustainability.
Nevertheless, a fully driverless future comes along with some non-negligible concerns on how
AV will affect traffic, environment, space, etc. A great proportion of them will be discussed in
the following section, while examination of perception-related, legal and ethical issues will take
place in individual sections afterwards.
2.1.2
Reduction in human error
The literature has also claimed that AV leads to fewer accidents as it can help prevent human
errors. Around 90% of all road accidents happen due to human error (Kyed 2017). It has been
elaborated with an example that extracting human factor from the equation may decrease the
number of accidents. Safety gains have already occurred just from the embodiment of ADAS
into conventional vehicles. In specific, NHTSA (2017) shows that after Tesla embedded the
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Autosteer function. In Models S and X, crash rates of those models fell by 40%. This could be
considered a very positive indication of AV's potential to make roads substantially safer:
Actually, we are not at level 3 yet, and it is very promising if, already at level 2, accidents have
decreased by 40% (Kyed 2017).
2.1.3
Reduction in Cost
Another benefit of Autonomous vehicles for businesses is that they can help reduce costs.
Labour costs count for 50% - 75% of total public transportation cost (Sørensen 2017), thus
constituting a largely preventive factor in achieving sustainability of demand responsive
transport (DRT) systems and maybe of general public transport as well. Flextrafik cars
(taxis/minibuses) charge 300-400 DKK per hour, while approximately 210 DKK out of them (up
to 2/3 of the price) is the cost of the driver. Hence, the potential reduction of transportation cost
to that extent brought by unmanned operation can even redefine this field's economics, opening
up a vast array of new possibilities (Sørensen 2017; Endres 2017). These involve higher
frequencies, increased capacity, enhanced off-peak services and better coverage of sparsely
populated areas (Alessandrini et al. 2014). Moreover, lack of drivers' availability issues will
enable "just in time" operations, allowing for better allocation of the public transport system's
resources (Alessandrini et al. 2014).
2.1.4
Improved safety
It has also been claimed that AV can promote the optimised use of infrastructure in many
ways. First, AV will move more accurately than human drivers; therefore, they will be able to
keep lower longitude and latitude safety distances along with being able to move in platoons
(Wietholt & Harding 2016; Egense 2017; Lioris et al. 2017). According to estimates, this may
induce a 30% increase in the capacity of motorways (Danish Road Directorate website 2017;
Lange 2017). Second, the distribution of road space and traffic lanes will vary according to
demand. For instance, in a six lanes motorway that connects suburbs and city centre, centre
bound flow can possibly acquire four out of them during morning inward peak, while the
opposite will happen in afternoon outward peak. Additional capacity benefits could occur by
converting the emergency lane from permanent to temporary and use it as an emergency lane
again when needed (Egense 2017). This would be feasible by using vehicle to vehicle (V2V) and
vehicle to infrastructure (V2I) systems, which will instantly inform all approaching vehicles
when an accident happens to leave the lane unoccupied. Third, introducing this technology will
8
probably lead to an increase in the speed limit in many cases, first due to missing out on human
fault and second due to the car's enhanced knowledge about the infrastructure and the rest of
traffic. For instance, in intersections, since the car will be aware of the amount and speed of
crossing vehicles, it will be able to either accelerate in case available time is sufficient to do so or
to slow down earlier to make the trip smoother and save energy.
2.1.5
The societal perception of AV and the opportunity for businesses to position as
"Highly Sustainable"
According to some scholars, Perceptions of people towards AV are favourable to a great
extent (Christie et al. 2016; Hohenberger et al. 2016; Lange 2017). On the other hand, some have
claimed that further relevant research should occur (Bazilinskyy, Kyriakidis & de Winter 2015).
Uncertainty arises due to many factors, beginning from this new technology's lack of
applications, especially on a wide scale (Yap et al. 2016). Most acceptance surveys take either
point of departure from how people imagine driverless mobilities or, at best, follow up a shortterm AV trial (Kyriakidis et al. 2015; Christie et al. 2016; Piao et al. 2016). Moreover, in many
public surveys, it happens that participants have not experienced AV or might be misinformed to
an extent on how this technology works; thus, expressing some hesitations or reluctance (Piao et
al. 2016). Indeed, greater knowledge and/or richer experience on automation in mobility induce
better perceptions towards it (Alessandrini et al. 2014; König & Neumayr 2017). Existing
evidence suggests younger people and men appear as more positive towards AV, in opposition to
women and elderly who tend to show less trust in driverless technology (Hohenberger et al.
2016; Kyriakidis et al. 2015; Haboucha et al. 2017).
Urban residents seem to be more positive towards driverless mobilities (Kyriakidis et al.
2015; König & Neumayr 2017). A reason for that can be the more significant drawbacks (gas
emissions, occupation of public spaces etc.) and the limited freedom of the driver-driven vehicles
causing (congestion, restrictions of access to the city centre due to high parking cost or
congestion pricing, etc.) in cities. For instance, Lu et al. (2017) indicated that in Atlanta, more
people would choose to live in Transit-Oriented Development (TOD) districts, combined with
AV rather than in traditional car-dependent suburbs. Another survey by Payre, Cestac &
Delhomme (2014) among French drivers shows they would be more willing to switch to
automated driving in motorways, congested traffic, and parking. Preference upon driverless
mobilities has significantly been linked with the personal focus on control (Payre, Cestac &
Delhomme 2014; Choi & Ji 2015). These societal trends offer fashion industry brands the
9
opportunity to adopt autonomous transportation and position themselves as highly sustainable by
reducing emission, congestions, etc.
2.2 Challenges of implanting autonomous transportation technology
2.2.1
Higher costs
In contrast to those scholars who have proposed that autonomous technologies reduce costs,
some studies have shared entirely different perspectives, arguing that AV increases the overall
cost instead of reducing them. This technology’s cost can also be considered one of the
constraints in its launching on public streets. A standard 15-passenger electric driverless pod,
like the one in the Aalborg East trial, costs around 1,7 million DKK (approx. 250,000 euros)
(The Telegraph 2016). That may be pricey for a low-capacity and low-speed – therefore lowrange – means of transportation. On top of that, electricity also constitutes a non-negligible
expense, especially in countries where electric power taxation is rigorous (Endres 2017).
However, a prominent advantage of this technology is low manufacturing lead-times (Kyed
2017). In addition to that, data needed for the vehicle to move (mapping, etc.) will not have a
considerable cost (Endres 2017).
Personal Rapid Transit (PRT) could be regarded as the predecessor of AV, but with a
considerable drawback; it needs its own guideways. MPM’s cost reached a noticeable sum of
319 million USD in 2004 for an 8,7 miles (14 km.) network or 22 million USD per km. This is
greatly owed to political pressure for quick construction, risk of new technology and the fact it
was not a mass-construction project, thus not getting the benefit of extensive network economies
(Raney & Young 2004). Creating a modern PRT system is not a low-price solution either, as it
can cost between 7 and 15 million USD per km, without tunnelling or other extra features
(Raney & Young 2004; Ultra Global PRT 2017). That could be the main reason that even though
PRT first appeared in 1975, it took 22 years for its second implementation and 11 more years to
come seriously to the forefront (CityLab 2014), while very few projects have been realised since
then. Thus, AV needs to run mostly on conventional infrastructure so that deployment of fully
driverless mobilities is not prohibitively expensive.
2.2.2
Legislation – liability issues
All businesses planning to adopt AV have to comply with multiple legislations, especially the
supply chains that have international operations, and any lack of compliance can cause serious
strategic and financial losses. It is so because any vehicle shall comply with specific safety and
10
operational regulations in order to be allowed to get in traffic. AV will eventually have no
drivers, and their operation will be entirely based on technology. So legal framework governing
AV shall regulate both movement-related issues (speed, traffic planning, liability in case of an
accident) and technology-oriented matters (system security, hacking protection). Lack of full
legal recognition is, for many, the main reason AV has not been widely introduced so far
(Alessandrini et al. 2014). However, waiting is preferred from acting ahead of time for most
policymakers, as it is of critical importance for this innovation not to run on the streets before
legal and liability issues have been clearly defined (Raptis 2017; Kyed 2017). On a global scale,
structural laws and principles about road traffic – and hence about AV - are formed by two
international conventions Vienna Convention on Road Traffic (VCRT) and Geneva Convention
on Road Traffic (GCRT). Since these conventions are considerably old, it makes sense they do
not make any provision for driverless technology. As it would be necessary to discriminate the
relatively more changeable national legislation from the less flexible international conventions,
this categorisation also applies to the examination of legislation regarding AV in this chapter.
Following are some examples of the international and national legislations that fashion
companies have to follow if they choose to adopt autonomous transportation.
2.2.3
International conventions and law
VCRT is the leading international legal text by which AV operations have to abide. VCRT is
ratified by 75 countries, mainly in Europe and Asia, and some in Africa, Central and South
America (United Nations Economic Commission for Europe 2016; United Nations Treaty
Collection 1968). Countries that are not parties to this VCRT, like the US and Australia, may
abide by the GCRT of 1949 (United Nations Treaty Collection 1949). VCRT addresses the
driver’s role as following (article 8, s1 and s5):
(1) Every moving vehicles or combination of vehicles shall have a driver.
(5) Every driver shall at all times be able to control his vehicle or to guide his animals.
As set above, the driver’s role is legally dominant for any driving activity in countries party to
the convention. However, an update of VCRT, which sets up the foundation for legal recognition
of AV, came into force on 23 March 2016:
As of that date, automated driving technologies transferring tasks to the vehicle will be
explicitly allowed in traffic, provided that these technologies are in conformity with the United
11
Nations vehicle regulations or can be overridden or switched off by the driver (United Nations
Economic Commission for Europe website 2016).
Another update regarding the adoption of automated steering technologies is under discussion
by United Nations Economic Commission for Europe (UNECE). It is expected to become a
component of VCRT in the next period of time (UNECE website 2016). Both amendments
constitute a sort of legal recognition of semi-autonomous vehicles, yet they preserve the leading
role of the driver in any driving-related decision making. Hence, the driver, either present at the
vehicle or monitoring it from a distance, is liable for any accident or incident that might happen
(CityMobil2 2016; Pillath 2016; Frisoni et al. 2016). Further legal recognition of AV is expected,
claims Raptis (2017), and necessary in order for “true” benefits of automation to be more visible
to the society
2.2.4
National law
At a national level, many countries like Greece, Denmark, Germany, France, UAE and so
forth allow or are shortly allowing fully autonomous vehicle trials (Endres 2017; CityMobil2
2016). Simultaneously, commercially available semi-autonomous cars, offered by many
manufacturers, Mercedes Benz, Tesla and BMW (Car and Driver website 2016), embed a vast
array of ADAS like autopilot, LKA, which are also allowed in large parts of the world. Various
interpretations of VCRT are used around different countries of the world. However, liability in
all cases lies either with the physical driver, in the case of a semi-autonomous car, or with the
remote operator, in the case of the fully driverless pod (Raptis 2017; CityMobil2 2016).
Discussions concerning switching responsibility from the driver to car manufacturer or other carrelated humans (or non-human) parties are still at an immature stage, both on a national and
international scale (Egense 2017; Lange 2017). This is partly because driving automation
technology is believed not to be 100% ready to perform an entire driving task under all
conditions (Lange 2017; Raptis 2017), as pointed out in the recent Tesla accident (Forbes 2016;
Reuters 2016). Therefore, redefinition of legislation or redistribution of liability should proceed
in accordance with - the though steady - progress in making AV capable of undertaking more
parts of the driving task (Collingwood 2017).
2.2.5
Ethical pressures
AV will lead to a substantial increase in safety both for their passengers and other road users,
yet some accidents will still happen (Goodall 2014). When an accident is foreseen, the AV’s
12
reaction will not be shaped by the driver but by a programmer. This raises an amount of ethical
and operational concerns. Ethics refer to which practical, moral etc., criteria AV will be based on
for decision-making in critical situations. These issues have not been defined yet according to
Egense (2017); Kyed (2017); and Lange (2017), and respective dialogue could be expected to
proceed when automation technology is safe enough to move - at least parts of - the
responsibility from the driver to the car (Lange 2017). One of the most common situations of this
kind is “the trolley problem”. This problem refers to a situation where an accident is inevitable,
and there is somebody inside or outside the vehicle that can decide who will be hurt by somehow
affecting the vehicle’s movement (Sandel 2009).
It is often put forward in discussion what should the vehicle do in a situation when an accident
is inevitable, and AV is going to choose if it is going up to an old woman or a mother with her
child; and of course, the situation there has to be taken into account. There is also a question on
how capacity is used on the road. For example, AVs waiting to be loaded or unloaded can park
outside the city centre to avoid adding to the city centre’s traffic. There will also be many
dilemmas in the transition period when there is a mix of autonomous cars and conventional cars,
and it might be challenging due to their interaction. For example, a test was made with truck
platooning in Denmark last May, where the trucks that were connected could drive closer to each
other, save energy and increase safety. Trucks were driving at 80 km/h (which was the speed
limit), and another truck was overtaking them at 81 km/h. It was a situation where this truck did
not have to overtake one truck, but three, meaning all traffic was stuck behind the trucks in both
lanes for a minute, which created an increase in congestion. So, there will be many situations
faced in the transition period; thus, replacement needs to be optimised along the way.
There is no doubt that self-driving cars are much safer than human-driven vehicles. Google’s
autonomous vehicles have passed the longest safety test, travelling for more than seven million
miles without accidents caused by the vehicle itself (Meyer & Beiker 2019). Shladover and
Nowakowski (2019) show in a collision report that for a significant number of minor accidents
encountered by Google’s self-driving cars in the US, not only that no injuries were involved, but
that a large percentage of the accidents are caused due to human errors rather than by the cars.
Despite its tested safety, the self-driving car requires making ethical decisions before it is
completely accepted. If it encounters a troubling ethical dilemma, as exemplified by Lin (2015),
swerving left and killing a young girl or right to hit a grandmother; decisions can be made only
according to moral codes. However, no simple algorithm or math can choose between sacrificing
13
its passengers or risking a more significant number of pedestrians (Shariff, Rahwan & Bonnefon
2016).
Another candidate solution is to pre-program self-driving cars with the crash-optimisation
system. Crash-optimization is a program that chooses the way that will lead to the least extent of
damage by which driverless cars make corresponding ethical decisions (Lin 2015). This strategy
can be realised by calculating the cost-function algorithm and is more reasonable because it
minimises lawsuits and fits humanity’s common sense (Lin 2015). Virginia Tech researcher
Noah Goodall points out that pre-programming self-driving cars to select an action with the
lowest harm and possibility of collision makes them more ethical than human drivers (Meyer &
Beiker 2019). Accessible as it sounds, the crash-optimisation system still faces difficulty in
market acceptance. Whichever action cars make to lower the cost, the ethical decisions cannot
satisfy everyone. Lin (2015) illustrates the trolley problem to minimise the cost.
Consequentialism justifies switching the train tracks and saving five people by killing one, but
non-consequentialisms think selecting, either way, is an act of killing, which is worse than letting
die.
Moreover, because people persist on their own beliefs, it is still a dilemma between
autonomous programming cars to be self-protective and utilitarian (Shariff, Rahwan & Bonnefon
2016). Furthermore, there is a lack of transparency in the discussion of ethics. It is still a crime if
driverless cars automatically sacrifice the owner without consent (Lin 2015).
3 Research methodology
The underlying research is based on evaluating both positive and negative effects of
autonomous transportation on logistics management in the UK’s fashion industry. The core
objectives of the research are to examine the positive effects of digital transportation on the
logistics management function in the fashion industry in the UK, to reveal possible challenges of
digital transportation which can negatively impact the supply chain management and to propose
recommendations of how the fashion industry can overcome the challenges and benefit from the
opportunities offered by digital transportation. This part of the dissertation will clearly state the
methodology by which all of these aims and objectives will be attained (Dźwigoł & DźwigołBarosz 2018).
14
3.1 Research Design
A research design is a collection of methodologies and strategies to be incorporated for
answering predetermined research questions and attaining the research aim. The three types of
research designs that are widely used are descriptive, casual research and exploratory design.
Each of the three types has its characteristics and unique features that make it preferable over the
other types depending on the nature of the research problem and the objectives to be attained
(Burns & Groove 2014).
Based on the nature of the research and the topic selected, different research designs could be
used. The three broad categories of the research design include exploratory research, descriptive
research and causal research designs. To attain the purpose of this underlying research, a
combination of descriptive and exploratory research designs has been used (Burns & Groove
2014).
3.2 Research Onion
To develop a framework for how to proceed with the research, the research Onion proposed
by Saunders, Lewis and Thornhill (2011) has been selected. This onion is visually represented in
Figure 1 below, and each activity involved in conducting the research has been shown
systematically.
Figure 1.
15
Figure 1. The Research onion framework (Saunders, Lewis & Thornhill 2011)
To follow this research onion, the underlying research has followed the same sequence where
research philosophy has been selected, the research approach has been decided, strategies have
been chosen, time horizon has been identified, and techniques and procedures have been
finalised in the same sequence.
3.3 Research philosophy
The research philosophy can be defined as a belief regarding the method by which data about
the underlying phenomena should be collected, analysed and interpreted. The main research
philosophies are interpretivism, positivist, pragmatist and realist research philosophy. The
positivist research philosophy believes that the social world can be understood objectively and is
based on concentrating on objective results and avoiding personal values and opinions. On the
other hand, interpretivism research philosophy states that the researcher plays a key role in
observing the social world, and the findings depend upon the researcher’s interest. Pragmatism
research philosophy is based on a very different concept as it states that there is no clear
definition of reality and truth, and there can be single or multiple truths and realities that are open
to empirical enquiry. Moreover, realistic research philosophy is concentrated on the idea of
16
independence of reality from the human mind, which is further divided into critical and direct
realism (Crossan 2003).
For the underlying research, the most suitable research ideology is positivism. This
philosophy has been chosen for this research because it allows the use of both quantitative and
objective data, which adds authenticity to the argument. Moreover, this philosophy offers higher
reliability and generalisability and helps understand the patterns and trends in data which is
necessary for attaining the predetermined research objectives of this study.
3.4 Research approach
It is vital to select a research approach that correlates with the purpose of the search. There are
different research approaches, including deductive, abductive and inductive research approaches,
out of which inductive and deductive approaches are more commonly used for academic
research. Inductive research is conducted on a larger scale to come up with new theories. On the
other hand, the deductive approach is adopted to validate existing theories. For this study, the
deductive approach has been chosen considering the limited resources and the number of
sufficient literature available on this topic (Snyder 2019). Moreover, due to Covid-19
restrictions, it is impossible to conduct face-to-face interviews or conduct a survey based on a
large sample size, making the inductive research approach infeasible; hence, the deductive
approach is most suitable.
3.5 Research strategy
The research strategy provides a roadmap for the research, which leads towards systematically
attaining the research objectives. The most valued research strategy is selected based on its
appropriateness for the research objectives to be attained. The most commonly used research
strategies include survey-based research, archival research, action research, case study analysis
and grounded theory research (Singh 2006). Based on the nature of the topic under discussion,
the underlying study incorporates critical and comprehensive literature review-based analysis
along with a survey method (questionnaire) to collect both secondary and primary data, which
has been synthesised for forming a reliable conclusion.
3.6 Research choice
There are three widely adopted research choices, which are the qualitative, quantitative and
mixed research choices. Qualitative research enables to conduct the analysis more
17
comprehensively and, in more detail, while quantitative research helps analyse the phenomenon
with factual evidence and statistical support. In contrast to these, the mixed research choice helps
gain benefits from both types of research choices. It helps examine the research topic in detail
and with supportive factual information and evidence. For the underlying research, the study
plans to incorporate the mixed choice because evaluating the positive and negative effects of
autonomous transportation on logistics management in the UK’s fashion industry requires
detailed analysis. The mixed approach has been chosen because it allows the detailed and
comprehensive analysis based on qualitative data and allows for quantitative data to be used as
supportive evidence. The qualitative data was gathered by critically reviewing the literature,
whereas quantitative data will be gathered through closed-ended questions included in the
questionnaire. The findings gained from these closed-ended questions will be summarised in
tables, graphs and pie charts using Microsoft Office excel (McCusker & Gunaydin 2015).
3.7 Time horizon
Examples of time horizons considered when starting research include the longitudinal time
horizon and the cross-sectional time horizon. These are essentially the point in time beyond
which the findings of research lose credibility and dependability. In simple words, the cross-
18
sectional time horizon is selected when the research findings are applicable and acceptable for
the short run. However, the longitudinal time horizon is selected when the research results can be
applied in the long run. As information technology is transforming the logistics operations with
each passing year and the logistics function has evolved in the fashion industry. Thus, it can be
expected that the positive and negative impacts of automated transportation logistics will not
remain the same in the coming decade. In this regard, it is not appropriate to claim that results
are applicable in the long run. Hence, this underlying research is considered to have a crosssectional time horizon (Ørngreen & Levinsen 2017).
3.8 Techniques and procedure
3.8.1 Population and Sampling
The population of this underlying research is logistics managers working in the fashion
industry in the UK. Out of this population, 30 management members have been collected
through various ways and contacted to participate in the questionnaire (Burns & Groove 2014).
3.8.2
Data collection
For the underlying study, both secondary and primary data have been collected, where the
secondary data has been collected from authentic web sources, journal articles, and books. The
primary data has been collected through a questionnaire based on both closed and open-ended
questions. The questionnaire was emailed to all 30 collected potential participants to maximise
the data collected. The participants’ contact details have been collected through their respective
companies’ websites, LinkedIn accounts, CILT (Chartered Institute of Logistics and Transport)
and some industry connections. However, this research involved no face-to-face interaction, and
the whole research was conducted virtually only after gaining their consent to participate in
research by sending consent forms via email. The questionnaire was created using google forms
then sent to all participants to allow easier and faster data collection (Dźwigoł & Dźwigoł-Barosz
2018). Only 12 of all contacted logistics managers responded and answered the questionnaire.
3.8.3
Question formation
The questionnaire has been formed after completing the introduction and literature review.
After setting the research aim and objectives, literature has been reviewed in relevance to each
objective. Then questions have been formed to validate the information gained from a critical
review of the literature. Each of the questionnaire questions is linked to one of the research
objectives and the claims made by scholars in the literature. Asking about potential positive
19
effects of AV, possible challenges along with examining perception-related, legal and ethical
issues. The purpose of each question is to validate or reject the results of the literature review.
The following questions have been included in the questionnaire.
Closed-ended questions:
1. In your opinion, which of the following is a positive effect of digital transportation on the
logistics management function in the fashion industry in the UK?

Reduced travel time

Reduction in human error

Reduction in Cost

Improved safety
2. Do you agree that the Societal perception of AV is an opportunity for businesses to
position themselves as “Highly sustainable”?

Yes

No
3. Which of the following is the biggest challenge of implanting autonomous transportation
technology?

Higher costs

International conventions and law

National law

All of the above
4. Ethical issues associated with the use of Autonomous vehicles will not let this technology
come into practice by businesses for at least a decade. What is your opinion on this
statement?

Strongly agree

Agree

Strongly disagree

Disagree
5. Do you believe that AV will lead to a substantial increase in safety both for their
passengers and other road users?
20

Yes

No
Open-ended Questions:
1. There is a conflict of opinion in the literature regarding the cost of AV as some scholars
claim that AV reduces the logistics cost, whereas some argue that it increases the costs.
Share your opinion on this.
2. According to your experience, can AV’s social and ethical issues negatively impact the
fashion companies’ CSR image?
3. Share some ideas and strategies for fashion industry participants to overcome the
challenges and benefit from the opportunities offered by digital transportation.
3.9 Data analysis
The data collected should be analysed by various means to draw correct and fair conclusions.
Data analysis depends on the method used by the researcher to collect data. Current research uses
mixed methods to analyse data since it helps scientists understand a great deal of research
information (Padgett 2016). For this study, data has been qualitatively and quantitatively
analysed, where findings of the questionnaire results have been compared with secondary data
discussed in the literature. The primary data has been summarised in tables, graphs and pie
charts. Results have been critically evaluated to identify similarities and differences between
primary and secondary data results.
3.10 Ethical consideration
All moral considerations were conducted so that the study remains reliable and authentic at
the same time. To make the assembled data factual and relevant, all theories, philosophy, and
methods must fulfil the purpose of the research, the questions, and the research aims. The
researcher should check for any typographical or calculation errors and have appropriate links.
Also, confidential information must be kept, and proper referencing has been provided, and
questionnaire participants were kept anonymous (Dźwigoł & Dźwigoł-Barosz 2018).
For underlying research following are the Ethical considerations

For preventing intellectual property theft, data has been shared along with proper in-text
citations, and a detailed bibliography has been shared at the end of the study.
21

The proper consent of all participants has been gained before sending the questionnaire.

The information collected for the underlying study will not be used for any other purpose.

The personal information of the research respondents will be kept confidential.
4 Findings and discussion
After collecting primary data from the 12 fashion industry participants and critically
reviewing the literature, this part of the research is based on examining the primary and
secondary data results comparatively to identify similarities and differences of opinions. The
results gained through each of the questions will be evaluated by comparing the results with
scholarly perspectives discussed in the literature to identify if the collected data supports or
disproves the secondary data collected. To aid in the analysis of the data collected through the
questionnaire, tables and figures were created.
4.1 Closed-ended questions
1. In your opinion, which of the following is a positive effect of digital transportation
on the logistics management function in the fashion industry in the UK?
Categories of Response
No. of
Participants
Reduced travel time
6
Reduction in human error
3
Reduction in Cost
2
Improved safety
1
22
8%
Reduced travel time
17%
The
50%
above
that 50%
25%
Reduction in human error
pie chart
Reduction in Cost
shows
Improved safety
of
the
respondents believe that reduced travel time is a positive effect of the implementation of digital
transport in the fashion industry’s logistics operations. This primary data result could be backed
by Lange (2017), who elaborated on the positive implications of autonomous vehicles not
needing any driver input and relying on their embedded state of the art, smart technologies.
Lange stated that this makes it possible to enhance the usefulness and productivity of travel time
by eliminating the stops needed by the driver and allowing the vehicle to finish the trip in a
significantly shorter time which can help reduce lead time or delivery time, consequently
benefiting the business.
25% of respondents, however, selected the option of reduction in human error to be a
prominent benefit of digital transportation. This result can be backed by Shladover and
Nowakowski (2019), who explained a Google self-driving car collision report that for a
significant number of minor collisions encountered in the US, a large percentage of them are
caused by human errors rather than by the vehicles. Kyed (2017) has also shared that around
90% of all road accidents happen due to human error, and extracting the human factor from the
equation may significantly decrease the number of accidents. Further elaborating that there is a
potential for AV to make roads substantially safer.
Additionally, 17% of the questionnaire participants shared that the key benefit of autonomous
logistics on the UK’s fashion industry is the reduced cost. This result is consistent with multiple
studies, such as the research by Alessandrini et al. (2014); Sørensen (2017); and Endres (2017),
who have claimed that Labour costs are 50% - 75% of total public transportation cost. Therefore,
using autonomous vehicles can help eliminate this considerable cost that can even redefine the
23
economics of this field, opening up a vast array of new possibilities. In contrast to those scholars
who have proposed that autonomous technologies reduce costs, Kyed (2017); and The Telegraph
(2016) have shared entirely different perspectives. Some studies have argued that instead of
reducing the costs, AV increases the overall cost. This technology’s cost can also be considered
one of the constraints in its launching on public streets. Hence, it is evident that some studies
comply with these primary data results, whereas some have claimed contradictory ideas as it
seems to be a controversial subject.
Moreover, only 1 participant selected improved safety, which reflects that most participants
do not consider improved safety a primary benefit of automated transportation in the UK’s
fashion industry. A report shared by Web urbanist (2015) can support this finding as this scholar
has also raised concern for road safety associated with AV use. It has been added that automation
is expected to increase mobility, improve road safety and facilitate sustainability. Nevertheless, a
fully driverless future comes with some non-negligible concerns on how AV will affect traffic,
environment, space and other factors. A fundamental contradiction is that some scholarly
perspectives do not agree with considering reduced cost a benefit of AV, but that contradiction is
not only between the primary data result and secondary findings; in fact, the literature also has a
contradiction of opinion in this context.
2. Do you agree that the Societal perception of AV is an opportunity for businesses to
position themselves as “Highly sustainable”?
Categories of Response
No. of
Participants
Yes
9
No
3
24
25%
Yes
was
a
No
Question 2
75%
straightforward question asking the participants whether they agree with the claim that AV offers
fashion industry businesses an opportunity to position themselves as highly sustainable, thus
increasing their popularity as people shift towards more environmentally conscience
consumption. In response to this question, very clear answers were generated where a vast
majority of 9 out of 12 answered with yes. It is crucial to clarify that this primary data result does
not indicate that AV is a highly sustainable technology. It is merely a reflection that fashion
brands can take advantage of society's perceptions of AV to improve their image by positioning
as highly sustainable brands due to their use of AVs, which can help in controlling gas emission,
environmental damage by reducing travel time, and many other environmental benefits of AV.
This primary data outcome can be backed by the studies of Kyriakidis et al. (2015); and
König and Neumayr (2017). These studies have revealed that urban residents seem to be more
positive towards driverless mobilities, and they perceive AV as an option to reduce gas
emissions, occupation of public spaces congestion, etc. People are willing to accept automated
driving in motorways to avoid congested traffic and long travel times contributing to high
emission. According to both primary and secondary data results, societal trends definitely offer
the fashion industry brands an opportunity to adopt autonomous transportation and position
themselves as highly sustainable by reducing emission, congestions, etc.
3. Which of the following is the biggest challenge of implementing autonomous
transportation technology?
25
Categories of Response
No. of
Participants
Higher Cost
6
International conventions and law
1
National law
3
All of the above
2
17%
Higher Cost
50%
25%
International conventions
and law
National law
8%
All of above
As one question was on the benefits of AV for UK fashion industry participants, it was also
imperative to incorporate one question specifically to reveal the challenges of implementing
autonomous logistics. This multiple-choice question also generated mixed results, but a very
clear result was that half of the questionnaire participants considered higher cost as a major
challenge. This result can is supported by many studies such as Endres (2017); and Kyed (2017);
however, a key research that has elaborated this idea is by Raney and Young (2004). It has
explained that AV, just like PRT, is not a simple and cost-effective technology to be
implemented. Ultra-Global PRT (2017) has also demonstrated that autonomous logistic is a
costly innovation that may increase businesses' costs instead of reducing them.
As for the remaining respondents, 8% selected international conventions and laws as a
challenge, 25% chose national laws, and 17% chose all of the above. This is understandable
since, other than the cost, another big challenge of implementing AV in logistic operations is
26
national and international laws. A study by Alessandrini et al. (2014) claimed that all businesses
planning to adopt autonomous vehicles have to comply with multiple legislations, especially if
their supply chains have international operations, and any lack of compliance can cause severe
strategic and financial losses. It is so because any vehicle shall comply with specific safety and
operational regulations in order to be allowed to get in traffic. AV will eventually have no
drivers, and their operation will be entirely based on technology. So legal framework governing
AV shall regulate both movement-related issues (speed, traffic planning, liability in case of an
accident) and technology-oriented matters (system security, hacking protection).
Raptis (2017) and Kyed (2017) have also shared that structural laws and principles about road
traffic – and hence about AV - are formed by two international conventions on a global scale.
These are the Vienna Convention on Road Traffic (VCRT) and the Geneva Convention on Road
Traffic (GCRT), depending on the country. Since these conventions are considerably old, it is
understandable that they do not make any provision for driverless technologies. A wide range of
countries worldwide have applied laws that allow the embodiment of AV in mixed traffic under
certain conditions. Furthermore, it is essential to distinguish between the relatively more
changeable national legislation from the less flexible international conventions, this
categorisation also applies to examining legislations regarding AV in this chapter. However, an
update of VCRT, which sets up the foundation for legal recognition of AV, came into force on
23 March 2016. stating that automated driving technologies transferring tasks to the vehicle will
be explicitly allowed in traffic, provided that these technologies are in conformity with the
United Nations vehicle regulations or can be overridden or switched off by the driver.
CityMobil2 (2016) has also shared that various interpretations of VCRT are used around
different countries of the world. These secondary data results suggest that international and
national laws are challenging for implementing autonomous logistics in the UK's fashion
industry due to the complete compliance between primary and secondary data results.
4. Ethical issues associated with the use of Autonomous vehicles will not let this technology
come into practice by businesses for at least a decade. What is your opinion on this
statement?
27
Categories of Response
9
8
No. of
Participants
Strongly Agree
8
Agree
3
Neutral
1
Disagree
0
Strongly Disagree
0
8
7
6
5
4
3
3
2
1
1
0
Strongly
Agree
Agree
Neutral
0
Disagree
0
Strongly
Disagree
This was a Likert scale-based question where participants were asked to share their level of
agreement and disagreement about the statement that ethical issues associated with Autonomous
vehicles use will not let businesses fully utilise this technology for at least a decade. This
question generated clear results as a vast majority selected strongly agree, and some selected
agreed, while no one disagreed or strongly disagreed. This outcome can be supported by Egense
(2017), which has explained that AV will lead to an increase in safety both for their passengers
and other road users. Yet, some accidents will still happen, and the AV's reaction when an
accident is foreseen will not be shaped by the driver but by the AV's programme. This raises an
amount of ethical and operational concerns. Ethics refer to which practical, moral etc., criteria
will the AV follow in its decision-making in critical situations. For instance, if the automated
vehicle is surrounded by cars on each side of the road and an emergency happens forcing the AV
to change lanes, it is programmed to turn to the side with fewer passengers. This decision may
28
raise some ethical issues, but it can lead to the attainment of the safety objective of these
vehicles. Still, complete automation is not practically possible, at least not for the next decade.
These secondary data claims suggest an absolute agreement between primary and secondary data
results in this context.
5. Do you believe that AV will lead to a substantial increase in safety both for their
passengers and other road users?
Categories of Response
No. of
Participants
Yes
4
No
8
33%
Yes
No
67%
In response to this question, 67% said no, and only 33% said yes, which reflects that most
participants believe that autonomous logistics will not increase safety. The opinions on this
aspect in secondary data are also mixed. Some scholars such as Lioris et al. (2017); and Egense
(2017) have shared that AV will move more accurately than human-driven vehicles by keeping
lower longitude and latitude safety distances and being able to move in platoons. Moreover,
additional capacity benefits could occur by converting emergency lanes from permanent to
temporary and use them as emergency lanes again when needed. For instance, in intersections,
since the car will be aware of the amount and speed of crossing vehicles, it will be able to either
accelerate in case available time is sufficient to do so or to slow down earlier to make the trip
29
smoother and save energy. Thus, increasing driving safety as AVs are perhaps more aware of
their environment than human drivers are. These are the opinions that suggest that AV will
increase safety. But there are many other scholarly claims discussed in the literature that suggest
that AV will not improve safety. Instead, it is more likely to increase the risk since it is a
machine that relies on certain smart technologies that control all of its operations without any
human intervention if any problems occur. So, on the whole, both primary and secondary data
results are contradictory, and it is difficult to reach a conclusive opinion in this regard.
4.2 Open-ended questions:
1. There is a conflict of opinion in the literature regarding the cost of AV as some scholars
claim that AV reduces the logistics cost, whereas some argue that it increases the cost.
Share your opinion?
In response to this question, several different arguments were mentioned. One experienced
participant shared that there is no wonder the advantages of using autonomous vehicles are
attractive. Still, there is a potential of increasing the number of injuries, which will eventually
increase the cost. Another participant shared that the expenses incurred in implementing AV
technology can be recovered, but some additional costs are very draining and cause grave
damage to revenues. It has been elaborated that there are federal laws, and negligence of any law
can cause not only financial penalties but can also cause damage to goodwill if media gets
involved.
Moreover, it was also shared by another participant that it could cost the livelihood of truckers
and drivers. A very different perspective was shared by a participant who shared that there are
some costs to the social well-being involved due to AV, auto repair shops and mechanics would
probably have less business, and there could be layoffs. Further, if an accident with an
autonomous vehicle occurs, it is likely to disrupt public trust, which is a crucial component of
universal driver-less vehicle adoption. The general inference gained from this question is that
there are many benefits that automated logistics can offer to fashion industry participants;
nevertheless, costs associated with this technology cannot be ignored as well.
2. According to your experience, can social and ethical issues of AV negatively impact the
fashion companies’ CSR image?
In response to this question, most participants shared that if not managed carefully, social and
ethical issues associated with AV can negatively impact the fashion companies’ CSR image. One
30
participant elaborated that the public may also demand that onboard autonomous vehicle
computers be capable of such analysis as to be able to predict a potentially dangerous situation
and offer manual driver control if they desire it. This seems to be a thin line where drivers would
be comfortable holding their lives’ destiny in their own hands. Such an option, of course, opens
up a host of problems. For example, what classification of potential danger warrants drivers to
have manual control and against which altruistic criteria should such control be waived? These
and other such questions need to be pondered and analysed carefully. Anyone is capable of
accepting that computers, by far, are capable of faster, more accurate and timely decisions than a
human being. Another manager shared that it will definitely put the CSR image into question due
to the lack of ability of a machine to make the most rational and ethical choice when faced with
an extreme situation. Aside from the vanity that precludes humans from accepting that machines
are better than them, the most critical issue is whether the choice to take another human being’s
life should be placed in a machine’s domain. Artificial intelligence, however intelligent, are still
machines, and it is a tough sell that a robot or computer could be responsible for one’s demise
and it’s difficult to hold them accountable for a human’s death as they are just machinery.
3. Share some ideas and strategies for fashion industry participants to overcome the
challenges and benefit from the opportunities offered by digital transportation
For this last question, participants were asked to share some suggestions for fashion industry
companies. A manager suggested no artificial systems are completely safe and that crashavoidance, breaking the cars or relinquishing control over the wheel cannot help autonomous
vehicles out of facing ethical dilemmas. For example, some subjects on roads are too small to be
detected by today’s technologies, and transferring control over cars to drivers requires time,
which are potential causes of accidents. He proposed a candidate solution to pre-program selfdriving vehicles with a crash-optimisation system. Another participant pointed out that preprogramming self-driving cars to select an action with the lowest harm and possibility of
collision makes them more ethical than human drivers. Accessible as it sounds, the crashoptimisation system still faces difficulty in market acceptance. And whichever action cars make
to lower the cost, the ethical decisions cannot satisfy everyone.
One shared that federal governments should oversee the advancement of autonomous driving
technologies. All countries within the UK will be governed by Federal legislation. Federal will
have to have all the vehicles compliant with the same requirements and legislation. When it
comes to electronics, malware, security systems, health protocols, and checks, the cars should be
31
allowed to exclude those requirements. Without federal rules for country use, the countries may
vary from country to country. And, as the vehicle moves into another country for some reason, it
may not be able to render the vehicle liability to other countries specifications. He also shared
that ethical dilemmas in advanced robotics and artificial intelligence have seemed like a distant
matter left to the scope of science fiction movies. However, with their reality insight, it is time
for these ethics to be explored seriously. Having resigned ourselves to the fact that machines will
undertake ethical decisions such as life and death decisions. The next best thing is to try, to the
furthest extent possible, to design algorithms with a clear and pure moral compass that can form
the “morality engine” for these bits of intelligence.
Another suggestion to avoid the legal issues was made as one participant elaborated that
government regulation is risky to contemplate because once set, it becomes hard to revise, but it
is nevertheless an inescapable reality. A strong push should be made towards basic regulation by
setting minimum safety standards for manufacturers. The customer will be the loser in such a
scenario, being incapable of influencing policy and unable to abstain from purchasing the
vehicles altogether. Another reason for the call to minimise government involvement is the usual
government bureaucracy and incompetence. Many studies have shown that most governments
tend to be complicit in crimes against their citizenry, primarily by negligence and ineptitude.
Government regulation, therefore, should mainly exist in an oversight capacity once minimum
standards have been established to allow market forces to propel innovation and improvement.
5 Conclusion
This chapter is based on evaluating the extent to which all research objectives have been
attained, the research implications and limitations, what could have been done better, and
proposing future research recommendations.
5.1 Attainment of research objectives
The first objective was to examine the positive effects of digital transportation on the UK’s
fashion industry’s logistics management function. It has been revealed that reduced travel time is
a crucial benefit, as through employing AV, it will be possible to enhance the utilisation and
value of travel time due to the elimination of the human factor, thus eliminating the excess time
endured because of it. Another critical benefit of Autonomous vehicles for businesses is that it
helps in lowering down the costs. AV also leads to fewer accidents as it prevents human errors,
and societal views on AV provide an opportunity for the fashion industry brands to adopt
32
autonomous transportation and set themselves as highly sustainable by reducing emission,
congestions, etc. The introduction of this technology could also lead to an increase in the speed
limit in many cases, first due to the missing out of human fault and second due to the vehicle’s
enhanced knowledge about the infrastructure and the rest of the traffic. Subsequently, this
increase in the speed limit could also reduce travel time significantly.
The second research objective was to reveal possible digital transportation challenges, which
could negatively impact supply chain management. This objective has also been attained as it has
been revealed that AV has a considerable drawback of high costs due to its advanced technology
and some AVs would need their own guideways and supporting infrastructure. This is
significantly owned to political pressure for quick construction, risk of new technology and the
fact it was not a mass-construction project, thus not being able to get the benefit of extensive
network economies. Creating a modern PRT system is not a low-price solution either, as it can
cost between 7 and 15 million USD per km, without tunnelling or other extra features. The legal
pressures are the second major challenge as in the US some states like Florida and Arizona are
promoting safe vehicle growth, testing, and service on public roads. Delaware set up the Wired
and Autonomous Vehicles Advisory Board, which was charged with providing guidelines for
new technologies and approaches, and several other states took this step as well. However, the
UK is still behind in implementing procedures to accelerate the spread of AV use throughout the
country.
Moreover, some international and conventional laws impose that every moving vehicles or
combination of vehicles shall have a driver; the driver shall at all times be able to control their
vehicle. Driver’s role is legally dominant for any driving activity in countries party of the
convention. It is of critical importance for this innovation not to run on the streets before legal
and liability issues have been clearly defined. Accountability lies either on the physical driver, in
the case of a semi-autonomous car, or on the remote operator, in the fully driverless pod.
Therefore, redefinition of legislation or redistribution of liability should proceed in accordance
with the steady progress in making AV capable of undertaking more parts of the driving task.
Moreover, safety risks are inevitable as it is hardly possible to build autonomous cars with
perfectly safe systems because traffic accidences are unavoidable. In the event of an emergency,
AVs are designed to take action with the lowest chance of destruction or injury.
The third objective was to propose recommendations of how the fashion industry can
overcome the challenges and benefit from digital transportation opportunities. The comparative
33
examination of secondary and primary data has helped in attaining this objective as well. It has
been shown that Crash-optimization is a program that chooses the way that will lead to the least
extent of damage by which driverless cars make corresponding ethical decisions. This strategy
can be realised by calculating a cost-function algorithm and is more reasonable because it both
minimises lawsuits and fits humanity’s common sense. It can also make AV safer and more
practical for fashion industry companies. Automated vehicle manufacturers should further ensure
that cars are made to the best possible standards to provide a relative guarantee of structural
integrity that assures human’s safety in the event of accidents. Currently, the decisions to make a
vehicle’s outer shell depend on factors such as aerodynamic quality, lightness, speed, cost of
metals, etc.
In a future where autonomous vehicles are abundantly used, some of these factors, such as
lightness and aerodynamic ability, can be dismissed since they only appeal to the vanity that is
the pursuit of speed and fuel consumption considerations (assuming that non-renewable fuels
will not be a factor). As such, car manufacturers will have one primary concern, the cost of
production, which is influenced mainly by the cost of raw materials, primarily metals. To
minimise the cost, consequentialism justifies switching the train tracks and save five people by
killing one, but non-consequentialists think selecting, either way, is an act of killing worse than
letting die. In this regard, manufacturers must adhere to the strictest ethics in designing vehicles
capable of withstanding a reasonable degree of impact while incorporating the highest in safety
features. Coming up with such a morality engine will necessitate the synthesis and collation of
the entire world’s populous to create an amalgam of moral code that can then be written into the
core autonomous vehicles’ algorithms to enable computation as to the ethical decisions they can
make. Thus, self-driving cars will make ethical decisions concerning life and death according to
the moral limits, parameters, and tolerances by which most people can live.
A strong push should be made towards basic regulation, such as setting minimum safety
standards for car manufacturers. This is so they can then be motivated by the free-market forces
to compete for and design better cars that adhere to regulatory statutes and continuously seek to
improve autonomous vehicles. Where official regulation sets the standards of autonomous
vehicle production safety standards and algorithms settings, manufacturers will likely lobby to
keep them at a certain level in a bid to keep down costs. Regardless of advancements, ethical
issues associated with the use of automated logistics cannot make this technology widely
adopted in the next couple of years. AVs must be programmed in such a way that they should
34
minimise the amount of danger caused by accident by being programmed by the manufacturer
with consideration for the existing legislation on safety.
5.2 Research implications
As this research is based on a combined evaluation of primary and secondary data, it can be
used not only by academic researchers but can also guide the decision making of the logistics
managers working in the fashion industry of the UK who may consider adopting AV in their
logistics operations.
5.3 Research limitations
This research is based on the comparison of review literature relating to this subject and
primary data collected from employees in the industry in question. And due to the Covid-19
pandemic, the primary data collection method has been changed from face-to-face interviews
with selected participants to an online questionnaire sent via email. Furthermore, as digital
transportation use in logistics management is still not a widespread idea, a limited number of
resources were available talking about the specific relationship of AVs use in fashion industry
supply chains, especially in the UK. Additionally, this report's word count limit has affected the
number of factors of the impact of AV on the UK's fashion industry logistics operations
discussed.
5.4 Future research recommendations

Future studies can gather data from a larger sample size to generate more reliable
outcomes.

More than one industrial sector can be studied, and results can be compared to reveal the
usefulness of AV in different sectors.

More primary and secondary quantitative data can be collected to support the findings
further.
35
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Appendix:
Progress Logbook:
Review topic choices – initial
research
Select supervisor
Supervisor allocated
Meeting with supervisor to discuss
dissertation topic
Further research.
Email requesting literature related to the
topic of interest from supervisor.
Email on initial dissertation topic
Email finalising the dissertation topic
Methodology Research and
selection
Initial Background Literature
Research
Draft Interim Report & Literature
Review
Meeting about Interim report
(Methodology and Ethical Approval)
Obtain Ethical Approval
Interim Report Deadline (30/11)
Collect potential respondents
Design questionnaire questions
Email about dissertation word count and
literature review
Contact selected questionnaire
participants.
Obtain respondents.
Writing introduction and literature
review sections of the final report.
Email Informing supervisor of obtaining
respondents and data collection process
Sending questionnaires to the
respondents collected.
Email Informing supervisor of completion
of writing introduction and literature
review and waiting to collect questionnaire
responses.
Draft of introduction and literature review
Sep
2020
Oct
2020
Nov
2020
Dec
2020
Jan
2021
4
11
14
19
27
28
7
4
11
25
42
Feb
2021
Mar
2021
Apr
2021
sent.
Data Collection – Questionnaires
Email Informing supervisor about data
collection process and beginning the data
analysis process.
Meeting about feedback on first two parts
and discussing methodology writing.
Writing methodology section of
the final report.
Analysis of collected results.
Methodology draft sent to supervisor and
received feedback on it.
Writing findings and discussion
section of the final report.
Findings and Discussion draft sent to
supervisor.
Writing conclusion section of the final
report.
Conclusion draft sent to supervisor.
Draft of all sections sent to supervisor.
Received feedback on the findings &
discussion and conclusion sections.
Editing Final Project Report.
Meeting to enquire about
presentation of final report.
Final Report Deadline (01/0413/04)
15
23
5
14
23
25
43