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Received: 19 September 2022 / Accepted: 17 January 2023 / Published online: 3 February 2023
© The Author(s) 2022 This article is published with Open Access at www.bvl.de/lore
ABSTRACT
The logistics industry is facing a transformation.
Automated driving has been gaining importance
in the commercial vehicle industry and trucks with
SAE L4 are expected by 2030 for the hub-to-hub
scenario. Driven by the research question of what the
direct logistics environment of automated trucks will
look like in 2030 a two-round Delphi-based scenario
study was conducted for domestic goods transport in
Germany. 19 projections were developed and evaluated
by 27 experts from different industries. With complete-
linkage clustering, four logistics scenarios for 2030
were created. The results show that environmental
and social sustainability as well as digitalization are
expected to be the most important drivers. These
include the shift to electric drive systems, improved
working conditions, and increasing transparency and
connectivity of the supply chain. The experts forecast
an increase in the importance of software services
and a continuing shortage of skilled workers. Rather
controversial are the topics of charging infrastructure
for electrified transport and the degree of automation of
loading systems. Overall, the results provide a reliable
basis for strategic decision-making in order to ensure
the introduction of automated trucks into the logistics
of the future and their surrounding environment.
KEYWORDS: Automated Trucks · Future · Logistics ·
Trends · Delphi Study · Delphi-based Scenario Study
Logistics Research (2023) 16:1
DOI
_
10.23773/2023
_
1
1. INTRODUCTION
Increasing vehicle automation is playing a significant
role in the commercial vehicle industry [22, 56]. With
its share of 73 %, the truck is the most important
means of freight transport in Germany. Consequently,
automation in this area has great potential on an
economic, ecological and social level [20, 49]. Through
automation at SAE Level 4 [77], improved fuel
efficiency and reduced driver costs for instance can
reduce total cost of ownership in the long term [20, 54,
86]. Further, automated trucks are expected to optimize
traffic flow, reduce the occurrence of traffic jams
and therefore enable improved planning of transport
times for the parties involved in logistics [20]. In view
of these advantages, companies in the commercial
vehicle sector are focusing on developing the necessary
technologies for automated driving. However, it should
not be forgotten that the truck is only one part of the
entire logistics chain. Therefore, it is just as important
to investigate the future logistics environment of
automated trucks to ensure that the logistic processes
can also be implemented with automated vehicles.
Just how the logistics industry will change in the
coming years has been widely discussed [99, 103].
Several authors assume that the logistics value chain
will be restructured by increasing digitalization and
strict emissions regulations [43, 61]. Moreover, it can be
assumed that further platform-based logistics business
models will develop in the course of digitalization,
driven in particular by start-ups [50, 93]. Increased
global competition and demographic change are also
cited as factors influencing logistics systems [55, 103].
Faced by these developments, it is of great importance
not only to shape the technical progress of automation,
but also to consider the future framework conditions in
which automated trucks will operate because their use
may be fundamentally influenced by possible future
trends and developments in logistics.
Automated Trucks and the Future of Logistics –
A Delphi-Based Scenario Study
S. Escherle1*
, E. Darlagiannis1, A. Sprung2
Svenja Escherle1*
Emilia Darlagiannis1
Anna Sprung2
1Technical University of Munich,
School of Engineering and Design,
Boltzmannstr. 15., Garching, Germany
2MAN Truck and Bus SE,
Dachauer Str. 667, Munich, Germany
*Corresponding author. Tel.: +49-162-1081541;
E-Mail: svenja.escherle@tum.de
2
2. LITERATURE REVIEW:
TRENDS IN LOGISTICS
To approach the future environment of automated
trucks, current trends in logistics next to automation
play a crucial role. The extent and influence of these
trends are widely debated in the literature. Conclusions
for the logistics and transport sector are derived from
various global developments. Therefore, various
factors influencing the environment of the logistics
sector can be identified, many of which are interlinked
and mutually dependent. Figure 1 illustrates the trend
areas and developments in logistics that are particularly
relevant in the context of this research question. In the
following, possible development trends in different
areas are described and their effects on the logistics
industry are explained.
2.1. Globalization
Global trade relations are growing due to the increasing
cross-border flows of goods as well as to intra-company
trade between globally distributed production sites and
subsidiaries. This development is driven by the decline
in prices for international transport [103]. Automated
trucks and the associated drop in transport costs
are expected to further increase globalization [72].
Due to the internationalization of online commerce,
global trade flows and logistics networks are steadily
increasing in importance [5, 99]. In globalized markets,
competitive pressure increases and new growth
opportunities are generated, at the same time existing
drivers for the logistics industry, such as cost pressure,
are further intensified [5]. Global competition therefore
demands new business processes in logistics [45].
1.1. Research question and scope of study
By 2030, automated trucks are expected to be in use for
long-distance transport between main transshipment
hubs outside conurbations [62]. At automation level 4
the system takes over complete control of the vehicle
for pre-defined routes. In the limiting case, the system
is able to assume a risk-minimizing state without the
need for human interaction [77]. Therefore, it is not
necessary for a driver to be inside the vehicle on these
defined routes. However, currently truck drivers are
responsible for many other tasks in addition to driving
that integrate them deeply in the logistics process.
These tasks include, for example, inspecting the truck,
route planning or loading and unloading the vehicle
[19]. Since it must be ensured that logistics processes
function with automated trucks in the future, it is
important to investigate what the future logistics
environment of automated trucks in hub-to-hub
scenario will look like. This will allow the truck to
be developed in such a way that it can meet its future
requirements and be integrated into future logistics
processes.
To be able to make statements about the future,
suitable forecasting instruments from futurology are
needed [39]. Scenarios are a helpful means of gaining
a deeper understanding of potential future business
environments [78]. Therefore, the aim of this study
is to develop differentiated future scenarios for the
logistics of domestic hub-to-hub transport in Germany,
and thus to provide reliable statements about the future
framework conditions for the logistics of automated
trucks.
Research Question: What will the direct logistics
environment of automated trucks look like in 2030?
Figure 1: Sele ction o f inter relat ed t rends in logistics .
3
Automated Trucks and the Future of Logistics – A Delphi-Based Scenario Study
As a result of the growing connectivity between
logistics players, uniform platform models and defined
interfaces are required, which represents a major hurdle
in complex value chains [37, 81]. Lack of trust and a
lack of standardization among the many participating
supply chain members are preventing data exchange,
especially in the heterogeneous European logistics
market [81, 102]. Trends toward the elimination of
manual work steps through eCMR, an increasingly
automated process at depots or robot-assisted logistics
processes will not only influence the working world of
truck drivers, but will also bring about a redistribution
of tasks in different logistics processes.
An overlying trend in this field is the trend toward
intelligent transport systems (ITS). ITS are smart
systems using innovative developments in information
and communication technologies [75] to „enable more
intelligent use of infrastructure and vehicles and to
enhance the management of traffic and mobility”
[23]. There are multiple components to the system
such as an overall communication infrastructure,
sensor technology, the digital transmission of data
and navigation satellite systems for locating the
vehicle [3, 94]. ITS include and make use of the above
mentioned trends such as the digital connection of
the parties involved or the real-time transmission of
data. The introduction of ITS however is dependent
on the infrastructure as sensors, cameras, etc. have to
be integrated in the road transport network [75]. Thus,
ITS can provide information on e.g. traffic conditions
and traffic flows [36] and among others can be used
for fleet management in order to react dynamically
to disturbances and increase the fleet efficiency [3].
Therefore, ITS can have a positive influence on CO²
efficiency and sustainability [2].
2.3. Sustainability
Environmental sustainability is considered to be a
significant driver in the need for transformation in
logistics [66, 76, 103]. Due to the increasing acceptance
of and demand for green products and services, players
in the logistics industry are being forced to address
this issue [76]. Environmental, transport and economic
policies are also indicative of the consensus in the
public debate [76]. EU regulation [18] for reduced CO2
emissions from new trucks is resulting in far-reaching
changes in the trucking industry [25]. Platooning
and automation can contribute to environmental
sustainability [63] and tr uck manufacturers are
increasingly pursuing the use of alternative drive
systems. However, the further development of batteries,
the associated prices [46], and current limited storage
capacities [99] are a source of major uncertainties in
this field. The high electricity demand as well as the
lack of space for suitable electric charging stations
along main traffic arteries are further obstacles
opposing electrification in long-distance transport [25].
Therefore, Fuel Cell Electric Vehicles could also play
an important role [21]. The 2030 innovation program of
For instance, those companies offering value-added
services beyond transport have a competitive advantage
relative to traditional transport companies [5].
2.2. Digitalization
Advancing digitization is often cited as a key driver of
change in the truck and logistics industry [37, 43, 79,
102]. Driverless transport systems, tracking and tracing,
and platform-based freight and loading exchanges
have long been established solutions in the logistics
industry [93]. A frequently described topic in respect
of Industry 4.0 is networking and connectivity in the
value and supply chain. The trend is towards connected
intelligent systems and communication throughout the
whole supply chain, linking suppliers, manufacturers,
and service providers [37, 45, 93, 102]. Further,
identification and localization technologies such as GPS
and RFID offer increasing transparency and control
capabilities thus enabling efficient transportation [99,
102, 103]. The benefits are greatest with real-time
transmission [103], because automatically transmitted
environmental data extend traditional tracking and
tracing to include ongoing information thus enabling
possible intervention in the transport process [102].
The digitization of products and services is becoming
a differentiating feature because of the increase
in technology relating to Big Data (BD), Cloud
Computing, Internet of Things, Artificial Intelligence
(AI) and Machine Learning [45, 84, 99, 102]. Fifth-
generation mobile communications (5G) is seen as a
key technology for the networking and integration of
large sensor networks, which enables monitoring of
the entire intralogistics and production logistics [38,
76]. It is assumed to have innovative potential for
logistics efficiency in terms of time- and failure-critical
communication with or between automated vehicles and
machines [50, 76]. Moreover, intelligent logistics robots
and autonomous vehicles are increasingly in demand
because of higher wage costs and demographic change
[73, 101, 102]. Intelligent networking that goes beyond
the direct vehicle environment is additionally changing
processes at depots. Thus, it is possible to integrate
trucks into a dynamic control system that controls the
coordination of vehicles as well as the assignment of
tasks at the depot [55].
In addition to AI, blockchain (BC) technology
offers potential for crucial efficiency-enhancements
and secure applications [33]. With the use of this
innovative technology, smart contracts can be realized
in the form of the automated execution of contracts
between different parties [26] and thus the digital
waybill (eCMR; 9, 16, 59). However, BC is still in a
developmental state and further research is needed
prior to widespread application of the technology
[26, 33]. In addition, the legal framework for the use
of BC as well as AI remains unclear [26]. Either way,
traditional logistics players will need to invest highly
in digitalization if they wish to remain competitive [70,
81].
4
from manufacturer to service provider to respond
to the changing market [43, 49, 79]. With the use of
automated trucks, manufacturers are already working
on so-called “Transport as a Service” (TaaS) concepts
[70]. Trends are also moving towards a transformation
of truck manufacturers into hardware and software
companies, emphasizing the development of “Software
as a Service” (SaaS) areas by OEMs [49, 79].
Furthermore, collaborations and strategic partnerships
will be necessary, especially with digital players, as
they can offer complex logistics services based on their
expertise in digital processes as well as their data assets
in respect of consumer and consumption behavior and
thus be able to advance further into logistics with their
own transport capacities [84, 85].
Digitalization is also enabling the entry of numerous
logistics start-ups, in the form of digital freight
forwarders or digital platforms [93]. Digital freight
forwarders are gaining in importance and are competing
with large, traditional freight forwarders [81, 93, 102] by
handling the traditional brokerage service of exporting
goods exclusively digitally [102]. Furthermore, with the
help of collected route planning and freight data as well
as the use of AI, optimal routes can be identified, thus
reducing empty runs up to 20% resulting in enormous
cost savings [70, 96]. Also, the growing collaborative
use (sharing) of transport, storage and truck capacities
via digital platforms highlights the trend towards the
“access over ownership” principle [47].
2.6. Logistic processes and infrastructure
As part of urbanization, large distribution centers are
becoming increasingly close to conurbations and thus
to consumers. The goal is to shorten the last mile, which
in the future will be limited by access restrictions [79].
Nowak et al. [61] assume a future hub-to-hub delivery
executed by automated trucks. In this context, highway
routes between hubs are suitable as an application area
for automated trucks because the transport is highly
standardized [31, 42, 62]. Also, according to Ritz [72], a
network of automated hubs is possible in that automatic
loading and unloading systems make the arrivals of
automated trucks independent of on-site personnel.
Deliveries from the hubs to urban areas can then be
made using light commercial vehicles [61, 79]. Factors
such as dwindling transport space are becoming an
increasing challenge for logistics processes, especially
near metropolitan areas [26, 57]. Schuckmann et al.
[82] conclude from their futures study that insufficient
availability of funding could lead to poor transport
infrastructure because government investment will
be concentrated in urban areas. To counteract this
and finance the required transport infrastructure or
its maintenance, private investors could become more
involved [82].
2.7. Society and risks
Demographic change is causing labor shortages in
many places, from unskilled workers to qualified
the German Federal Ministry of Transport and Digital
Infrastructure (BMVI) addresses the topics of fuel
cell technology, electromobility, and the expansion of
charging infrastructure [7].
Moreover, the demand for the social sustainability of
logistic players is increasing due to public discussion
about drivers’ working conditions [99, 103]. Repeated
criticism concerns issues such as low wages, partly
precarious employment conditions, and the lack of
a work-life balance [99]. Through quality seals such
as FairTruck, logistics service providers and freight
forwarders are demonstrating an increased appreciation
of their employees [7, 69, 99]. Accordingly, these will
continue to act as a differentiator between logistics
companies in the future.
2.4. Combined Transport
With the aim of being able to meet the increasing
demand for road freight traffic volumes and ongoing
sustainability requirements, the cooperation between
different transport modes such as rail, water and
road, so-called combined transport (CT), is being
promoted [6] in order to achieve optimum performance,
economic efficiency and environmental friendliness
in the transport sector [6, 45, 103]. The related trend
of synchromodality focuses on the combination
of different transportation modes in a structured
and synchronized way with the aim of increasing
intermodal transportation [15, 44, 65]. The idea of
synchromodality is that actors along the supply chain
are interconnected and cooperatively plan the flow
of goods being able to flexibly switch between the
available means of transport [44, 68]. Despite the strong
growth in CT, freight transport continues to remain
significantly concentrated on roads, accounting for over
70% of total transit volume, and this share is continuing
to increase [26, 45, 103]. This is because of the many
current advantages of road transport over other modes
including dense road network, shorter transport times,
high adaptability to customers’ requirements [45].
The use of automated trucks could also deepen the
focus on road transportation [17]. The main obstacle
for CT is interface problems for the transshipment of
intermodal transport goods [103]. Therefore, it remains
questionable as to whether a breakthrough in CT and
thus a change of use of trucks in freight transport can
be expected.
2.5. Competition and new business models
Truck manufacturers must prepare for increasing
consolidation among the logistics players: smaller
customer numbers with greater purchasing power. A
concentration process and increasing fleet sizes are
to be expected in the next ten years, especially in
long-distance transport [79]. In addition, it is likely
that truck manufacturers will be repositioned in the
future, as digital reinvention will be a prerequisite for
future success [40]. For example, OEMs could offer
flexible rental and sharing concepts [79] and move
5
Automated Trucks and the Future of Logistics – A Delphi-Based Scenario Study
Delphi method can be used to collect expert knowledge
in a structured way [13, 29] with the aim of reaching
a reliable consensus within an expert group about
future developments and events [28, 32, 90]. At the
same time, the scenario method is particularly suitable
for estimating future developments and long-term
planning, and is useful in strategic decision making in
an uncertain, rapidly changing environment [89, 95].
The scenario technique is often used in combination
with the Delphi method, as it can increase the quality
of the study in terms of creativity, objectivity and
credibility [20, 60]. Therefore, a Delphi-based scenario
study was conducted for the year 2030 following a
standard phase-based procedure [28] visualized in
Figure 2. The study focuses on hub-to-hub transport
in Germany using automated trucks of level 4 or higher
according to the classification of SAE International
[77].
3.1. Development of projections
The projection phase included three processes consisting
of the development, formulation and evaluation of
the projections. For the development of projections,
we first identified the current trends, drivers and
developments in the logistics industry, digitalization
and society by conducting a broad literature research.
For this literature research, scientific publications,
current draft legislation, governmental regulations,
current future studies, company and association reports
as well as press releases were consulted. Further, we
used a variety of data bases including Web of Science,
Scopus and Google Scholar. The search string
initially included the keywords trends in logistic,
trends in digitalization and trends in society. Based
on the results the following key words were chosen
more precisely on the identified topics and included
digitalization in logistics,globalization,competition
in logistics,business models in logistics,sustainability,
individualization,alternative drives,freight transport,
infrastructure,society, and criminality. The identified
trends were categorized into suitable thematic areas and
initially 45 future theses were developed.
Furthermore, we conducted five explorative
workshops with experts from logistics and the
commercial vehicle industry. Their areas of expertise
specialists, as well as a shortage of truck drivers [37, 81,
99]. By 2030, the logistics work environment will have
changed as a result of digitalization and automation [7,
37]. As a result of the use of automated trucks and new
driving methods such as platooning, it will be possible
for vehicle drivers to carry out new tasks [7, 61, 83].
It may be possible to account for the time when the
driver is not driver (passive journey part) differently
to periods of active driving. This would result in an
enormous increase in efficiency for logistics companies
[83].
Furthermore, crime represents a major risk for the
logistics industry [37, 41]. In addition to cargo theft,
cyber attacks on IT infrastructure in particular are
increasingly occurring [41]. As a result, cyber security
is becoming an important part of the service portfolio
for logistics companies [11, 97]. In addition, extreme
weather events are cited as a growing disruptive factor
for closely timed supply chains [41]. Trade conflicts,
rising tariffs, sanctions, and political instability are
also disrupting established supply chains worldwide,
so that supply chain volatility must always be taken
into consideration [8, 37, 41].
2.8. Summary
The trends in logistics show that hub-to-hub traffic
will be a suitable application area for automated trucks
in 2030. Important drivers such as digitalization and
sustainability indicate a clear direction of development
for the logistics industry, whereas other factors such as
the future role of CT, for instance, remain questionable
based on the literature. As automated trucks will have
to be integrated in the future logistics processes of
2030, clarification is required as to which of the trends
will prevail, what state they will be in by 2030 and
whether these trends will have an influence on the
direct logistics environment of the automated trucks.
3. METHODOLOGY AND
RESEARCH DESIGN
Since there is often insufficient quantitative data
available for futures studies, experts have to be
consulted. As a powerful research technique, the
Figure 2: Study design adapted from Gracht & Darkow [22] and Fritschy & Spinler [15]
6
participants of the targeted time period [80]. Finally,
the projections were evaluated regarding consistency,
ambiguity and content validity by three experts from
the fields of psychology, foresight in the commercial
vehicle industry, and academic research in logistics.
Based on their feedback, the projections were adjusted
and, thus, their suitability for the research purpose
was ensured. For better structure and understanding
for the reader, the 19 projections were grouped into
four thematic areas: Society, Environment and Freight
Transport (SEF), Infrastructure and Alternative Drives
(IAD), Digitalization in Logistics (DIL), and Business
Models and Competition (BMC). The projections as
well as their underlying sources are presented in Table
1. Please note, that the projections were originally
formulated in German for this study and were only
translated for use in this paper.
concentrated on innovation research, product strategy,
logistic processes and telematics services. Each
workshop consisted of a free brainstorming on the
driving trends in logistics by the experts followed by
an evaluative assessment of the so far identified trend
areas and future theses from the literature review. This
two way approach via literature and expert knowledge
allowed us to ensure that no relevant trend area was
overlooked for our specific research question [29].
To filter the core theses, redundancies were removed
and the initial 45 future theses were reduced to the final
19 projections by the experts’ prioritization of trends
and relevance for the research topic. In a further step,
these projections were formulated with concrete and
precise wording. Irrelevant information and conditional
statements were eliminated and an appropriate question
length was ensured [20, 29, 51, 74]. Each projection
was introduced with “In 2030 ...” to always remind
Table 1: Formulated projections with included sources from literature and expert workshops.
No. Projections for 2030 Source
Society, Environment and Freight Transport (SEF)
1In 2030, automated trucks will partially redistribute tasks along the logistics process.
The shortage of qualified personnel persists as an unsolved problem. [7, 37, 61, 69, 81, 99]; Workshop
2
In 2030, driving in an automated truck will be divided into active manual and passive
automated driving time. Passive automated driving will count differently towards the
prescribed driving time.
[83]; Workshop
3
In 2030, social and environmental sustainability will become increasingly important
for logistics players e.g. as manifested by improved working conditions and climate
neutrality.
[14, 18, 25, 37, 69, 76, 99, 103];
Workshop
4
In 2030, combined transport consisting of land transport and other modes of transport
(e.g. rail or water) will still be held back by factors such as a lack of infrastructure for
reloading goods. Freight traffic will continue to be concentrated on the roads.
[6, 26, 45, 59, 92, 103]; Workshop
Infrastructure and Alternative Drives (IAD)
5In 2030, automated trucks will mainly be used for transportation between logistics
centers connected by major roads or highways. [42, 61, 62, 72, 79]; Workshop
6In 2030, the condition of the transport infrastructure between logistics centers will
largely be unchanged from today. [82]; Workshop
7In 2030, it will be possible to use electric drive systems based on battery and fuel
cell technology for long-distance transportation. [7, 21, 25, 46, 67, 99]; Workshop
8In 2030, an adequate charging infrastructure with sufficient charging stations will
enable the expansion of electrified transport between logistics centers. [7]; Workshop
Digitalization in Logistics (DIL)
9
In 2030, a large part of the logistics chain will be digitalized. This will enable cross-
modal and cross-company connectivity between all players in the supply chain
(customer, service provider, supplier, etc.).
[7, 37, 45, 81, 93, 102]; Workshop
10
In 2030, transport and environmental data will be recorded automatically and
transmitted in real time along the entire supply chain. This will enable the seamless
tracking of goods.
[7, 99, 102, 103]; Workshop
11
In 2030, coordination of trucks at logistic centres will be automated and digital. The
loading or unloading position, as well as the route to it will be transmitted digitally
to the truck.
[55]; Workshop
7
Automated Trucks and the Future of Logistics – A Delphi-Based Scenario Study
with information on the research topic, study design,
timing, and the invitation to participate. 30 experts
agreed to participate and completed the questionnaire
of the first round of the Delphi study. In round two 27
out of the original 30 experts took part. This dropout
rate is below average [60] and speaks for the quality
of the survey design and the experts’ interest in the
research topic. The 27 final participants of both Delphi
rounds represent different stakeholder groups and
industries. The panel composition is shown in Figure
3. Half of the expert panel has more than 15 years of
professional experience in their industry (M= 15.41,
SD = 9.68). On average, each of the experts holds three
to four competency areas amongst the named subject
profiles (M= 3.56, SD = 1.87). Thus, an appropriate
heterogeneity across the involved stakeholder groups
and knowledge areas was ensured. Furthermore, the
average overall expertise is rated as rather high (Likert
Scale 1-5; M= 3.96, SD = 0.80). None of the experts
rated their expertise as low (worse than 3), and therefore
no one had to be excluded from the results analysis due
to a lack of expertise.
12
In 2030, delivery and export process in logistics centers will be automated. This
includes the automated loading and unloading of trucks as well as the automated
identification and allocation of goods.
[61, 72]; Workshop
13 In 2030, freight and customs documents will be widely digitalized and will no longer
need to be carried in paper form. [7, 8, 26, 33, 59]; Workshop
Business Models and Competition (BMC)
14
In 2030, digital players such as Amazon or Google will increasingly be competing in
the logistics field by also offering transport and logistics services and, where applicable,
owning their own truck fleets.
[7, 61, 81, 84, 85]; Workshop
15
In 2030, the transport of goods between logistics centers will be carried out by a few
logistics service providers with very large truck fleets, rather than by a large number
of small logistics service providers with very small fleets.
[79, 93, 102]; Workshop
16 In 2030, OEMs are also increasingly offering transportation services. OEMs thus
own fleets of automated trucks and offer transport as a service. [43, 49, 70]; Workshop
17
In 2030, OEMs have evolved into hardware and software service providers. In addition
to the automated truck, they also offer other telematics systems by means of their own
platforms and interfaces.
[49, 79]; Workshop
18
In 2030, digital freight forwarders will play a significant role in logistics. They will
take over the brokerage of transport services and offer further services in planning
and controlling logistics processes.
[70, 81, 84, 85, 93, 102]; Workshop
19
In 2030, sharing of truck fleets and storage areas will be becoming increasingly
established as a way of avoiding empty runs and vacant storage areas to the greatest
extent possible.
[14, 79, 84, 96]; Workshop
3.2. Selection of Experts
Correct selection of experts is of great importance for
the validity and reliability and thus for the success of
a Delphi study [30, 48, 87]. Principles for the selection
of experts include heterogeneity, an appropriate
level of knowledge and the size of the group [74].
Therefore, following Schuckmann et al. [82], potential
representatives of all relevant stakeholder groups were
identified for this study: Academic research, logistics
players, politics and associations, consulting companies
active in the field of logistics and transport, automotive
industry with representatives of commercial vehicle
manufacturers and tier 1 suppliers, and digital logistics
services. The recommended panel size of a group of
20 – 30 experts [64] was aimed for. Following the
approach of Fritschy and Spinler [20] and Kluge et al.
[39], the qualification of the experts was measured on
the basis of their affiliation to the respective industry,
their position, professional experience in years, and
their self-assessment of their own expertise.
In total, we considered 63 experts for this study.
They were contacted via email or LinkedIn message
Figure 3: Characteristics of selected experts (N=27)
8
3.3.2. Delphi Round 2
The second survey took place in August 2021.
Participants were given a period of three weeks to
respond. In total, 27 of the 30 participants from the
first round completed the questionnaire.
The layout of the second round questionnaire
included the summarized feedback as well as the
experts’ own individual answers from round one.
Therefore, an individual questionnaire was created for
each expert. In addition to the histogram, exemplary
arguments of the other experts for a high or low
probability assessment per projection were compiled
from the codes derived in the coding phase. Based
on the feedback given, the experts again assessed the
same projections from round 1 in terms of probability.
Desirability was not reassessed, as it is assumed that
this factor would not change over this short time
period [28]. However, impact was reassessed because
this factor may change given the feedback presented
[20]. No new arguments or comments were requested.
Finally, the self-assessment on overall expertise was
recorded again to check whether the expert had revised
it during the course of the study.
3.4. Development of future Scenarios
For clustering, the average expected probability of
occurrence and the average impact for all 19 projections
were plotted. Clustering along these two dimensions is
a useful and common method for deriving appropriate
measures and strategies [29]. To identify structure
and similarities in the data, the hierarchical complete
linkage method with the proximity measure of
Euclidean distance was applied. Ordinal categories can
be evaluated using this method and it is suitable when a
high homogeneity of the clusters is a key requirement
[88]. The clusters formed and the previously categorized
comments of the experts were used to identify patterns
in the qualitative data and to formulate plausible
scenarios and conclusions for 2030 [29]. Scenarios
comprise a qualitative, verbal representation of a future
situation including the environment and risks [90] and
should be concrete and comprehensible.
After the scenarios had been formulated, they were
validated by two independent experts with regard
to certain quality criteria. These were consistency,
plausibility and relevance [1, 10, 98] and can be
explained as follows: A consistent scenario does
not have any internal contradictions and is therefore
comprehensible. Plausibility refers to scenarios that
are generally credible and capable of happening. The
relevance of a scenario refers to its utility to contribute
to specific insights regarding the research question.
To present the results clearly, the scenarios were
named individually [80]. In addition to the expert
comments, the qualitative description of the scenarios
is also based on supporting evidence from the literature.
If this is the case, it is explicitly indicated by the source
citation.
3.3. Execution of the Delphi study
The Delphi study took place as an online survey using
a two-step process, as in numerous prior Delphi studies
[see 20, 39, 52]. This is based on the fact that more than
two rounds of interviews do little to improve the quality
of the outcome and that most adjustments occur after
the first iteration [91, 100].
3.3.1. Delphi Round 1
The first survey took place in July 2021. Participants
had a period of three weeks to complete the survey.
All 30 participants completed the questionnaire in full.
The whole questionnaire can be found in the Appendix.
The first part of the questionnaire contained an
initial explanation of data protection and the research
objective, definitions of impor tant constructs,
instructions as well as an example question. In the
main part, the 19 projections were to be evaluated.
The experts rated the projections on an ordinal five-
point Likert scale (1 to 5) in respect of the following
criteria: probability of occurrence (P) by 2030 (unlikely
to likely), impact (I) on the logistics environment of
automated trucks if the projection were to occur in
this way (weak to strong), and desirability (D) of the
formulated projection (undesirable to desirable). For
a balance between quantitative and qualitative data,
experts were asked to give reasons for their probability
assessments. Comments on effects and desirability
were not requested, as this would have significantly
lengthened the questionnaire [20]. After each thematic
cluster, the experts gave a self-assessment of their
expertise in the given subject area. In the third section of
the questionnaire, sociodemographic data on position,
industry, work experience, and a self-assessment on
overall expertise were collected [39]. In each case,
the self-assessment was given on a five-point Likert
scale from low (1) to high (5). To ensure the quality of
the questionnaire [24], a pretest was conducted with
four persons who were not participating experts in
the study. In order to guarantee anonymity as a core
element of the Delphi study [60], the experts received
a randomized access key to the survey.
The qualitative and quantitative results of the first
round were analyzed and prepared as feedback for the
second Delphi round. The distribution of the aggregated
results was presented in the form of histograms and the
median additionally marked. The coding method by
Corbin and Strauss [12] was applied for the analysis
of the comments and comments were divided into the
three categories for a high, low, or neutral probability
rating. Subsequently, those arguments for a neutral
assessment were assigned to the high or low level
depending on their salience and tendency, resulting in
two groups for comments. Comments were categorized
into codes within the groups to identify core factors and
important arguments mentioned multiple times for a
high or low probability rating [29].
9
Automated Trucks and the Future of Logistics – A Delphi-Based Scenario Study
operations (projection 3) and real-time tracking
in the supply chain (projection 10). Looking at the
various topics, the projections relating to progress in
digitalization in logistics (projections 9 to 13), as well
as developments in society, the environment and freight
transport (projections 1 to 4), are the most expected and
desired by the experts. Their impact on the logistics
environment of automated trucks is also estimated to
be the strongest in total. Projections of the topic area
of business models and competition (projections 14 to
19), on the other hand, are the least desired.
4.2. Clustering
Based on the dimensions of probability and impact, the
projections were clustered using the complete linkage
method. Based on the recommended number of clusters
� √
[4, 34], in total four clusters were identified
differing in size. The clusters and their associated
projections are visualized in Fig. 4. The first cluster
contains projections 3, 10, and 11, whose occurrence is
rated as most likely (M= 4.58). At the same time, the
occurrence of the ten projections in cluster 2 is also
rated as rather likely (M= 4.06). Cluster 3 contains
the two projections about which the experts could not
reach a consensus (projections 8 and 12). It contains the
projections whose occurrence is disputed or uncertain,
but still possible (M= 3.48). The last cluster is based
on projection 16 with the TaaS business model. The
occurrence of this projection is not yet foreseeable at
the current level by the experts (M= 2.56) and would
therefore be a surprising scenario if it occurred by 2030.
4. RESULTS OF THE DELPHI STUDY
4.1. Quantitative Analysis
The descriptive statistics of the analysis are shown in
Table 2. The inter quartile ratio (IQR) is a commonly
used method for measuring consensus in Delphi
studies [27]. An IQR ≤ 1 is considered an appropriate
consensus indicator for 4- or 5-unit scales [71]. The
results of the IQR show that in the first round 15 of
the 19 projections (79%) reached consensus among the
experts regarding their probability assessment. After
the second round, 17 of the 19 projections reached
consensus (89%). Projection 8 and projection 12 differ
within the experts‘ opinions (IQR = 1.50). The change
in the standard deviation between the first and second
Delphi round illustrates the increasing convergence
of the experts [80]. A total of 95 out of 513 possible
changes in the probability ratings were made by the
experts in the second round, of which 61 (64%) were
revised upward and 34 (36%) were revised downward.
On average, each of the 27 experts changed the rating
for 3.5 projections in the second round compared to the
responses in the first survey (SD = 2.40).
The most probable topics for 2030 are sustainability
(projection 3), real-time tracking (projection 10) and
automated coordination of trucks at the hubs (projection
11). The experts consider the relevance of digital players
in the market (projection 14) and the use of the digital
waybill (projection 13) to be more likely. Projection 16
(TaaS) is rated least probable.
The impact on the logistics environment of automated
trucks is estimated to be strongest for sustainable
Table 2: Overview of quantitative results.
P
Round 1 (N= 30)
P
Round 2 (N= 27)
Nr. Projection IQR Mdn M SD IQR Mdn M SD SD
change IaDb
SEF
1 Lack of staff 1.00 4.0 4.20 0.65 0.00 4.0 4.22 0.42 -36.35% 3.81 3.52
2 Driving time regulation 1.75 4.0 3.43 1.26 1.00 4.0 3.56 0.92 -27.08% 3.63 4.11
3 Sustainability 1.00 5.0 4.57 0.72 0.00 5.0 4.81 0.39 -45.72% 4.33 3.59
4 Combined transport 0.00 4.0 3.87 0.92 0.00 4.0 3.89 0.83 -9.75% 3.33 3.59
IAD
5 Hub-to-hub transport 0.75 4.0 3.97 0.95 0.00 4.0 3.74 0.97 1.87% 4.11 4.19
6 Condition of infrastructure 1.00 4.0 3.57 1.05 0.00 4.0 3.81 0.82 -22.42% 3.52 2.93
7 Alternative drives 1.00 4.0 4.23 0.80 1.00 4.0 4.22 0.74 -8.27% 3.67 3.74
8 Charging infrastructure 2.00 4.0 3.47 1.23 1.50 4.0 3.56 1.13 -7.96% 3.70 3.89
DIL
9 Digitalized logistic chain 1.00 4.0 4.23 0.84 1.00 4.0 4.04 0.88 4.41% 4.15 4.78
10 Real-time tracking 1.00 4.5 4.37 0.75 1.00 5.0 4.56 0.68 -8.92% 4.19 4.74
11 Terminal coordination 1.00 5.0 4.30 0.97 1.00 5.0 4.37 0.87 -10.73% 4.11 4.70
12 Loading and unloading 2.00 3.0 3.23 1.23 1.50 3.0 3.11 0.87 -28.85% 3.67 4.37
13 eCMR 1.00 4.0 4.20 0.87 1.00 4.0 4.26 0.70 -19.84% 3.74 4.70
10
one of the two projections for which no consensus was
reached, and thus is controversial. The 530 comments
obtained in this study were used to formulate the four
scenarios and conclusions that will be presented in the
following section.
5. DISCUSSION
Guided by the research question of what the direct
logistics environment of automated trucks could look
like in 2030, scenarios based on the results of the
Delphi study are described narratively below. Please
4.3. Qualitative Analysis
In this Delphi study, the experts were asked to give
reasons for their probability assessments. In total, the
experts gave 530 out of 570 possible comments. The
largest number of comments for a high probability was
given for projection 1 (staff shortage) with 28 comments
and for projection 9 (digitalized supply chain) and
10 (real-time tracking) with 26 arguments each.
This emphasizes the potential for discussion and the
relevance of these projections. The largest number of
comments for low probability was given for projection
12 (loading and unloading) and projection 17 (TaaS)
with 17 and 16 comments each. Projection 12 is also
BMC
14 Digital players 1.00 4.5 4.20 1.01 1.00 5.0 4.30 1.05 3.39% 3.74 2.81
15 Consolidation 1.00 4.0 3.53 0.99 1.00 4.0 3.59 0.87 -12.04% 3.70 3.15
16 TaaS 2.00 3.0 2.87 1.31 1.00 3.0 2.56 1.03 -21.33% 3.00 2.89
17 SaaS 1.00 4.0 4.17 0.93 1.00 4.0 4.22 0.87 -6.32% 3.93 4.15
18 Digital freight forwarder 1.00 4.0 4.03 0.95 0.00 4.0 3.93 0.90 -5.11% 3.67 3.78
19 Sharing concepts 1.00 4.0 3.50 1.02 1.00 4.0 3.56 0.92 -10.58% 3.52 3.85
Note. IQR = Interquartile range. Mdn = Median. M = Mean. SD = Standard deviation. P = Probability of occurrence. I = Impact.
D = Desirability. SEF = Society, environment, and freight transport. IAD = Infrast ructure & alter native drives. DIL = Digitalisation
in logistics. BMC = Business models & competition. IQR values in italics indicate that no consensus was reached.
Note. Numbers correspond to the respective projection; dot = consen sus reached; square = no consensus reached .
Cluster 1: Sustainable logistics with widespread digital, automated track ing and coordination; Cluster 2: Progress in electrification
and digitalization more significant than in automation and infrastructure; key players in logistics challenged; Cluster 3: Unexploited
potentials in electric charging, efficient use of truck s and automated (un)loading; Cluster 4: No new business models on automated
trucks yet.
3
10
11
1
4
5
67
9
13
14
17
18
2
15
12 8
19
16
2.5
3
3.5
4
4.5
5
2 2.5 3 3.5 4 4.5
5
Impact
Expected Probability of Occurrence
1. Erwartetes Szenario
2. Wahrscheinliches Szenario
3. Mögliches Szenario
4. Überraschendes Szenario
Cluster 1
Cluster 2
Cluster 3
Cluster 4
Figure 4: Scatter plot and clusters of future projections
11
Automated Trucks and the Future of Logistics – A Delphi-Based Scenario Study
anchoring of sustainability in the strategic corporate
orientation.
Next to the growing importance of sustainability,
in this scenario the experts expect substantial
progress in digitalization and automation until 2030.
However, there will be differences between the various
actors along the supply chain regarding the level of
development. More precisely, widespread real-time data
tracking (projection 10) is expected to occur by 2030,
with the reasoning of current innovation thrusts and
current trends in tracking and tracing and the Internet
of Things. These innovations listed by the experts are
also widely cited in the literature [93, 99, 102, 103].
More specifically, the experts of this Delphi study state
that solutions via transponders, GPS, and RFID, the
higher availability of environmental data as well as the
decreasing hardware and communication costs by 2030
will be enablers of this widespread tracking of trucks
and goods. This development is seen as an inevitable
and logical next step and has also been classified as
probable in the Delphi study by Gracht and Darkow
[29]. Nevertheless, the experts of this study do not
foresee automated recording and seamless tracking
for all means of transport and goods by 2030. Because
there are many actors involved in the supply chain,
some will still be operating non-digitalized in 2030
and further, permanent tracking is not considered to
be useful or necessary for all kinds of goods. In this
scenario the situation is similar for the digitalized
and automated coordination of trucks on site. The
experts expect solutions for this kind of coordination
to function efficiently by 2030 (projection 11), because
current progress already shows the development into
this direction as automated real-time route planning
is already possible albeit with restrictions. However,
the experts assume that more extensive digitalization
will first take place in larger logistics centers with a
high degree of standardization and comparatively
simple processes. Implementation will be slower for
small depots because the automated coordination of
trucks requires specific infrastructure. Although digital
coordination is necessary for automated trucks, it can
be assumed that in 2030 such vehicles will not yet be
widespread, because not every hub will be able to offer
these digital services.
5.2. Cluster 2: Progress in electrification and
digitalization more significant than in
automation and infrastructure; key players
in logistics challenged
Cluster 2 consists of ten projections (1, 4 – 7, 9, 13,
14, 17, 18) that are rated as rather likely with an
expected probability of occurrence between M=
3.74 and M= 4.30. The projections are therefore very
valuable from a strategic perspective. Furthermore,
the impacts of the projections are assessed as relevant
(M≥ 3.33). Consensus was reached for all of the
included projections. In this scenario, small fleets
of automated trucks are initially used in hub-to-hub
note that the scenarios differ in size due to the selected
clustering method. For a better overview, each scenario
is named accordingly, briefly summarized, followed
by a detailed description. The resulting probabilities
of occurrence of the original projections are justified
by the comments of the experts interviewed and thus
underpin differently expected developments by 2030. If
not explicitly indicated by a source, the argumentation
is based on the comments of the experts of this study.
5.1. Cluster 1: Sustainable logistics with
widespread digital, automated tracking and
coordination
Cluster 1 includes the three projections (3, 10, 11)
rated most probable for 2030. Consensus was reached
for all of the included projections. Moreover, the
range of average impact of the projections (M= 4.11 –
4.33) suggests that the events will have a particularly
relevant and consequential impact on the logistics
environment of automated trucks. It describes
increasingly sustainable and digitalized logistics and
can be summarized as follows: By 2030, sustainability
has become an increasing differentiator for logistics
service providers and companies that do not think
sustainable are disappearing from the markets. In
this scenario, there is substantial progress in terms of
digitalization, like the widespread real-time tracking
of trucks and goods as well as automated and digital
coordination of trucks on hub premises. However,
by 2030 this progress is not yet seamless and partly
limited to large logistic centers and players with a high
degree of standardization. Below, these conclusions are
described and justified in more detail.
For this scenario, the experts agree that both social
and environmental sustainability will continue to gain
in importance until 2030 (projection 3) as it is already
an enormous driver in logistics today. Concerning
environmental sustainability, by 2030 the political
pressure on logistics companies will be increased
due to emission standards, stricter regulations and
CO2taxation. Social acceptance and the demand for
sustainable transport services as well as competitive
pressure are further reasons for the development
towards sustainability and climate neutrality. The
transport industry will therefore meet its responsibilities
in the area of sustainable use of resources by 2030.
Facing the impeding shortage of skilled workers,
logistics companies will be taking actions in respect
of social sustainability. As a consequence of the trend
toward greater sustainability, costs are likely to rise for
companies by 2030. However, companies that do not
adopt a sustainable approach will be disappearing from
the market. The associated projection 3 is therefore
rated as the most consequential for the environment
of automated trucks. These results are in line with the
conclusions of Ruess and Litauer [76], who forecast cost
and competitive disadvantages as a result of insufficient
12
such as intersections or highway entrances. Further
requirements for the traffic infrastructure may arise, for
example, control towers on highways, troubleshooting
centers or dynamic obstacle warnings. Consequently,
these aspects will not be completely implemented by
2030. As the government will pay all the associated
costs logistics companies themselves would have to
drive forward and pay for upgrades to the infrastructure
for faster changes [82].
Although automation will reduce the shortage of
drivers, there will still be a high demand and shortage
of qualified truck drivers in 2030 due to the relatively
low proportion of automated transport. As a result
of demographic change, the steady decline in the
availability of labor will continue for rather unattractive
jobs such as truck drivers but also for logistics experts
and IT specialists (projection 1). In addition, the use
of automated trucks will change logistics in terms of
personnel and the distribution of tasks. In the experts’
opinion, the tasks previously performed by the driver
such as departure checks or documentation might
be replaced by digital solutions where the trucks are
driverless. The digitalization of vehicles must therefore
be accompanied by the digitalization of the entire
logistics process [7, 37]. As a result, new tasks and
new required competencies related to the emerging
digitalized and automated processes are expected in
logistics by 2030. Therefore, the experts expect an
increased shortage of skilled workers in this scenario,
which is in line with literature [37, 81].
In this scenario, the electrification of trucks will
also be further advanced by 2030. The majority of
experts considers the use of electric drive systems in
long-haul transport (projection 7) to be rather likely
or likely. OEMs indicate that electric trucks for long-
distance transport will be produced and sold in the
next few years. According to the experts, around 30
to 50% of all trucks on the road by 2030 could have
electric drives, as a result of political pressure on
climate targets, the development of battery technology
and price degression. However, experts’ opinions
are divided regarding the use of battery and fuel
cell technology in long-distance transport as these
represent two different drive systems with different
future application profiles. Fuel cell electric vehicles
tend to be used for long-distance transport because they
differ only slightly from conventional trucks in terms
of operating procedures. However, the development
of this currently energy-intensive technology depends
on the production of the underlying material. It is
therefore doubtful that this type of drive will be
widely introduced by 2030. Battery electric vehicles
will tend to be used for short distances, unless there is
a rapid breakthrough in research on lightweight energy
storage. Overall, the number of electrified trucks will
grow slowly and the impact will initially be small. The
discussion about the drive technology of the future
among the experts underscores the uncertainty that is
also apparent from discussions in the literature [58, 59].
transport by 2030. Partly due to the infrastructural
situation being largely unchanged from today the
proportion of automated trucks by 2030 is still low. As
a result, there is still a high demand for drivers and a
shortage of qualified personnel. Next to automation,
the electrification of trucks has proceeded as well
and about 30% of the trucks drive on electric drives.
Small changes in infrastructure have concentrated
on providing the required charging infrastructure.
With the perceived increase of sustainability due to
electrification, the transportation of goods still focuses
on the road in 2030. Another area with a lot of progress
in this scenario is digitalization: Freight and customs
documents are digitalized and largely established in
Europe and large parts of the supply chain are digitally
connected by 2030. The market for telematics systems
however continues to be fragmented and leaves a
need for standardization. Due to the progress in
digitalization, digital players like Amazon have become
growing competitors in logistics and digital freight
forwards continue to gain importance. The detailed
reasoning for these conclusions are presented below.
The likelihood for the necessary technology for
automated trucks being available by 2030 has increased
significantly in recent years, as stated by the experts.
In addition, legislation in Germany has recently paved
the way for autonomous vehicles. However, as the
experience with the use of automated trucks will still
be limited in 2030, automated driving will initially be
limited to specific application areas in this scenario.
According to the experts, highways and thus long-
distance traffic between logistics centers are best
suited for the use of automated trucks for numerous
reasons: influences on the system, complexity of the
traffic area and, thus, safety concerns are lower than
in cities and municipalities. Therefore, deployment
on federal highways and freeways is the easiest and
quickest to implement [42] and has the highest benefits
for logistics companies carrying the largest transport
volumes. Accordingly, the majority of experts considers
it to be more likely that automated trucks will be
mainly used between logistics centers connected by
major roads in 2030 (projection 5). In addition to that,
some experts also see great potential for automation
in company and terminal premises as well as clearly
defined short distances between logistics centers.
However, the market penetration of automated trucks
will initially be slow and many things will still occur in
a conventional manner in 2030 according to the experts
and literature [53]. One reason for the low proportion
of automated trucks by 2030 is that according to
the experts the infrastructure will remain largely
unchanged from its current state until 2030 (projection
6) due to the investment and repair backlog as well
as long approval and construction times. It remains to
be determined which components and systems will be
required for the infrastructure of automated transport.
Adaptations may be necessary at complex locations
13
Automated Trucks and the Future of Logistics – A Delphi-Based Scenario Study
corresponding platforms will be a central element of
future logistics processes. In addition, data availability,
ideally across the entire logistics chain, is a decisive
factor in increasing efficiency. However, they name
standardized interfaces in transport management and
telematics systems as the prerequisites for it. Although
the supply chain is already partially digitalized and
telematics systems are established on the market, a
connection between all actors and a changeover of all
manual to digital standards is still seen as unlikely by
2030 by some experts. With more than 50 telematics
system providers in Europe alone, the market is very
fragmented and thus transporters may be required to
use different platforms in parallel. OEMs also offer
telematics systems, platforms and interfaces in addition
to their main product the truck (e.g. RIO for MAN
Truck & Bus; FleetBoard for Daimler Trucks). From the
experts’ point of view, this development will continue
and OEMs will increasingly become integrated system
providers by 2030 (projection 17). Therefore, the
problem of insufficient interface integration in transport
management systems will remain as long as OEMs
offer their own platforms and telematics, which do not
allow sufficient networking. However, it is questionable
whether OEMs will be able to maintain these offerings
in the highly fragmented telematics market or whether
third-party solutions will dominate the market in 2030.
In view of the progress in digitalization in this
scenario, the experts are of the opinion that digital
freight forwarders will continue to grow in importance
and take over the intermediation of transport services
(projection 18). The experts see open platforms as an
increasing necessity for SMEs to remain competitive
in 2030. In recent years, there has been a trend toward
4PL and 5PL, a business model without assets but
with expert knowledge and digital tools, which will
continue in the experts’ opinion. This is also in line
with literature [70, 81, 93]. As a complement to the
specialization in transportation services the business
model of the specialized supply chain architect is
developing and will play an important role by 2030
according to the experts. Nevertheless, they share the
opinion that digital freight forwarders will not dominate
the market when compared to the large logistics
companies in 2030. However, global corporations
will become growing competitors in logistics by 2030
(projection 14). The experts see a great advantage
of Tech Groups in the optimal control of logistics
processes as well as through the use of collected
logistics and customer data. This advantage is also
emphasized in the literature [81, 84, 85]. The experts
cite the recent investments by Amazon in Rivian
(electric cars) and by Google in Waymo (automation
technology) further illustrating their intention to offer
autonomous logistics and transport capacities in the
future. Nevertheless, some experts consider an in-depth
participation in logistics to be unlikely and are of the
opinion that they will only offer services in the area of
In view of the electrification of trucks, the focus of the
above mentioned few changes in infrastructure will be
on providing truck charging and hydrogen refueling
stations rather than on modernizing the infrastructure
for automation or CT, according to the experts.
Facing the increasing importance of sustainability
(cluster 1), electrified trucks are seen as a way to
achieve the climate goals, which will play a significant
role in slowing down the expansion of CT in this
scenario. Although CT is currently driven by political
considerations in view of climate targets and growing
transport volumes, the experts think that CT will
continue to grow only slowly until 2030 and freight
transport will still be concentrated on the roads
(projection 4). According to the expert panel, freight
transport by road continues to offer the greatest
reliability, speed and flexibility in both urban and rural
areas, and will be even more attractive as a result of
automation. The fact that only a certain proportion
of road freight transport is suitable for an economic
transfer to rail supports this prediction and is in line
with the literature [26, 103]. However, the experts are
aware of the possibility that there will be a shift towards
rail if the climate-friendly use of vehicles cannot be
realized.
As already mentioned above, the automation and
digitalization of vehicles must be accompanied by the
digitalization of the entire logistics process. Therefore,
this scenario considers a significant progress in
digitalization: The experts expect freight and customs
documents to be largely digitalized in Europe by 2030
(projection 13). As a reason, they cite that current pilot
concepts and initiatives by logistics providers point
to this development and the desire to profit from the
expected efficiency potential [33]. Logistics service
providers are increasingly researching BC technology
so that the freight and customs documents can be
realized with smart contracts [26]. In this context,
the legal framework plays an important role. The
experts consider Germany’s accession to the eCMR
Additional Protocol a critical step for a Europe-wide
roll-out of the digital consignment note. The next
step is the recognition of the legal security of digital
documents by the legislator. Even though the experts
assume that the digitalization of freight and customs
documents will be widespread in Europe by 2030, they
also acknowledge that this will not be the case for each
end every area, region or country, as already pointed
out in cluster 1. Limiting factors will be deficient
international coordination and a lack of standardization.
Missing standardization is also a reason for the experts
rating the digital networking of the various actors along
the supply chain as just rather probable (projection
9). The experts acknowledge the increasing trend
towards networked systems that is also reported in
the literature [37, 45, 93] and makes cross-modal and
cross-company connectivity between all players in the
supply chain probable for 2030. The experts predict that
optimal logistics control using telematics systems and
14
private depots can serve as a backbone. The skepticism
in this projection coincides with current doubts in the
literature [25]. Since this projection is controversial
among experts and not subject to consensus, it should
be interpreted with caution.
The unexploited potentials continue in terms of
sustainability. Even though sustainability, cost and
competitive pressures will call for an optimization of
logistics processes, the experts consider a strengthening
of the trend toward shared use of transport and storage
space (projection 19) to be rather unrealistic by 2030.
According to the experts the development of sharing
is slowed down by fragmented systems and a lack of
standardization. Further, OEMs are more likely to
work towards providing truck capacity flexibly to their
customers through models like pay per use or short-
term leasing (cluster 4). Large logistics companies will
optimize empty runs and storage areas within their own
volumes.
In terms of automation the experts consider the
differentiation between active manual and passive
automated driving time (projection 2) to be a
prerequisite for selling automated driving as this will
first reduce the costs of logistics services enormously.
The legislator is therefore expected to react to the
corresponding pressure from the logistics industry.
The majority of experts consider an adjustment of
the driving time regulations to be more likely and to
have a greater impact. However, the revision of the
current regulations will be difficult as working time
legislation will play an important role. In the view of
several experts, benefits for the freight forwarder based
on the driving time regulations cannot be expected by
2030, not least because Europe-wide changes will not
be easy and changes will depend on the reliability of
automation.
Further, the assessment of the role of automation in
the (un-)loading of trucks and the identification and
allocation of goods by 2030 (projection 12) differs
among the experts. No consensus was reached for the
corresponding projection. Some experts expect growing
networking due to the already increasing autonomous
intralogistics with loading robots, drones and driverless
transport systems. The rapid development of recording
systems for goods (e.g. trackers, barcodes, QR codes)
also favors this trend. However, a complete automation
of loading and unloading will be difficult to implement
depending on the seasonality of the industry, the
heterogeneity of the goods and load carriers, and the
complex load securing. Many experts are of the opinion
that by 2030 initial implementations will only exist for
large logistics centers that can afford the technology
and have standardized goods and packaging and a high
turnover rate. Unloading robots, especially for non-
standardized goods and special loads, are not yet ready
for use. Consequently, by 2030 automated trucks will
be able to approach the loading zone in an automated
manner, but personnel will still be needed for most
general cargo and courier, express, and parcel services
(CEP), which only form a small part of freight traffic.
5.3. Cluster 3: Unexploited potentials in electric
charging, efficient use of trucks and
automated (un)loading
The scenario derived from cluster 3 includes five
projections (2, 8, 12, 15, 19) of which two (8, 12)
did not reach consensus among the experts. The
expected probability of the projections in this cluster
is rated as neutral to somewhat likely (M= 3.11 –
3.59) and is, thus, more ambiguous. In addition,
this cluster is characterized by its intermediate to
rather higher estimates of impact (≥ 3.52) and rather
higher desirability (≥ 3.85). This suggests that these
projections tend to be viewed by the experts as an
opportunity rather than a threat. In summary, this
scenario describes the following situation: In spite of
the fact that sustainability plays an important role in
2030 (cluster 1), the charging network for electric trucks
is not yet widespread. Charging stations are initially
found on routes between large logistics centers or
privately in depots. Also, the concept of sharing (trucks
and storage space) among large logistics operators for
higher sustainability still remains unrealistic until
2030. Unexploited potentials like these also show in
other areas in this scenario: The adaption of driving
times to the requirements of automated driving and
regulation of passive driving is in progress, but not yet
finalized by 2030. The same goes for automated (un)
loading of trucks and the automated identification of
goods which is not yet widespread in 2030. In general,
the proportion of automated and sustainable trucks in
2030 is low, mainly owned by large companies due to
the necessary large investments. These conclusions
were drawn as follows.
This scenario is characterized by unexploited
potentials in the logistics environment of automated
trucks in 2030. Starting with the charging infrastructure
to enable electrified transport between logistics centers,
almost half of the experts surveyed considers it to be
only rather likely that it will be adequately developed
by 2030 (projection 8). The experts argue that the
energy and space requirements are far too high for
an adequate infrastructure to be realized by 2030.
Due to the charging times, more parking areas will
have to be created. Necessary investments and slow
construction developments are delaying extensive
infrastructural expansion. At the same time, some
experts argue that battery capacities are improving
thanks to the massive upswing in technology. As
already mentioned in cluster 2, the experts assume
that a charging infrastructure network will either be
politically driven or built up through current initiatives
by the OEMs to achieve climate targets. Some of the
experts are of the opinion that this will enable an
adequate charging infrastructure by 2030 especially
in hub-2-hub transport, as charging infrastructure in
15
Automated Trucks and the Future of Logistics – A Delphi-Based Scenario Study
as already expected by Schiller et al. [79]. TaaS is seen
as the model of the future and OEMs will eventually
own their own fleets in the future. However, the extent
to which it will occur cannot yet be estimated by the
experts due to the costs of automated trucks and the
high transport volume.
6. LIMITATIONS
There are some limitations to the results of the
presented study which are discussed in the following.
The composition of the expert panel is to be mentioned
in this context. With 27 participants we reached the
recommended panel size [64] and integrated different
points of views on the research question. However,
59 % of the respondents were from the field of academic
research and the automotive industry. This distribution
may have led to a bias in the results. Furthermore, it
must be noted that some of the stakeholder groups are
rather small. For example, the group of digital services
with two representatives might be underrepresented.
However, it is to emphasize that all identified relevant
areas of expertise were covered by the expert panel
multiple times, so that appropriate heterogeneity
could be ensured. In addition, 89% of the respondents
in the present study did not revise their self-rating of
expertise in the second round, but rated it the same
after seeing the feedback from the first round in the
second questionnaire. This speaks for the stability of
the experts’ assessed expertise.
Furthermore, the second questionnaire provided a
limited number of the categorized and most frequent
arguments for and against the occur rence of a
projection, as it is common practice in Delphi studies
(e.g. [20]). This selection of comments can have an
influence on the experts which is a known limitation
of the Delphi method [91] and has to be taken into
account when interpreting the results.
In addition, it has to be stated, that some of the
formulated projections are of a slightly bigger scope
than others and partially combine multiple statements
(e.g. projection 3, 11, or 15). The formulation of the
projections can have an influence on the outcome
of the Delphi study, which is a general limitation of
Delphi studies [91]. To counteract this limitation, the
projections’ formulation was evaluated regarding
consistency, ambiguity and content validity by three
experts in the projection phase, as described above. If
a projection included several aspects, the expert panel
was able to address these aspects separately in the
comments. This information was used for the creation
of the scenarios. Further, it must be noted that scenarios
do not provide forecasts but are subject to uncertainties.
This is partly due to the fact that the future projections
cannot be viewed in isolation, but are interlinked in
many ways [89].
of the subsequent process steps, such as opening the
container, the swap body or releasing the load restraint.
A possible reason for the unexploited potentials in
this scenario might be that sustainability, automation
and digitalization involve investments that small
fleet operators are unable to meet. Therefore, the
experts expect that logistics service providers will
be confronted with changes in view of the continuing
consolidation process in their industry (projection 15).
However, the experts cite that consolidation will not
have occurred on a large scale by as early as 2030.
Especially in rural areas, a heterogeneous environment
of small logistics service providers with small, manual
truck fleets (less than 10 trucks) is to be expected. Once
automated transportation becomes common practice,
the market will focus on large key players, but this is
not yet to be expected by 2030.
5.4. Cluster 4: No new business models
on automated trucks yet
Cluster 4 consists of only one projection (projection
16) and deals with new business models for OEMs in
regard to automated trucks. The expert panel agrees
that a widespread offering of transportation services
by OEMs by 2030 should be classified as mid-range or
rather unlikely (M= 2.56). This scenario describes a
small amount of pilot projects but no established new
business models in 2030. The reasoning is as follows.
A far-reaching offering of transport services by OEMs
by 2030 can be classified as rather unlikely according to
the experts (projection 16). Therefore, in this scenario
there will only be an early phase of the initiation of
the OEM business model transport as a service by
2030. As already described in the previous scenarios,
only a few automated vehicles are expected to be in
service by 2030 (cluster 2) and the driver might still be
needed to some extent. This is partly due to the fact that
automated trucks will initially be a high investment that
only a limited number of companies will be able to take
(cluster 3) and not every hub will be able to provide the
required infrastructure for the digital and automated
coordination of trucks (cluster 1). In addition to that, the
expected growing shortage of skilled personnel (cluster
2) might complicate the integration of automated trucks
into the existing hub processes. These challenges, as
well as the lack of logistics expertise of OEMs, slow
processes in strategic development and unattractive
low margins in the logistics sector could hinder the
development of this new OEM business model in the
view of the experts. In addition, OEMs would have to
compete with their customers. From the experts’ point
of view, most OEMs will therefore stick to their core
competencies. Nevertheless, the experts assume that
automated trucks will enable new business models in
the field of mobility. OEMs attach great importance to
offering mobility services [43, 79]. Automated trucks
provide the basis for offering cargo space capacity or
business models such as pay per use or pay per mile,
16
processes and load packaging also plays a role for the
implementation of automated loading and unloading of
trucks (cluster 3) which promises great benefits but can
not be expected by 2030. In summary, standardization
is presented as one of the key elements for progress
in the logistics environment of automated trucks.
Therefore, the pursuit of standardization should be
prioritized and focused on in the further development
of the specific areas.
In spite the fact that environmental sustainability
will be a very important factor (cluster 1), the results
of this Delphi study show that there will still be room
for improvement by 2030. The concept of sharing load
and storage space is not expected to grow (cluster 3)
despite its contribution to increased sustainability. This
aspect should be reconsidered for strategic decisions.
Further, the electrification of trucks holds great benefits
in terms of environmental sustainability but will still
be limited in 2030 (cluster 2). The results indicate that
infrastructural changes for better charging options
could enable more electrified transport of goods.
It is therefore recommended to further drive this
development.
Moreover, the scenarios indicate that further
infrastructural changes like additional lanes for
automated trucks or communication between
infrastructure and vehicle could speed up the market
penetration of automated trucks (cluster 2) and increase
the initially low proportion of automated trucks in
use by 2030. Therefore, the role of infrastructural
changes should be further researched and considered
instead of leaving the enablement of automated driving
exclusively to vehicle functions. This also applies
to the infrastructure of logistic centers. It has to be
addressed whether there will be defined prerequisites
for the use of automated trucks that have to be met by
the hub infrastructure or whether other solutions can be
created to enable automated transport in 2030. These
other solutions could lie in human assistance for the
automated truck which could in turn have an influence
on the design of the future cab.
Because of the initial low proportion of automated
trucks in 2030 many transports will still be in a
conventional manner (cluster 2). That means that
there will be mixed transport with automated as well
as manually driven trucks between logistic centers,
which has to be kept in mind for designing the in-
hub processes. Further, automated trucks will change
logistics processes and introduce new tasks and
qualification requirements. At the same time, there will
still be tasks that have to be carried out manually by
2030 such as (un)loading the truck or (dis)connecting
the trailer (cluster 3). Facing the expected shortage
of skilled workers (cluster 2) and the striving for
social sustainability in 2030 (cluster 1), hub operators
should also focus on ensuring that the future jobs and
working conditions in the context of automated trucks
are attractive and ergonomically well designed for the
limited number of workers in 2030. In this context, the
7. IMPLICATIONS AND OUTLOOK
As automated trucks are expected to provide benefits
on an economic, environmental, and social level in the
commercial vehicle industry, OEMs are increasingly
dedicated to developing the necessary technologies [20].
At the same time, the logistics industry is changing,
driven by recent technological, social, and political
developments [99]. In light of these developments, it
is of great importance not only to shape the technical
progress of automation, but also to consider the
future framework conditions automated trucks will
operate in. In this paper, a Delphi-based scenario
study was conducted to investigate the future logistics
environment of automated trucks in Germany in
2030. 19 projections for the year 2030 were developed
and evaluated by 27 selected experts in a two-round
Delphi study. The experts included perspectives from
the automotive industry, logistics representatives,
consultants, academic and political representatives. The
methodological rigor of the Delphi study was ensured in
all steps and consensus could be found for 17 of the 19
projections. Based on complete-linkage clustering, four
narrative scenarios were developed for the year 2030.
By compiling the information on the current trends
in the logistics field and presenting the current state-
of-the-art, this paper provides a useful overview based
on literature and expert knowledge. Further, with the
detailed description of the execution of the Delphi
study and the presentation of the found limitations,
this research can contribute to the guidance for and the
improvement of future Delphi studies. Moreover, with
the four developed scenarios, we provide the research
community with collected, organized and elaborated
expert knowledge from a 360° point of view and
valuable insights on how the current trends in logistics
will most likely emerge until 2030. These results on
the future logistics environment in general provide a
sound basis for strategic decisions in the automotive
and logistics sectors and can serve as a basis to further
investigations.
In detail, the results of this Delphi study show many
unexploited potentials in the environment of automated
trucks in 2030. Especially in the field of digitalization
there are a lot of optimization opportunities: For
example, the beneficial digital connection between
all actors along the supply chain will not be finalized
by 2030 (cluster 1) and the required infrastructure for
the digital coordination of (automated) trucks will
not yet be available to all depots (cluster 1). The lack
of standardization is mentioned as a central limiting
factor throughout the scenarios. Because of missing
standardization integration of the many telematics
systems on the market will not be possible by 2030
(cluster 2) which hinders connectivity along the supply
chain. Missing standardization also plays a role for
the international implementation of digital freight
documents which limits the benefits of this progress to
certain regions and areas in Europe. Standardization of
17
Automated Trucks and the Future of Logistics – A Delphi-Based Scenario Study
within the project RUMBA (19A20007E). The authors
are solely responsible for the content of this publication.
COMPLIANCE WITH ETHICAL STANDARDS:
The authors declare no conflict of interest. The funders
had no role in the design of the study; in the collection,
analyses, or interpretation of data; in the writing of the
manuscript, or in the decision to publish the results. The
experts of this study all gave their informed consent to
participation.
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future task distribution in hub-to-hub transport in 2030.
Currently, truck drivers in hub-to-hub transport take
on tasks that go beyond driving the truck. Examples
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relevant influencing variables in the projection phase,
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study in order to keep the focus on the direct logistics
environment of automated trucks. Subsequent research
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factors such as cybercrime, extreme weather events,
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logistics environment of automated trucks. Moreover,
the presented scenarios focus on the context of heavy
commercial vehicles in German domestic transport.
As transportation of goods often crosses boarders, the
results of this study could serve as a basis to expand the
focus to the European level or to explore other markets
in future research.
ACKNOWLEDGEMENTS:
This research was funded by the German Federal
Ministry for Economic Affairs and Climate Action
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