Exploiting the technological capabilities of autonomous vehicles as assembly items to improve assembly performance

Abstract

The automotive industry is on the brink of transitioning to autonomous vehicles (AVs). This will require highly flexible assembly systems. This paper focuses on exploiting the capabilities of the technology base, e.g., sensors and image recognition, of AVs as assembly items and employing their self-driving function in assembly systems. This fundamentally new approach to matrix manufacturing systems based on autonomously navigating automated guided vehicles (AGVs) and the elimination of set assembly sequences is a growing topic of discussion. This study develops a conceptual framework, based on a systematic literature review and interviews with fifteen experts from three carmakers, for exploring the field of research and assessing the feasibility of employing the technology base of autonomous driving instead of AGVs. This study is intended for assembly planners and researchers of assembly systems in automotive manufacturing.

Full text

Advances in Industrial and Manufacturing Engineering 6 (2023) 100111
Available online 28 December 2022
2666-9129/© 2022 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
Exploiting the technological capabilities of autonomous vehicles as
assembly items to improve assembly performance
Tom Kathmann
a
,
*
, Daniel Reh
a
, Julia C. Arlinghaus
b
a
Assembly Planning, Volkswagen Commercial Vehicles, Mecklenheidestrasse 74, 30419, Hannover, Germany
b
Production Systems and Automation, Otto-von-Guericke University Magdeburg, Universit¨
atsplatz 2, 39104, Magdeburg, Germany
ARTICLE INFO
Keywords:
Autonomous vehicle
Assembly
Production
Self-driving
Automated guided vehicle
Conveyor
ABSTRACT
The automotive industry is on the brink of transitioning to autonomous vehicles (AVs). This will require highly
exible assembly systems. This paper focuses on exploiting the capabilities of the technology base, e.g., sensors
and image recognition, of AVs as assembly items and employing their self-driving function in assembly systems.
This fundamentally new approach to matrix manufacturing systems based on autonomously navigating auto-
mated guided vehicles (AGVs) and the elimination of set assembly sequences is a growing topic of discussion.
This study develops a conceptual framework, based on a systematic literature review and interviews with fteen
experts from three carmakers, for exploring the eld of research and assessing the feasibility of employing the
technology base of autonomous driving instead of AGVs. This study is intended for assembly planners and re-
searchers of assembly systems in automotive manufacturing.
1. Introduction: Autonomous vehicles and exible assembly
systems
Automotive original equipment manufacturers (OEMs) are currently
transitioning from combustion to electric vehicles. Market observers
anticipate another revolution in vehicle control in the next few years.
Autonomous vehicles (AVs) equipped with highly sophisticated tech-
nology, such as sensors and intelligent image recognition, will enable
self-driving. Tigadi et al. (2021) assert that AVs ‘are going to inuence
the near future’ and Maurer et al. (2016) conclude that self-driving
technology is about to reach a level suitable for road use. Germany
amended laws governing AVs in July 2021 (German Government,
2021). Kaltenh¨
auser et al. (2020) predict market share of AVs in Ger-
many will reach 31.5% by 2040. Growing sales are necessitating the
expansion of manufacturing capacities for series AV production in the
near future. The use of AVs’ self-driving function alone, made possible
by additional sensors and processing intelligence, promises economic
benets for assembly by requiring less handling equipment.
Moreover, assembly planning is grappling with the challenge of a
wider variety of products as product personalization grows (Fries, H.-H.
Wiendahl, and Foith-F¨
orster, 2020), placing greater demands on as-
sembly systems’ exibility and production systems’ capability to
manufacture different products or combinations of products (Bellmann,
Himpel, and B¨
ohm, 2010). At the same time, shorter product life cycles
will give rise to more requirements for assembly systems’ adaptability, i.
e., production systems’ capability to switch product families and pro-
duction capacities proactively (Rauch, 2013). Researchers and com-
panies are consequently exploring approaches to handle these
requirements better. Digitalization is paving the way for exible as-
sembly systems (FAS), such as matrix-like assembly systems (MLAS),
that will supersede conventional assembly lines. Autonomously navi-
gating automated guided vehicles (AGVs) will route orders through as-
sembly systems based on the product structure of the specic option
being manufactured (Kern et al., 2015; Fries, H.-H. Wiendahl, and
Foith-F¨
orster, 2020).
Current research essentially concentrates on the development of
technology for AVs (Gamba, 2020; Meng et al., 2017; Wishart et al.,
2020) and the ramications of expanded functionality for on-road
vehicle use (Anderson et al., 2014; A. Herrmann, Brenner, and Stadler,
2018) but rarely explores the requirements for and capabilities of AVs in
series production. Identifying the capabilities for assembly performance
that ensue from using AVs’ self-driving function would be highly rele-
vant, though. AVs could move themselves from workstation to work-
station as assembly items, for instance, replacing conventional conveyor
systems, such as assembly lines, or AGVs as of a certain stage of as-
sembly. Since current assembly systems only give a manufactured
* Corresponding author.
E-mail address: Tom.Kathmann@volkswagen.de (T. Kathmann).
Contents lists available at ScienceDirect
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https://doi.org/10.1016/j.aime.2022.100111
Received 1 September 2022; Received in revised form 28 November 2022; Accepted 23 December 2022

Advances in Industrial and Manufacturing Engineering 6 (2023) 100111
2
vehicle its driving function at the end of assembly, exploiting the driving
function will require changing existing assembly systems to provide the
self-driving function as early as possible.
The number of AGVs in use is growing (Automatica, 2022). Among
other things, they act as enablers for MLAS, which are attracting more
and more attention (Kern, L¨
ammermann, and Thomas Bauernhansl,
2017; Kirmse and B¨
ar, 2020). Boosting assembly performance by
rendering the handling of assembly items more responsive and adapt-
able, MLAS are transforming the structure of assembly systems. Given
the structural changes required, combining the implementation of
self-driving assembly items in assembly with the introduction of MLAS
to cut capital expenditures for AGVs and boost logistics performance
would be a logical step.
This study is intended to contribute to closing the research gap at the
interface of research in the eld of AVs and FAS, such as MLAS. We
therefore pursue the goal of devising a conceptual framework with all
related input variables, such as product or infrastructure requirements,
which inuence the use of AVs’ technological capability during assem-
bly, and resultant elds of activity for assembly planners of OEMs for
their actual implementation. This paper explores the following research
question:
How can the technology base of autonomous driving (AD) be
exploited in MLAS to improve logistics performance in terms of capacity
utilization, cost, exibility and adaptability?
What elds of activity in research and practice ensue from this
approach?
In this study, we combine a systematic literature review (SLR) at the
interface of ‘assembly’ and ‘automated driving/AV’ research with a set
of exploratory interviews with fteen production specialists and vehicle
developers.
The rest of this study is organized as follows. In section 2, we present
the theoretical background of research streams on AVs, exible matrix-
like production systems, PPC and AGVs to identify the research gap. In
section 3, we describe our research design. In section 4, we present the
ndings of our SLR and our interviews with experts, which are compiled
in an initial framework that presents input variables and correlations. In
closing, we discuss the main elds of activity in research and practice as
the basis for further studies.
2. Theoretical background and research gap
Levels 0 to 2 of the Society of Automotive Engineers’ (SAE) six Levels
of Driving Automation require a driver supervising the vehicle (SAE,
2021). The vision of AD has attracted increased attention in recent years.
Vehicle technology has matured enough during this time that newer SAE
Level 3 vehicles can automatically take over driving in certain situa-
tions, such as trafc jams or parking, and the driver only has to intervene
at a feature’s requested. OEMs are currently developing SAE Level 4
vehicles that do not require any driver to intervene under predened
conditions (city limits, weather), whereas SAE Level 5 (full automation
everywhere in all conditions) still remains a vision because of the high
intelligence required to respond to unknown situations (Grush and Niles,
2018; Z. Liu et al., 2020). Production of SAE Level 3 and 4 AVs has
already started or is imminent (see Fig. 1) and market observers expect
the number of manufactured AVs to grow in the coming years (VDA,
2020a; Irle, 2021; Coppola and Eszterg´
ar-Kiss, 2019; Grush and Niles,
2018; SAE, 2021; Martínez-Díaz and Soriguera, 2018).
SAE Level 3 AVs and upward replace human driving with automated
features, essentially detection (sensing), mapping, localization and
navigation (Kocic, Jovicic, and Drndarevic, 2018; Maurer et al., 2016).
Vehicle manufacturers combine additional sensors and control units to
do this. Moreover, elimination of the driver’s cockpit opens new op-
portunities for use by passengers during a drive. A. Herrmann, Brenner,
and Stadler (2018) and Roeckle et al. (2019) consequently expect that
interiors will undergo a change since other activities, such as sleeping,
working and playing, will be possible. Although researchers are working
on the improvement of features (Tigadi et al., 2021; Altintas, 2018),
resultant legal and ethical issues (Maurer et al., 2016; Anderson et al.,
2014) and potential use cases (Peging, Rang, and Broy, 2016; Teleki,
Fritz, and Kreimeyer, 2017) of AVs, they have largely paid little atten-
tion to the ramications and capabilities for manufacturing. OEMs must
install and test AD features and assemble additional or modied com-
ponents, including redundant safety components, such as sensors, con-
trol units and cable connections (Ondruˇ
s et al., 2020). They could,
however, exploit AVs’ AD features during assembly to have them travel
from one workstation to another automatically much like AGVs do.
AGV systems have dominated the conveyance of assembly items in
manufacturing for decades. Unlike simple electromagnetically and line-
guided AGVs, advanced exible AGVs are laser- and GPS-guided, iden-
tify different objects and communicate with each other or a factory
system (Ullrich and Albrecht, 2019). The advantages of AGVs (e.g.,
exible routing, open routes and accessibility to machines and auto-
mated material movement) pay off for more FAS with variable material
movement, such as MLAS (T. Schmidt, 2019).
One approach to demand-responsive assembly systems is MLAS. It
divides an assembly system into individual subsystems (Greschke et al.,
2014) with independent modular assembly stations connected by AGVs,
which convey items between stations (Kern et al., 2015) so that ‘the
assignment of assembly tasks to individual stations is only inuenced by
the products to be assembled and not by a xed conveyor system or a
common cycle time for each workstation.’ (Kern et al., 2015) Since
different product models follow different paths through the system
depending on the steps required, assembly systems rely on intelligent
production planning and control systems (PPC) for short lead times,
on-time delivery, optimal inventory and efcient capacity utilization
(Trierweiler, Foith-F¨
orster, and Thomas Bauernhansl, 2020). PPC ‘refers
to the activities of loading, scheduling, sequencing, monitoring, and
controlling the use of resources and materials during production.
Loading concerns how much to do; scheduling concerns when to do
things; sequencing concerns in what sequence to do things; and
Fig. 1. Vehicle technologies timeline (based on Coppola and Eszterg´
ar-Kiss, 2019; Grush and Niles, 2018; Irle, 2021; Martínez-Díaz and Soriguera, 2018; VDA,
2020a; SAE, 2021).
T. Kathmann et al.

Advances in Industrial and Manufacturing Engineering 6 (2023) 100111
3
monitoring and control is concerned with whether activities are going to
plan, and corrective actions needed to bring activities within plan.’
(Oluyisola et al., 2021) All these tasks become more variable and chal-
lenging in more exible MLAS (Oluyisola et al., 2021), i.e., PPC must
deal with ‘the routing problem to determine for each individual product
an assembly route” (Sawik, 1999). This makes exible production
planning essential. It must be possible to change production plans
automatically at any point in the process (Halevi, 2014).
AGV systems usually consist of the vehicles themselves, a power
supply, routes, charging stations, a communication system and a control
system (Martin, 2014). These components require relatively high capital
expenditures that can hinder or impede the introduction of an MLAS.
The cost for the AGV itself is particularly noteworthy in the automotive
industry due to the high payload required there, which is additionally
increasing due to the growing use of heavy HV batteries.
Hence, assembly planners who usually select the AGV by considering
the workpieces’ attributes could consequently decide to use AVs instead
of AGV systems and would cut most component costs signicantly.
Moreover, a self-driving assembly item would eliminate manufacturers
disadvantageous use of AGVs not only as a means of conveyance but also
as workholders at manual workstation where they stay parked during
assembly, thus reducing their capacity utilization as a means of
conveyance (Martin, 2014). In this context, researchers discuss among
other aspects assembly systems where “robots not only perform the pro-
cessing but also the transferring of the parts” (Michalos, Makris, and
Chryssolouris, 2015) which shows the signicance of using technolog-
ical capabilities since this approach only work with 100% automated
assembly systems.
In current literature, this conceptual approach is rarely explored: a
comprehensive overview on the impacts of AVs as assembly items and
their capabilities to boost assembly performance is lacking. Only few
researchers already envisaged the utilization of “even the automobile
itself” (Krüger et al., 2017) to substitute AGVs in FAS and started to
mention several advantages and challenges for implementing such a
concept (Wenning, Kawollek, and Kampker, 2020). Next to detailing the
potentials of this approach and developing possible assembly structures,
it is of particular interest how car manufacturers can realize automated
driving of AVs as assembly objects from a process and product
perspective. Motivated by these initial considerations, we contribute in
this study to exploiting AVs’ self-driving function in OEMs’ assembly
systems to boost assembly performance. Given the novelty of our study,
we take an exploratory research approach. Exploiting the technology
base of AVs in assembly would entail using an ‘incomplete vehicle’. This
raises the questions of which factors facilitate or hinder the imple-
mentation of this approach and what tasks ensue for assembly planning.
We therefore design a conceptual framework based on an SLR and
semi-structured interviews with experts from OEMs to obtain an over-
view of the research eld and to describe the impact on the logistics
performance qualitatively.
3. Research design
We selected a dual exploratory approach to synthesize the literature
covering relevant research streams related to the topic of this paper and
to gather important input variables and relationships from literature and
practice. First, we conducted a descriptive SLR to identify the conceptual
content of the eld (Meredith, 1993; Seuring and M. Müller, 2008).
Then, we discussed possible input variables, such as infrastructure or
product specications and resulting activities for assembly planning,
with fteen experts from the automotive industry in semi-structured
interviews. We used the ndings from the review and the interviews
to develop a conceptual framework that is intended to serve as a basis for
further study of this topic.
3.1. Systematic literature review: ‘assembly’ and ‘AVs’
First, we formulated the research question and used it to extract
relevant research streams. Then, we developed a literature review pro-
tocol that specied important information, such as the purpose of the
study, research questions, inclusion criteria, search strategies, quality
assessment criteria and screening procedures, as recommended by Y.
Xiao and M. Watson (2019). This enhanced the reliability of the subse-
quent review by reducing the possibility of researcher bias when
selecting and analyzing data (Kitchenham and Charters, 2007). We
followed the four steps of a descriptive SLR proposed by Y. Xiao and M.
Watson (2019).
(1) Literature search: We searched the Web of Science and Scopus
databases for related English or German studies for the SLR. We
derived search terms combining the keywords ‘production” OR
‘assembly” with ‘automated driving” (and synonyms thereof)
derived from the research question with Boolean operators to
identify the research gap by identifying literature from adjacent
research elds. We searched paper titles and/or abstracts for
publications from 2010 onward, roughly concomitant with the
rise of AVs. The initial search yielded 1349 results. We later
enlarged the number of results by conducting backward searches
using the listed references to obtain a complete list of literature.
We also conducted key author searches in the research elds to
ensure that their relevant studies were included (Levy and Ellis,
2006). Moreover, we conducted forward searches to nd studies
that cite the articles reviewed (Webster and R. T. Watson, 2002).
(2) Inclusion screening: After eliminating duplicates, we screened
each of the 1121 articles remaining from the initial list of refer-
ences to decide whether they should be included in data extrac-
tion and further analysis. We followed a two-stage procedure. We
applied predened inclusivity criteria in our initial screening of
abstracts to ensure their applicability to our research. We only
included papers on AGVs’ mechanical/electrical engineering or
logistics in the subsequent full-text review and excluded publi-
cations on software engineering, social sciences, sensor devel-
opment, production and autonomous underwater or aerial
vehicles. This left 186 papers for full-text review, the nal pre-
paratory phase prior to data extraction and synthesis (Y. Xiao and
M. Watson, 2019, 106). We read the remaining studies and
reviewed the inclusivity criteria. This yielded a base of
seventy-seven studies from which data could be extracted for the
heuristic framework. At that point, we conducted the aforemen-
tioned backward and forward searches with the same inclusivity
criteria and added literature recommended by experts in the eld
after cross-checking the completeness (Okoli and Schabram,
2010). We ultimately arrived at a list of 109 publications from
which data could be extracted, analyzed and synthesized. Fig. 2
shows the screening process.
(3) Data extraction: After collecting the relevant sources, we
deductively coded the remaining articles to generate a clearer
view of the research eld’s structure and to describe rst-order
constructs. We entered general information (authors, title, year,
journal) and focus, objective, outlook and impacts related to the
research question in a database.
(4) Data analysis and synthesis: We interpreted coded rst-order
constructs to derive second-order constructs to synthesize data
into a heuristic framework that comprises relevant requirements
and activities ensuing from this research approach. These second-
order constructs served as the basis for the heuristic framework
and the subsequent semi-structured interviews. We then con-
ducted a descriptive analysis of the publications. The annual
distribution of publications presented in Fig. 3 is larger in the
second half of the years analyzed. Publications dated 2018 or
later account for more than 70% of the publications selected.
T. Kathmann et al.

Advances in Industrial and Manufacturing Engineering 6 (2023) 100111
4
Forty-one of the 110 publications selected were scientic articles in
journals, forty-six conference papers, nine book chapters and nine other
sources, such as VDI guidelines or academic theses. The articles in the
forty-one indexed journals were widely distributed. Each of a large
number of journals contributed one publication to the SLR. The journals
Assembly Automation, Journal of Intelligent Manufacturing and Interna-
tional Journal of Robotics Research and Application each contributed two.
Fig. 4 is an overview of the document types selected and the publications
per journal.
3.2. Interviews with experts
We conducted qualitative semi-structured interviews with practi-
tioners involved in assembly planning and vehicle development at three
OEMs in order to expand the scope of this study. Qualitative guided
interviews with experts are a widespread, diversied and comparatively
well-devised method for producing qualitative data (Helfferich, 2019).
In our case, we aim to expand, detail and validate the input variables
obtained from the literature review, such as product or infrastructure
requirements, which inuence the exploitation of AVs’ technological
capabilities during assembly. We followed the three steps advanced by
Helfferich (2009) to select a representative sample of experts. First, we
specied our interest in the interviewees’ expertise (assembly planning
or development of current or future vehicle models or related expertise
in automotive assembly). Second, we selected fteen suitable in-
terviewees for interviews of 30–60 min (see Appendix). Third, once the
interviews had been conducted, we determined which professional
constellations were not indicative of the group of interviewed experts in
order to be able to dene the scope of the survey.
We conducted all interviews in German by videoconference or face-
to-face in 2021 and 2022, anonymizing data to ensure condentiality.
Whenever interviewees consented, we recorded the interviews to sum-
marize them extensively for subsequent analyses.
We followed an interview guide (see Appendix), which was based on
the research questions and the concept in the form of a framework
prelled by the SLR, to obtain specic factual information. It focused on
ascertaining which requirements and moderating or hindering factors
respective interviewees considered relevant to the development of
concepts for the exploitation of the self-driving function in assembly.
Furthermore, we inquired about capabilities related to assembly per-
formance. Then we conducted a qualitative content analysis of the ex-
perts’ recorded responses (Mayring, 2016) in a topic matrix to
categorize the various statements (Kuckartz, 2016). We did this to
obtain an overview of interviewees’ statements so that we could eval-
uate and validate them and correlate the results with ndings from the
literature review in order to create a conceptual framework.
Fig. 2. Systematic literature review.
Fig. 3. Annual distribution of publications (2022 only partially).
Fig. 4. Document types and publications per journal (select journals with one article each).
T. Kathmann et al.

Advances in Industrial and Manufacturing Engineering 6 (2023) 100111
5
4. Findings from the literature review and interviews with
experts: conceptual framework
A number of studies identied in the initial SLR search indicate that
AV manufacturing is imminent but focus on work packages that must be
completed before introducing series manufacturing. Among others,
these include.
•evaluating and validating AV safety and reliability (D. Zhao et al.,
2017; Elrofai, Worm, and Op den Camp, 2016; Wishart et al., 2020),
•developing a test method for correct decision-making in potentially
dangerous scenarios (Tak´
acs et al., 2019; Xinxin, Fei, and Xiangbin,
2020–2020),
•ascertaining the effect of automated driving on passengers’ interac-
tion with AVs (Albert et al., 2015)
•continuously monitoring the driver’s readiness to take over driving
(Perello-March et al., 2021).
We decided not to analyze them further in the screening process
since they do not refer to manufacturing. All the same, they demonstrate
the need for the development of a framework that incorporates the
research eld of AV manufacturing since every author views
manufacturing as one of the next steps toward broad use of AVs on the
road.
We identied two distinctive areas of research, i.e. ‘production/as-
sembly” and ‘autonomous/automated vehicles” (and synonyms) in the
screened literature. A mere thirty-seven of the studies analyzed actually
deal with the manufacture/assembly of AVs, albeit often tangentially.
Six papers describe AV manufacturing processes related to additional
sensor system but most examine AV product designs transferrable to
assembly (31). More authors concentrate on AD in assembly (68), mostly
AGV technology (59), scheduling or implications for production sys-
tems, and a minority (9) examine the use of AVs’ self-driving function in
manufacturing showing a big research gap. This is a key nding of our
literature review. We were able to draw analogies to known constraints
on the use of AGVs when devising the approach on which this paper is
based.
We incorporated the two research areas identied in the SLR and the
interviews with experts aiming to identify elds of activity for future
studies of assembly planning in order to describe and conceptualize the
research eld of AV technology base exploitation in assembly precisely.
We present the resulting framework in three steps, correlating the results
of the SLR with statements from the interviews with experts.
4.1. Autonomous driving in assembly systems (ADIAS) as a factor
contributing to exible assembly systems (FAS): capabilities to improve
assembly performance
OEMs could replace their conveyor systems, e.g., conveyor belts or
AGVs, with self-driving assembly items as of a certain stage of assembly.
AVs would act as their own workholders, eliminating most drawbacks of
conventional conveyor systems, i.e., limited exibility and adaptability
and high capital expenditures. Some approaches in the literature
screened treat the assembly item as a means of conveyance. Krüger et al.
(2017) discuss the exibility of transformable factories comprising
process modules or workstations where ‘a eet of AGVs individually
routes car bodies through assembly stations depending on the car’s type
and variant’. They also intend to use self-driving vehicles, such as AGVs,
in the next step on the way to exibly connected material movement in
transformable factories. Kampker et al. (2017), Wenning, Kawollek, and
Kampker (2020) and Sch¨
afer (2018) present a disruptive concept of a
self-driving chassis and focus on the assembly of non-autonomous
electric vehicles in small series production. The concept replaces con-
ventional conveyor systems with the goal of assembling components
relevant to driving readiness as early as possible to reduce
manufacturing costs, especially for non-value-added conveyance of
assembly items from workstation to workstation, which no longer incurs
labor costs or requires capital expenditures once a certain stage of as-
sembly has been reached, and to increase the exibility of assembly
systems. K. D. Kreisk¨
other (2019) and Kirchner (2019) discuss similar
concepts and identify responsiveness to changed production volume and
lower capital expenditures as the main advantages of self-driving chassis
in assembly. Sch¨
onauer (2020b) examines options for automated vehicle
driving during manufacturing like AGVs, depending on their function,
domain of use and outside inuences, and presents a decision-making
morphology for production planning. He additionally denes various
use cases, assesses them with a specially developed taxonomy
(Sch¨
onauer, 2020a) and introduces a tool for developing recommended
actions (Sch¨
onauer, 2021). Jesse (2017) postulates that ADIAS will open
new options for material movement in factories and examines auto-
mated driving from the conveyor belt to logistics zones to improve
future automotive assembly performance. Macher et al. (2019) identify
‘connected and automated vehicles and their exible production’ as the
‘main research trends of the radically changing automotive industry’.
Eighteen studies identied by the SLR consider AGVs the basis for
FAS, revealing a strong correlation between automated driving in as-
sembly with exibilization of assembly structures. Many studies
describe automated driving with AGVs as a factor that facilitates the
introduction of MLAS. Kern et al. (2015) develop a modular assembly
concept intended to increase exibility and adaptability by imple-
menting a variable material ow and describe an integrated logistics
concept for it based on AGVs (Kern, L¨
ammermann, and Thomas
Bauernhansl, 2017). Martinez-Barbera and Herrero-Perez (2010)
conclude that exible material handling systems such as AGVs are
‘highly convenient for exible manufacturing systems in order to pro-
vide a exible solution to use alternative routes for compensating ma-
chine failures or production changes’. Mayer et al. (2019a, 2019b;
2019–2019; 2019c; 2021) work on adaptive production control in
modular assembly systems using AGVs, which ´
A. B´
anyai et al. (2019)
consider the basis for ‘adaptable and exible material handling solu-
tions” of internal factory processes in matrix production. Moussa and
ElMaraghy (2019) present a method for establishing a master assembly
network with different assembly sequences that enable smart AGVs to
follow alternative assembly sequences and increase the system’s exi-
bility and adaptability. Wider product variety requires workstations
with variable cycle times for every product model and thus a variable
material ow from FAS. This increases capacity utilization by reducing
waits caused by the different amounts of work required to assemble
different product models (Michalos et al., 2010). This and the lower
likelihood of a complete production stoppage in the event of a mal-
function (since there are fewer central weak points) (Moussa and
ElMaraghy, 2019) directly improves the capacity utilization of work-
stations (Kern et al., 2015). Hottenrott and Grunow (2019) discuss AGVs
as a means of conveyance in exible layouts for mixed vehicle model
assembly, arriving at an average efciency gain of 24.5%. They conclude
that exible layouts nevertheless need more space than conventional
assembly lines to accommodate the AGVs and buffers. Treating AGVs as
a exible means of conveyance, Fries, H.-H. Wiendahl, and Foith-F¨
orster
(2020) focus on the adaptability of automotive manufacturing and
present a method for planning MLAS implementation to modify as-
sembly systems for new models of products (Kirmse, 2022). Table 1
summarizes the studies discussed.
Every expert questioned about the capabilities and challenges of
ADIAS in the exploratory interviews (Fig. 5) indicated that they consider
the reduction of expensive conveyor equipment ADIAS’s biggest capa-
bility. Over 80% of the interviewees associate ADIAS with more FAS,
drawing parallels to AGV systems. Whereas workholders have to be
modied for particular product models when AGVs are used, AVs would
be a universal means of conveyance in assembly. In conjunction with
this, experts discuss easier ejection of assembly items for reworking or
specic activities since the essential driving function would be tested
earlier on. Experts see challenges for occupational safety similar to those
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of AGVs since vehicles must detect all obstacles. Interviewees contem-
plating exible MLAS see logistical challenges if they depart from set
assembly sequences. Finally, some experts mentioned ergonomic chal-
lenges arising from assembling components underneath the chassis or at
non-ergonomic heights without conventional conveyor systems capable
of hoisting vehicles.
When asked about potential moderators of ADIAS’s impact on pro-
ductivity, experts mainly focused on the stage of assembly at which self-
driving could be exploited. We qualitatively summarize the impacts
different levels of AD in assembly have on the target dimensions quality,
(manufacturing) speed, economics and variability (Erlach, 2010; Nyhuis
and H.-P. Wiendahl, 2009) and related assembly performance criteria,
such as exibility, adaptability, capacity utilization and capital expen-
ditures in Fig. 6. OEMs currently do not exploit the self-driving function
in assembly until assembly is (almost) 100% complete (blue). The earlier
AVs can replace standard conveyor systems in assembly, the longer
OEMs could exploit the self-driving function and the greater its inuence
on manufacturing performance would be (Wenning, Kawollek, and
Kampker, 2020; Kampker et al., 2018). In this future vision (yellow),
OEMs will exploit AVs’ self-driving function early in assembly once they
will have tested and established their driving readiness in the assembly
Table 1
Overview of literature on automated driving in assembly/manufacturing.
Categories Studies
ADIAS as a possible goal for future
production systems
Krüger et al., (2017); Jesse (2017)
Analyses of ADIASs’ impact on and
consequences for production
systems
Kampker et al., (2017); Kampker et al.,
(2018); Kirchner (2019); K. D. Kreisk¨
other
(2019); Sch¨
afer (2018); Sch¨
onauer (2020a),
2020b, 2021; Wenning, Kawollek, and
Kampker (2020)
Mention of strong correlation
between AD and assembly
exibilization
´
A. B´
anyai et al., (2019); Cronin, Conway,
and Walsh (2019); Fries, H.-H. Wiendahl,
and Foith-F¨
orster (2020); Hottenrott and
Grunow (2019); Kern et al., (2015); Kern,
L¨
ammermann, and Thomas Bauernhansl
(2017); Kirmse (2022); Macher et al.,
(2019); Martinez-Barbera and Herrero-Perez
(2010); Mayer et al., (2019a); Mayer et al.
(2019b); Mayer et al. (2019c); Mayer and
Endisch 2019–2019; Mayer, Classen, and
Endisch (2021); Michalos et al., (2010);
Moussa and ElMaraghy (2019)
Fig. 5. Frequent statements extracted from interviews with experts.
Fig. 6. Effects of different levels of AD.
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environment. The literature screened and the interviews conducted with
experts indicate that exibility, adaptability and capacity utilization
increase as the level of AD in assembly increases, while capital expen-
ditures for conveyor equipment and personnel cost for driving activities
decrease. Furthermore, rework cost could be reduced due to earlier
identication of malfunctions and easier rejection of assembly items for
quality checks. This also has an impact on lead time and work-in-process
(WIP) inventories, although the impact is not clear based on the in-
terviews and literature review, as two effects counteract each other: On
the one hand, lead time and WIP inventories may be reduced by indi-
vidual routes through assembly stations characteristic for exible as-
sembly systems, ensuring that unnecessary assembly stations are not
approached (Kern et al., 2015; Fries, H.-H. Wiendahl, and Foith-F¨
orster,
2020). On the other hand, lead time and WIP inventories may increase
due to limited opportunities to continuously move the vehicle along
with workers on a conveyor, which may result in additional work sta-
tions to compensate for missing assembly time during workpiece
transport time.
Experts additionally indicate the variety of assembly items is rele-
vant since a wider variety of vehicle types in one assembly system in-
creases the need for exibility. Furthermore, vehicle classes of differing
complexity and size could affect cost effectiveness and the ratio of AVs to
non-AVs in an assembly environment. Some experts foresee difculties
in the interaction between AVs and non-AVs when they are both man-
ufactured together in one system and fewer benets from reducing
conveyor systems since they will still be required for non-AVs.
According to the experts, these aspects can inuence the impact of
automated driving of AVs in assembly systems as contributing factors for
exible assembly systems on the assembly performance. Hence, we
correlate the experts’ statements with the ndings from the literature
review in the framework presented in Fig. 7.
4.2. Manufacturing driving readiness: the key to exploiting technological
capabilities
AVs enhance vehicles’ driving function – powered, controllable
forward, backward and sideways motion (SAE Level 0) – by adding
intelligent automated features. Basic automated features are 360◦
sensing, mapping, localization and navigation (A. Herrmann, Brenner,
and Stadler, 2018). In conjunction with assembly systems, experts
emphasize that the driving intelligence concomitant with SAE Level 3 or
higher constitutes a completely new form of driving readiness, which
OEMs must be able to exploit. Assembly items must have a sufcient
level of manufacturing driving readiness, which covers all functions
required for their use in a factory environment, to exploit the techno-
logical capabilities of self-driving in assembly. Vehicles must conse-
quently have a minimum of features by the stage of assembly at which
they will be used. OEMs can base their denition of functional re-
quirements for manufacturing driving readiness on the range of func-
tions of AGVs in use in assembly, while factoring in ergonomic and
occupational safety factors specic to the use of unnished vehicles in
manufacturing (VDI, 2010).
We refer to the descriptions of AGV technology, specically features
and components for motion, detection, localization and navigation, and
communication, in some of the papers from the SLR when specifying
functions relevant to AVs’ manufacturing driving readiness.
(1) AGVs typically move at walking speed (Ullrich and Albrecht,
2019), reducing hazardous situations and the need to detect
far-eld environments. Meißner and Massalski (2020) focus on
electrical power distribution with the intention of improving
energy efciency. Most of the power ow between batteries,
embedded PCs, microcontrollers, sensors and motors must be
functional for AV manufacturing driving readiness. Hirz, Walzel,
and Brunner (2021) examine inductive and conductive charging
technologies for electric vehicles, such as AGVs. When dening
motion requirements for manufacturing driving readiness, as-
sembly planners must consider whether or not a vehicle will
require require charging during assembly and provide this func-
tion early on if it does. Ullrich and Albrecht (2019) describe
mechanical motion components for accelerating, braking and
steering required in AVs used in assembly.
(2) AGVs have sensor systems for near-eld detection of their envi-
ronment and obstacles. Manufacturers sometimes add off-board
systems to improve safety, as outlined by Borges et al. (2013).
Other requirements for detection exist depending on the type of
navigation. Melacini et al. (2019), Ullrich and Albrecht (2019),
Cronin, Conway, and Walsh (2019) thoroughly review AGV
localization and navigation systems, from xed wire guidance to
new exible navigation technologies that enable AGVs to navi-
gate relying ‘on a virtual map of the area based on natural
landmarks’. The more exible the system is, the greater the
computational requirements are. Dixon, Bright, and Harley
(2012) propose a distributed localization system for AGVs, calling
attention to the computing required to correctly determine a ro-
bot’s location and pose in a coordinate system, Almeida et al.
(2020) detect landmarks placed in a factory for localization.
Herrero, Villagra, and Martinez (2013) work on automatically
conguring an AGV’s initial location, the starting point of ma-
neuver navigation, and introduce a corresponding optimization
method. Run and Z.-Y. Xiao (2018) conduct a feasibility study of
indoor AGV navigation based on infrared technology that accu-
rately determines vehicle position and touch on other technolo-
gies such as indoor GPS.
Assembly engineers must dene the extent to which an AV ought to
take over localization and navigation and the extent to which external
infrastructure ought to be used to implement these functions.
Fig. 7. Moderators in the relationship of ADIAS and the assembly performance.
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(3) AGVs must be able to communicate machine-to-machine with
workstations, other AGVs and higher-level factory systems to
send information on their current status and receive information
on their next destination (Cupek et al., 2020, 2021). VDA 5050
denes the communication interface required between a master
control and an AGV (VDA, 2020b). Many studies included in the
SLR focus on AGV communication: Javed et al. (2021) conduct
safety and security analyses of AGV platooning, examining sen-
sors, actuators and controllers that detect and process sensor data
into movement and a ‘communication component for receiving
and sending commands to other AGVs and … the cloud infra-
structure” as one node of a system. When this is transferred to AVs
in assembly, OEMs have to view the entire system’s driving
readiness holistically. Connected and intelligent movement of
many vehicles in one system necessitates communication be-
tween AGVs (Horatiu et al., 2019; Burmeister et al., 2021) and
with the factory (Cupek et al., 2020; Indri et al., 2019; Bujari
et al., 2020). They employ a communication module with a WiFi
antenna for this purpose (Horatiu et al., 2019; Ullrich and
Albrecht, 2019; Indri et al., 2019).
AVs do not need to meet legal road requirements for use in assembly
(Sch¨
onauer, 2020b). Interviewees indicated a scaled-down version
would sufce since the restrictions are different. Vehicles would move at
a lower speed and along predetermined paths in a dened environment
with fewer external inuences, such as weather. Only having to modify
specic driver readiness requirements for respective stages of assembly
will benet the feasibility of ADIAS. Whereas the requirements are
relatively low during assembly, vehicles must meet higher standards for
nal testing before delivery or use in internal logistics (higher speeds,
weather conditions) until they are fully roadworthy for the customer.
OEMs must nevertheless sufciently test levels of driving readiness
before use in assembly. This, in turn, makes it easier to detect and rectify
quality problems at an early stage. Table 2 presents the related studies
from the SLR.
4.3. Fields of activity in assembly planning research and practice
We identify three main elds of activity in assembly planning
research and practice from the screened literature and interviewee re-
sponses about ADIAS, namely assembly processes, products and as-
sembly environments. The earlier the AD function can be exploited in
the course of assembly by adapting processes, the product and the as-
sembly environment, the earlier assembly performance can be boosted.
4.3.1. AV-specic product features and implications for assembly processes
The authors of studies that deal directly or indirectly with AV as-
sembly/manufacturing and examine different vehicle components
describe mechanical and electrical product changes. First, we examine
self-driving technology and additional or changed components and their
implications for assembly. Then we present ideas about establishing
driver readiness early in assembly put forth by studies that deal with
this.
Self-driving systems: Many authors (Jagannathan et al., 2018; Z.
Liu et al., 2020; Meinel, 2018; A. Herrmann, Brenner, and Stadler, 2018;
Taraba et al., 2018; Hussain and Zeadally, 2019) point out that AVs
require a greater variety of sensors to ensure sensor system reliability as
SAE Levels ascend. Whereas SAE Level 1 vehicles have six sensors, SAE
Level 3 and 4 vehicles need twenty-nine (Meinel, 2018). Z. Liu et al.
(2020) describe the latest advances in environment sensing and
demonstrate sensor fusion with data detected by various radar, LiDAR,
camera and ultrasonic sensors. Ilas (2013) reviews the main sensing
technologies used in AV prototypes. Apart from the large number of
external environment sensors, Jagannathan et al. (2018) and Hussain
and Zeadally (2019) discuss vehicle odometry sensors that gather in-
formation on vehicle motion and actuators that translate a vehicle’s
actions into movement as essential AV components that OEMs must
assemble. Ondruˇ
s et al. (2020) provide an overview of self-driving
technology, including numerous types of sensors as well as CPUs or
computers as essential AV components, the latter having to be ‘mounted
on the inside of the vehicle’. Shadrin, Varlamov, and Ivanov (2017)
examine technical issues when production vehicles are modied for AD
by adding additional hardware, including GPS receivers for AV
localization.
Self-driving systems require validation and calibration to ensure
correct assembly and accurate environment sensing (Weber, 2014). You
et al. (2021) identify static alignment inspection as an essential assembly
process/test for AVs and point out that OEMs must cross-calibrate all
sensors. Their novel static procedure for sensor alignment inspection has
the advantage of not requiring complex technology or space to rotate
either the vehicle or the sensor targets. Solmaz and Holzinger (2019)
and Solmaz et al. (2021) introduce a novel test bench for the calibration
of AD functions, which permits the vehicle to control its front wheels
independently ‘by rotating the front set of rollers around respective
vertical axes’, thus responding to Neumann, Tentrup, and Weck’s (2018)
call for new sensor system calibration and testing methods in
manufacturing and end-of-line testing. Discerning a ‘great burden to
calibrate huge amount of cameras’ in vehicle manufacturing lines, Ren
and Hu (2021) work on position guidance to improve calibration
accuracy.
Electrical/electronic (E/E) architecture: AVs are subject to higher
safety standards than human-driven vehicles. Safety components usually
require a redundant design (Singh and Saini, 2021; Behere and
T¨
orngren, 2016; Dominic et al., 2016; Reschka, 2016) and their as-
sembly must meet high quality standards (Meiners et al., 2020; Kuhn
et al., 2018; Nguyen, Kuhn, and J¨
org Franke, 2021; Arena2036 e.V.
2020). This mainly pertains to a vehicle’s E/E architecture. Behere and
T¨
orngren (2016) discuss key elements of a functional AV architecture
and present an example exploring redundancy as one of the major
changes to conventional vehicles. Dominic et al. (2016) discuss E/E
architecture in the context of risk assessment, emphasizing the ‘impor-
tance of redundancy and not establishing too much trust in any one
subsystem’. Singh and Saini (2021) analyze redundant braking, steering
and power systems that ensure ‘alternate systems can take control in
event of a power loss’. Backup power sources are available for various
components, e.g., computers, sensors and braking and steering systems.
Reschka (2016) compares an AV with an airplane in terms of safety
concepts, concluding that ‘actuators must therefore have several
redundant control circuits”. Redundancy results in more hardware
needing to be assembled and connected during vehicle manufacturing.
Meiners et al. (2020), Nguyen, Kuhn, and J¨
org Franke (2021) and an
ARENA2036 innovation initiative (Arena2036 e.V. 2020) expect and
explore quality standards for the assembly of cable harnesses, which are
safety-related AV components. Murphy et al. (2019) anticipate a greater
Table 2
Studies of AGV technology identied in the SLR.
Categories Studies
´
AGV/AV motion features A. Herrmann, Brenner, and Stadler (2018); Hirz,
Walzel, and Brunner (2021); Meißner and
Massalski 2020; Sch¨
onauer (2020b); Ullrich and
Albrecht (2019); VDI 2010
AGV detection, localization and
navigation features
Almeida et al., (2020); Borges et al., (2013);
Cronin, Conway, and Walsh (2019); Dixon,
Bright, and Harley (2012); Herrero, Villagra,
and Martinez (2013); Melacini et al., (2019);
Run and Z.-Y. Xiao (2018); Ullrich and Albrecht
(2019)
AGV connectivity/
communication features
Bujari et al., (2020); Burmeister et al., (2021);
Cupek et al., (2021); Cupek et al., (2020);
Horatiu et al., (2019); Indri et al., (2019); Javed
et al., (2021); Ullrich and Albrecht (2019); VDA
2020b
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recall risk for OEMs because of higher quality standards for E/E com-
ponents. More closely monitored automated processes will be employed
in lieu of manual operations to meet this challenge. In turn, we can
transfer this to AV assembly processes, which also entail safety-related
operations. Moreover, the growing number of electronics and software
in AVs (S. Otten et al., 2019) is giving rise to longer assembly and
commissioning times (Jesse, 2017) and stricter electrical safety stan-
dards during assembly (Rass˜
olkin, Sell, and Leier, 2018).
AVs’ E/E architecture also needs a high-level vehicle-to-X (V2X)
communication interface to send and receive information from the
surroundings. The growing number of access points makes cybersecurity
threats an important issue (Schoitsch, 2016).
Use of the interface with additional manufacturing software during
assembly to control vehicle movement would require rendering the
interface inaccessible to cyber attackers once an assembled vehicle hits
the road. Interviewees therefore propose deleting the gateway to the
SDS system entirely at the end of manufacturing. Some interviewees
reported longer commissioning times for current models equipped with
advanced driver assistance systems and expect commissioning times to
grow longer as more computer systems are added. Some interviewees
consider changes in software and E/E architecture the biggest
manufacturing challenge.
Interior and exterior design changes stemming from different
use cases: One main difference between AVs and conventional human-
driven vehicles is the vehicle body. The driver’s cockpit and re-
quirements for an unobstructed view and interaction with the vehicle
are no longer pertinent as of SAE Level 4. This opens new options for
exterior and interior design (Winner and Wachenfeld, 2016; A. Herr-
mann, Brenner, and Stadler, 2018). The elimination of a driver’s cockpit
will enable designers to incorporate new personal or commercial
non-driving activities, as described by Peging, Rang, and Broy (2016),
Balogh et al. (2019) and Teleki, Fritz, and Kreimeyer (2017). This will
change essential vehicle components and related assembly systems:
cockpits, seats, door designs and positions, passenger interaction with
the vehicle, etc. (Winner and Wachenfeld, 2016; A. Herrmann, Brenner,
and Stadler, 2018) Since the vehicle’s seats ‘no longer need to be aligned
with the moving direction’, seat design will change because function
controls must still be available regardless of alignment, Cust´
odio et al.
(2022) designed a new car seat with integrated passenger controls, e.g.,
window controls. Roeckle et al. (2019) surveyed and analyzed potential
features of future AVs, identifying sleeping and relaxing as the use cases
most relevant for autonomous personal conveyance and proposed spe-
cial equipment options that enable users to relax in a comfortable po-
sition. Blankenbach et al. (2020) consider exterior and interior signage
and lighting relevant to safety. In addition to signal tones, interior
vehicle lights will indicate whether a vehicle is in AD mode or a driver
must take control. This will require passenger and pedestrian informa-
tion and increase the complexity of vehicle manufacturing.
Responses to the interview question about changes to components
apart from the SDS system focused on changes to the vehicle interior,
swivel seats, no steering wheel, additional interior sensors that monitor
passengers (during carpooling) and communication interfaces, such as
additional screens that display live trip status. Most of the interviewees
think these changes will have less impact on the complexity of vehicle
manufacturing than the self-driving system but some expect interiors
will vary more widely as the range of use cases grows, thus making more
FAS particularly advantageous.
Modularization: Wedler and Vietor (2019) mention AV assembly
tangentially in conjunction the potential modularization of AVs
comprising a drive module (wheels-drive-steering), a basic module with
energy storage and a life module that could be interchanged depending
on the plan for use. Their approach is based on the assembly of different
easily interchangeable bodies on one platform. Clausen and Klingner
(2019) also see great potential in the modularization of electric AVs, i.e.,
‘concentrating all driving and control functions in one basic module and
completing these base modules with case-specic body modules’.
Modularization would exibilize AV body manufacturing since it could
‘be implemented signicantly more consistently for autonomous electric
vehicles” without a driver’s cockpit.
ADIAS-specic product and process requirements: The exploita-
tion of AVs’ technological capabilities at an early stage of assembly re-
quires altering the assembly sequence (Wenning, Kawollek, and
Kampker, 2020; K. D. Kreisk¨
other, 2019). Wenning, Kawollek, and
Kampker (2020) identify essential components, such as ‘front and rear
axle including all wheels, the … the battery pack, …the drivetrain, cable
harness and the power and vehicle control unit’, that must be assembled
at the start of assembly system. OEMs’ assembly planners and vehicle
developers must rethink assembly process sequences, incorporating the
components and processes required for minimum manufacturing driving
readiness and a product design conducive to this. Among other things,
Kampker et al. (2018) identify the use of high-voltage batteries as a
challenge to occupational safety and proposes a low-voltage approach
that would use just one battery module. This, in turn, would only be
possible when it is included in software development. Their concept
relies on external infrastructure for vehicle localization end environ-
ment perception to control the vehicle’s next actions. The communica-
tion module must be installed in the AV early on to send and receive
information.
Since the SLR yielded no other in-depth analyses of approaches to
implementing automated driving in assembly, further research is needed
at this time, something with which the interviewees concur. A number of
key research topics were extracted from the interviews. Assembly
planners ought to include testing in these analyses since vehicles must be
sufciently operational and have been tested before use in the assembly
system. New tests specically for AVs will be needed (e.g., sensor veri-
cation and calibration) and some conventional testing procedures
might be eliminated (e.g., steering wheel tests). OEMs ought to schedule
tests of manufacturing driving readiness earlier. Many interviewees
recommend a detailed analysis of assembly owcharts and foresee more
difcult challenges in the E/E vehicle components and special software
required for manufacturing than in mechanical interconnections. One
expert identied assembly owcharts as crucial to the implementation
Table 3
Studies related to AV-specic product features and implications for factory
processes identied in the SLR.
Categories Studies
Self-driving system A. Herrmann, Brenner, and Stadler (2018);
Hussain and Zeadally (2019); Ilas (2013);
Jagannathan et al., (2018); Z. Liu et al.,
(2020); Meinel (2018); Neumann, Tentrup,
and Weck (2018); Ondruˇ
s et al., (2020);
Ren and Hu (2021); Shadrin, Varlamov, and
Ivanov 2017; Solmaz and Holzinger (2019);
Solmaz et al., (2021); Taraba et al., (2018);
Weber (2014); You et al., (2021)
E/E architecture Arena2036 e.V. 2020; Behere and T¨
orngren
2016; Dominic et al., (2016); Jesse (2017);
Kuhn et al., (2018); Meiners et al., 2020;
Murphy et al., (2019); Nguyen, Kuhn, and
J¨
org Franke (2021); S. Otten et al., (2019);
Rass˜
olkin, Sell, and Leier (2018); Reschka
(2016); Schoitsch (2016); Singh and Saini
(2021)
Interior and exterior design changes
stemming from different use cases
Balogh et al., (2019); Blankenbach et al.,
(2020); Cust´
odio et al., (2022); A.
Herrmann, Brenner, and Stadler (2018);
Peging, Rang, and Broy (2016); Roeckle
et al., (2019); Teleki, Fritz, and Kreimeyer
(2017); Winner and Wachenfeld (2016)
Modularization Clausen and Klingner (2019); Wedler and
Vietor (2019)
ADIAS-specic product specications Kampker et al., (2018); K. D. Kreisk¨
other
(2019); Wenning, Kawollek, and Kampker
(2020)
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of FAS since they are the basis of constraints on the material ow.
Table 3 presents the related literature from the SLR.
4.3.2. Assembly environment
Scheduling, sequence and path planning: Combining ADIAS with
FAS necessitates modifying the assembly environment and sequence.
AGV scheduling is a primary concern of research the SLR identied as
useful for AVs in assembly. Authors often stress the need for smart PPC
in exible production systems. H. F. Rahman, Janardhanan, and P.
Nielsen (2020) propose an integrated approach to line balancing and
AGV scheduling with the intention of organizing smart assembly systems
supplied by AGVs. They conclude that an efcient material handling
system using AGVs requires a suitable planning and scheduling system
that ensures that material delivery activities are effectively distributed
among AGVs and materials can be loaded, unloaded and moved in the
shortest time possible. Fazlollahtabar (2016) proposes a framework for
two layers of hierarchical, dynamic decision-making for allocating re-
sources and assigning activities, incorporating the manufacture of
multiple products in one exible manufacturing system. M. R. Bahuba-
lendruni et al. (2019) describe a new hybrid conjugated method to
generate assembly sequences which is one main eld of activity for as-
sembly planners that want to establish MDR earlier. A number of other
studies examine sequence planning, scheduling algorithms and mathe-
matical models for organizing an AGV eet (see Table 4), underscoring
the need for a higher-level system that plans AVs’ paths to destinations
in the assembly system and factors in all other AGVs handling material
in the assembly environment.
Infrastructure: This higher-level system must be able to send in-
formation to and receive information from AVs and all workstations the
same way AGV communication modules do (Bujari et al., 2020; Bur-
meister et al., 2021; Cupek et al., 2020). The communication interface
must transfer data on an AV’s real-time-position in the factory, work-
station and test station availability and an AV’s destination as well as
data from test stations. Since the implemented V2X communication
interface required for road use (Johanning and Mildner, 2015) could be
used for AV communication during manufacturing, a factory must pro-
vide a map of the assembly environment similar to cloud-based map
services used by on-road AVs (Wong, Gu, and Kamijo, 2021). A
high-level internet connection is required for communication (Grote-
pass, Eichinger, and Voigtl¨
ander, 2019). Wenning, Kawollek, and
Kampker (2020) and Kampker et al. (2018) introduce an approach in
which external cameras attached to infrastructure and an external
control system are used to control unnished vehicles in manufacturing.
Buildings are also an important factor. VDI 2710 (VDI, 2010) is a
guideline for interdisciplinary AGV system design, including building
design for AGVs (e.g., ooring features), infrastructure and peripheral
units, such as stationary positioning and routing equipment, which must
be factored into assembly planning. Interviewees believe existing fac-
tories with conveyor systems and designated areas for logistics, assem-
bly or testing will hamper the implementation of ADIAS since OEMs will
have to remove some conveyor systems and workstations in addition to
redesigning the material ow and reorganizing oorspace, OEMs would
have to partially remove conveyor systems and assembly stations. One
interviewee noted that AVs’ larger turning radius than AGVs’ must be
allowed for in the layout of vehicle areas.
Factory and human factors: Studies also explore the interaction
between AVs and pedestrians, which are applicable to workers (Deb
et al., 2018), and the occupational safety challenges of AGVs (Tubis and
Poturaj, 2021; Brezina, Spackova, and Mensik, 2019; Oravec, 2021).
Most interviewees recommend minimizing encounters with other vehi-
cles or workers and propose corresponding organizational (work in-
structions) and/or infrastructural (physical separation) actions. Workers
must be able to activate an emergency stop button or the like at all times
to halt an AV’s movement in the event of a malfunction (also discussed
for AGVs by Brezina, Spackova, and Mensik (2019)]). In addition to
interacting with workers, AVs must be able to handle non-road situa-
tions in factories, such as driving onto a conveyor belt or chassis dyna-
mometer. Assembly planners must establish standardized processes for
self-driving assembly items. Workstations ought to have AV posi-
tioning systems and controls workers can use to dispatch/start vehicles
upon completed assembly. Table 4 presents related studies from the SLR.
4.4. Toward a conceptual framework: Consolidating and discussing
research ndings
This study presents the rst comprehensive literature review on AVs
and their manufacture and assembly. The ndings reveal a research gap
in the eld of AV manufacturing. Only a few studies mention
manufacturing processes modied for AVs (You et al., 2021; e.g. Solmaz
et al., 2021). More studies deal with aspects of vehicle development and
testing (D. Zhao et al., 2017; e.g. Elrofai, Worm, and Op den Camp,
2016; Wishart et al., 2020) that must be addressed before manufacture.
This demonstrates that even though research anticipates manufacturing,
it seldom truly explores manufacturing. We detect a need for further
research ensuing from the business environment changed by modied
product designs and the capability to boost assembly performance. Our
review reveals that authors mostly contemplate exploiting AVs’ AD
function in assembly (Krüger et al., 2017; Jesse, 2017). The research
project presented by Wenning, Kawollek, and Kampker (2020) focuses
on using assembly items – electric vehicles – in a low-cost assembly
system. Their studies leave elds of research on the use of AVs in
large-scale assembly open.
4.4.1. The correlation between the exploitation of the AD function in
assembly and logistics performance
A larger number of studies tangential to assembly and AD examine
AGVs, which interviewees consider a concurrent technology. We iden-
tify strong parallels between the exploitation of AVs’ technological ca-
pabilities in assembly and the impacts of AGV technologies. Many
studies in the SLR, e.g., Kern et al. (2015) and Martinez-Barbera and
Herrero-Perez (2010), emphasize the need for AGVs as a means of
conveyance fundamental to rendering matrix-like production systems
exible and adaptable. We hypothesize that AVs as self-driving assembly
items would have additional positive effects on assembly performance
similar to the use of AGVs to boost MLAS exibility and adaptability as
constraints on its exibility and adaptability as a means of conveyance
disappear. At the same time, capital expenditures would be eliminated
or reduced. Clearly, the sooner OEMs exploit the self-driving function,
Table 4
Studies related to assembly environment.
Categories Studies
Scheduling, sequence and
path planning
M. V. A. R. Bahubalendruni and Biswal (2018); M. R.
Bahubalendruni et al., (2019); Bocewicz, I. E. Nielsen,
and Banaszak (2016); Confessore, Fabiano, and Liotta
(2013); Emde and Gendreau (2017); Fazlollahtabar
(2016); Fazlollahtabar, Saidi-Mehrabad, and
Masehian (2015); A. K. Gulivindala et al., (2020); A.
K. Gulivindala et al., (2021); Heger and Voss (2018);
H. F. Rahman, Janardhanan, and P. Nielsen (2020);
Shi, Tang, and Zou (2021); Sipahioglu and Altın 2019;
Tabatabaei, Torabi, and Paitoon (2018); Xia et al.,
(2020); Xidias, Zacharia, and Nearchou (2016); Yao
et al., (2018); Zhang and Li (2018); X. F. Zhao et al.,
(2020); Y. Zhao et al., (2020); Zhu and Y. He (2019)
Infrastructure Bujari et al., (2020); Burmeister et al., (2021); Cupek
et al., (2021); Cupek et al., (2020); Grotepass,
Eichinger, and Voigtl¨
ander (2019); Horatiu et al.,
(2019); Indri et al., (2019); Javed et al., (2021);
Johanning and Mildner (2015); Kampker et al.,
(2018); Ullrich and Albrecht (2019); VDA 2020b; VDI
2010; Wenning, Kawollek, and Kampker (2020);
Wong, Gu, and Kamijo 2021
Factory and human factors Tubis and Poturaj (2021); Brezina, Spackova, and
Mensik (2019); Deb et al., (2018); Oravec (2021)
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the greater the inuence on the aforementioned target variables will be.
There are, however, limits on moving up the time at which self-driving
function is exploited. At a certain point, the benets of increased as-
sembly performance will no longer offset the costs of changing products
in order to change the assembly sequence. Studies are needed in addi-
tion to the qualitative analysis of the impacts on assembly performance
to better understand the time at which the self-driving function can be
exploited.
4.4.2. Fields of activity in research and practice related to assembly system
modication and conguration
AVs must meet a set of minimum functional requirements to be self-
driving-ready in manufacturing. Sch¨
onauer (2020b) describes the dif-
ferences between industrial and on-road use. This study conrms his
conclusion. OEMs ought to draw parallels to AGVs because gradations of
driving readiness are conceivable. The assembly system could succes-
sively establish driving readiness as requirements increase during
manufacturing. This would facilitate the implementation of self-driving
assembly items since the time at which an AV is self-driving-ready could
be expected earlier. This would make dening minimum functional re-
quirements one of assembly planners’ primary activities as they imple-
ment this concept.
The literature review and the interviews conducted also reveal a
large number of activities and obstacles that need to be factored into
implementation and analyzed in more detail by assembly planners.
Three main elds of activity in research and practice represent the
greatest challenges.
1. For one thing, assembly planners must take new manufacturing re-
quirements stemming from product changes related to the self-
driving function into account. Sensor system installation and
commissioning will play a major role since it is instrumental to
manufacturing driving readiness. We expect new vehicle designs to
have an impact on this concept’s feasibility since OEMs will have to
assemble new components and design new manufacturing processes.
The current literature frequently describes new and modied AV
components (Roeckle et al., 2019; Winner and Wachenfeld, 2016;
Dominic et al., 2016) but rarely deals with manufacturing. We pro-
pose concentrating more on the assembly of new components
because they will probably not only affect exploitation of the AD
function in assembly but also conventional manufacturing processes.
2. Assembly planners must analyze the assembly sequence. We assume
that OEMs will only be able to maximize sufcient capabilities when
assembly is fundamentally reorganized. This will frequently involve
product design requirements, something also mentioned by Wen-
ning, Kawollek, and Kampker (2020). Some researchers discuss
higher quality standards for E/E component assembly (Dominic
et al., 2016; Arena2036 e.V. 2020; Singh and Saini, 2021). We expect
that interconnected on-board electronics will give rise to bigger
challenges in the domain of the electrical and electronic architecture
and related software when redesigning assembly sequences. OEMs
must keep product changes as manageable as possible so that they
remain nancially and organizationally feasible during
implementation.
3. At the same time, the assembly environment must be changed. The
factory layout as well as the building infrastructure and the
communication infrastructure that locates vehicles and sends driving
commands must be set up similarly to those of smart AGV systems.
Kampker et al. (2018) outline the challenges of implementing
external sensing systems. We expect bigger challenges in existing
factories since existing conveyor systems will have to be removed
rst and existing infrastructure might impede new communication
channels. The substantial research streams on scheduling (e.g.,
Fazlollahtabar (2016)], Emde and Gendreau (2017)], H. F. Rahman,
Janardhanan, and P. Nielsen (2020)]) and AGV machine-to-machine
communication (e.g., Grotepass, Eichinger, and Voigtl¨
ander (2019)],
Johanning and Mildner (2015)], Cupek et al. (2021)]) indicate that
assembly planners will have to factor both into the design of control
and communication systems for the assembly environment.
The aforementioned factors and their correlations are the basis for
the conceptual framework (Fig. 8) in which we provide an overview of
the research eld and correlate all the attendant issues: ADIAS that re-
quires MDR acts as a contributing factor for FAS and can improve the
assembly performance. For exploiting the potential, three elds of ac-
tivity for assembly planning remain (product, assembly process and
assembly environment), e.g. analyzing and changing the assembly
sequence. Thus, for the rst time in the literature, we discuss AVs as
assembly objects, establish the link to FAS, and simultaneously highlight
the most important elds of activity on the way to improved assembly
performance.
5. Theoretical and managerial contribution, limitations and
outlook
We are chiey contributing to the research streams of automotive
assembly planning and internal logistics. Main contributions to future
research are the following:
First, we present the rst literature review on assembly/production
and AVs and thereby address a major research gap, namely the lack of a
comprehensive overview of the impacts and capabilities of AVs as as-
sembly items and an overview of the ensuing elds of activity in
research and practice for the exploitation of the technological potential
of AVs.
We thus continue the thought of Krüger et al. (2017), who state that
vehicles as assembly items may take over the internal transportation in
factories themselves in the future by providing an overview of current
state of research and practice in related elds. This reveals parallels to
the conceptual approach of Wenning, Kawollek, and Kampker (2020),
who present a new manufacturing concept for low-volume battery
electric vehicles based on self-driving chassis. Our conceptual approach
goes one step further and does not consider electric vehicles, but rather
takes future AVs, which are about to be launched on the market, as the
object of consideration, and our comprehensive overview thus points
out that assembly planning must illuminate many other inuencing
variables such as AV-specic components and processes, in addition to
the points already mentioned in the literature.
Second, the literature review was supplemented with the inter-
viewed experts’ practical knowledge, so we were able to develop a ho-
listic conceptual framework for the implementation of this concept. It
merges important requirements, potentials and interrelationships for the
use of AVs’ driving function in assembly systems – also in the context of
FAS – and provides an overview of task areas during implementation
and performance criteria to be inuenced.
Third, we began by using the literature analysis and interviews with
experts to describe potential technological capabilities of AV use in as-
sembly qualitatively. This was our motivation for extracting and clus-
tering the requirements for AV use in assembly from the literature. This
provides OEMs’ assembly planners starting points for planning AV self-
driving in assembly and can thus represent one driver for exible as-
sembly systems in comparison to conventional linear systems. While
specifying the requirements and inuences, we realized that a denition
of all functional AV requirements is needed for use in assembly and that
challenges arising from changing the assembly sequence have to be
tackled. Given the dearth of information on AV manufacturing we
discovered, we recommend stepping up work on holistically reorganiz-
ing assembly systems for AVs by integrating additional processes and
exploiting capabilities. We see three essential elds of practical activity
for OEMs’ assembly planners and managers: (1) focusing more on AV-
specic assembly processes and factoring future changes into interior
and exterior design, (2) analyzing current assembly sequences and
necessary product design changes, and (3) including all the assembly
T. Kathmann et al.

Advances in Industrial and Manufacturing Engineering 6 (2023) 100111
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peripherals that reorganization will change.
Specically, this means for responsible planners that, as early as
possible in the concept planning phase, all project assessments should
include what AVs entail for special assembly processes, capabilities and
requirements, such as higher quality standards or additional assembly
and test processes. These must include all departments responsible for
assembly peripherals, such as IT or logistics, affected by this funda-
mental change in assembly strategy, as several time-intensive separate
subprojects will result.
Although our study makes important contributions, it has its limi-
tations, which open avenues for further research. This paper is intended
to describe the input variables and elds of activity in assembly planning
research and practice. The generalized ndings need to be explored in
more detail in further research. Since our research design does not
identify quantitative capabilities and challenges, additional dialogue
with experts and analyses of case studies would provide the necessary
basis for a technical and economic feasibility study. We also recommend
expanding the number of different assembly performance criteria dis-
cussed that could be inuenced by this research approach. Especially in
the context of exible assembly systems, we see a challenge to ADIAS
analogous to AGV scheduling. Future research should accordingly
consider the different mechanisms for solving the AGV scheduling
problem.
Further research should scrutinize the concept of manufacturing
driving readiness and identify OEMs’ current assembly sequences, the
changes required in assembly sequences and product design and the
constraints on implementation. As we see it, this novel approach needs
to be subjected to a case study before the practical challenges to and
capabilities for OEMs’ assembly sequences can be thoroughly studied.
The results, opinions and conclusions expressed in this paper are not
necessarily those of Volkswagen Aktiengesellschaft.
Declaration of competing interest
The authors declare that they have no known competing nancial
interests or personal relationships that could have appeared to inuence
the work reported in this paper.
Data availability
No data was used for the research described in the article.
Appendix
Table 5
Interviewees
Interviewee Work eld Interview date Location Duration [min]
Anonymized Assembly planning 2021/09/27 Online 30
Anonymized Assembly planning 2021/10/01 Hanover 30
Anonymized Assembly planning 2021/10/05 Online 45
Anonymized Production planning and quality management 2021/10/22 Online 45
Anonymized Vehicle development 2021/10/13 Online 60
Anonymized Vehicle development 2021/02/09 Online 30
Anonymized Assembly planning 2021/11/11 Online 30
Anonymized Production planning 2022/02/15 Online 30
Anonymized Vehicle development 2021/12/22 Online 45
Anonymized Assembly planning 2021/12/17 Online 30
Anonymized Assembly planning 2022/02/09 Online 30
Anonymized Assembly planning 2022/02/18 Online 30
Anonymized Assembly planning 2021/12/17 Online 30
Anonymized Assembly planning 2021/11/29 Online 30
Anonymized Assembly planning 2021/12/09 Online 30
Fig. 8. Framework correlating ADIAS, FAS and assembly performance.
T. Kathmann et al.

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Table 6
Interview questionnaire translated from German
Interview guide “Exploiting the Technological Capabilities of Autonomous Vehicles as Assembly Items to Improve Assembly Performance”
Interview partner:
Work eld:
Date and duration:
Part 1: Introduction into the topic and relationship with the interview partner
Topic: >Introductory presentation of the conceptual approach:
“Autonomous Vehicles as Assembly Items”<
Work: In which eld of work are you active?
How is your work related to this conceptual approach?
Part 2: Technology base of autonomous driving and automotive assembly
Key Question 1:
Supportive, guiding questions:
How can the technology base of autonomous driving (AD) be exploited in automotive assembly?
•… in the context of Flexible Assembly Systems?
•Which requirements ensue for assembly environment?
•What assembly performance criteria would be inuenced?
•How would the performance criteria named be inuenced? Positive, negative?
•How does shifting the point in time from which MDR is exploitable affect performance criteria?
Part 3: Fields of activity in practice
Key Question 2:
Supportive, guiding questions:
What elds of activity in research and practice ensue from this approach?
•What aspects ensue for assembly planners in automotive industry?
•What moderating or hindering factors are relevant to the development of concepts for the exploitation of the self-driving function in assembly?
•In your opinion, which aspect is most important for the implementation of such a concept?
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