F=
rom
fabric to factory floor: contrasting the implementation of industry 4.0 in =
the
textile and automotive industries
|
Ana Maria Barbosa Dias=
https://orcid.org/0009-0003-8187-2846 |
Mestre
em Engenharia Têxtil. UFSC – campus Blumenau. Brasil. anama.dias@gmail.com |
|
Ana Julia Dal Forno https://orcid.org/0000-0003-2441-5385 |
Doutora
em Engenharia de Produção. UFSC – campus Blumenau. Brasil. =
ana.forno@ufsc.br |
|
Fernanda Steffens https://orcid.org/0000-0003-3402-3641 |
Doutora
em Engenharia Têxtil. UFSC – campus Blumenau. Brasil. fernanda.steffens@ufsc.br |
RESUMO
A indústria 4.0 tem trazido fortes transformações
digitais a todos os setores industriais, incluindo a cadeia têxtil, que é
considerada um segmento tradicional e que apresenta resistência para aplica=
r as
tecnologias advindas dessa revolução. Em contrapartida, há o setor automoti=
vo,
considerado benchmarking para várias áreas. Neste sentido, o objetivo desse
estudo é comparar a adoção das tecnologias advindas da indústria 4.0 entre o
setor têxtil e o setor automotivo. A metodologia utilizada foi revisão sist=
emática
de literatura que teve as seguintes etapas: definição dos termos e estratég=
ias
de busca; eleição das bases referenciais; definição dos critérios de inclus=
ão e
exclusão e designação dos softwares a serem utilizados para triagem.=
Foi
verificado que as instituições de ensino brasileiras se destacaram no númer=
o de
publicações científicas associando os termos da Indústria 4.0 com a área tê=
xtil
e automotiva na base dados Scopus no período entre 2018-2022, evidenciando =
que
o Brasil se encontra na vanguarda dentro dessa temática. Com base nas análi=
ses
dos estudos, destaca-se que as áreas de logística e, dentre as linhas de
produção, a confecção são os setores têxteis que buscam uma maior aplicação=
de
tecnologias em seus processos. No entanto, a indústria têxtil, em nível
mundial, tem se mostrado hesitante na implementação da transformação digita=
l,
implicando em menor uso das tecnologias da indústria 4.0. Destaca-se o
ineditismo da comparação entre a indústria têxtil e a indústria automotiva.
Além disso, a apresentação de um diagnóstico das práticas nos dois setores.=
As
implicações práticas são fornecer subsídios às empresas têxteis para uma to=
mada
de decisão assertiva.
Palavras-chave: transformação dig=
ital.
Revisão sistemática da literatura. Quarta =
revolução
industrial. Tecnologias em=
ergentes.
ABSTRACT
Industry 4.0 has brought substantial digital transformations to all
industrial sectors, including the textile chain, which is considered a
traditional segment that tends to resist the adoption of technologies arisi=
ng
from this revolution. In contrast, the automotive sector is often regarded =
as a
benchmark for several areas. In this context, this study aimed to compare t=
he
adoption of Industry 4.0 technologies in the textile and automotive sectors.
The methodology employed was a systematic literature review, with the follo=
wing
stages: definition of search terms and strategies, selection of reference
databases, definition of inclusion and exclusion criteria, and designation =
of
software to be used for screening. It was found that Brazilian educational
institutions stood out in the number of scientific publications linking the
terms “Industry 4.0” with the textile and automotive fields in the Scopus
database during the period 2018–2022, highlighting that Brazil is at the
forefront of this theme. Based on the analysis of the studies,
it is highlighted that the logistics area and, among the production
lines, the apparel line are the textile sectors that most actively seek to
apply technologies in their processes. However, on a global level, the text=
ile
industry has been hesitant in implementing digital transformation, resultin=
g in
a lower level of adoption Industry 4.0 technolog=
ies. This
study is original in its comparison between the textile and automotive
industries. In addition, it offers a diagnostic overview of practices in bo=
th
sectors. The practical implication is to provide subsidies for more asserti=
ve
decision-making by textile companies.
Keywords: Digital transformation. Systematic literature
review. Fourth industrial revolution. Emerging
technologies.
Recebido
em 05/10/2024. Aprovado em 10/09/2=
025.
Avaliado pelo sistema double blind
https://doi.org/10.22279/navus.v16.2=
034 <=
br
clear=3Dall style=3D'mso-special-character:line-break;page-break-before:alw=
ays'>
1 INTRODUC=
TION
The textile industry is amid the digital
revolution driven by Industry 4.0, which stimulates a significant
transformation in the manufacturing sector. This phenomenon, which gave ris=
e to
the fourth industrial revolution, is characterized by technological advances
such as the Internet of Things (IoT) and cloud-based systems, which integra=
te
manufacturing processes and promote continuous improvements in the producti=
on
chain (Sharma et al., 2021a) The textile sector stands out as a fertile gro=
und
for the introduction of such innovations, although facing significant
challenges in the adoption of these technologies, in contrast to sectors su=
ch
as the automotive, in which the application of Industry 4.0 is already a
reality (CNI, 2021). In addition, it is observed that the textile sector has
primarily invested in the introduction of technologies aimed at increasing =
the
efficiency of production process, reducing operating costs and improving
business management (Dal Forno et al., 2021).
Thus, this study aimed to compare the eme=
rging
technologies most commonly used in the automotive
sector, which serves as a benchmark for their implementation in the textile
sector. The research question addressed was: Which Industry 4.0
technologies are most commonly applied in the te=
xtile
and automotive industries?
The main motivation lies in the historical
importance of the textile sector, which played a vital role in previous
industrial revolutions and continues to be a key industry in developing
economies (Majumdar et al., 2021).
Therefore, this study conducted a Systema=
tic
Literature Review (SLR) to map technological advancements in the textile
sector, highlighting specific areas adopting the emerging Industry 4.0
technologies. Moreover, the research aimed to compare the implementation of
Industry 4.0 in the textile and automotive sectors, offering insights for
textile companies seeking to improve their competitiveness and stand out in=
an
increasingly challenging global economy.
This study not only aimed to identify gap=
s and
trends in the adoption of Industry 4.0 technologies but also to provide cle=
ar guidance
for the textile industry on its path toward efficient and sustainable digit=
al
transformation.
2 METHODOLOGY<= o:p>
For the development of this study, the
following steps were undertaken: i) defining the
search terms and strategies; ii) selecting the survey sources, iii) establi=
shing
the inclusion and exclusion criteria, iv) choosing the screening software, =
and v)
bibliographic management.
To collect information, a search was cond=
ucted
for articles addressing the topic in question, performing a theoretical sur=
vey
in the Web of Science (WoS) and Scopus database=
s,
which are recognized for their multidisciplinary scope and the large number=
of
available titles. Regarding the bibliographic survey, specific keywords were
identified for the textile and automotive sectors, combined with the term
"Industry 4.0". The search was carried out in these databases usi=
ng
the terms defined for the industrial segments in question, as presented in
Table 1.
Table 1
=
De=
fined
search terms
|
Sector |
Initial strings=
|
|
Textile |
("Textile" OR
"Clothing" OR "Apparel" OR "Fashion" AND
"Industry 4.0") |
|
Automotive |
("Automotive Industry" OR "Automobile Manufacture&q=
uot;
OR "Automotive Sector" OR "Car Manufacturers" OR
"Automotive" AND "Industry 4.0") |
As inclusion criteria, academic articles
published in English in engineering and related fields between 2018 and 2022
were included. However, the study areas had to be adjusted for each sector
since each database has its own categorization. Nevertheless, the other
criteria remained consistent and unchanged. Table 2 shows an increase in pu=
blications
for both analyzed sectors, reinforcing that the theme of the Fourth Industr=
ial Revolution
is on the rise.
Table 2
=
Do=
cuments
from the Scopus and WoS databases from 2018 to =
2022
for the textile and automotive sectors
|
Ano/Base |
Textile |
Automotive |
||||
|
Scopus |
WoS<=
span
lang=3DEN-US style=3D'font-family:"Myriad Pro",sans-serif;mso-fareast-fon=
t-family:
"Times New Roman";mso-bidi-font-family:Arial;mso-bidi-theme-font:minor-bi=
di;
mso-ansi-language:EN-US'> |
Total |
WoS<=
span
lang=3DEN-US style=3D'font-family:"Myriad Pro",sans-serif;mso-fareast-fon=
t-family:
"Times New Roman";mso-bidi-font-family:Arial;mso-bidi-theme-font:minor-bi=
di;
mso-ansi-language:EN-US'> |
Scopus |
Total |
|
|
2018 |
147 |
6 |
153 |
196 |
13 |
209 |
|
2019 |
163 |
10 |
173 |
216 |
32 |
248 |
|
2020 |
184 |
14 |
198 |
251 |
34 |
285 |
|
2021 |
265 |
24 |
289 |
291 |
42 |
333 |
|
2022 |
209 |
26 |
235 |
260 |
53 |
313 |
|
Total |
968 |
80=
|
1048 |
1214 |
174 |
1388 |
Specialized tools were selected to conduc=
t the systematic review and meta-analysis projects. Rayy=
an
software, developed by the Qatar Computing Research Institute, simplifies t=
he
process of screening and selecting articles, offering functionalities such =
as data
export, duplicates removal, and collaborative screening
After applying the defined limitations, a total of 2.437 documents were identified, with=
968
related to the textile sector and 1.214 to the automotive industry found in=
the
WoS database. In the Scopus database, 80 and 174
documents were identified regarding the textile and automotive sectors,
respectively.
The results were organized based to the relevance criteria determined by the databases
algorithms, which consider keywords frequency, scientific journal quality, =
and
citation count. For this study, the 30 most relevant articles from each of =
the
four searches were selected, totaling 120 studies, whose metadata were stor=
ed
for further analysis. The identification and screening process is illustrat=
ed in
the PRISMA flowchart in Figure 1.
Figure 1 – PRISMA flowchart with the study
screening
Next, the 120 documents, 60 from each sec=
tor, obtained
from the two databases were screened. The Rayyan online tool was used to
facilitate this process. The first screening aimed to i=
dentifying
duplicates among the documents. Then, the document titles were analyzed to
assess their relevance relative to the objectives of this study. Subsequent=
ly,
the abstracts were read to ensure the inclusion of only those documents
relevant to the scope of this work. In the end, 34 articles remained, which
were stored and imported into the Mendeley bibliographic manager, where
critical reading was performed and the necessary information was extracted.
Table 3 lists the 17 articles analyzed from the textile industry, and Table=
4
shows the 17 articles analyzed from the automotive industry, both organized=
in
descending order of recency.
Table 3
Selected articles on Industry 4.0 in the textile sector
|
Reference |
|
Title |
Country |
|
(O=
lgun
& Turan, 2022) |
Digital Transformation in Texti=
le
Industry and A Study to Determine the Conceptual Awareness Level of Texti=
le
Firms Regarding Industry 4.0 |
Turkey |
|
|
(Agrawal et
al., 2021)=
|
Blockchain-based framework for supply chain
traceability: A case example of textile and clothing industry |
Switzerland |
|
|
(Bonnard et
al., 2021)=
|
Big data/analytics platform for
Industry 4.0 implementation in advanced manufacturing context |
Brazil |
|
|
(dos Santos et al., 2021) |
Decision-making in a fast fashion company in the
Industry 4.0 era: a Digital Twin proposal to support operational planning=
|
Brazil |
|
|
(L=
ee
& Lin, 2021)<=
/span> |
A Two-Phase Fashion Apparel
Detection Method Based on YOLOv4 |
Taiwan |
|
|
(Longo et a=
l.,
2021) |
Towards a mass customization in the fashion indus=
try:
An evolutionary decision aid model for apparel product platform design and
optimization |
Italy |
|
|
(Sharma et =
al.,
2021) |
Development of an Intelligent
Data-Driven System to Recommend Personalized Fashion Design Solutions |
France |
|
|
(Chen, 2020=
) |
Cross-disciplinary innovations by Taiwanese
manufacturing SMEs in the context of Industry 4.0 |
Taiwan |
|
|
(Santos et =
al.,
2021) |
A new concept of full-automated
equipment for the manufacture of shirt collars and cuffs |
Portugal |
|
|
(Verleysen, Holvoe=
t,
Proesmans, Den Haese, & wyffels,
2020) |
Simpler Learning of Robotic Manipulation of Cloth=
ing
by Utilizing DIY Smart Textile Technology |
Belgium |
|
|
(Ou et al.,
2019) |
SensorKnit: Architecting Textile
Sensors with Machine Knitting |
USA |
|
|
(Ślusarczyk et
al., 2019)=
|
Fourth industrial revolution: a way forward to at=
tain
better performance in the textile industry |
Poland |
|
|
(Ten Bhömer et al., 2019) |
Designing Predictive Tools for
Personalized Functionalities in Knitted Performance Wear |
China |
|
|
(Agrawal et
al., 2018)=
|
A secured tag for implementation of traceability =
in
textile and clothing supply chain |
France |
|
|
(Bertola & Teunissen, 2018)<=
/span> |
Fashion 4.0. Innovating fashion
industry through digital transformation |
Italy |
|
|
(Serrat et =
al.,
2018) |
Learning to measure for preshipment garment sizin=
g |
Spain |
|
|
(Tsai, 2018=
) |
Green Production Planning and
Control for the Textile Industry by Using Mathematical Programming and
Industry 4.0 Techniques |
Taiwan |
Table 4
Selected articles on Industry 4.0 in the automotive sector
|
Reference |
Title |
Country |
|
(Aivaliotis et al., 2022) |
An augmented
reality software suite enabling seamless human robot interaction |
Greece |
|
(de Ma=
ttos
Nascimento et al., 2022) |
A sustainable circular 3D print=
ing
model for recycling metal scrap in the automotive industry |
Spain |
|
(Jiménez-Jiménez et al., 2022) |
Advances and
challenges in the automotive industry-driving towards sustainable mobilit=
y |
Spain |
|
(Nagy & Lăzăroiu, 2022) |
Computer Vision Algorithms, Rem=
ote
Sensing Data Fusion Techniques, and Mapping and Navigation Tools in the
Industry 4.0-Based Slovak Automotive Sector |
Slovakia |
|
(Turner et al., 2022) |
Circular
production and maintenance of automotive parts: An Internet of Things (Io=
T)
data framework and practice review |
United Kingdom |
|
(Bavelos et al., 2021) |
Enabling Flexibility in
Manufacturing by Integrating Shopfloor and Process Perception for Mobile
Robot Workers |
Greece |
|
(Brunheroto et al., 2021) |
Implications=
of
Industry 4.0 to compan’es' performance: a comparison between Brazil and
Germany |
Brazil |
|
(Sanz et al., 2021) |
BiDrac Industry 4.0 framework:
Application to an Automotive Paint Shop Process |
Spain |
|
(Bysko et al., 2020) |
Automotive P=
aint
Shop 4.0 |
Poland |
|
(Redondo et al., 2020) |
A Decision-Making Tool Based on
Exploratory Visualization for the Automotive Industry |
Spain |
|
(Fraga-Lamas & Fernández-Caramés, 2019) |
A
Review on Blockchain Technologies for an Advan=
ced
and Cyber-Resilient Automotive Industry |
Spain |
|
(Mourtzis et al., 2019)<=
span
style=3D'mso-bookmark:_Hlk4746433'> |
Augmented reality application to
support the assembly of highly customized products and to adapt to produc=
tion
re-scheduling |
Greece |
|
(Para et al., 2019) |
Analyze, Sen=
se,
Preprocess, Predict, Implement, and Deploy (ASPPID): An incremental
methodology based on data analytics for cost-efficiently monitoring the
industry 4.0 |
Spain |
|
(Sell et al., 2019) |
Integration of autonomous vehic=
les
and Industry 4.0 |
Estonia |
|
(Krugh & Mears, 2018) |
A complement=
ary
Cyber-Human Systems framework for Industry 4.0 Cyber-Physical Systems |
USA |
|
(Silva et
al., 2018)=
|
A Customer Feedback Platform for
Vehicle Manufacturing Compliant with Industry 4.0 Vision |
Brazil |
|
(Yin et al., 2018) |
The evolutio=
n of
production systems from Industry 2.0 through Industry 4.0 |
China |
3 RESULTS
This section
presents the results of the systematic literature review based on the selec=
ted
articles, as previously presented, for both the textile and automotive sect=
ors.
3.1 Repercussion on the text=
ile
industry
3.1.1 Countries and institut=
ions
that published on Industry 4.0 in the textile sector
The educational institutions that publish=
ed on
Industry 4.0 in the textile and automotive sectors were analyzed and separa=
ted
according to the database used (Scopus and WoS)=
. Focusing
first the Scopus database and the textile sector,
Table 5 shows that the vast majority of institut=
ions
had two documents published. Four documents were published by two instituti=
ons
in Brazil, and four documents were published by two institutions in Italy.
However, the vast majority of institutions are l=
ocated
on the Asian continent, which plays a prominent role in the international
textile market.
Table
5
Institutions with the most studies indexed=
in
the Scopus database on the textile industry and Industry 4.0 (2018–2022)
|
Institution / Country |
Number |
|
2 |
|
|
Donghua University (DHU) / China |
2 |
|
National
Tsing Hua University (NTHU) / Taiwan |
2 |
|
Polytechn=
ic
University of Milan (Polimi) / Italy |
2 |
|
State
University of Campinas (Unicamp) / Brazil |
2 |
|
Marche
Polytechnic University (UNIVPM) / Italy |
2 |
|
Pakistan
National University of Sciences and Technology (NUST) / Pakistan |
2 |
|
Universit=
y of
Novi Sad (UNS) / Serbia |
2 |
|
Universitas
Pendidikan Indonesia (UPI) / Indonesia |
2 |
|
FEI University Center (FEI) / Brazil |
1 |
During the collection of documents associ=
ating
the textile industry and Industry 4.0 in the WoS
database, the same protocol used for the Scopus database was employed, whil=
e respecting
the characteristics of each database. After implementing all restrictive se=
arch
criteria, the advanced search identified 968 relevant documents by searching
only the titles, abstracts, and keywords of the studies. Considering the
studied period, the ten most prominent affiliated institutions, i.e., those=
to
which the authors of the scientific articles were associated, are presented=
in
Table 6.
Table 6
Institutions wi=
th the
most studies indexed in the WoS d=
atabase
on the textile industry and Industry 4.0 (2018–2022)
|
Institution / Country=
|
Number |
|
National Center for Scientific Research (CNRS) /
France |
29 |
|
Donghua Universi=
ty
(DHU) / China |
28 |
|
University of Lille (Univ-Lille) / France |
24 |
|
National Higher School of Tex=
tile
Arts and Industries (ENSAIT) / France |
20 |
|
Hong Kong Polytechnic University (PolyU) / China |
17 |
|
RWTH Aachen University (RWTH)=
/
Germany |
15 |
|
Swiss Federal Institute of Technology / Switzerl=
and |
15 |
|
Indian Institutes of Technolo=
gy
(IITs) / India |
14 |
|
KU Leuven University (KU
Leuven) / Belgium |
13 |
|
National Institute of Applied
Sciences of Lyon (INSA Lyon) / France |
12 |
From Table 6, it can be observed that 70 =
% of
the institutions are European, while the remainder are in Asia. When analyz=
ing
the countries with the highest number of publications in the WoS database, France stands out with 45 % of publicat=
ions,
which is consistent with its prominence in the international textile scenar=
io
due to fashion. China accounts for 20 % of the institutions, reflecting its
leading role in global textile production. Despite Germany being the pionee=
r in
studies related to the Fourth Industrial Revolution, it presented only 8 % =
of
the publications.
3.1.2 Emerging Industry 4.0 technologies =
in the
textile sector
The technologies and areas explored in the
textile sector show the multidisciplinarity of =
these
studies. Some topics include the automation of manufacturing, mainly relate=
d to
the production of shirt cuffs and collars through the development of a new
concept of almost fully automatic equipment (Santos et al., 2021). Other
studies address the implementation of traceability in the textile and cloth=
ing
supply chain (Agrawal et al., 2018), studies related to advanced methods of
clothing identification (Lee & Lin, 2021), big data/analytics platforms=
for
the implementation of Industry 4.0 (Bonnard et al., 2021), blockchain-based
frameworks for traceability in the supply chain (Agrawal et al., 2021),
predictive tools for customized functionalities in knitted clothing (Ten
In addition, a focus was observed on
incorporating sustainable principles and practices, such as green productio=
n,
sustainable planning and control, and the pursuit of mass customization in =
the
fashion industry. Some studies highlighted digital transformation as a mean=
s of
innovation (Bertola & Teunissen, 2018). This concept reflects the need =
for
the sector to adapt in order to remain competiti=
ve in
the era of Industry 4.0.
It was also observed that production is o=
ne of
the segments that benefits most from the applying
Industry 4.0 technologies. Examples include the use of collaborative robots=
in
activities such as folding clothing (Verleysen, Holvoe=
t,
Proesmans, Den Haese, & Wyffels, 2020).
Additionally, the use of the Digital Twin strategy to improve production
decisions in the fast fashion (dos Santos et al., 2021) and inventory
optimization (Tsai, 2018) segments stood out.
The studies revealed an upward trend in t=
he
textile industry toward incorporating Industry 4.0 technologies to improve
processes, products, and services. Digitization, data analytics, automation,
and customization are emerging as crucial elements to address challenges and
capitalize on opportunities in the contemporary textile industry.
Of the 17 articles analyzed, the most
frequently identified technologies were system integration, virtual simulat=
ion,
and robots, each mentioned in three articles. Other technologies were menti=
oned
only once, namely cybersecurity, artificial intelligence, blockchain, big d=
ata,
cloud computing, digital twins, the Internet of Things (IoT), cyber-physical
systems, and digital transformation. The relationship of each technology wi=
th
the textile sector was perceived as follows and summarized in Figure 2.
Figure 2 – Summary of Industry 4.0
technologies applied in the textile sector
3.2 Repercussion on the auto=
motive
sector
3.1.2 Countries and institut=
ions
that published on Industry 4.0 in the automotive sector
Following the limiting search criteria re=
sulted
in 174 documents from the Scopus database. Table 7 presents the ten leading
institutions of affiliation representing the authors associations during the
study period.
Table 7
Institutions with the most studies indexed=
in
the Scopus database on the automotive sector and Industry 4.0 (2018–2022)
|
Institution / Country |
Number |
|
Pontifical Catholic University of Paraná (PUCPR)=
/
Brazil |
5 |
|
NOVA University Lisbon / Portugal |
5 |
|
Silesian University of Technology / Poland |
4 |
|
University of Warwick / United Kingdom |
4 |
|
Chalmers University of Technology / Sweden |
3 |
|
Politehnica Universi=
ty
Timisoara / Romania |
3 |
|
Cranfield University / United Kingdom |
3 |
|
Tecnológico de Monte=
rrey
/ Mexico |
3 |
|
University of Brescia / Italy |
3 |
|
University of Michigan, Ann Arbor / USA |
3 |
The advanced search identified 1.214 docu=
ments.
From this result, Table 8 presents the ten institutions of affiliation with
which the authors of the scientific articles were associated that gave rise=
to
the studies indexed in the database under analysis.
Table 8
Institutions with the most
studies indexed in the WoS database on Industry=
4.0
in the automotive sector (2018–2022)
|
Institution / Country<=
/b> |
Number |
|
Technical University =
of Košice (TUKE) /<=
/span> Slovakia |
31 |
|
National Center for Scientific Research
(CNRS) / France |
22 |
|
Helmholtz Association=
/<=
/span> Germany |
20 |
|
University of Porto /<=
/span> Portugal |
20 |
|
Chalmers University of
Technology / Sweden |
19 |
|
Technical University of Munich (TUM) /<=
/span>Germany |
18 |
|
Karlsruhe Institute of
Technology (KIT) <=
span
lang=3DEN-US style=3D'font-family:"Myriad Pro",sans-serif;mso-bidi-font-f=
amily:
Arial;mso-bidi-theme-font:minor-bidi;mso-font-kerning:1.0pt;mso-ligatures:
standardcontextual;mso-ansi-language:EN-US'>/ Germany |
17 |
|
Polytechnic University of Turin =
/<=
/span> Italy |
16 |
|
Indian Institutes of
Technology (IITs) =
/ India |
15 |
|
BBA (joint venture between BMW and
Brilliance Auto Group) / China |
14 |
According to Table 8, the Technical Unive=
rsity
of Košice, a Slovak public university, appears =
first,
standing out with 31 indexed studies. The five most cited studies produced =
by
the institution address several aspects, including the implementation of
Digital Twin technology for manufacturing processes, the evaluation of the
capacity of the production process, the impact of parameters on gearbox
emissions, the application of advanced measurement methods for structural
analysis, and the design of large-scale logistics systems using computer
simulation.
In the tenth place is a joint venture for=
med by
the German automotive group BMW and the Brilliance Group, called BBA. It is=
a
research and development center in Shenyang, China, which opened in 2013 and
expanded in 2020. BMW increased its stake in BBA from 50 % to 7 5% in Febru=
ary
2022, becoming the first foreign automaker to gain majority control of its
joint venture in China. In addition, an investment was made in a battery
assembly plant with the aim of starting the production of electric vehicles=
.
Regarding geographic distribution, after
compiling the scientific documents from the two databases used, it was found
that several countries have implemented strategies focused on Industry 4.0
technologies related to the automotive sector in the development of indexed
scientific production. European countries accounted for 82 % of the documen=
ts, with
only Germany standing out with 55 %. Asia followed, representing 12% of the
documents.
3.2.2 Emerging Industry 4.0
technologies in the automotive sector
Figure 3 – Summary of Indust=
ry 4.0
technologies applied in the automotive sector
With the analysis of the articles related=
to
the automotive sector, the technology most often present was the
IoT (seven documents), followed by cloud computing (four documents).=
In
sequence, with three documents, the technologies of augmented reality, robo=
ts,
big data, and 3D printing were identified. In turn, digital twins, virtual
simulation, virtually guided self-service, and artificial intelligence each
appeared in two documents. Finally, sustainable mobility, cybersecurity, sy=
stem
integration, blockchain, and cyber-physical systems each appeared in one
article. The relationship of each technology to the automotive sector was
perceived as follows: Afterwards, Figure 3 summarizes the application of th=
ese
technologies applied in the automotiv sector
identified in the literature.
3.3 Comparison between the a=
doption
of Industry 4.0 technologies in the textile and automotive sectors
Following the systematic analysis of the
literature on the adoption of emerging Industry 4.0 technologies in the tex=
tile
and automotive sectors, Figure 4 was developed to illustrate the results
obtained. The review of 17 articles from each area revealed the identificat=
ion
of technologies in the textile sector. Among these, virtual simulation and
system integration were the most frequently cited, each appearing in three
studies. The remaining technologies were identified once in each article:
cybersecurity, artificial intelligence, blockchain, big data, cloud computi=
ng,
digital twins, IoT, robots, cyber-physical systems, and digital transformat=
ion.
Figure 4 – Emerging Indus=
try
4.0 technologies identified in the literature in the textile and automotive
sectors
In the automotive sector, IoT technology =
was
identified in seven articles. Subsequently, four publications highlighted t=
he
use of robots, a technology that proved to be particularly prominent in this
sector. As noted by Silva et al. (2018), the sector stands out for achieving
advanced levels of customization, exemplified by the creation of the Intern=
et
of Intelligent Vehicles (IoIV), an evolution of=
the
IoT principles applied to the automotive context. This breakthrough promotes
significant improvements in both intelligent vehicles and industrial
automation. Cloud computing technology was also reported in four publicatio=
ns.
Additionally, augmented reality, robots, big data, and 3D printing where each identified in three articles.
The use of virtual simulation and system
integration predates Industry 4.0 in the automotive sector. This practice w=
as
verified in the lean product development process in the study by Dal Forno =
et
al. (2016), who surveyed companies in Brazil and found that at least 80 % h=
ad
already consolidated these practices. Moreover, virtual simulation is also =
present
in the textile industry, particularly in the development of digital prototy=
pes aimed
at reducing the costs associated with physical prototypes. Additionally, th=
ere
is system integration with suppliers to reduce waste (Dal Forno et al., 202=
2).
4 CONCLUSION
Based on the analysis of the studies, it =
is
highlighted that the areas of logistics (supply chain) and apparel producti=
on
are the textile sectors that seek a more significant application of
technologies in their processes. While the textile sector is in the early
stages of implementing Industry 4.0 technologies, with a focus on software =
and
tools for process and product development, the automotive industry demonstr=
ates
an advanced level of application of these concepts, especially within the
retail scope, in which there are high levels of customization and enhanced
interaction with customers.
It was noted that the textile sector is
embarking on a significant journey toward the digital transformation of its
processes along its complex chain. The adoption of these technologies may v=
ary
between companies, but, in general, it is still relatively incipient, with =
the
introduction of technologies such as robotics (for the clothing folding
process) and blockchain (in supply chain traceability) especially standing =
out.
In contrast, the automotive industry has =
widely
adopted the emerging technologies of the fourth industrial revolution to
optimize manufacturing processes, improve product quality, and meet growing
consumer demands. Elements such as automation, real-time data analysis, and
connectivity along the supply chain have become an integral part of the
automotive industry landscape.
Therefore, from this systematic literature
review, the studied articles indicated that, although both industries have =
been
incorporating Industry 4.0 technologies, the automotive sector leads in ter=
ms
of implementation and comprehensiveness.
The contribution of Industry 4.0 technologies to render the textile
chain more agile, efficient, sustainable, and customer-oriented while remai=
ning
competitive in a constantly evolving global market is undeniable.
Research Trends and Future Agenda
Using the same databases and the same sea=
rch
strings from 2023 were checked some current trends overview, gaps and
limitations, proposals for future studies e potential impact for the textile
industry. The main studies highlighted for the textile industry (Chatchawanchanchanakij et al., 2023; Fani et al., 202=
4;
Ferlito, 2024; Haq et al., 2025; Marshall et al., 2024; Pant and Palanisamy,
2025; Safavi Jahromi and Ghazinoory, 2025; Van =
Ta et
al., 2024):
· =
Adoption of emerging Industr=
y 4.0
Technologies: The textile sector is impl=
ementing
several Industry 4.0 technologies, such as CPS, IoT, cloud computing, RFID,
blockchain, augmented reality and digital twins, to increase automation,
traceability and efficiency.
· =
Adoption challenges: despite the potential, adoption is hampered by lack of leadership,
educational misalignment, high costs, scalability difficulties, and low
utilization of advanced technologies.
· =
Transition to Industry 5.0: there is a movement towards Industry 5.0, which emphasizes
collaboration between humans and machines, mass customization, business
resilience, economic efficiency and sustainability.
· =
Focus on sustainability: Industry 4.0 plays a crucial role in promoting sustainability in t=
he
textile supply chain, through waste reduction, optimization of resource use=
and
better environmental management.
· =
Need for integration and dev=
elopment: for a successful transition to Industry 4.0, it is essential to
integrate technologies, develop supporting policies and focus on profession=
al
training and practical validation of technologies.
For the automotive industry, which has pr=
oven
to be more advanced than the textile industry, some trends and challenges a=
re
repeated. In summary, the following stand out (El Affa=
ki
et al., 2023; Agarwal et al., 2024; Aljuaid et =
al.,
2023; Macpherson, 2024; Patel and Patel, 2025; Piepoli=
et al., 2024; Schröder et al., 2024):
· =
Adoption of emerging Industr=
y 4.0
technologies: still needs to integrate
cyber-physical systems (CPS), Internet of Things (IoT), artificial intellig=
ence
(AI) and industrial automation technologies to increase production efficien=
cy,
reduce waste and optimize the supply chain.
· =
Implementation challenges: as in the textile and possibly other sectors, the barriers in the
automotive sector are financial constraints, a shortage of specialized
suppliers, data security concerns and a lack of employee training. In addit=
ion,
small and medium-sized companies face difficulties due to a lack of digital
infrastructure and investment.
· =
Focus on sustainability and =
green
manufacturing
· =
Supply chain transformation:=
Industry 4.0 optimizes the supply chain through the integration of =
IoT,
cloud computing and big data analytics, improving visibility, traceability =
and
risk management. Automotive mergers also benefit from Industry 4.0, with ga=
ins
in inventory turnover and reduced delivery times. In addition, there is a t=
rend
towards customized and localized manufacturing supported by 3D printing
technology.
· =
Quality and standardization<=
/span>: in addition to improving automotive quality, technologies such as =
Big
Data and IIOT (Industrial Internet of Things) need to be aligned with the
requirements of IATF 16949:2016. Standardizing processes and integrating
technologies are crucial to optimizing flexibility, productivity and
operational efficiency.
· =
Digitalization and talent ma=
nagement
strategies: companies are adopting dif=
ferent
digitalization strategies, focusing on automation, self-regulation and grad=
ual
experimentation. Talent retention is essential, requiring customized
strategies, continuous training and attention to the Sustainable Development
Goals (SDGs).
REFERENCE=
S
Affaki<=
/span>, O., Benh=
adou,
M. and Haddout, A. (2023), “Synergy between In=
dustry
4.0 Technologies and Automotive Standard Requirements: Guide for
Implementation and Interactions Model Proposal”, International Journal of
Engineering Trends and Technology, Vol. 71 No. 3, pp. 368–376, doi: 10.14445/22315381/IJETT-V71I3P239.
Agarwal, S., Saxena, K.K., Agrawal, V., Dixit, J.=
K.,
Prakash, C., Buddhi, D. and Mohammed, K.A. (2024), “Prioritizing the barri=
ers
of green smart manufacturing using AHP in implementing Industry 4.0: a case
from Indian automotive industry”, TQM Journal, Vol. 36 No. 1, pp.
71–89, doi: 10.1108/TQM-07-2022-0229.
Agrawal, T. K., Koehl,
L., & Campagne, C. (2018). A secured tag for implementation of traceability in textile and
clothing supply chain. The International Journal of Advanced Manufactur=
ing
Technology, 99(9–12), 2563–2577.
https://doi.org/10.1007/s00170-018-2638-x
Agrawal, T. K., Kumar, V., Pal,=
R.,
Wang, L., & Chen, Y. (2021). Blockchain-based framework for supply cha=
in
traceability: A case example of textile and clothing industry. Computers
& Industrial Engineering, 154(May 2020), 107130.
https://doi.org/10.1016/j.cie.2021.107130
Aivalio=
tis, S., Lots=
aris,
K., Gkournelos, C., Fourt=
akas,
N., Koukas, S., Kousi, N., & Makris, S. (2=
022).
An augmented reality software suite enabling seamless human robot interact=
ion.
International Journal of Computer Integrated Manufacturing, 36(1),
3–29.
Aljuaid=
, A.A., Masood, S.A., Tipu, J.A=
.K.
and Shah, I. (2023), “Development of a localized production model for the
automotive industry, built into the concept of industry 4.0 in the Kingdom=
of
Saudi Arabia”, Eastern-European Journal of Enterprise Technologies,
Vol. 4 No. 13 pp. 101–113, doi:
10.15587/1729-4061.2023.282297.
Bavelos, A. C., Kous=
i,
N., Gkournelos, C., Lotsa=
ris,
K., Aivaliotis, S., Micha=
los,
G., & Makris, S. (2021). Enabling Flexibility in
Manufacturing by Integrating Shopfloor and Process Perception for Mobile R=
obot
Workers. APPLIED SCIENCES-BASEL, 11(9).
https://doi.org/10.3390/app11093985
Bertola, P., & Teunissen, J.
(2018). Fashion 4.0. Innovating fashion industry through digital
transformation. Research Journal of Textile and Apparel, 22(=
4),
352–369. https://doi.org/10.1108/RJTA-03-2018-0023
Bonnard, R., Arantes, M. D. S.,=
Lorbieski, R., Vieira, K. M. M., & Nunes, M. C.
(2021a). Big data/analytics platform for Industry 4.0 implementation in
advanced manufacturing context. The International Journal of Advanced
Manufacturing Technology, 117(5–6), 1959–1973.
https://doi.org/10.1007/s00170-021-07834-5
Brunher=
oto, P. H., Tomanek, D. P., &
Deschamps, F. (2021). Implications of Industry 4.0 to companies’ performan=
ce:
A comparison between Brazil and Germany. Brazilian Journal of Operations
& Production Management, 18(3), 1–10.
https://doi.org/10.14488/BJOPM.2021.009
Bysko, S., Krystek, J., & Bysko, S. (2020). Automotive Paint Shop 4.0. Comp=
uters
and Industrial Engineering, 139(xxxx),
1–13. https://doi.org/10.1016/j.cie.2018.11.056
Chatcha=
wanchanchanakij, P., Jerm=
sittiparsert,
K., Chankoson, T. and Waiyawuththanapoom, P. (=
2023),
“The role of industry 4.0 in sustainable supply chain: Evidence from the
textile industry”, Uncertain Supply Chain Management, Vol. 11 No. 1,
pp. 1–10, doi: 10.5267/j.uscm.2022.12.006.
Chen, C.-L. (2020).
Cross-disciplinary innovations by Taiwanese manufacturing SMEs in the cont=
ext
of Industry 4.0. Journal of Manufacturing Technology Management,
CNI. (2021). CONFEDERAÇÃO
NACIONAL DA INDÚSTRIA. Sondagem Especial - Ano 21, n=
. 83
(abril 2022). Brasília/D=
F.
Dal Forno, A. J., Bataglini, W. V.,
Steffens, F., & Ulson de Souza, A. A. (2021). Industry 4.0 in textile and apparel sector: a
systematic literature review. Research Journal of Textile and Apparel.
https://doi.org/10.1108/RJTA-08-2021-0106
Dal Forno, A. J., Bataglini, W. V.,
Steffens, F., & Ulson de Souza, A. A. (2022). Maturity model toll to diagnose Industry 4.0 in t=
he
clothing industry. Journal of Fashion Marketing and Management.
https://doi.org/10.1108/JFMM-09-2021-0241
Dal Forno, A. J., Forcellini, F. A=
.,
Kipper, L. M., & Pereira, F. A. (2016). Method for evaluation via benchmarking of the lean
product development process: Multiple case studies at Brazilian companies.=
Benchmarking,
23(4). https://doi.org/10.1108/BIJ-12-2013-0114
de Mattos Nascimento, D. L., Mury Nepomuceno, R., Caiad=
o,
R. G. G., Maqueira, J. M., Moyano-Fuentes, J., & Garza-Reyes, J. A.
(2022). A
sustainable circular 3D printing model for recycling metal scrap in the
automotive industry. Journal of Manufacturing Technology Management=
, 33(5),
876–892. https://doi.org/10.1108/JMTM-10-2021-0391
dos Santos, C. H., Gabriel, G. T., do Amaral, J. V. S.,
Montevechi, J. A. B., & de Queiroz, J. A. (2021). Decision-making in a fast fashion company in the =
Industry 4.0 era: a Digital Twin proposal to support
operational planning. International Journal of Advanced Manufacturing
Technology, 116(5–6), 1653–1666. https://doi.org/10.1007/s00170-021-07543-z
Fani, V., Bucci, I., Rossi, M. =
and
Bandinelli, R. (2024), “Lean and industry 4.0 principles toward industry 5=
.0:
a conceptual framework and empirical insights from fashion industry”, J=
ournal
of Manufacturing Technology Management, Vol. 35 No. 9, pp. 122–141,
Ferlito, R. (2024), “Industry 4=
.0
and sustainability: the case of the Italian textile district of Prato”,
Fraga-Lamas, P., & Fernández-C=
aramés,
T. M. (2019). Uma revisão sobre tecnologias Blockchain para um ambiente
avançado e resiliente cibernético Indústria automobilística.
Jiménez-Jiménez, A., Gessa-Perera,=
A.,
& Sancha-Dionisio, P. (2022). Advances and challenges in the automotive industry: driving towards
sustainable mobility. Dyna (Spain), 97(4), 1–10.
https://doi.org/10.6036/10487
Haq, U.N., Khan, M.M.R., Khan,
A.M., Hasanuzzaman, M. and Hossain, M.R. (2025=
),
“Global initiatives for industry 4.0 implementation and progress within the
textile and apparel manufacturing sector: a comprehensive review”, Inte=
rnational
Journal of Computer Integrated Manufacturing, pp. 1-26, doi: 10.1080/0951192X.2025.2455655.
Krugh, M., & Mears, L. (201=
8).
A complementary Cyber-Human Systems framework for Industry 4.0 Cyber-Physi=
cal
Systems. Manufacturing Letters, 15, 13–21.
https://doi.org/10.1016/j.mfglet.2018.01.003
Lee, C.-H., & Lin, C.-W.
(2021). A Two-Phase Fashion Apparel Detection Method Based on YOLOv4. A=
pplied
Sciences, 11(9), 3782. https://doi.org/10.3390/app11093782
Longo, F., Padovano, A., Cimmin=
o,
B., & Pinto, P. (2021). Towards a mass customization in the fashion
industry: An evolutionary decision aid model for apparel product platform
design and optimization. Computers and Industrial Engineering, 1=
62.
https://doi.org/10.1016/j.cie.2021.107742
Majumdar, A., Garg, H., & J=
ain,
R. (2021). Managing the barriers of Industry 4.0 adoption and implementati=
on
in textile and clothing industry: Interpretive structural model and triple
helix framework. Computers in Industry, 125. https://doi.org/10.1016/j.compind.2020.103372
Macpherson, W.E. (2024), “The
adoption of talent retention strategies in Industry 4.0 automotive organisations: Employees’ perspective”, SA Journa=
l of
Human Resource Management, Vol. 22, doi:
10.4102/sajhrm.v22i0.2789.
Marshall, J., Thompson-Whitesid=
e,
S. and Jan, T. (2024), “Barriers to the adoption of industry 4.0 technolog=
ies
within the Australian fashion industry”, International Journal of Fashi=
on
Design, Technology and Education, pp. 1-11, doi:
10.1080/17543266.2024.2409895
Mourtzis, D., Zogopou=
los,
V., & Xanthi, F. (2019). Augment=
ed
reality application to support the assembly of =
highly
customized products and to adapt to production re-scheduling. Internati=
onal
Journal of Advanced Manufacturing Technology, 105(9), 3899–3910.
https://doi.org/10.1007/s00170-019-03941-6
Nagy, M., & Lăzăr=
oiu,
G. (2022). Computer Vision Algorithms, Remote Sensing Data Fusion Techniqu=
es,
and Mapping and Navigation Tools in the Industry 4.0-Based Slovak Automoti=
ve
Sector. Mathematics, 13–21. https://doi.org/10.3390/math10193543
Olgun, B. A., & Turan, F. K.
(2022). Digital Transformation in Textile Industry and A Study to Determine
the Conceptual Awareness Level of Textile Firms Regarding Industry 4.0. Tekstil ve Mühendis<=
/span>, 29(125), 28–40.
https://doi.org/10.7216/1300759920222912504
Ou<=
/span>, J., Oran, D., Haddad, D. D.,
Paradiso, J., & Ishii, H. (2019). <=
span
class=3DSpellE>SensorK=
nit: Architecting Textile Sensors =
with
Machine Knitting. 3D Printing and Additive Manufacturing, 6(=
1),
1–11. https://doi.org/10.1089/3dp.2018.0122
Ouzzani=
, M., Hammady, H., Fedorowicz, =
Z.,
& Elmagarmid, A. (2016). Rayyan—a web and =
mobile
app for systematic reviews. Systematic Reviews, 5(1), 210.
https://doi.org/10.1186/s13643-016-0384-4
Pant, K. and Palanisamy, P. (20= 25), “Navigating the path to Industry 4.0: a study on key barriers in Indian textile supply chain”, Benchmarking, Vol. ahead-of-print No. ahead-of-print doi: 10.1108/BIJ-11-2024-0970.<= o:p>
Para, J., Del Ser, J., Nebro, A. J., Zurutuza, U., & Herrera, F. (2019)=
. Analyze,
Sense, Preprocess, Predict, Implement, and Deploy (ASPPID): An incremental
methodology based on data analytics for cost-efficiently monitoring the
industry 4.0. Engineering Applications of Artificial Intelligence, =
82(March),
30–43. https://doi.org/10.1016/j.engappai.2019.03.022
Patel, M.P. and Patel, J.D. (20=
25),
“Synergy Gains in Supply Chain for Automobile Manufacturing in Post-Merger
Integration: A Case Study of Stellantis”, International Journal of
Innovative Science and Research Technology, pp. 1647–1659, doi: 10.38124/ijisrt/25a=
pr933.
Piepoli=
, A., Arcidiacono, F., Basile,
L.J., Pellegrino, R., Schupp, F. and Zuehlke, T. (2024), “The Interplay
Between Industry 4.0 Technologies and Business Performance: Evidence from a
Multiple Case Study in the Automotive Sector”, IEEE Engineering Managem=
ent
Review, Vol. 52 No. 1, pp. 108–120, doi:
10.1109/EMR.2023.3328780.
Redondo, R., Herrero, A., Corchado,
E., & Sedano, J. (2020). A
Decision-Making Tool Based on Exploratory Visualization for the Automotive
Industry. Applied Sciences-basel, 10=
(12).
https://doi.org/10.3390/app10124355 WE - Science Citation Index Expan=
ded
(SCI-EXPANDED)
Safavi Jahromi, G. and Ghazinoory, S. (2025), “Clothing industry in transit=
ion
from Industry 4.0 to Industry 5.0”, Journal of the Textile Institute,
Vol. 116 No. 3, pp. 365–379, doi:
10.1080/00405000.2024.2336438.
Santos, P. M. M., Campilho,=
R. D.
S. G., & Silva, F. J. G. (2021). A new concept of full-automated equipment for the manufacture of sh=
irt
collars and cuffs. Robotics and Computer-Integrated Manufacturing, =
67,
102023. https://doi.org/10.1016/j.rcim.2020.102023
Sanz, E., Ble=
sa,
J., & Puig, V. (2021). BiDrac Industry 4.0 framework: Application to an Automo=
tive
Paint Shop Process. Control Engineering Practice, 109(April
2020), 104757. https://doi.org/10.1016/j.conengprac.2021.104757
Schröder, M., Mokudai,
T. and Holst, H. (2024), “Industry 4.0 and lean augmentation? Digital
transformation in the German and Japanese automotive industry”, Interna=
tional
Journal of Automotive Technology and Management, Vol. 24 No. 6, doi: 10.1504/IJATM.2024.144148.
Sell, R., =
Rassõlkin,
A., Wang, R., & Otto, T. (2019). Integration of autonomous vehicles and
industry 4.0. Proceedings of the Estonian Academy of Sciences, 6=
8(4),
389–394. https://doi.org/10.3176/proc.2019.4.07
Serrat, J., Lumbreras, F., &
Ruiz, I. (2018). Learning to measure for preshipment<=
/span>
garment sizing. Measurement, 130, 327–339.
https://doi.org/10.1016/j.measurement.2018.08.019
Sharma, S., Koehl, L., Bruniaux, P., Zeng, X., & Wang, Z. (2021). Devel=
opment
of an Intelligent Data-Driven System to Recommend Personalized Fashion Des=
ign
Solutions. Sensors,
21(12), 4239. https://doi.org/10.3390/s21124239
Silva, M., Vieira, E., Signoretti<=
/span>,
G., Silva, I., Silva, D., & Ferrari, P. (2018). A Customer Feedback Platform for Vehicle
Manufacturing Compliant with Industry 4.0 Vision. Sensors, 18(10),
3298. https://doi.org/10.3390/s18103298
Ślusarczyk, B., Haseeb, M.,
& Hussain, H. I. (2019). Fourth industrial revolution: A way forward to
attain better performance in the textile industry. Engineering Manageme=
nt
in Production and Services, 11(2), 52–69.
https://doi.org/10.2478/emj-2019-0011
Ten=
Bhömer, M.,
Liang, H.-N., Yu, D., Liu, Y., Zhang, Y., De Laat,
E., & Leegwater, C. (2019). Designing Predictive Tools for
Personalized Functionalities in Knitted Performance Wear. Temes de Disseny, 35, 42–75.
https://doi.org/10.46467/TdD35.2019.42-75
Tsai, W.-H. (2018). Green
production planning and control for the textile industry by using mathemat=
ical
programming and industry 4.0 techniques. Energies, 11(8). https://doi.org/10.3390/en11082072
Turner, C., O=
korie,
O., Emmanouilidis, C., & Oyekan,
J. (2022). Circular
production and maintenance of automotive parts: An Internet of Things (IoT)
data framework and practice review. Computers in Industry, 136. https://doi.org/10.1016/j.=
compind.2021.103593
Van Ta, C., Jin, B.E. and Cho, =
H.J.
(2024), “Examining the relationship between firm characteristics and Indus=
try
4.0 technology adoption in Vietnam’s apparel industry”, International
Journal of Fashion Design, Technology and Education, Vol. 17 No. 3, pp.
404–419, doi: 10.1080/17543266.2024.2326937.
Verleysen, A., Holvoet,
T., Proesmans, R., Den Haese, C., & wyffels, F. (2020). Simpler Learning of Robotic
Manipulation of Clothing by Utilizing DIY Smart Textile Technology. App=
lied
Sciences, 10(12), 4088. https://doi.org/10.3390/app10124088
Yin, Y., Stecke, K. E., & L=
i,
D. (2018). The evolution of production systems from Industry 2.0 through
Industry 4.0. International=
Journal of Production Research, 56(1–2), 848–861.
https://doi.org/10.1080/00207543.2017.1403664
Do tecido ao chão de fábrica:
contrastando a implementação da indústria 4.0 nas indústrias têxtil e
automotiva
Ana Maria Barbosa
Dias; Ana Julia Dal Forno; Fernanda Steffens
|
IS=
SN
2237-4558 •<=
/span> Navus • Florianópolis • SC •
v. 16 • p. 01- |
|
|
ISSN 2237-4558 • Navus • |
|