Access to this full-text is provided by Springer Nature.
Content available from SN Applied Sciences
This content is subject to copyright. Terms and conditions apply.
Vol.:(0123456789)
SN Applied Sciences (2023) 5:387 | https://doi.org/10.1007/s42452-023-05587-2
Review
Artificial intelligence andsustainability inthefashion industry:
areview from2010 to2022
LeoRamos1,4 · FrancklinRivas‑Echeverría1,2 · AnnaGabrielaPérez3 · EdmundoCasas4
Received: 15 August 2023 / Accepted: 8 November 2023
© The Author(s) 2023 OPEN
Abstract
The fashion industry often falls short of sustainability goals, but contemporary technological advancements oer a wide
range of tools to address this issue. Articial Intelligence (AI) has emerged as a particularly promising ally in promoting
sustainability in fashion. This literature review explores how AI can contribute to the fashion industry’s sustainability,
highlighting its potential benets and limitations. Following PRISMA guidelines, we conducted a review of scientic
documents, focusing on the period from 2010 to 2022. After a meticulous selection process, we analyzed 37 scholarly
articles to distill their key insights and contributions. Our ndings demonstrate that AI has diverse applications in dierent
aspects of the fashion industry, enhancing sustainability eorts in supply chain management, creative design, sales and
promotion, waste control, and data analysis. While AI oers signicant potential, it is important to acknowledge limita‑
tions, such as the volume of data required and associated implementation costs. The reviewed literature aligns with the
multifaceted nature of sustainability, emphasizing responsible resource management, accessible services, and ecient
customer satisfaction, both now and in the future. In conclusion, despite some reservations, AI stands as a crucial partner
in guiding the fashion industry toward a more sustainable future.
Article Highlights
• AI presents promising that can be applied to design,
sales, and wastemanagement, all aimed at boosting
sustainability.
• Many of the research studied focus on environmental
dimension, includingecofriendly manufacturing and
defect detection.
• Despite reluctance, AI is becoming a vital ally in moving
the fashion industrytoward sustainability.
Keywords Articial intelligence· Fashion industry· Sustainability· Sustainable fashion· Sustainable development
* Leo Ramos, theleothomasramos@gmail.com; Francklin Rivas‑Echeverría, frivas6@gmail.com; Anna Gabriela Pérez, gabipm23@
gmail.com; Edmundo Casas, edmundo.casas@kauel.com | 1School ofMathematical andComputational Sciences, Yachay Tech University,
Urcuquí100119, Imbabura, Ecuador . 2Ponticia Universidad Católica del Ecuador Sede Ibarra, Ibarra10102, Imbabura, Ecuador
. 3Laboratorio de Sistemas Inteligentes, Universidad de Los Andes, Mérida05101, Venezuela . 4Kauel Inc., Houston, TX77027, USA.
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Vol:.(1234567890)
Review SN Applied Sciences (2023) 5:387 | https://doi.org/10.1007/s42452-023-05587-2
1 Introduction
Sustainability, as defined in Ref. [1], entails meeting pre‑
sent needs without endangering the requirements of
future generations. It involves safeguarding the natu‑
ral environment and social well‑being while ensuring
that economic growth and development do not pose
threats. Sustainability is a critical concern in the industry
[2]. Today, it is imperative to integrate social and envi‑
ronmental considerations into business decision‑making
and operations [3]. In specific industries, including agri‑
culture, mining, renewable energy, and manufacturing,
sustainability has emerged as a central objective aimed
at mitigating adverse impacts on the environment and
society [4].
Sustainability comprises three key dimensions: eco‑
nomic, social, and environmental, as noted by Kristensen
[5]. The economic dimension ensures that production
meets present needs without compromising future capac‑
ity [5, 6]. The social dimension focuses on parameters for
social equity, access to essential services, security, and
citizen participation in governance [7]. The environmen‑
tal dimension emphasizes responsible resource manage‑
ment and waste control to prevent overconsumption and
environmental degradation [5, 8]. These dimensions are
interconnected and integral to sustainable development.
The United Nations argues that strategies to gener‑
ate economic growth must go hand in hand with strate‑
gies to promote prosperity and protect the planet [9].
These include a range of social needs such as education,
health, and job opportunities, while at the same time
ensuring that climate change is halted and the environ‑
ment is protected.
The fashion industry, notorious for its sustainability
shortcomings [10], consumes vast resources in clothing
and accessory production, particularly straining water
resources. It ranks among the largest water consumers,
often with inadequate treatment post‑use [2]. Countries
like Bangladesh face issues of heavy metal and micro‑
plastic pollution in water sources [11, 12], leading to
health problems among nearby residents who consume
this water [13].
Furthermore, the fashion industry is marred by sig‑
nificant labor exploitation, often subjecting workers to
grueling shifts exceeding eight hours a day. Additionally,
the transportation of clothing and accessories further
exacerbates greenhouse gas emissions. Moreover, the
fast fashion model has accelerated the production and
disposal of clothing, resulting in a surge in waste gen‑
eration and unsustainable resource consumption [10].
As a countermeasure to all these problems, the fash‑
ion industry is increasingly trying to find solutions and
tools that will enable it to achieve sustainability goals.
As a result, the fashion industry is increasingly turning
to AI to help improve sustainability [14]. AI is a branch
of computing that develops systems for simulating the
cognitive capabilities of humans, especially in problem‑
solving tasks [15]. AI is a trending area, and its use has
spread to multiple areas, such as medicine, science, and
industry.
Through AI, the fashion industry can optimize vari‑
ous processes in the apparel production process [16, 17].
Moreover, AI algorithms enable companies to use their
resources better, leading to cost reduction, increased e‑
ciency and eectiveness, and increased production speed
[17]. In addition, this benets the environment and soci‑
ety as AI makes it possible to process and use natural and
human resources better [18].
This study provides a focused examination of how AI
can enhance sustainability within the fashion industry. We
concentrate on evaluating specic areas where AI can be
applied in fashion, as well as assessing the performance
of dierent AI techniques in bolstering sustainability. Our
primary aim is to discern both the merits and drawbacks
associated with these AI approaches, oering valuable
insights for experts and stakeholders in these elds.
2 Methodology
This systematic review was conducted using the Preferred
Reporting Items for Systematic Reviews and Meta‑Analy‑
ses (PRISMA) statements [19], the most commonly used
reporting guidelines for systematic reviews [20]. The fol‑
lowing is a description of the stages used to carry out this
work.
2.1 Research questions
This article aims to explore how AI is used in the fashion
industry to improve sustainability and answer the follow‑
ing questions:
Q1 In which ways can articial intelligence improve sus‑
tainability in the fashion industry?
Q2 What are the advantages of using articial intelli‑
gence as a tool to achieve sustainability?
Q3 What are the limitations of using articial intelligence
as a tool to achieve sustainability?
2.2 Eligibility criteria
2.2.1 Inclusion criteria
Regarding the inclusion criteria (IC), an article was
included as long as it met all of the following criteria:
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Vol.:(0123456789)
SN Applied Sciences (2023) 5:387 | https://doi.org/10.1007/s42452-023-05587-2 Review
IC1 Empirical research, not books, manuals, or tutorials.
IC2 Research that explicitly uses AI techniques as a
potential tool to drive sustainability in the fashion
industry.
IC3 Research published between 2010 and 2022.
IC4 Research published in peer‑reviewed journals only.
2.2.2 Exclusion criteria
Regarding exclusion criteria (EC), an article was excluded
if it failed to meet any of the following criteria:
EC1 Research that does not involve approaches based
on AI.
EC2 Research not published in a peer‑reviewed journal.
EC3 Research not written in English.
EC4 Document not available.
2.3 Information sources
We decided to use multiple databases and search engines
to expand the number of relevant articles considered.
Details of these are given in Table1.
Other sources, such as trial registers or other grey litera‑
ture sources, were not used.
2.4 Search strategy
We performed a search string based on the previously
mentioned inclusion criteria to search for articles in the
databases. This was: ( ( ( "sustainability" AND " fashion indus-
try" ) OR "sustainable fashion" ) AND ( "articial intelligence"
OR "machine learning" OR "deep learning" OR "expert sys-
tems" OR "knowledge-based systems" ) ). Likewise, we apply
a series of lters, where possible, in each of the databases
to obtain only relevant articles for our review. Details of
this can be seen in Table2. The databases were searched
on December 30, 2022.
2.5 Selection process
From the preselected articles, titles and abstracts were
imported into Covidence1 systematic review software
for screening. First, using Covidence tools, duplicate
articles were identied and removed. Then, the articles
were manually reviewed by two researches (LR and FR) to
remove any remaining duplicates. Next, all the research‑
ers independently screened the titles and abstracts of
the articles. In case of disagreement, the consensus was
reached to determine articles to screen in the next stage
by discussion.
The next step was to remove articles that could not be
accessed. That is, articles whose full‑text was unavailable
were removed and not included for full‑text screening.
This was done by one researcher (LR).
Then, the next step consisted of retrieving the articles
and screening them by full‑text reading. In this step, the
inclusion and exclusion criteria were considered to deter‑
mine which articles passed to the next stage and which
did not. This was done by two researchers (LR and FR) and
veried by the other (AGP and EC).
Finally, the articles that met the inclusion criteria and
evidenced a relevant contribution to the objectives of this
study were included in the review.
2.6 Data collection process
A data extraction sheet was developed for this stage. The
first version of the extraction sheet was made by two
researchers (LR and FR). This was rst tested with ten ran‑
domly selected articles. Subsequently, the other research‑
ers (AGP and EC) veried and validated that the extraction
sheet worked correctly and allowed all relevant informa‑
tion to be obtained. They also made corrections to the
extraction sheet when necessary.
2.7 Information extraction
To address our research questions, the articles selected for
review were thoroughly examined to extract the following
main information:
• Potential application domain or area.
• AI class used.
• Aim of the work.
• Main technique(s) used.
• Relevant ndings.
• Publication year.
Table 1 Online sources used in this work
No. Source Url
1 Scopus www. scopus. com
2 ScienceDirect www. scien cedir ect. com
3 ACM Digital Library https:// dl. acm. org
4 IEEE Xplore https:// ieeex plore. ieee. org
5 Springer Link www. link. sprin ger. com
6 Google Scholar https:// schol ar. google. com/
1 https:// www. covid ence. org/.
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Vol:.(1234567890)
Review SN Applied Sciences (2023) 5:387 | https://doi.org/10.1007/s42452-023-05587-2
In a complementary manner, the publishing company and
the journal in which each article was published were also
extracted. The information extraction was carried out and
cross‑checked by all the researchers in this work.
3 Results
We identied 616 articles from the selected online sources.
Articles were screened and selected, as shown in Fig.1,
resulting in 37 studies meeting our inclusion criteria and
ultimately being included in the review.
Figure2 shows the distribution of relevant articles
retrieved per year. From this graph, it can be seen that from
2019 onwards, there has been an increase in sustainability,
the fashion industry, and AI research. Most of the research
is focused on the year 2020. The year 2022 presents a low
number of articles since most of the articles we could not
access are from this year. Concerning the publishers, it is
evident that Elsevier is the academic publishing company
that contributes most to this work. It provides more than
40% of the articles retrieved, as shown in Table3. In sec‑
ond place is Springer, followed by the other publishing
companies.
Given that we studied dierent domains in this work, a
wide variety of journals host this type of research. Table4
below highlights the top four journals among those that
contributed signicantly to this study.
From this table, the Journal of Cleaner Production stands
out as the journal that contributes the most to this study,
providing 10.81% of the articles reviewed. To integrate
the textile industry with technology, the International
Journal of Clothing Science and Technology is the journal
that makes the second most signicant contribution, with
8.11%. This could be due to the signicant development
of technology and its irruption in dierent industries in
recent years. Then, the journals Multimedia Tools and Appli-
cations and Textile Research Journal contribute 5.41% of the
articles. Finally, all the other journals have a contribution
of 2.7% each.
Subsequently, we categorized the articles based on
their potential applications in promoting sustainability
within the fashion industry. The allocation of each arti‑
cle to a specic domain was achieved through extensive
discussions and consensus among all the researchers
involved in this study. The ndings reveal that a majority of
the reviewed articles focus on optimizing the supply chain,
comprising 40.54% of the total articles. Following closely,
Table 2 Filters and specications for searching online sources
Source Search string Filters
Scopus TITLE‑ABS‑KEY(("sustainability" AND "fashion industry") OR
"sustainable fashion") AND TITLE‑ABS‑KEY("articial intel‑
ligence" OR "machine learning" OR "deep learning" OR
"expert systems" OR "knowledge‑based systems")
Article type: Research article
Year(s): 2010–2022
ScienceDirect (("sustainability" AND "fashion industry") OR "sustain‑
able fashion") AND ("articial intelligence" OR "machine
learning" OR "deep learning" OR "expert systems" OR
"knowledge‑based systems")
Article type: Research article
Year(s): 2010–2022
ACM Digital Library [[[All: "sustainability"] AND [All: "fashion industry"]] OR [All:
"sustainable fashion"]] AND [[All: "articial intelligence"]
OR [All: "machine learning"] OR [All: "deep learning"]
OR [All: "expert systems"] OR [All: "knowledge‑based
systems"]]
Content type: Research article
Publication: Journals
Publication date: 2010–2022
IEEE Xplore ((("All Metadata":sustainability AND "All Metadata":fashion
industry) OR "All Metadata":sustainable fashion)
AND ("All Metadata":articial intelligence OR "All
Metadata":machine learning OR "All Metadata":deep
learning OR "All Metadata":expert systems OR "All
Metadata":knowledge‑based systems))
Type: Journals
Year(s): 2010–2022
Springer Link (("sustainability" AND "fashion industry") OR "sustain‑
able fashion") AND ("articial intelligence" OR "machine
learning" OR "deep learning" OR "expert systems" OR
"knowledge‑based systems")
Content type: Article
Date published: 2010–2022
Language: English
Google Scholar (("sustainability" AND "fashion industry") OR "sustain‑
able fashion") AND ("articial intelligence" OR "machine
learning" OR "deep learning" OR "expert systems" OR
"knowledge‑based systems")
Exclude: Patents
Date published: 2010–2022
Without the words: “review", “meta‑analysis", “systematic
review", “exploratory study", “bibliometric review",
“literature review", “systematic literature review"
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Vol.:(0123456789)
SN Applied Sciences (2023) 5:387 | https://doi.org/10.1007/s42452-023-05587-2 Review
27.03% of the articles are dedicated to sustainable cloth‑
ing design and sales. Reducing waste represents 18.91% of
the articles, while data analysis contributes 13.52%. These
insights are summarized in Table5.
Upon closer examination of the technologies used in the
retrieved articles, articial neural networks (ANNs) are the
most frequently employed. Figure3 illustrates the prevalent
use of ANNs in various forms, including traditional multi‑
layer feed‑forward neural networks, convolutional neural
networks (CNNs) for image analysis, and generative adver‑
sarial networks (GANs) for image generation. Additionally,
fuzzy logic and classical machine learning (ML) algorithms
Fig. 1 PRISMA ow diagram
used in this work
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Vol:.(1234567890)
Review SN Applied Sciences (2023) 5:387 | https://doi.org/10.1007/s42452-023-05587-2
like k‑means, random forests, and support vector machines
are commonly featured. Other technologies, such as block‑
chain and the Internet of Things (IoT), are also utilized.
We also classified the articles based on the sector to
which their contributions are oriented, including govern‑
ment, business, or customer orientation. Similarly, we cat‑
egorized each article according to the sustainability dimen‑
sion that best represents its contribution. In cases where an
article could belong to more than one dimension or target
sector, we selected the most representative one through
consensus among all the researchers involved in this study.
The results regarding the sustainability dimension are
summarized in Table6. These ndings reveal that the major‑
ity of the articles are situated within the economic dimen‑
sion, focusing primarily on measures aimed at satisfying the
present and future needs of customers. Specically, 41% of
the reviewed articles fall within this dimension. Following
closely is the environmental dimension, encompassing 35%
of the articles. This sector is notable for its contributions
related to waste management, control, and reduction, with
several works dedicated to enhancing recycling practices.
Lastly, the social dimension comprises 24% of the articles,
driven by contributions aimed at improving accessibility and
services
Regarding the target sector, the outcomes of this clas‑
sication are detailed in Table7. The data indicates that the
majority of the articles are business‑oriented (84%), empha‑
sizing sustainability measures implemented within company
operations. These encompass actions concerning the sup‑
ply chain, manufacturing processes, and design, all aimed at
enhancing sustainability. In the second position is the cus‑
tomer sector (16%), focusing on initiatives aimed at improv‑
ing the customer’s shopping experience while encouraging
the consumption of sustainable products. Conversely, the
government sector exhibits minimal relevance in this con‑
text (0%).
The summary of selected articles in terms of their main
characteristics is presented in Table8. This data extraction
table serves as a comprehensive guide to understanding the
landscape of the research included in this review. It details
key aspects such as the year of publication, methodologies
employed, objectives, and key ndings. This consolidation of
information is designed to oer readers an easily navigable
overview, thereby facilitating a deeper comprehension of
the review’s scope, methods, and results.
4 Discussion
From the reviewed articles, it was possible to highlight four
main application areas in which AI can help to improve
sustainability in the fashion industry. Each of these is
described below.
Fig. 2 Overall distribution of retrieved articles over time
Table 3 Distribution of articles based on publisher
Publisher Amount Percent (%)
Elsevier 16 43.24
Springer 7 18.91
Emerald 3 8.11
MDPI 3 8.11
SAGE 2 5.41
Wiley 2 5.41
Taylor and Francis 2 5.41
IEEE 1 2.7
Growing Science 1 2.7
Total 37 100
Table 4 Four leading journals contributing to this review
Journal Amount Percent (%)
Journal of Cleaner Production 4 10.81
International Journal of Clothing Sci‑
ence and Technology 3 8.11
Multimedia Tools and Applications 2 5.41
Textile Research Journal 2 5.41
Table 5 Distribution of articles based on domain of application
Domain Amount Percent (%)
Supply chain optimization 15 40.54
Design and sale of sustainable
clothing 10 27.03
Reducing waste 7 18.91
Data analysis 5 13.52
Total 37 100
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Vol.:(0123456789)
SN Applied Sciences (2023) 5:387 | https://doi.org/10.1007/s42452-023-05587-2 Review
4.1 Supply chain optimization
The supply chain encompasses the entire process, from
the creation of a product or service to its delivery to the
end consumer. It can be visualized as a network [59] com‑
prising human and material components, all striving to
minimize costs and maximize eciency without compro‑
mising the nal product or service’s quality. However, real‑
world supply chains encounter numerous challenges [57],
including shortages, delayed deliveries, and diculties in
adapting to changing market demands [57, 60].
Sustainability has become a central theme in supply
chains, especially within the fashion industry. However,
many supply chains in this sector still need to become
more sustainable [61]. AI offers a myriad of benefits to
supply chains that would be otherwise unattainable
[57]. It provides a range of tools applicable throughout
the supply chain, from procurement and raw material
processing to manufacturing, distribution, and final
product delivery, all while promoting sustainability [62].
Numerous works have already incorporated AI to opti‑
mize supply chains. For instance, in Refs. [31, 30], ML and
ANNs, respectively, are proposed for classifying cloth‑
ing categories. Both approaches achieved high accuracy
rates (> 80%), correctly categorizing clothing and sub‑
categories. Models like these enable fashion companies
to automate classification tasks, ensuring organized and
efficient product category management.
In the manufacturing domain [36], suggests using ML,
specifically, support vector machine to detect common
fabric defects, such as neps, broken ends, broken picks,
and oil stains from images, obtaining high accuracy (>
98%). Similarly, in Ref. [33], CNNs are employed for color
difference detection, a common defect in warp‑knitted
fabrics. The authors utilized the YOLO neural architecture
and achieved real‑time accuracy. These examples illus‑
trate AI’s positive impact on sustainable apparel manu‑
facturing, enhancing efficiency and resource optimiza‑
tion by identifying manufacturing faults and preventing
defective garments from reaching consumers, ultimately
reducing waste.
Continuing with manufacturing [52], proposes a ML‑
based system that combines dimensionality reduction
techniques and k‑means based on 3D scans to define
adaptive morphotype mannequins. This innovative
approach eliminates the need for predefined tables,
ensuring garments better fit customers’ shapes, thus
optimizing manufacturing resources.
AI can also be utilized to select the best components
for garments. For example, in Ref. [27], an expert sys‑
tem is developed to identify and select the best type
of cotton fiber for product creation, leveraging docu‑
mented knowledge sources and customer experience.
The system optimizes clothing manufacturing, reducing
Fig. 3 Distribution of articles
by AI used over time. It was
selected the most relevant
technology used in each
retrieved article
Table 6 Articles according to the sustainability dimension assigned
to each
Sustainability dimension Amount Percent‑
age (%) References
Economic 15 41 [6, 21–34]
Environmental 13 35 [35–47]
Social 9 24 [48–56]
Table 7 Articles according to the target sector assigned to each
Target sector Amount Percentage
(%) References
Business 31 84 [6, 21–48, 50, 57]
Customer 6 16 [49, 51, 52, 54–56]
Government 0 0 –
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Vol:.(1234567890)
Review SN Applied Sciences (2023) 5:387 | https://doi.org/10.1007/s42452-023-05587-2
Table 8 Summary of reviewed articles
References Domain AI class Aim Main technique Key ndings Year
[21] Data analysis Machine learning To perform customer segmenta‑
tion to obtain market segments
and characterize subgroups of
observations.
K‑medoids Six market segments and 49
rules that allowed a better
understanding of customer
preferences in a highly custom‑
ized fashion manufacturer/e‑
tailor were obtained.
2015
[35] Data analysis Machine learning Evaluate the Brazilian consumer’s
ecological footprint and the
awareness of sustainability in
the clothing production stages.
J48 It was found that the majority of
Brazilian consumers surveyed
wear cotton clothing but are
not aware of how it is manu‑
factured. Also, most consumers
seek to buy sustainable prod‑
ucts, but do not know if the
manufacturers are sustainable.
2019
[48] Design and sale of sustainable
clothing Deep learning To propose an ANN model that
will take the latest fashion
trends and the clothes bought
by users as input and generate
new clothes recommendations.
Generative adversarial network The clothes generated by the
system is personalized for dif‑
ferent types of consumers. This
will help the manufacturing
companies to come up with
the designs, which will directly
target the customer.
2020
[49] Design and sale of sustainable
clothing Deep learning To develop an image‑based
virtual tting system that real‑
istically reects the appearance
and the behavior of garment.
Generative adversarial network The obtained system allows the
user to try on clothes from their
own home without having to
go out to the stores. This pro‑
vides an advanced virtual shop‑
ping experience to the user.
2022
[28] Design and sale of sustainable
clothing Deep learning To propose an intelligent, semi‑
autonomous decision support
system for the designers’ crea‑
tive processes.
Multi‑layer feed‑forward neural
network The proposed system allows
reducing long lead times,
eliminating the burden on
the manufacturing process
and transforming traditional
processes into intelligent
processes.
2020
[50] Design and sale of sustainable
clothing Machine learning To predict pattern and outt
based on the images collected
from New York Fashion Week
Fall/Winter 2019 Instagram
posts.
Logistic regression The model analyzed colour,
pattern, and outts and
predicted the patterns that
retailers might use in the com‑
ing season for mass‑market
consumers.
2020
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Vol.:(0123456789)
SN Applied Sciences (2023) 5:387 | https://doi.org/10.1007/s42452-023-05587-2 Review
Table 8 (continued)
References Domain AI class Aim Main technique Key ndings Year
[46] supply chain optimization Expert system Develop a framework to assess
sustainability in multi‑tier sup‑
ply chains.
Fuzzy logic It was found that the important
indicators for assessing sustain‑
ability are "Environmental
issues", "Economic issues", "Pol‑
icy and governance", "Participa‑
tion", "Social issues", "Trans‑
parency", "Leadership and
support". The expert system
allows a consistent assessment
of sustainability and provides
advice for decision making.
2021
[37] Design and sale of sustainable
clothing Expert system Help fashion designers in assess‑
ing consumers’ perception
of eco‑style and ensure the
success of sustainable product
development.
Fuzzy logic The proposed process proved
to be ecient when applied
to the analysis of the style of
the garment with respect to
the eco‑fashion style. This will
benet fashion designers in
the design and development of
sustainable products.
2019
[38] Reducing waste Expert system Development of a blockchain‑
enabled circular supply chain
management system in the fast
fashion industry with the goal
of reducing waste.
Blockchain Three relevant issues were
identied for circular supply
chain management in fast
fashion in terms of economic,
environmental and social
responsibility. The proposed
blockchain‑based system is
able to manage the challenges
in the fast‑fashion supply
chains to achieve a zero‑waste
circular economy.
2020
[33] Supply chain optimization Deep learning To propose a model to detect
color dierences in warp‑knit‑
ted fabric.
Convolutional neural network The system showed good
real‑time performance and
accuracy and can meet the
fabric inspection requirements
of warp‑knitted fabric factories.
2021
[51] Design and sale of sustainable
clothing Deep learning To develop a clothing recom‑
mendation system that consid‑
ers the user’s social circle and
fashion style consistency of
clothing items as important
factors in the user’s decision
making.
Convolutional neural network The proposed model showed
high precision in recommend‑
ing personalized clothing.
However, it proved to be a bit
slow when exposed to massive
data sets.
2017
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Vol:.(1234567890)
Review SN Applied Sciences (2023) 5:387 | https://doi.org/10.1007/s42452-023-05587-2
Table 8 (continued)
References Domain AI class Aim Main technique Key ndings Year
[24] Data analysis Machine learning To generate a better grouping
of products that have similar
behavior in terms of sales to
provide better information for
predictions, by a modication
of the K‑means algorithm.
K‑means The proposal produces a smaller
standard deviation in each
cluster compared to the con‑
ventional K‑means clustering
technique. This provides more
relevant and realistic informa‑
tion to use in predictions and
interpretations.
2017
[47] Supply chain optimization Expert system Propose a system for the most
appropriate sustainable selec‑
tion of suppliers in circular
supply chains.
Fuzzy logic The system showed its eective‑
ness and applicability when
tested with a case study. Unlike
other systems, the system
provides greater interactivity
with the user since it allows
customizing the evaluation
criteria and the importance of
the parameters.
2021
[39] Reducing waste Deep learning Complement or replace manual
waste sorting with an articial
intelligence‑powered robot to
perform this task.
Convolutional neural network The robot was successfully built
and trained and installed near
Barcelona in a municipal waste
sorting plant. It demonstrated
that this type of technology
is increasingly important in
the transformation of supply
chains, from product design to
disposal.
2021
[40] Reducing waste Deep learning Use dierent combinations of
cameras, lighting, and data
augmentation techniques to
create image databases to train
deep ANNs for recognition and
classication of ber materials.
Convolutional neural network The databases created were
used to train ANN systems
that obtained high accuracy
numbers in recognizing and
classifying ber materials. The
eectiveness of data aug‑
mentation and ANNs applied
to waste classication was
evidenced.
2019
[27] Supply chain optimization Expert system Development of an expert sys‑
tem that allows the identica‑
tion and selection of the best
type of cotton ber to create
products that meet the needs
of customers.
Quality function deployment The system was tested with two
study cases in which dierent
parameters and importance for
the dierent properties of the
bers were used. In both cases,
the system delivered a coher‑
ent result that, supported by
the numbers, was apt to satisfy
the user’s needs.
2018
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Vol.:(0123456789)
SN Applied Sciences (2023) 5:387 | https://doi.org/10.1007/s42452-023-05587-2 Review
Table 8 (continued)
References Domain AI class Aim Main technique Key ndings Year
[41] Reducing waste Deep learning Development of a methodology
that allows the identication,
classication and automatic
recycling of dierent materials.
Convolutional neural network It was shown that using ANNs
can automatically identify and
classify dierent materials,
including glass, paper, plastic,
and organic matter. This and
similar systems could dierenti‑
ate between materials that can
be cleaned for reuse and those
that can no longer be reused.
2021
[42] Reducing waste Knowledge‑based system Investigate the problems related
to waste management and
develop a system for the
sustainable and intelligent
management of these using
industry 4.0 technologies.
Internet of Things The proposed system uses essen‑
tial information on the truck,
weight scale, collection and
segregating room, conveyors,
hazardous bins, tumbler, heavy
materials compartment, light
material compartment, and
nal waste material bins. This
makes it possible to use all the
information and data on the
ow, input, process, and output
of waste for proper manage‑
ment of these.
2020
[25] Supply chain optimization Deep learning Predicting the burst strength and
air permeability of single jersey
knitted fabrics using regression
models and ANNs.
Multi‑layer feed‑forward neural
network The ANN proved to be better
than the linear regression and
allowed to nd the critical
factors inuencing the air
permeability and the bursting
strength of the single jersey
fabrics with low error.
2012
[26] Design and sale of sustainable
clothing Knowledge‑based system Propose a color predicting
method to capture the fashion
trend, which is an essential fac‑
tor that leads to winning a sale.
Fuzzy logic The prediction system met the
trend color capture criteria as
its predictions had low error
rates and high accuracy. The
system proved to have much
potential since it would allow
industries to make decisions to
select the color trend.
2016
[30] Supply chain optimization Deep learning To propose a technique for
tackling visual fashion clothes
analysis in images, aiming to
achieve clothing category clas‑
sication and attribute predic‑
tion by producing regularized
landmark layouts.
Convolutional neural network The proposed model enhances
classication by eectively rec‑
ognizing key features and their
location in an image by repre‑
senting multilevel contextual
information of landmarks.
2020
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Vol:.(1234567890)
Review SN Applied Sciences (2023) 5:387 | https://doi.org/10.1007/s42452-023-05587-2
Table 8 (continued)
References Domain AI class Aim Main technique Key ndings Year
[53] Design and sale of sustainable
clothing Deep learning To propose a neural network
system to design and recom‑
mend new clothing patterns
and styles.
Generative adversarial network The proposed system generates
attractive designs appreciated
by users. This was evidenced in
the evaluations since the sys‑
tem was evaluated with online
surveys and the volunteers
prefer the clothing generated
by the proposal.
2020
[36] Supply chain optimization Machine learning Address the problem of fabric
defect recognition from images
using ML techniques.
Support vector machine The results showed that the use
of support vector machine
for the recognition of tissue
defects produces accurate
results, and it emerged that
this type of classier has great
potential for the automatic
inspection of tissue defects in
the industry.
2011
[52] Supply chain optimization Machine learning To dene a methodology to
obtain a clustering of human
morphology shapes repre‑
sentative of a population and
to extract the most signicant
morphotype of each class.
K‑means The porposed technique facili‑
tates the automatic handling of
3D scans and calculating their
geodesic shape description
with optimized performance
for maximum dierentiation
ability. This approach has
the potential to signicantly
enhance the sizing systems
used by clothing companies.
2017
[32] Supply chain optimization Deep learning To use ANNs and regression
methods for exploring the rela‑
tionship between various drape
parameters and mechanical
fabric properties.
Multi‑layer feed‑forward neural
network The use of an ANN provided the
most comprehensive quan‑
titative understanding of a
material’s drapability, includ‑
ing shape parameters and
measurement technology. This
makes it highly benecial for
producing high‑quality textiles
and garments.
2010
[44] Reducing waste Deep learning To design a time series model for
predicting monthly based solid
waste generation.
Multi‑layer feed‑forward neural
network The proposed model showed to
give accurate predictive results.
The error percentages obtained
were low and allowed to obtain
a realistic prediction about the
generation of garbage.
2018
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Vol.:(0123456789)
SN Applied Sciences (2023) 5:387 | https://doi.org/10.1007/s42452-023-05587-2 Review
Table 8 (continued)
References Domain AI class Aim Main technique Key ndings Year
[45] Reducing waste Deep learning To develop a model to estimate
the annual amount of packag‑
ing waste.
Multi‑layer feed‑forward neural
network Dierent approaches were
examined, and the ANN was
the one that produced the best
results. Compared to conven‑
tional regression procedures,
the ANN was demonstrated to
have better explanatory power
and to be less susceptible to
outliers. The technique can be
used in other areas like fashion,
manufacturing, and construc‑
tion because the forecasts were
accurate.
2019
[58] Supply chain optimization Deep learning To test whether a simplied
neural‑network computational
model can make routing deci‑
sions in a logistics facility more
eciently than ve intelligent
routing heuristics.
Multi‑layer feed‑forward neural
network The results showed that the ANN
model performs better than
the best‑tested routing heuris‑
tics. Simulations with complex
logistics scenarios were carried
out with real‑world data, and
the ANN showed potential to
be applied in other elds, such
as supply chains and public
transport.
2016
[29] Data analysis Deep learning Propose a system for fast and
ecient sales forecasting for
fashion products.
Multi‑layer feed‑forward neural
network It was found that using ML and
traditional statistical methods
allows for controlling all the
time cost and forecast errors
so that a fast and eective
forecast can be obtained. The
system made sales forecasts
accurately.
2011
[54] Design and sale of sustainable
clothing Expert system To propose an hybrid model of
intelligent classication for size
recommendation.
Multi‑layer feed‑forward neural
network The system proposed demon‑
strated high accuracy and can
serve as a size recommenda‑
tion expert, reducing time and
increasing customer satisfac‑
tion by identifying the ideal
clothing size.
2013
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Vol:.(1234567890)
Review SN Applied Sciences (2023) 5:387 | https://doi.org/10.1007/s42452-023-05587-2
Table 8 (continued)
References Domain AI class Aim Main technique Key ndings Year
[43] Supply chain optimization Deep learning To examine the eects of waste
material composition on the
best truck route’s time, dis‑
tance, and air emissions. Create
a model that predicts the rate
of garbage generation, and
identify optimal waste collec‑
tion routes with minimal travel
distance.
Multi‑layer feed‑forward neural
network According to the proposed
model, investigations on the
optimization of geographic
information system routes
must take into account
temporal variations in trash
composition and characteris‑
tics. Additionally, the model’s
error was moderately low and
allowed the optimization of the
routes.
2019
[23] Supply chain optimization Expert system To propose a decision support
model for determining the
chance of meeting on‑time
delivery in a complex supply
chain environment.
Fuzzy logic The system demonstrated its
ability to supply crucial data
to the production and supply
chain planning departments
within the organization, while
also oering real‑time monitor‑
ing of delivery timeliness.
2013
[22] Supply chain optimization Deep learning To propose a system to deter‑
mine the optimum level of
nished goods inventory.
Multi‑layer feed‑forward neural
network The model was successful in
terms of its agreement with
actual values in a manufac‑
turing industry, with a good
performance by the ANN and
a low mean absolute error
percentage.
2011
[55] Design and sale of sustainable
clothing Deep learning To propose a novel ANN frame‑
work that simultaneously
provides outt recommenda‑
tions and generates abstractive
comments.
Convolutional neural network The experiments showed that
the proposal signicantly
improved over state‑of‑the‑art
baselines for outt recommen‑
dation. In addition to generat‑
ing the recommendation, the
system generates explanations
for these results with high
precision compared to human‑
written comments.
2019
[56] Design and sale of sustainable
clothing Deep learning To propose an ANN model to
generate complementary
fashion items by measuring
the compatibility between
complementary fashion items
comprehensively from both
item‑item and item‑template
perspectives.
Generative adversarial network The proposed method can eec‑
tively assess the compatibility
of complementary fashion
items both on an item‑to‑item
basis and in item‑template.
Results of testing indicated that
the proposed scheme has a
high level of accuracy.
2020
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Vol.:(0123456789)
SN Applied Sciences (2023) 5:387 | https://doi.org/10.1007/s42452-023-05587-2 Review
Table 8 (continued)
References Domain AI class Aim Main technique Key ndings Year
[34] Data analysis Machine learning To apply image mining for
fashion analysis, clustering
and predicting using fashion‑
related images collected from
the social network.
Mean‑shift clustering The suggested method dem‑
onstrated its robustness in
handling uncertainty present in
the input images. Additionally,
it proved to be benecial for
the fashion industry in making
informed decisions regarding
the production of fashion items
with specic features.
2020
[31] Supply chain optimization Machine learning To use data mining and symme‑
try‑based learning techniques
on product data to create a
classication model for predict‑
ing the garment category and
for predicting the garment
sub‑category.
Decision tree, naive Bayes, ran‑
dom forest, and Bayesian forest The study found that the random
forest classier performed
better than other methods in
classifying garments and sub‑
categories (upper body, lower
body, whole‑body). These
classications are useful for
decision support systems and
recommendation algorithms,
aiding customers in making
informed purchases.
2020
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Vol:.(1234567890)
Review SN Applied Sciences (2023) 5:387 | https://doi.org/10.1007/s42452-023-05587-2
long‑term costs and waste generation. Promising results
were obtained through real case testing.
Likewise, in Ref. [25], ANNs are employed to discover
the properties influencing burst strength and air per‑
meability in single jersey knitted fabrics, as well as to
predict these properties. In [32], ANNs are used to pre‑
dict the relationship between drape parameters and
fabric mechanical properties. These AI applications pro‑
vide quantitative insights into material characteristics
and their influencing parameters, facilitating resource
optimization and high‑quality garment and textile
manufacturing.
In the rst case, it was found that the burst strength of
single knit fabrics is aected by ber strength, ber elon‑
gation, and ber mean length. Similarly, air permeability
is aected by ber mean length, yarn twists per inch, yarn
count and number of wales and courses. In the second
case, bending, shear and aerial density were found to
aect the drape parameters the most. In both cases, AI
helped to understand quantitatively and in more detail
the materials’ characteristics and the parameters that inu‑
ence them.
In a complementary vein [22], employs ANNs to deter‑
mine the optimal inventory level for nished products,
considering setup costs, holding costs, material costs, and
product demand. This optimization prevents overproduc‑
tion of merchandise that may go unsold, ultimately reduc‑
ing waste and resource expenditure.
In terms of resource and product distribution, AI also
proves benecial. For instance [58, 43], utilize ANNs to
address routing problems, optimizing distribution vehi‑
cle routes to minimize distances, reduce gas emissions,
and ensure on‑time product delivery based on real‑time
geographical context information.
Furthermore, in Ref. [23], a decision support model lev‑
erages fuzzy logic to predict on‑time delivery chances in
a complex supply chain environment, mitigating negative
consequences of delivery variations, demand forecasting
inaccuracies, materials shortages, and distribution lead
time uncertainties.
Addressing supplier selection in sustainable supply
chain management [47], presents an expert system for
circular supply chains that manufacture, dispose of, and
recycle, reducing costs and waste. The system combines
multi‑criteria decision‑making, ML, and fuzzy logic to
select the most suitable suppliers, as demonstrated in a
real‑world case study.
Finally, [46] introduces an expert system based on fuzzy
logic to evaluate supply chain sustainability comprehen‑
sively. This system analyzes various aspects, including envi‑
ronmental, economic, policy, governance, participation,
social issues, transparency, and leadership support, yield‑
ing a sustainability score that assists fashion companies
in evaluating their operations and making decisions to
achieve sustainability objectives.
Common limitations in these articles include the high
data and processing demands of some AI approaches,
such as those based on ANNs. Additionally, the reluctance
to adopt this technology due to a lack of knowledge can
hinder its widespread use. Nonetheless, these works col‑
lectively demonstrate AI’s substantial potential to optimize
various aspects of a fashion company.
4.2 Design andsale ofsustainable clothing
Sustainable fashion is often perceived as less exciting, of
lower quality, or not aligned with the latest fashion trends
[63]. Despite this misconception, the sustainable fashion
industry and its products often struggle to gain market rel‑
evance [64]. However, AI emerges as a potentially valuable
tool for sustainable fashion companies, aiding in both the
design and promotion of clothing that aligns with current
fashion trends [65]. These technologies can signicantly
contribute to driving the purchase and use of sustainable
garments [66].
For instance, in Ref. [28], an application of Articial Neu‑
ral Networks (ANNs) is proposed to create a semi‑autono‑
mous intelligent system supporting designers during the
creative process. This system leverages user preferences,
fashion trends, seasonal data, and company constraints to
make predictions and design suggestions.
Similarly, in Ref. [37], an expert system is employed to
assess consumer perceptions of eco‑style. The objective
is to analyze consumers and gain deeper insights into
eco‑fashion and consumer perceptions, thereby ensur‑
ing the success of eco‑fashion and sustainable product
development.
Other works focus on analyzing the latest fashion trends
to inform clothing design. In Ref. [50], machine learning
methods are used to analyze trends from events like the
New York Fashion Week, predicting new design patterns
based on this data. Likewise, in Ref. [26], a fuzzy logic‑
based system is proposed to analyze fashion trends related
to color and suggest new color combinations for manufac‑
turers to consider. Both approaches have been evaluated
and demonstrated their utility in supporting designers’
decision‑making processes.
Design suggestion systems are also explored in Refs.
[48, 53], but with a twist. These works introduce Genera‑
tive Adversarial Networks (GANs) to generate new clothing
designs based on fashion trends and user purchasing data.
What sets these systems apart is their ability to generate
recommendations and graphical design suggestions. This
innovation streamlines the design and manufacturing pro‑
cess, oering designers textual feedback and visual repre‑
sentations, signicantly enhancing eciency.
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Vol.:(0123456789)
SN Applied Sciences (2023) 5:387 | https://doi.org/10.1007/s42452-023-05587-2 Review
While these developments are instrumental in making
sustainable fashion more appealing and aligned with cur‑
rent fashion trends, the ultimate goal of gaining market
relevance hinges on increasing the attractiveness and
accessibility of sustainable clothing for consumers. This
can be achieved through technologies that promote sus‑
tainable clothing and improve the search and purchasing
experience.
For instance, in Ref. [51], CNNs are employed to provide
recommendations that consider not only customer pref‑
erences but also their social network. A similar approach
is proposed in [55], which generates visual recommenda‑
tions along with explanations for the recommendations.
In Ref. [56], GANs are used to recommend complementary
fashion items, assisting customers in completing their out‑
ts by suggesting items that complement their selections.
Furthermore, in Ref. [54], a system is introduced to oer
size recommendations, utilizing size tables and ANNs to
create an intelligent sizing system. This system was tested
in an Iranian store, resulting in time savings and increased
customer satisfaction by assisting customers in selecting
the right clothing size.
Continuing to enhance the shopping experience, a vir‑
tual try‑on interface based on GANs is presented in Ref.
[49]. This interface allows users to virtually try on clothes
from the comfort of their homes, facilitating online shop‑
ping and providing a realistic visualization of how the
clothes would appear when worn.
In conclusion, AI has the potential to reshape sustain‑
able fashion by aligning it with current trends, enhancing
the design process, and improving the shopping experi‑
ence. This technology bridges the gap between sustain‑
ability and market relevance, making eco‑friendly fashion
more appealing and accessible to consumers.
4.3 Reducing waste
Effective waste management is critical for the fashion
industry’s sustainability, especially considering its histori‑
cal negative environmental impact [67]. Over recent years,
the proliferation of fast fashion and a throwaway culture
has led to a signicant surge in textile production and
consumption [68]. Unfortunately, the majority of textiles
and clothing ultimately nd their way into landlls, with
only a small fraction being recycled, making textile waste
a pressing global concern [69].
AI oers innovative solutions to address these waste
management challenges. In Ref. [42], an intelligent knowl‑
edge‑based system is applied to sustainable waste man‑
agement. This comprehensive approach analyzes various
facets of waste collection, transportation, and processing.
Moreover, it considers critical dimensions of sustainable
development, including well‑being, health, clean water,
and climate change.
Within the realm of supply chains [38], introduces an
expert system aimed at waste management. This system
focuses on the return of products at the end of their life
cycle to various supply chain components for reuse and
value recovery. Notably, it incorporates blockchain tech‑
nology, enabling the transparent processing and record‑
ing of data across the entire product lifecycle, thus foster‑
ing a circular economy.
AI’s predictive capabilities are harnessed in waste pre‑
diction models like those seen in Refs. [44, 45], both uti‑
lizing ANNs. By leveraging historical data, these systems
provide more accurate waste generation predictions.
Consequently, these insights empower the development
of strategies to curtail waste, boost recycling rates, and
promote sustainability.
AI‑driven waste classication is a rapidly advancing
eld. For instance [40], presents a methodology reliant on
CNNs for classifying dierent ber materials, even when
confronted with limited data. Similarly Ref. [41], demon‑
strates the eectiveness of CNNs in classifying various
materials, including glass, paper, plastic, and organic mat‑
ter, using images generated from smartphones.
Taking waste sorting to the next level [39], combines
CNNs with robotic technology. Equipped with sensors
and mechanical grippers, a robot continuously monitors
waste flow and autonomously performs sorting tasks.
The practical deployment of this system in a major Span‑
ish waste sorting plant underscores its industry relevance
and potential for ecient waste management.
In summary, AI presents a powerful toolkit for address‑
ing the fashion industry’s waste management challenges.
These innovative applications not only oer solutions for
sustainable waste disposal but also align with broader
sustainability goals. By leveraging AI for waste prediction,
classication, and intelligent systems, fashion companies
can not only reduce their environmental impact but also
enhance their operational eciency. These advancements
underscore the transformative role of AI in promoting sus‑
tainability within the fashion industry, emphasizing the
path towards a more eco‑friendly and responsible future.
4.4 Data analysis
Data analysis plays a pivotal role in the fashion industry,
oering valuable insights and predictive capabilities that
can be harnessed to drive sustainability eorts [57, 70, 71].
Customer segmentation, as demonstrated in studies like
[21, 24], allows fashion companies to gain a deeper under‑
standing of their customer base. By tailoring strategies to
specic market segments, these companies can eectively
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Vol:.(1234567890)
Review SN Applied Sciences (2023) 5:387 | https://doi.org/10.1007/s42452-023-05587-2
promote and sell sustainable clothing to a diverse range of
consumers.
Moreover, predictive analytics, exemplied in research
such as Refs. [29, 34], empowers fashion businesses to make
informed decisions and anticipate market trends. Sales pre‑
dictions and style forecasting enable these companies to
optimize inventory management, streamline supply chains,
and proactively address future challenges, all of which are
essential for sustainable fashion practices.
In the context of sustainable fashion, studies like [35] shed
light on consumer behavior and attitudes towards sustain‑
ability. By utilizing ML techniques to analyze consumer
responses, these studies reveal valuable insights into con‑
sumer knowledge and preferences regarding sustainable
clothing. The ndings underscore the importance of edu‑
cating consumers about sustainable fashion practices and
strategies to encourage their adoption.
In sum, data analysis serves as a powerful tool for fash‑
ion companies striving to enhance sustainability. From
customer segmentation to predictive analytics, these
data‑driven approaches empower the fashion industry to
make informed decisions and tailor strategies that promote
sustainable clothing consumption and production. This
underscores the potential of data analysis in driving posi‑
tive change within the fashion industry.
4.5 Limitations ofthestudy
One notable limitation of this study is its primary reliance
on existing literature and readily available sources. This
approach may inadvertently omit certain non‑academic,
ongoing, or unpublished works that could potentially con‑
tribute valuable insights to the eld of AI in sustainable fash‑
ion. Nevertheless, it is worth noting that a comprehensive
array of relevant online sources was diligently incorporated,
making a concerted eort to ensure that the ndings pre‑
sented in this paper oer a representative and meaningful
overview.
Furthermore, this study primarily operates at a high‑level
examination of the contributions of AI within the sustainable
fashion industry. This means that it provides a broad over‑
view and general insights into how AI is being applied to
promote sustainability in the fashion sector. However, it may
not encompass the intricate details and specic nuances
that some readers, particularly those looking for in‑depth
technical or sector‑specic information, may seek.
5 Conclusions
This study aimed to assess the application of Artificial
Intelligence (AI) in the fashion industry to promote
sustainability. To achieve this goal, we conducted a
systematic review of 37 articles sourced from relevant
online publications. These articles were subsequently
reviewed and analyzed.
The analysis of the selected articles unequivocally dem‑
onstrates that AI plays a pivotal role in the fashion indus‑
try’s transition towards sustainable development. The
ndings underscore a multitude of contributions that can
be harnessed across various facets of the fashion indus‑
try, encompassing supply chain optimization, sustainable
clothing design and sales, waste management and control,
and data analysis.
Our analysis reveals the pervasive presence of ANNs as
the primary technological cornerstone in the integration
of AI within the fashion industry. ANNs, with their versatile
capabilities, occupy a central role across a diverse array
of applications. These encompass but are not limited to
waste classication, where ANNs excel in their ability to
accurately categorize materials. Furthermore, they play a
pivotal role in garment defect detection, swiftly identify‑
ing and rectifying manufacturing aws. In addition, ANNs
signicantly contribute to the augmentation of sustain‑
able clothing design and production, ensuring that the
industry’s environmental footprint is minimized.
Moreover, our investigation uncovered a balanced dis‑
tribution among sustainability dimensions in the reviewed
articles. The majority of contributions are situated within
the economic dimension, prioritizing the fulllment of
current and future customer needs. Subsequently, the
environmental dimension encompasses a signicant por‑
tion of articles, primarily focusing on responsible waste
management and the promotion of recycling. Finally,
the social dimension concentrates on creating accessible
environments and providing services that cater to diverse
market needs.
The articles predominantly targeted the business and
customer sectors. In the business sector, contributions
primarily centered on improving garment manufacturing,
design, and handling processes. Conversely, the customer
sector emphasized equipping users with tools to encour‑
age the purchase of sustainable products while ensuring
a delightful and attractive shopping experience. Notably,
there were no signicant contributions directed towards
the government sector.
However, it’s crucial to acknowledge the limitations
encountered on this journey towards AI‑driven sustainabil‑
ity in fashion. One notable challenge lies in the substantial
volume of data required to train neural networks eec‑
tively. Acquiring and managing extensive datasets, espe‑
cially in a domain as dynamic as fashion, can prove daunt‑
ing. The fashion industry also faces the cost considerations
associated with the implementation of AI technologies.
These investments encompass not only the acquisition of
cutting‑edge hardware and software but also the training
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Vol.:(0123456789)
SN Applied Sciences (2023) 5:387 | https://doi.org/10.1007/s42452-023-05587-2 Review
and upskilling of personnel to harness the full potential of
AI systems.
6 Future work
As of 2023, the landscape of AI has undergone a remark‑
able surge in popularity, marking a global trend like never
before. This surge has permeated various industries,
including fashion, where AI’s transformative potential
is becoming increasingly evident. Therefore, for future
research endeavors, it is highly advisable to conduct a
dedicated review with a specic focus on the develop‑
ments and impacts witnessed in this pivotal year.
An exploration centered around the year 2023 can shed
light on how this unprecedented surge in AI’s popularity
has resonated within the fashion industry. This examina‑
tion can provide valuable insights into how the fashion
sector has harnessed the momentum of AI, whether it
be through innovative applications, novel solutions, or
heightened integration. Such a study can also help identify
emerging trends, challenges, and opportunities unique to
this period, oering a comprehensive view of the indus‑
try’s trajectory.
Moreover, a complementary study centered around
2023 would not only serve as a testament to the dyna‑
mism of AI but also provide an invaluable perspective
for researchers, businesses, and stakeholders seeking to
navigate the evolving landscape of AI‑driven sustainabil‑
ity in fashion. It can illuminate how the fashion industry
has adapted and innovated in response to the surge in
AI adoption, potentially uncovering novel strategies and
best practices for sustainable growth and development.
Author Contributions LR: Conceptualization, Formal analysis and
investigation, Writing—original draft preparation, Writing—review
and editing. FRE, AGP, and EC: Formal analysis and investigation, Writ‑
ing—review and editing. All authors reviewed the manuscript.
Funding No funding was received to assist with the preparation of
this manuscript.
Data availability Data sharing not applicable to this article as no
datasets were generated or analysed during the current study.
Declarations
Conflicts of interest The authors have no relevant nancial interests
in the manuscript and no other potential conicts of interest to dis‑
close.
Open Access This article is licensed under a Creative Commons Attri‑
bution 4.0 International License, which permits use, sharing, adap‑
tation, distribution and reproduction in any medium or format, as
long as you give appropriate credit to the original author(s) and the
source, provide a link to the Creative Commons licence, and indicate
if changes were made. The images or other third party material in this
article are included in the article’s Creative Commons licence, unless
indicated otherwise in a credit line to the material. If material is not
included in the article’s Creative Commons licence and your intended
use is not permitted by statutory regulation or exceeds the permitted
use, you will need to obtain permission directly from the copyright
holder. To view a copy of this licence, visit http:// creat iveco mmons.
org/ licen ses/ by/4. 0/.
References
1. Hawken P, Lovins AB, Lovins LH (1999) Natural capitalism: creat‑
ing the next industrial revolution. Little Brown, Boston
2. Moretto A, Macchion L, Lion A, Caniato F, Danese P, Vinelli A
(2018) Designing a roadmap towards a sustainable supply
chain: a focus on the fashion industry. J Clean Prod 193:169–184.
https:// doi. org/ 10. 1016/J. JCLEP RO. 2018. 04. 273
3. Jamwal A, Agrawal R, Sharma M, Kumar V, Kumar S (2021) Devel‑
oping a sustainability framework for industry 4.0. Procedia CIRP
98:430–435. https:// doi. org/ 10. 1016/J. PROCIR. 2021. 01. 129
4. Ruggerio CA (2021) Sustainability and sustainable develop‑
ment: a review of principles and denitions. Sci Total Environ
786:147481. https:// doi. org/ 10. 1016/J. SCITO TENV. 2021. 147481
5. Kristensen HS, Mosgaard MA (2020) A review of micro level
indicators for a circular economy—moving away from the three
dimensions of sustainability? J Clean Prod 243:118531. https://
doi. org/ 10. 1016/j. jclep ro. 2019. 118531
6. Becker C, Betz S, Chitchyan R, Duboc L, Easterbrook SM, Pen‑
zenstadler B, Sey N, Venters CC (2016) Requirements: the key
to sustainability. IEEE Softw 33:56–65. https:// doi. org/ 10. 1109/
MS. 2015. 158
7. Alshehhi A, Nobanee H, Khare N (2018) The impact of sustain‑
ability practices on corporate nancial performance: literature
trends and future research potential. Sustainability. https:// doi.
org/ 10. 3390/ su100 20494
8. Huang L, Wu J, Yan L (2015) Dening and measuring urban sus‑
tainability: a review of indicators. Landscape Ecol 30:1175–1193.
https:// doi. org/ 10. 1007/ S10980‑ 015‑ 0208‑2/ METRI CS
9. Carlsen L, Bruggemann R (2022) The 17 united nations’ sustain‑
able development goals: a status by 2020. Int J Sustain Dev
World Ecol 29(3):219–229. https:// doi. org/ 10. 1080/ 13504 509.
2021. 19484 56
10. Wren B (2022) Sustainable supply chain management in the fast
fashion industry: a comparative study of current eorts and best
practices to address the climate crisis. Clean Logistics Supply
Chain 4:100032. https:// doi. org/ 10. 1016/J. CLSCN. 2022. 100032
11. Akter MMK, Haq UN, Islam MM, Uddin MA (2022) Textile‑apparel
manufacturing and material waste management in the circular
economy: a conceptual model to achieve sustainable devel‑
opment goal (SDG) 12 for Bangladesh. Clean Environ Syst
4:100070. https:// doi. org/ 10. 1016/J. CESYS. 2022. 100070
12. Hossain MN, Rahman MM, Afrin S, Akbor MA, Siddique MAB,
Malafaia G (2023) Identication and quantication of microplas‑
tics in agricultural farmland soil and textile sludge in Bangla‑
desh. Sci Total Environ 858:160118. https:// doi. org/ 10. 1016/J.
SCITO TENV. 2022. 160118
13. Hossain L, Sarker SK, Khan MS (2018) Evaluation of present
and future wastewater impacts of textile dyeing industries in
Bangladesh. Environ Dev 26:23–33. https:// doi. org/ 10. 1016/J.
ENVDEV. 2018. 03. 005
14. Bruzzone AG, Massei M, Frosolini M (2022) Redesign of sup‑
ply chain in fashion industry based on strategic engineering.
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Vol:.(1234567890)
Review SN Applied Sciences (2023) 5:387 | https://doi.org/10.1007/s42452-023-05587-2
Procedia Comput Sci 200:1913–1918. https:// doi. org/ 10.
1016/J. PROCS. 2022. 01. 392
15. Ramos L (2022) Artificial intelligence for cancer detection
using medical image: highlights and limitations. Green World
J. https:// doi. org/ 10. 53313/ gwj51 011
16. Pournader M, Ghaderi H, Hassanzadegan A, Fahimnia B (2021)
Artificial intelligence applications in supply chain manage‑
ment. Int J Prod Econ 241:108250. https:// doi. org/ 10. 1016/J.
IJPE. 2021. 108250
17. Thomassey S, Zeng X (2018) Artificial intelligence for fashion
industry in the big data era. Springer Ser Fashion Bus. https://
doi. org/ 10. 1007/ 978‑ 981‑ 13‑ 0080‑6_1
18. Ahmad T, Zhang D, Huang C, Zhang H, Dai N, Song Y, Chen
H (2021) Artificial intelligence in sustainable energy indus‑
try: status quo, challenges and opportunities. J Clean Prod
289:125834. https:// doi. org/ 10. 1016/J. JCLEP RO. 2021. 125834
19. ...Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann
TC, Mulrow CD, Shamseer L, Tetzlaff JM, Akl EA, Brennan SE,
Chou R, Glanville J, Grimshaw JM, Hróbjartsson A, Lalu MM,
Li T, Loder EW, Mayo‑Wilson E, McDonald S, McGuinness LA,
Stewart LA, Thomas J, Tricco AC, Welch VA, Whiting P, Moher D
(2021) The Prisma 2020 statement: an updated guideline for
reporting systematic reviews. BMJ. https:// doi. org/ 10. 1136/
BMJ. N71
20. Rethlefsen, M.L., Kirtley, S., Waffenschmidt, S., Ayala, A.P.,
Moher, D., Page, M.J., Koffel, J.B., Blunt, H., Brigham, T., Chang,
S., Clark, J., Conway, A., Couban, R., de Kock, S., Farrah, K.,
Fehrmann, P., Foster, M., Fowler, S.A., Glanville, J., Harris, E.,
Hoffecker, L., Isojarvi, J., Kaunelis, D., Ket, H., Levay, P., Lyon,
J., McGowan, J., Murad, M.H., Nicholson, J., Pannabecker, V.,
Paynter, R., Pinotti, R., Ross‑White, A., Sampson, M., Shields,
T., Stevens, A., Sutton, A., Weinfurter, E., Wright, K., Young, S.,
Group, P.‑S. (2021) Prisma‑s: an extension to the Prisma state‑
ment for reporting literature searches in systematic reviews.
Syst Rev 10:39. https:// doi. org/ 10. 1186/ s13643‑ 020‑ 01542‑z
21. Brito PQ, Soares C, Almeida S, Monte A, Byvoet M (2015) Cus‑
tomer segmentation in a large database of an online custom‑
ized fashion business. Robot Comput 36:93–100. https:// doi.
org/ 10. 1016/j. rcim. 2014. 12. 014
22. Paul SK, Azeem A (2011) An artificial neural network model
for optimization of finished goods inventory. Int J Ind Eng
Comput 2:431–438. https:// doi. org/ 10. 5267/j. ijiec. 2011. 01. 005
23. Nakandala D, Samaranayake P, Lau HCW (2013) A fuzzy‑based
decision support model for monitoring on‑time delivery
performance: a textile industry case study. Eur J Oper Res
225(3):507–517. https:// doi. org/ 10. 1016/j. ejor. 2012. 10. 010
24. Tehrani AF, Ahrens D (2017) Modified sequential k‑means clus‑
tering by utilizing response: a case study for fashion products.
Expert Syst 34:12226. https:// doi. org/ 10. 1111/ EXSY. 12226
25. Unal PG, Üreyen ME, Mecit D (2012) Predicting properties of
single jersey fabrics using regression and artificial neural net‑
work models. Fibers Polym 13:87–95. https:// doi. or g/ 10. 1007/
S12221‑ 012‑ 0087‑Y/ METRI CS
26. Hsiao SW, Lee CH, Chen RQ, Yen CH (2017) An intelligent sys‑
tem for fashion colour prediction based on fuzzy c‑means
and gray theory. Color Res App 42:273–285. https:// doi. org/
10. 1002/ COL. 22057
27. Chakraborty S, Prasad K (2018) A quality function deployment‑
based expert system for cotton fibre selection. J Institut Eng
99:43–53. https:// doi. org/ 10. 1007/ S40034‑ 018‑ 0111‑X/ METRI
CS
28. Papachristou E, Chrysopoulos A, Bilalis N (2021) Machine learn‑
ing for clothing manufacture as a mean to respond quicker and
better to the demands of clothing brands: a greek case study.
Int J Adv Manuf Technol 115:691–702. https:// doi. org/ 10. 1007/
S00170‑ 020‑ 06157‑1/ METRI CS
29. Yu Y, Choi T‑M, Hui C‑L (2011) An intelligent fast sales forecasting
model for fashion products. Expert Syst Appl 38(6):7373–7379.
https:// doi. org/ 10. 1016/j. eswa. 2010. 12. 089
30. Shajini M, Ramanan A (2021) An improved landmark‑driven
and spatial‑channel attentive convolutional neural network for
fashion clothes classication. Visual Computer 37:1517–1526.
https:// doi. org/ 10. 1007/ S00371‑ 020‑ 01885‑7/ METRI CS
31. Jain S, Kumar V(2020) Garment categorization using data mining
techniques. Symmetry 12(6) . https:// doi. org/ 10. 3390/ sym12
060984
32. Pattanayak AK, Luximon A, Khandual A (2010) Prediction of
drape prole of cotton woven fabrics using articial neural net‑
work and multiple regression method. Text Res J 81:559–566.
https:// doi. org/ 10. 1177/ 00405 17510 380783
33. Guosheng X, Yang X, Zhiqi Y, Yize S (2022) An intelligent defect
detection system for warp‑knitted fabric. Text Res J 92(9–
10):1394–1404. https:// doi. org/ 10. 1177/ 00405 17521 10600 84
34. Wazarkar S, Keshavamurthy BN (2020) Social image mining for
fashion analysis and forecasting. Appl Soft Comput 95:106517.
https:// doi. org/ 10. 1016/j. asoc. 2020. 106517
35. Garcia S, Cordeiro A, de AlencarNääs I, de OliveiraCosta Neto
P.L (2019) The sustainability awareness of Brazilian consumers
of cotton clothing. J Clean Prod 215:1490–1502. https:// doi. org/
10. 1016/j. jclep ro. 2019. 01. 069
36. Ghosh A, Guha T, Bhar RB, Das S (2011) Pattern classication
of fabric defects using support vector machines. Int J Cloth Sci
Technol 23:142–151. https:// doi. or g/ 10. 1108/ 09556 22111 11073
33
37. Wagner M, Curteza A, Hong Y, Chen Y, Thomassey S, Zeng X
(2019) A design analysis for eco‑fashion style using sensory
evaluation tools: consumer perceptions of product appearance.
J Retail Consum Serv 51:253–262. https:// doi. org/ 10. 1016/j. jretc
onser. 2019. 06. 005
38. Wang B, Luo W, Zhang A, Tian Z, Li Z (2020) Blockchain‑enabled
circular supply chain management: a system architecture for
fast fashion. Comput Ind 123:103324. https:// doi. org/ 10. 1016/j.
compi nd. 2020. 103324
39. Wilts H, Garcia BR, Garlito RG, Gómez LS, Prieto EG (2021) Arti‑
cial intelligence in the sorting of municipal waste as an enabler
of the circular economy. Resources. https:// doi. org/ 10. 3390/
resou rces1 00400 28
40. Vrancken C, Longhurst P, Wagland S (2019) Deep learning in
material recovery: development of method to create training
database. Expert Syst Appl 125:268–280. https:// doi. org/ 10.
1016/j. eswa. 2019. 01. 077
41. Nañez Alonso SL, Reier Forradellas RF, Pi Morell O, Jorge‑Vazquez
J (2021) Digitalization, circular economy and environmental sus‑
tainability: the application of articial intelligence in the e‑
cient self‑management of waste. Sustainability. https:// doi. org/
10. 3390/ su130 42092
42. Fatimah YA, Govindan K, Murniningsih R, Setiawan A (2020)
Industry 4.0 based sustainable circular economy approach
for smart waste management system to achieve sustainable
development goals: a case study of Indonesia. J Clean Prod
269:122263. https:// doi. org/ 10. 1016/j. jclep ro. 2020. 122263
43. Vu HL, Bolingbroke D, Ng KTW, Fallah B (2019) Assessment of
waste characteristics and their impact on GIS vehicle collection
route optimization using ANN waste forecasts. Waste Manage
88:118–130. https:// doi. org/ 10. 1016/j. wasman. 2019. 03. 037
44. Singh D, Satija A (2018) Prediction of municipal solid waste
generation for optimum planning and management with arti‑
cial neural network‑case study: Faridabad city in Haryana state
(India). Int J Syst Assurance Eng Managem 9:91–97. https:// doi.
org/ 10. 1007/ S13198‑ 016‑ 0484‑5/ METRI CS
45. Oliveira V, Sousa V, Dias‑Ferreira C (2019) Artificial neural
network modelling of the amount of separately‑collected
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Vol.:(0123456789)
SN Applied Sciences (2023) 5:387 | https://doi.org/10.1007/s42452-023-05587-2 Review
household packaging waste. J Clean Prod 210:401–409. https://
doi. org/ 10. 1016/j. jclep ro. 2018. 11. 063
46. Shayganmehr M, Kumar A, Luthra S, Garza‑Reyes JA (2021)
A framework for assessing sustainability in multi‑tier supply
chains using empirical evidence and fuzzy expert system. J
Clean Prod 317:128302. https:// doi. org/ 10. 1016/j. jclep ro. 2021.
128302
47. Alavi B, Tavana M, Mina H (2021) A dynamic decision support
system for sustainable supplier selection in circular economy.
Sustain Prod Consumpt 27:905–920. https:// doi. org/ 10. 1016/j.
spc. 2021. 02. 015
48. Singh M, Bajpai U, Vijayarajan V, Prasath S (2020) Generation
of fashionable clothes using generative adversarial networks.
Int J Cloth Sci Technol 32:177–187. https:// doi. org/ 10. 1108/
IJCST‑ 12‑ 2018‑ 0148
49. Ghodhbani H, Neji M, Qahtani AM, Almutiry O, Dhahri H, Alimi
AM (2022) Dress‑up: deep neural framework for image‑based
human appearance transfer. Multimedia Tools App. https:// doi.
org/ 10. 1007/ S11042‑ 022‑ 14127‑W/ FIGUR ES/ 16
50. Chakraborty S, Hoque SMA, Kabir SMF (2020) Predicting fashion
trend using runway images: application of logistic regression in
trend forecasting. Int J Fashion Design Tech Edu 13(3):376–386.
https:// doi. org/ 10. 1080/ 17543 266. 2020. 18290 96
51. Sun GL, Cheng ZQ, Wu X, Peng Q (2018) Personalized clothing
recommendation combining user social circle and fashion style
consistency. Multimedia Tools App 77:17731–17754. https:// doi.
org/ 10. 1007/ S11042‑ 017‑ 5245‑1/ METRI CS
52. Hamad M, Thomassey S, Bruniaux P (2017) A new sizing system
based on 3d shape descriptor for morphology clustering. Com‑
put Ind Eng 113:683–692. https:// doi. org/ 10. 1016/j. cie. 2017. 05.
030
53. Wu Q, Zhu B, Yong B, Wei Y, Jiang X, Zhou R, Zhou Q (2021) Cloth‑
gan: generation of fashionable Dunhuang clothes using genera‑
tive adversarial networks. Connect Sci 33(2):341–358. https://
doi. org/ 10. 1080/ 09540 091. 2020. 18227 80
54. Shahrabi J, Hadavandi E, Esfandarani MS (2013) Developing a
hybrid intelligent model for constructing a size recommenda‑
tion expert system in textile industries. Int J Cloth Sci Technol
25:338–349. https:// doi. org/ 10. 1108/ IJCST‑ 04‑ 2012‑ 0015
55. Lin Y, Ren P, Chen Z, Ren Z, Ma J, de Rijke M (2020) Explainable
outt recommendation with joint outt matching and comment
generation. IEEE Trans Knowl Data Eng 32(8):1502–1516. https://
doi. org/ 10. 1109/ TKDE. 2019. 29061 90
56. Liu J, Song X, Chen Z, Ma J (2020) Mgcm: multi‑modal generative
compatibility modeling for clothing matching. Neurocomputing
414:215–224. https:// doi. org/ 10. 1016/j. neucom. 2020. 06. 033
57. Wu L, Yue X, Jin A, Yen DC (2016) Smart supply chain manage‑
ment: a review and implications for future research. Int J Logist
Manag 27:395–417. https:// doi. org/ 10. 1108/ IJLM‑ 02‑ 2014‑
0035/ FULL/ XML
58. Becker T, Illigen C, McKelvey B, Hülsmann M, Windt K (2016)
Using an agent‑based neural‑network computational model
to improve product routing in a logistics facility. Int J Prod Econ
174:156–167. https:// doi. org/ 10. 1016/j. ijpe. 2016. 01. 003
59. Carter CR, Rogers DS, Choi TY (2015) Toward the theory of the
supply chain. J Supply Chain Manag 51(2):89–97. https:// doi.
org/ 10. 1111/ jscm. 12073
60. Wong C, Skipworth H, Godsell J, Achimugu N (2012) Towards a
theory of supply chain alignment enablers: a systematic litera‑
ture review. Supply Chain Manag 17:419–437. https:// doi. org/
10. 1108/ 13598 54121 12465 67/ FULL/ XML
61. Farooque M, Zhang A, Thürer M, Qu T, Huisingh D (2019) Circular
supply chain management: a denition and structured litera‑
ture review. J Clean Prod 228:882–900. h ttps:// doi. org/ 10. 1016/j.
jclep ro. 2019. 04. 303
62. Sharma R, Shishodia A, Gunasekaran A, Min H, Munim ZH (2022)
The role of articial intelligence in supply chain management:
mapping the territory. Int J Prod Res 60(24):7527–7550. https://
doi. org/ 10. 1080/ 00207 543. 2022. 20296 11
63. Dabas CS, Whang C (2022) A systematic review of drivers of sus‑
tainable fashion consumption: 25 years of research evolution. J
Glob Fash Market 13(2):151–167. https:// doi. org/ 10. 1080/ 20932
685. 2021. 20160 63
64. Hur E, Cassidy T (2019) Perceptions and attitudes towards sus‑
tainable fashion design: challenges and opportunities for imple‑
menting sustainability in fashion. Int J Fashion Design Technol
Educ 12(2):208–217. https:// doi. org/ 10. 1080/ 17543 266. 2019.
15727 89
65. Waheed MF, Khalid AM (2019) Impact of emerging technolo‑
gies for sustainable fashion, textile and design. In: Karwowski
W, Ahram T (eds) Intell Human Syst Integr 2019. Springer, Cham,
pp 684–689
66. Silva ES, Bonetti F (2021) Digital humans in fashion: will consum‑
ers interact? J Retail Consum Serv 60:102430. https:// doi. org/ 10.
1016/j. jretc onser. 2020. 102430
67. Jacometti V (2019) Circular economy and waste in the fashion
industry. Laws. https:// doi. org/ 10. 3390/ laws8 040027
68. Shirvanimoghaddam K, Motamed B, Ramakrishna S, Naebe M
(2020) Death by waste: fashion and textile circular economy
case. Sci Total Environ 718:137317. https:// doi. org/ 10. 1016/j.
scito tenv. 2020. 137317
69. Moorhouse D, Moorhouse D (2017) Sustainable design: circular
economy in fashion and textiles. Design J 20:1948–1959
70. Power DJ (2016) Data science: supporting decision‑making. J
Decis Syst 25(4):345–356. https:// doi. org/ 10. 1080/ 12460 125.
2016. 11716 10
71. Müller O, Fay M, vom Brocke J (2018) The eect of big data and
analytics on rm performance: an econometric analysis consid‑
ering industry characteristics. J Manag Inf Syst 35(2):488–509.
https:// doi. org/ 10. 1080/ 07421 222. 2018. 14519 55
Publisher’s Note Springer Nature remains neutral with regard to
jurisdictional claims in published maps and institutional aliations.
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
1.
2.
3.
4.
5.
6.
Terms and Conditions
Springer Nature journal content, brought to you courtesy of Springer Nature Customer Service Center GmbH (“Springer Nature”).
Springer Nature supports a reasonable amount of sharing of research papers by authors, subscribers and authorised users (“Users”), for small-
scale personal, non-commercial use provided that all copyright, trade and service marks and other proprietary notices are maintained. By
accessing, sharing, receiving or otherwise using the Springer Nature journal content you agree to these terms of use (“Terms”). For these
purposes, Springer Nature considers academic use (by researchers and students) to be non-commercial.
These Terms are supplementary and will apply in addition to any applicable website terms and conditions, a relevant site licence or a personal
subscription. These Terms will prevail over any conflict or ambiguity with regards to the relevant terms, a site licence or a personal subscription
(to the extent of the conflict or ambiguity only). For Creative Commons-licensed articles, the terms of the Creative Commons license used will
apply.
We collect and use personal data to provide access to the Springer Nature journal content. We may also use these personal data internally within
ResearchGate and Springer Nature and as agreed share it, in an anonymised way, for purposes of tracking, analysis and reporting. We will not
otherwise disclose your personal data outside the ResearchGate or the Springer Nature group of companies unless we have your permission as
detailed in the Privacy Policy.
While Users may use the Springer Nature journal content for small scale, personal non-commercial use, it is important to note that Users may
not:
use such content for the purpose of providing other users with access on a regular or large scale basis or as a means to circumvent access
control;
use such content where to do so would be considered a criminal or statutory offence in any jurisdiction, or gives rise to civil liability, or is
otherwise unlawful;
falsely or misleadingly imply or suggest endorsement, approval , sponsorship, or association unless explicitly agreed to by Springer Nature in
writing;
use bots or other automated methods to access the content or redirect messages
override any security feature or exclusionary protocol; or
share the content in order to create substitute for Springer Nature products or services or a systematic database of Springer Nature journal
content.
In line with the restriction against commercial use, Springer Nature does not permit the creation of a product or service that creates revenue,
royalties, rent or income from our content or its inclusion as part of a paid for service or for other commercial gain. Springer Nature journal
content cannot be used for inter-library loans and librarians may not upload Springer Nature journal content on a large scale into their, or any
other, institutional repository.
These terms of use are reviewed regularly and may be amended at any time. Springer Nature is not obligated to publish any information or
content on this website and may remove it or features or functionality at our sole discretion, at any time with or without notice. Springer Nature
may revoke this licence to you at any time and remove access to any copies of the Springer Nature journal content which have been saved.
To the fullest extent permitted by law, Springer Nature makes no warranties, representations or guarantees to Users, either express or implied
with respect to the Springer nature journal content and all parties disclaim and waive any implied warranties or warranties imposed by law,
including merchantability or fitness for any particular purpose.
Please note that these rights do not automatically extend to content, data or other material published by Springer Nature that may be licensed
from third parties.
If you would like to use or distribute our Springer Nature journal content to a wider audience or on a regular basis or in any other manner not
expressly permitted by these Terms, please contact Springer Nature at
onlineservice@springernature.com
Available via license: CC BY 4.0
Content may be subject to copyright.
