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WWD meets five graduating students from FIT's fashion design program.

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The Fashion Institute of Technology’s graduating fashion design students spent their entire senior year learning remotely due to the pandemic. This meant crafting their thesis collections — designs that are often seen as an entryway to getting a coveted design job or, in rare cases, a star-making order from an influential store — from their own homes.

With limited resources, students improvised by upcycling old or deadstock fabric and by cleverly constructing clothes on their living room floors.

Herein, WWD meets five members from FIT’s class of 2021 who explain the concepts and inspirations behind their thesis designs, as well as their hopes for the future.

Name : Abraham Azamy

Hometown : Mission Viejo, Calif.

Describe the concept behind your thesis collection : My concept is focused on capturing the playfulness of undressing and the subtle illusion of skin exposure. I find the irony in flaunting your body while being fully clothed to be mischievous yet seductive. My idea for this garment was based more on sex appeal as a suggestion rather than an obvious statement. The intention is to give the wearer the power to be provocative in a way that is subtle and refined.

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Where have you been studying from while school is closed? Do you have plans to move after the pandemic? The transition from in-person to remote learning has been a blessing in disguise. I have been studying from my apartment in Brooklyn, N.Y. Once COVID-19 hit, I was fortunate enough to be able to convert my common area into a work studio, and invest in a commercial sewing machine outfitted with my own cutting table and desk. I felt my creativity flourish in this environment that I created for myself, which enabled me to design more freely.

How has the pandemic affected your design aesthetic or process and the outcome of your thesis collection? The forced isolation from the pandemic has allowed me to find my voice, effectively express my vision, and further my creativity. I had the opportunity to marinate in my ideas and to cement exactly what it is I’m offering to future employers. The catalyst for my creative process begins with an idea, concept, or character and once that is established, designing from there. Creating on my own during the pandemic narrowed my focus for the better and everything came together in an organic way.

Has the pandemic changed your outlook on the fashion industry? If so, how? We as creatives are responsible for cultivating new and effective methods for the production process, as well as the quantity of merchandise we spew out into the market. In the beginning of the pandemic, it became increasingly difficult to gain access to resources as a fashion student, which further solidified the concept of “less is more.”

Who do you hope is reading this and what is your message to them?  I would hope a potential, future employer will see this and recognize my integrity and ability to design from a genuine and raw point of view.

Name : Hawwaa Ibrahim

Hometown : Mankato, Minn.

Describe the concept behind your thesis collection : Inspired by genderless fashion and art in the Islamic world, this collection offers clothing that is genderless and created for the parent and child to think freely about how they would like to express themselves now and in the future.

What techniques are you most proud of in your thesis collection? I created the prints used throughout my collection by taking items that I found in my house and putting them through a kaleidoscope effect to create geometric shapes that resembled what you might see in Islamic art. I also incorporated some embroidery to give the design some texture. I took the word “inclusive” and translated it into Arabic and machine-embroidered it on one of the jacket straps of my design.

What were some of the inspirations, concepts or important world events that helped lead your thesis work ? I decided to challenge the idea of color being associated with gender and used a mix of shades and tones to create something that no one has seen yet. A lot of gender-inclusive or gender-neutral clothing we see nowadays has a tendency to be a bit bland when it comes to color.

How has the pandemic affected your design aesthetic or process and the outcome of your thesis collection? Throughout this pandemic, I was able to narrow down the things that are important to me in life and as I paid more attention to how people around the world treat each other, I found myself growing closer to my religion, my Islamic faith. I found a way to showcase something that’s fundamental in my life and heavily influences the way I live — so with gender identity, this also plays a role in how I choose to express myself. Contrary to popular belief, gender expression and religious identity are not mutually exclusive. I try to live in harmony with both.

Has the pandemic changed your outlook on the fashion industry? If so, how? The pandemic has drastically changed my outlook on the fashion industry. There has been a disregard for not only other people’s identities, but also with who is represented. I always knew this was happening, but the resurgence of the Black Lives Matter movement across the world last summer forced me to wake up. I saw how quickly people and companies within the fashion industry suddenly switched up to act like they’ve cared about Black and Indigenous people and people of color this whole time as they continue to profit off of them with no recognition and no respect. However, finally seeing other designers emerge and get the attention they deserve gave me some hope that the fashion industry is slowly moving in the right direction.

What do you hope to accomplish most in your career as a fashion designer? Children, even at their young ages, have opinions and beliefs, so I would like to provide them with options of expression. I hope to create children’s clothing that sparks conversation around how people consciously choose to express themselves with clothing and how it intertwines with other aspects of their life. Fashion is much more than nice pieces of clothing. It’s identity, it’s comfort, it’s its own language. I personally take my views on gender identity and my religious beliefs and let those things open up a world of possibilities in design.

What are your plans for after graduation? After graduation, I am hoping to work in children’s wear for two or three years to gain some experience. After that, I want to work full time for my own apparel and accessories brand, Because, which I started in 2019. My goal is to incorporate genderless children’s clothing into the mix!

Name : Saemi Jeon

Hometown : Incheon, South Korea

Describe the concept behind your thesis collection: My thesis collection is called “The Memory Vessel.” The concept comes from seeing Memor Studio’s artwork on Instagram around memory jugs. The origins of the memory jug are somewhat vague, but they were made as memorials for deceased loved ones.

What techniques are you most proud of in your thesis collection? I used a variety of techniques, including hand-knitting, knitting done on a Stoll machine, weaving and latch-hook skills. I enjoyed incorporating the techniques to achieve the final look, which came together quite naturally. I am most proud of my hand-knit work in the balaclava and sleeveless drop-shoulder sweater top, because it was my first time knitting an entire piece by hand.

What were some of the inspirations, concepts or important world events that helped lead your thesis work? After learning the concept of the memory jug, I asked myself; “if I were to make a memory jug, what memory would I put inside of it?” Many memories came into my mind. A dogwood flower. I was once gifted white dogwood flowers at my graduation. This flower is delicate, and soft. Even though I don’t have the flowers anymore, the memory of that day still warms my heart.

Where have you been studying from while school is closed? Do you have plans to move after the pandemic? I have been studying both at home in Korea and here in New York. I went back to Korea last semester and took classes at night and early morning, which was very tough. I came back to New York this semester and studied at home. I don’t have a plan to move back to Korea after the pandemic. I will remain in New York City.

How has the pandemic affected your design aesthetic or process and the outcome of your thesis collection? I had limited access to school where the knitting labs are. It would have been a much easier process to work in the labs and have face-to-face communication with professors and technicians. However, I was very touched by how hard everyone worked to make up for the limitations placed on the students.

Has the pandemic changed your outlook on the fashion industry? If so, how? The pandemic has lifted up the curtains on worldwide pollution. The Himalayan mountain tops came into view, wild animals appeared in city streets, the air was cleaner. Through this unexpected situation, we had an opportunity to take a step back and see what we could have. The fashion industry should shift to a more sustainable chain of production.

Name a trend you are ready to see take off and a trend you are ready to see finish : I would love to see timeless classic designs, based on comfort become the trend. Flashy, showy trends that are ever-changing from season to season is something I’m ready to see fade away.

Name : Yitao Li

Hometown : Taiyuan, China

Describe the concept behind your thesis collection : The inspiration for this collection was the idea of distortion. In the digital world, in which we find ourselves so completely ensconced, I am constantly confronted with distortion whether it’s associated with media, art, or people’s biased opinions. Many things we see on the internet are not real but filtered through other people’s lenses.

What techniques are you most proud of in your thesis collection? I tried creating distorted “floating plaids” on the body with multiple materials including horsehair and 3D-printing material. The plaid fabric manipulation was moved and reshaped on the body to show movement. I also combined the plaid pattern with lace to juxtapose sharpness with softness and create a romantic effect.

What were some of the inspirations, concepts or important world events that helped lead your thesis work? My interest was piqued in how images in the media were different from reality, how software like Photoshop and other image-editing apps could easily change the appearance of anything and deceive the viewer. The process started by using distorted filters on regular objects. The distortion filters were then applied to my designs and drapes to find new silhouettes and possibilities.

Has the pandemic changed your outlook on the fashion industry? If so, how? I believe after the pandemic hit the industry there have been some positive effects. People in this industry have been doing things the same way for hundreds of years and suddenly everyone was forced into adjusting. I was amazed at how the creativity from the industry evolved in a remote environment. The collections were even better than previous years. There was also a bigger audience on social media platforms and new talents are getting a lot more exposure.

Name : Gabriela Villatoro

Hometown : Miami

Describe the concept behind your thesis collection : Taken from the idea of upcycling, I wanted to make multifunctional garments. By showing the wearer how the same pieces can be used to create different looks, the wearer can “upcycle” the garments themselves and extend the product’s life cycle.

What techniques are you most proud of in your thesis collection? One of the best things about being in knitwear is being able to create your own fabric. All of the fabrics used in the garments were handmade or designed and programmed by me. I used hand-knitting, machine-knitting techniques and felting wool on sheer silk.

What were some of the inspirations, concepts or important world events that helped lead your thesis work? I wanted my collection to serve as a prototype solution for the overproduction of apparel materials in the fashion industry. If we create pieces that can be used multiple ways, ideally it would reduce the overall number of textiles and clothing being produced.

How has the pandemic affected your design aesthetic or process and the outcome of your thesis collection? I think the pandemic has really forced me to look within myself to be a solution-driven designer. In a classroom environment you can easily turn to your peers and ask for their help when you’re stuck or ask for their opinion. Although we still meet and see each other online, the distanced environment has highlighted our individual thinking.

Name a trend you are ready to see take off and a trend you are ready to see finish : I’ve seen things that have the feeling of being handmade become increasingly popular. I think because of the pandemic a lot of people had the time to start new hobbies and make whatever they want, which I think is great to see.

A trend I’m ready to see go is sweatpants. With the pandemic, and nowhere to go, they’re the most practical solution, but I look forward to the days when we will have somewhere to go.

What are your plans for after graduation? I want to start working in knitwear because there’s still a lot I can learn, and then hopefully use what I’ve learned to start my own brand of clothing.

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Wearable technologies in the fashion value ecosystem: a conceptual model

Innovation & Management Review

ISSN : 2515-8961

Article publication date: 22 November 2021

Issue publication date: 29 March 2022

The fashion sector is complex. It involves multiple actors with distinct and potentially conflicting interests, forming a value ecosystem. Thus, knowing the interested parties and belonging to the fashion sector may be a means to promote technological innovation, such as products with wearables. The purpose of this paper to identify the participants of the fashion ecosystem from the perspective of wearable technologies and develop a conceptual model.

Design/methodology/approach

The present work aims to identify the participants (actors) and develop a conceptual model of the fashion ecosystem from the perspective of wearable technologies. The systematic literature review is the recommended method to qualitatively analyze documents and identify the interested parties (actors) in the fashion sector in order to design the proposed conceptual model.

From the studies, the conceptual model of the fashion value ecosystem was designed, and the wearable product was considered its core business. The studies identified addressed ecosystems of fashion value in general but not specific to wearable products and their relations with other complementary industries.

Research limitations/implications

The model was designed using secondary data only. Its validation is relevant through interviews with experts.

Originality/value

In terms of relevance, when conducting a systematic literature review, there were no studies that included wearable technologies in the fashion ecosystems discussed and their relations with other industries. The topic of wearables is an emerging subject that needs further research aiming to insert this technology in productive sectors.

• Fashion sector
• Fashion ecosystem
• Wearable technologies
• Systematic literature review

Serrano, R. , Fortunati, L. and Lacerda, D.P. (2022), "Wearable technologies in the fashion value ecosystem: a conceptual model", Innovation & Management Review , Vol. 19 No. 2, pp. 90-105. https://doi.org/10.1108/INMR-02-2020-0020

Emerald Publishing Limited

Copyright © 2021, Rosiane Serrano, Larissa Fortunati and Daniel Pacheco Lacerda

Published in Innovation & Management Review . Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence maybe seen at http://creativecommons.org/licences/by/4.0/legalcode .

1. Introduction

Making products with a short life cycle ( Abecassis-Moedas, 2006 ) is a characteristic of the fashion sector ( Boscacci, 2018 ). Therefore, the need to innovate, produce and sell items is continuous ( Tervilä, 2015 ), and the synergies to support the growth and development of the sector are essential ( European Commission, 2019 ).

As an economic sector, fashion employs 75 million people worldwide, and its market value is estimated at 1.7 trillion dollars ( Tervilä, 2015 ). The fashion industry’s value surpasses 3.0 trillion dollars (16 trillion Brazilian reais on June 29, 2020), representing 2% of the world GDP ( Fashion United, 2020 ). In the Brazilian market, fashion is an industrial sector with the positive job and income multipliers ( Serrano, Rodrigues, Lacerda, & Paraboni, 2018 ). The direct jobs generated in the textile and clothing sector total 1.5 million distributed in 25,000 formal companies, and other 8 million indirect jobs with income effects ( Associação Brasileira da Indústria Têxtil e de Confecção – ABIT, 2020 ).

However, the fashion sector is complex ( Jia, Yin, Chen, & Chen, 2020 ; Jin, 2004 ), since it involves multiple actors with distinct and potentially conflicting interests that need to be articulated to generate a co-evolution process ( Moore, 1996 ). Ecosystem participants are interconnected and depend on each other to survive ( Iansiti & Levien, 2004 ), creating value for products. Mapping the fashion sector (textiles and clothing) as an ecosystem is interesting as it enables developing joint actions with all actors ( Staicu & Pop, 2018 ). Thus, knowing the interested parties belonging to the fashion sector may be a means to promote technological innovation, stimulate demand and measure the impacts generated by this sector (European Apparel and Textile Organisation – EURATEX, 2017 ), which shows ever-increasing competitiveness.

As pointed out by EURATEX (2004) , the fashion sector is formed by several subsectors. Therefore, it is crucial to define which of them will be the object of this study since it aims to map the fashion value ecosystem from the perspective of wearable products. Wearable products have as functionality the user’s interaction with the environment ( Wood, 2018 ; Zhang, Stankovski, Saeed, Saeed, & Zhang, 2020 ) by placing the technology around the body employing sensors ( O'Nascimento, 2020 ).

Wearable devices have achieved fast growth in the electronics market, providing interested buyers with various products to satisfy their needs and desires ( Mardonova & Choi, 2018 ). Besides allowing constant, convenient, continuous and portable access for users ( Dehghani & Dangelico, 2018 ), wearable products seek to enhance reality by superimposing computer-generated images or audio clips over the real world and provide sensitivity to the outside context by informing the users about their environmental and personal status ( Billinghurst & Starner, 1999 ).

Wearable devices can be found in different industrial sectors ( Mardonova & Choi, 2018 ), for example, for medical assistance purposes aiming to collect data on patient health ( Heintzman, 2016 ). In the universe of the fashion sector, devices are found in aesthetic accessories combined or not with garments ( Cantanhede, Dias, Gammarano, & Arruda Filho, 2018 ; Lazaroiu, 2012 ). Their use aims to add value to a piece of clothing by inserting electronic components ( Marini, 2016 ). It is estimated that the sales of smart clothing will increase from 2.9 million pieces in 2018 to 10.5 million by 2022 ( Richter, 2018 ).

Patents issued for smart clothing are not a new category in the wearables market ( Dehghani & Dangelico, 2018 ); for example, between the 1960s and the 1990s, efforts concentrated on developing the first clothing with a wearable concept ( O'Nascimento, 2020 ; Wood, 2018 ). However, information about the presence of such technologies in the production process of the fashion sector is still limited, and uncertainties about the insertion of wearable technologies in the production process are frequent ( Dehghani & Kim, 2019 ). Furthermore, the production flow of fashion is different from conventional processes ( Han, Han, & Kim, 2014 ) in that it includes innovative features, such as the user interaction with the product. Technology, therefore, changes the way work is carried out in organizations ( Eidenhammer, 2018 ).

In addition, the development of products with wearable technologies faces difficulties in market positioning, as there is interaction with more than one industrial sector ( Wood, 2018 ). For example, there are relations between the fashion and the electronics sectors, which play complementary roles in the complex productive context. Therefore, a valuable ecosystem is formed capable of producing goods with innovative and technological resources resulting from the interaction of different actors. Therefore, in-depth studies that consider the relations between different actors are essential for the competitiveness of the fashion sector ( Serrano, Morandi, Veit, Mansilha, & Lacerda, 2020 ).

The present work aims to develop a conceptual model of the fashion value ecosystem from the perspective of wearable technologies. The participants of the fashion value ecosystem were identified in previous studies, and the systematic literature review ( Morandi & Camargo, 2015 ) was the working method. It is worthwhile noting that the studies addressed ecosystems of fashion value in general but not specifically to wearable products and their relations with other complementary industries. Based on these studies, a conceptual model of the fashion value ecosystem was developed about wearable products. In addition, the issue of sustainability was incorporated by analyzing the final destination of the garments.

This article is structured into five sections, Section 1 is the Introduction. Then, the theoretical framework, the methodology and the research results are presented in Sections 2– 4 , followed by the final discussions and considerations of the study in Section 5 .

2. Theoretical framework

This theoretical framework initially addresses value ecosystems and their relations with the fashion sector, followed by the premises for the insertion of wearable technologies in the fashion sector.

2.1 Value ecosystems and their relations with the fashion sector

Ecology is a science that examines complex relations and interactions between members or species of particular communities and their relations with the environment ( Mengi, 2017 ). When addressing this concept in an industrial sector, Moore (1996) considered companies as a network of interconnected organizations and individuals with the objective of generating a process of co-evolution. Thus, ecosystems are a living community of interacting organisms, requiring diversity to function ( Oksanen et al. , 2018 ).

According to Salonoja (2013) , the ecosystem is an important concept as it helps to understand the complex business environment since the ownership and roles of actors belonging to it are identified ( Staicu & Pop, 2018 ). Furthermore, the set of actors, comprising organizations, products and processes, are analyzed as a part of a comprehensive, interdependent system ( Aarikka-Stenroos & Ritala, 2017 ).

In the logic of business ecosystems, the health of an organization influences the success and survival of all other participants in the ecosystem ( Iansiti & Levien, 2004 ). In industries formed by subsystems, such as fashion ( Mengi, 2017 ), the network of relations and the subsequent dynamics represented by the different stakeholders are highly complex ( Staicu & Pop, 2018 ). Therefore, the alignment of views and the mutual support of interested parties are crucial ( Moore, 1996 ).

The concept of the ecosystem has several interpretations ( Aarikka-Stenroos & Ritala, 2017 ) and structures. It can be composed of eight dimensions, as proposed by Moore (1996) and Serrano (2018) ; four layers ( Baghbadorani & Harandi, 2012 ); or six categories ( Fragidis, 2017 ). Thus, the object of the study (core business) and the complexity of the environment define the necessary structure of the ecosystem. In this study, the core business is defined by products developed using the concept of wearable technologies.

The name of the ecosystem may vary depending on the focus of the study and the complexity of the sector, such as service ecosystems ( Fragidis, 2017 ), business ecosystems ( Moore, 1996 ) or value ecosystems ( Serrano, 2018 ). We opted for the term “fashion value ecosystem,” which enables identifying professionals, textile manufacturers, wholesalers and retail buyers ( Mengi, 2017 ) as necessary members to create value for the core business and seek its co-evolution.

Finally, in the complex fashion value ecosystem, in which competition is related to the development of products with different levels of technology ( Serrano et al. , 2018 ), producing competitive products that satisfy the needs of consumers ( Kawamura, 2005 ) without harming the environment ( Fletcher & Grose, 2012 ) is a constant challenge. In this perspective, it is interesting to understand fashion as a value ecosystem that adds economic, social and environmental value as it evolves ( Serrano et al. , 2018 ). The following section addresses the theoretical framework of products developed from the concept of wearable technologies.

2.2 Insertion of wearable technologies in the fashion sector

In the era of Industry 4.0 and the Internet of Things (IoT), connectivity between humans and machines grows increasingly ( Fernández-Caramés & Fraga-Lamas, 2018 ; Zhang et al. , 2020 ). As a result, new information and intelligence are generated for the industry ( Chen, 2019 ) and users. Connectivity devices include wearable technologies, which seek interactivity between the environment and the user, assisting in motor and cognitive activities without limiting movements ( Donati, 2004 ). Besides, they are characterized as products controlled by the user, always on and accessible ( Mann, 1997 ).

Wearable products are inserted in several industrial sectors ( Mardonova & Choi, 2018 ) and services, like health, agriculture, manufacturing, home automation and public safety ( Fernández-Caramés & Fraga-Lamas, 2018 ). Thus, the use of wearable technologies for monitoring the health of employees may become a valuable resource for companies ( Lavallière, Burstein, Arezes, & Coughlin, 2016 ). For instance, sensors can detect signs of health, social well-being ( Stoppa & Chiolerio, 2014 ) and personal productivity ( Fernández-Caramés & Fraga-Lamas, 2018 ; O'Nascimento, 2020 ), providing biometric data on the preferences and lifestyles of each user ( Heintzman, 2016 ).

Wearable products are at the boundary between the physical and the digital worlds, where communication with remote objects and servers enables advanced monitoring services ( Fernández-Caramés & Fraga-Lamas, 2018 ). They may drastically change the way we live and do business ( Dehghani & Kim, 2019 ). Wearable products have as premise not to attract attention during use, but to dress the body ( Eidenhammer, 2018 ) and provide the user with real-time information and experiences ( Fernández-Caramés & Fraga-Lamas, 2018 ).

However, due to the need to carry extra equipment to monitor the desired data, 40% of wearable product users tend to put the equipment aside ( Lavallière et al. , 2016 ). Consequently, the initial premise of dressing the body is still not being fully met. Advances are needed for a better user experience with wearable products ( Lavallière et al. , 2016 ), such as joining industries that previously worked separately. With different product development techniques and manufacturing practices, computing, electronics, clothing and textiles could work together to develop such technological products ( Wood, 2018 ).

Therefore, technical uncertainties about the manufacture of wearable products are numerous, and changes in production processes are frequent ( Dehghani & Kim, 2019 ). This is a result of the need to define the way of inserting wearable technologies in the production of garments: whether the garment or textile acts as a support for electronic sensors or computing devices, enabling data output; or having all devices integrated at the level of textile production, be it at the fiber, fabric manufacture or finishing stages ( Wood, 2018 ). Thus, making clothing using the wearable concept is still an object of study ( Eidenhammer, 2018 ).

The scholars and decision-makers need to determine which new forms of integration connect the various parts of the field as it continues to grow ( Dehghani & Kim, 2019 ). First, new technologies arising from the miniaturization of components foster research opportunities, such as incorporating conductors into the fabric thread ( Wood, 2018 ). Second, the functionality of devices and cost reduction of the leading technologies cause the wearable market to grow rapidly ( Dehghani & Kim, 2019 ). This research adopts this perspective, as it proposes to identify the necessary actors for the development of a product with a wearable concept then to propose actions of leverage and perpetuation to this sector.

3. Methodology

According to Silva and Menezes (2005) , the research seeks ways to solve problems still unanswered; therefore, it is a reflective and critical procedure which, to obtain relevant and well-founded results, must indicate how it was carried out, making it possible to be contested and verified ( Dresch, Lacerda, & Antunes, 2015 ).

In this research, the proposed working method combines systematic and rational practices that contribute to obtain the desired results ( Collatto, Dresch, Lacerda, & Bentz, 2018 ; Marconi & Lakatos, 2010 ). For this research, we applied the systematic literature review method, which seeks to answer a question put forth by the researcher and uses systematic and explicit methods for collecting and analyzing the material found ( Morandi & Camargo, 2015 ). Figure 1 shows how this study was conducted.

The definition of the question and the conceptual framework arise from the interest in identifying how wearable technologies are inserted in the fashion value ecosystem, initially detecting who the actors that belong to this ecosystem are. Regarding the conceptual framework, this research is configurative since keywords were searched a priori considering the topic of wearable technologies and their insertion in the fashion value ecosystem. The work team , in turn, comprised the researchers who worked on the project and those who had knowledge about the theme and the methodology used.

The strategy was the elaboration of a set of keywords on the proposed topic. Databases were selected to perform the search: Ebsco , Scielo , Web of Science , Scopus , Emerald and gray literature (Google Scholar and reports of funding agencies) ( Morandi & Camargo, 2015 ). Saturation was the selected search strategy, and no timeframe was defined. When entering the combination of keywords, the Boolean operator “AND” was used, which helped minimize search bias. To select the documents, we performed three analyses: titles, abstracts and studies selected for a full reading. This way, 6,881 titles were found, of which 902 studies (scientific articles, technical reports and journal articles) were selected for summary analysis, and 349 were finally included in our study, as Table 1 shows.

Out of the final 349 documents, 15 addressed ecosystems and value or supply chains and were read in full. This way, a second exclusion of titles was carried out: studies not relevant were excluded, leaving 13 documents (see Table 2 in Section 4).

The completion of search , eligibility and encoding represented the operationalization of the study. Thus, when conducting the search, adherence and relevance to the theme were considered as inclusion criteria, whereas documents that did not address the studied context were excluded. The evaluation of the quality of primary studies centered on the contents of the documents, that is, whether they addressed value ecosystem, value chain, supply chain, among others, concerning the researched theme. The similarity of primary studies was considered and synthesized in a heterogeneous way.

For the synthesis of the results , we applied interpretative criticism. It was performed in three moments: initially by separating materials that discussed value ecosystem, value chain, supply chain, among others. Then, these materials were indexed using the software ATLAS.ti. The second step was also performed in this software, which listed the elements/actors that composed the fashion value ecosystem. Finally, in the third step, a table was created containing the elements/actors, which was analyzed again in order to reorder the elements/actors the documents presented, excluding or joining similar ones.

Using the qualitative data analysis software allows grouping similar data into blocks related to the issue, hypothesis or topic of interest and its relations ( Miles, Huberman, & Saldana, 2013 ), thus enabling an efficient, consistent and systematic analysis of data management ( Gibbs, 2014 ). Based on this process, the framework for this research was prepared, and we show the results in the following section.

This section discusses the data obtained in the systematic literature review, its results and the fashion value ecosystem model from the perspective of wearable technologies. Therefore, as described in Section 3, we selected 13 documents, as Table 2 shows. We selected these documents because they discuss ecosystems, value chains or supply chains of the fabric and clothing sector, defined in this study as fashion .

The study of EURATEX (2004) was used as a basis for the development of the ecosystem proposed for this study, as it presents the actors and the interconnections between them, beginning by the presentation of the extraction industry and ending in the reverse chain. The study’s main objective was to plan strategies for the future of the fashion industry, allowing access to resources for the development of research and innovations. Thus, that study shows the need to know the actors that belong to the fashion sector to propose future actions, corroborating the objectives of our own study.

The other studies discuss other topics, such as the creation of competitive advantages for the fashion sector. Chen (2019) and Mengi (2017) used a local fashion ecosystem to understand the clothing sector; however, Chen (2019) demonstrated the relevance of using technology for the participation of small companies in the fashion supply chain, whereas Mengi (2017) proposed integration between the textile and clothing sectors.

Corroborating this stance, Salonoja (2013) noted that the lack of integration and collaboration between clothing companies might result in the ecosystem’s underdevelopment and difficulties in obtaining external capital. Pinar and Trapp (2008) explained that strategies for brand promotion and product differentiation might promote increased competitiveness of textile products.

Sandberg et al. (2018) explained value creation and appropriation processes in a reverse clothing chain to demonstrate sustainability in the fashion value ecosystem. Wang (2018) addressed the need for changes in the manufacture and disposal of clothing, pointing out solutions to minimize impacts generated in the production of garments. Both authors present a comprehensive view of the actions and the members in the reverse chain.

Corner and Stride (2015) and Strauss et al. (2010) addressed the search for the development of the local fashion industry. The evidence shows the relevance of cities as global fashion centers through the promotion of jobs, training and workspaces ( Corner & Stride, 2015 ), helping and informing interested parties regarding the future of the fashion industry ( Strauss et al. , 2010 ).

In turn, Oksanen et al. (2018) analyzed the ecosystem of the creative economy in three countries and provided recommendations for the fashion industry. Lin (2018) explored technological innovation as a means to expand the image of an innovation ecosystem. Kaplanidou (2018) demonstrated the influence of digital transformation of different companies on the clothing industry, emphasizing the importance of Greek clothing manufacturers in understanding digital technologies. Finally, Fontell and Heikkilä (2017) presented a circular business ecosystem for textiles and clothing and explained the circular economy principles in the textile context, the main material flows and the types of actors present in the value chain.

Thus, after reading the documents listed in Table 2 , the elements/actors present in the models and the subdivision of the products derived from them were extracted, as Table 3 shows. Then, as described in Section 2, a qualitative analysis was performed on the actors, using a qualitative statistics software as a tool and excluding duplicates or conflicting names.

Based on data on the actors, the conceptual model of the fashion value ecosystem was designed from the perspective of wearable technologies, as Figure 2 shows. The proposed ecosystem has as its core business a “technological clothing product.” This definition was adopted because wearable products had different descriptions in the literature review ( Mardonova & Choi, 2018 ). This diversity reflects in the separation carried out by Richter (2018) , who pointed out six types of marketed wearable products. In addition, Dehghani and Kim (2019) pointed to clusters of occurrence of terms related to wearable devices and product diversity. Corroborating this stance, O’Nascimento (2020) shows several types and classes of wearable products.

As a premise, Moore (1996) stated that for the core business value and leverage ecosystem, there are dimensions defined as extraction industry , textile transformation industry , goods/clothing industry , retail , customers , reverse chain and electronics . Furthermore, external actors/support who provide services to the actors directly linked to the core business were added to our model and labeled as distribution , professionals and others .

The extraction industry is divided into natural and chemical fibers, as this subdivision determines the fabric to be produced and the other processes they perform. The transformation industry remodels the fibers, converting them into threads and later into fabrics through weaving and improvements that increase the quality of threads, whether chemical, natural, conventional, frictional or technological ( EURATEX, 2004 ).

The goods/clothing industry develops and produces the clothing items, technological or not. In the proposed model, this dimension briefly addresses the main actors involved in the clothing manufacture process, namely, development and modeling, cutting and sewing ( Kaplanidou, 2018 ; Mengi, 2017 ) and improvement ( EURATEX, 2004 ). Thus, because it is a wide-scope, complex sector, other actors are present and vary according to the market segment considered.

The retail dimension relates to sales and embodies two possible forms of distribution: physical and online ( Chen, 2019 ; Fontell & Heikkilä, 2017 ; Strauss et al. , 2010 ). The current rise of shopping apps has highlighted online retailing, which now has more followers than ever ( Fontell & Heikkilä, 2017 ). The customers , in turn, are divided into conventional , seeking to supply their individual needs, and unconventional , such as medicine consumers, which obtain products intending to supply collective needs ( EURATEX, 2004 ).

When reviewing the documents in Table 3 , we noticed the presence of reverse chains . They are inserted in the ecosystem and divided into disuse and recycling ( EURATEX, 2004 ; Fontell & Heikkilä, 2017 ; Strauss et al. , 2010 ).

Finally, electronics is part of the computer and nanotechnology industry that produces components and parts. Thus, this actor supplies materials to build technological garment products. They can be inserted in both the extraction industry, thus producing technological fabrics that will later be used as raw material and the goods/clothing industry during the finalization of the product ( Wood, 2018 ). Therefore, sensors, nanotechnological goods, miniaturization, such as sensing, wireless communication or nanotechnology, become available to the manufacturing and goods/confection industries ( Eidenhammer, 2018 ). Currently, wearable technology is considered the joining of electronics/informatics and clothing and accessories ( Wood, 2018 ).

Other actors (external/support) may directly or indirectly influence the functioning of the ecosystem, such as intermediaries/negotiators, legislation, infrastructure and education/courses. Therefore, these actors comprise an essential dimension in the ecosystem since they disseminate knowledge and legitimize technological product development in different sectors.

Distribution and professionals refer to the availability of products or technical services for product development. They are divided into textile/apparel, services, technology providers, content providers, intermediaries/negotiators and transport/logistics. For example, considering distribution , there are software suppliers that permit the reading of the identified data. In the professionals dimension, there is a need for specialized people to analyze this data and insert this technology in the clothing product, including many technological areas, such as fashion, computer science, engineering and the Humanities, like Medicine and Psychology. Therefore, these professionals have joint action, with variable intensity depending on the technological product to be developed.

The model shown in Figure 2 represents the reality of the complex fashion value ecosystem, lists the actors and demonstrates their interconnection. Unfortunately, the fashion value ecosystem model from the perspective of wearable technologies is incomplete because, depending on the product, actors need to be inserted or excluded. The following section presents the final considerations of the study.

5. Final considerations

Identifying the actors in the fashion value ecosystem enables confirming the complexity in this sector and understanding its functioning. Furthermore, by designing a conceptual model using an ecosystem approach ( Moore, 1996 ), it is possible to verify the interconnection between actors aiming at the sector’s functionality and leverage. Finally, even though organizations, entities, actors and the society in general, which gravitate around as complex a business such as fashion, are not structured and identified, they are part of a value ecosystem ( Serrano et al. , 2018 ). Therefore, the proposed objective was achieved, i.e. to identify the participants (actors) and create a fashion ecosystem model from the perspective of wearable technologies.

In terms of relevance, in our systematic literature review, no studies included wearable technologies in the discussed fashion ecosystems, nor their relations with other industries. However, the use of wearable technologies in numerous industrial sectors is evident ( Fernández-Caramés & Fraga-Lamas, 2018 ). Furthermore, these technologies add safety at work by monitoring body data ( Mardonova & Choi, 2018 ). Besides, according to Zhang et al. (2020) , wearable technology can be helpful for governments and health departments to control pandemics and track people consistently and precisely.

In addition, wearables are an emerging subject that needs further research to insert this technology in productive sectors and enable the development of sensors, information technology, data fusion techniques, material science, communication technologies, flexible batteries and storage facilities ( Zhang et al. , 2020 ). Thus, it becomes interesting to systematically identify this sector’s possible limitations and leverage points with the insertion of wearable technologies aiming to increase its competitiveness. Systematically analyzing the sector, it is possible to understand the existing complex relations, which would not be possible to be done linearly ( Sterman, 2000 ). In addition, it is necessary to use different approaches for business effectiveness ( Chen, 2019 ).

Although the results of this study are satisfactory, there were limitations to it. The first is that the model considered secondary data, the studies and documents shown in Table 2 . Also, the search universe resulted in documents concerning ecosystem models, value chains and supply chains. Thus, many studies had to be excluded.

The second limitation refers to the structure of the value ecosystem presented in Figure 2 , which followed Moore (1996) and was divided into core business , direct dimensions and external actors/support . Thus, the positioning of the actors may not follow a proper order due to the sector’s complexity. Because of this, we propose for further works the validation of our conceptual model through interviews with experts, making it possible to neutralize the limitations. It is also crucial to identify existing differences in the current processes of product development and production.

Method for the systematic literature review

Conceptual model of fashion value ecosystem

Search results

Source(s): Prepared by the authors

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Acknowledgements

The authors are grateful to the IFRS – Campus Erechim (Federal Institute of Education, Science and Technology of Rio Grande do Sul – Campus Erechim) for the financial support for the development of this paper.

The authors wish to thank The National Council for Scientific and Technological Development (CNPq), and The Coordination for the Improvement of Higher Education Personnel (CAPES) for the support to conduct this research.

Corresponding author

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International Journal of Interdisciplinary Research

• Open access
• Published: 25 October 2023

Developing an AI-based automated fashion design system: reflecting the work process of fashion designers

• Woojin Choi 1 ,
• Seyoon Jang   ORCID: orcid.org/0000-0002-1033-1247 1 ,
• Ha Youn Kim 2 ,
• Yuri Lee 3 ,
• Sang-goo Lee 4 ,
• Hanbit Lee 4 &
• Sungchan Park 5  

Fashion and Textiles volume  10 , Article number:  39 ( 2023 ) Cite this article

5705 Accesses

3 Citations

Metrics details

With the recent expansion of the applicability of artificial intelligence into the creative realm, attempts are being made to use AI (artificial intelligence) in the garment development system in various ways, both in academia and the fashion business. Several IT companies have developed and possess AI-based garment design technologies that utilize StyleGAN2 for image transformation. However, they are not widely utilized in the fashion business. Since fashion brands need to create numerous designs to launch new garment products for at least two seasons per year, the adoption of AI-based garment design generation technology can be one way to increase work efficiency. Therefore, this research aims to collect and analyze existing cases of AI-based garment design tools in order to identify the similarities and differences between the garment development processes of human designers and the existing AI-based garment design tools. Based on this analysis, the research aims to develop an AI-based garment development system that takes into consideration the garment development process of human designers, incorporating fashion domain knowledge.

Introduction

Artificial intelligence (AI) is one of the key drivers shaping the transformation of contemporary society alongside big data, virtual reality, and other technological advancements. The fashion industry is undergoing a transformation driven by technological innovations centered around AI (Carvalho et al., 2019 ; Jang & Ha, 2023 ; Market.US, 2023 ). For instance, advancements in AI technology have improved the ability to analyze big data, enabling online retailers to track consumer purchasing data and provide personalized services, thereby enhancing sales. Moreover, AI-based technologies allow for more accurate predictions of upcoming fashion trends, enabling efficient inventory management. Currently, “generative AI” technologies that generate diverse and customized outcomes are garnering significant attention. A notable example is Chat GPT; released by OpenAI, it attracted the interest of over 100 million active users in only two months. Additionally, platforms such as DALL-E2 generate over four million images daily (Xu et al., 2023 ).

However, despite the fashion industry having a shorter product lifecycle than other industries, the field of fashion design has traditionally relied on designer intuition for decision-making (Dubreuil & Lu, 2020 ; Lin & Yang, 2019 ; Takagi et al., 2017 ). As a result, although the application of AI has evolved from analytical to generative AI, it has not been widely adopted in the field of fashion design. Experts in the fashion industry recognize the importance of AI-based garment development technology (Kim et al., 2022 ). Specifically, AI-based garment development technology, incorporating the design process of human designers and fashion domain knowledge, can reduce the workload of fashion designers and product planners, thus increasing work efficiency (Dubey et al., 2020 ).

This research aims to achieve two primary objectives. First, aims to collect and analyze existing cases of AI-based garment design tools in order to identify the similarities and differences between the garment development processes of human designers and the existing AI-based garment design tools. Second, based on this analysis, the research aims to develop an AI-based garment development system that takes into consideration the garment development process of human designers, incorporating fashion domain knowledge. By developing a system that supports fashion design generation based on an understanding of the work processes of fashion designers and the domain knowledge of the fashion industry, rather than focusing exclusively on technological development, this study will enable AI-based garment development tools to become more adaptable for practical use.

The structure of this research is as follows: First, an examination of previous cases related to AI-based fashion design is conducted. This research focuses on the cases that have emerged since the active development of conceptual studies related to deep learning-based image generation techniques. Second, the garment development processes of the collected cases are analyzed by comparing them to the garment development processes of human designers to thus propose an AI-based garment development system that incorporates fashion domain knowledge. Third, an AI-based garment development system is developed using StyleGAN2, and a pilot program is developed to evaluate its satisfaction among industry designers. Finally, the research findings are discussed, highlighting their implications for industry, and some recommendations for future research are provided.

Literature Review

Garment development process and fashion domain knowledge.

The garment development process is a special problem-solving activity that comprises a series of small steps in which a designer explores a problem (Schoen, 1983 ). It is the process of designing, planning, and developing saleable products reflecting their brand identity and the relevant season’s concept for the target consumers (Clodfelter, 2015 ; Kincade, 2010 ; Lee, 2004 ). Many studies have found that the garment development process of human designers sequentially and simultaneously undergoes several stages: analysis of the brand’s internal data (i.e., sales review) and global fashion trends, concept formation and design ideation, design generation and modification, and design finalization (Evans, 2014 ; Lamb & Kallal, 1992 ; Watkins, 1998 ).

The purpose of this study is to develop an AI-aided design tool optimized for fashion brands owned by a producer or distributor. Based on previous research, the following five processes comprise the most optimized garment product development for fashion brands (Evans, 2014 ; Lamb & Kallal, 1992 ; Watkins, 1998 ): (1) analyzing internal/external data, (2) determining the concept to be the season’s direction, (3) generating garment design according to the season’s concept, (4) modifying the newly generated design, and (5) finalizing the garment design.

Fashion brands generally begin garment design development half a year or a year before the start of their product sales season (Blaazer, 2022 ; Lee, 2004 ). When developing garment products, fashion brands consider and analyze two main sources of information: internal brand data and global fashion trends (Clodfelter, 2015 ; Jackson & Shaw, 2017 ; Kincade, 2010 ). Internal brand data refer to past sales data, best-selling brand items, consumer data, or other relevant information (Testa & Karpova, 2022 ). The entire garment development team reviews the performance in previous seasons, including the previous year, to identify key trends that have contributed to profitability (Jackson & Shaw, 2017 ). Products that performed well in the previous season are likely to impact sales in the next season, making it crucial to review them (Ha-Brookshire, 2015 ; Jackson & Shaw, 2017 ; Kunz, 2010 ). Furthermore, for garment product development, global fashion trends are reviewed based on the runway collections of high-end brands. Runway collections are essential trend information and serve as significant factors in garment product development among mass fashion brands (Choi et al., 2021 ; Zhao & Min, 2019 ).

Next, it is necessary to plan the season’s concepts. This stage involves determining the overall theme and mood of the whole garment design (Caniato et al., 2015 ; Clark, 2014 ). Since a fashion brand needs to create 20–30 pieces of garments per season, establishing a concept is crucial to ensure consistent designs across any season (Clark, 2014 ; Lee & Jirousek, 2015 ). Hence, garment design involves generating and modifying garment designs that reflect the brand’s identity and seasonal concept (Caniato et al., 2015 ). This step focuses on generating a garment design and modifying the newly generated design to find an alternative design. Finally, through evaluations by merchandisers, shop managers, and other relevant stakeholders in the fashion brand, the finalization process entails selecting designs suitable for the brand's sales in the respective season (Evans, 2014 ).

Meanwhile, domain knowledge refers to the valid knowledge in a specialized field of study or industry (Choi, 2017 ). Although the development and introduction of new technologies are replacing many aspects of human factors, domain knowledge plays a critical role in setting the direction for any industry (Muralidhar et al., 2018 ). In the garment development process, this domain knowledge includes brand identity, past sales data, brand design characteristics, bestselling items and consumer information (Chen et al., 2012 ; Lee, 2004 ). In fields such as fashion, where human ‘intuition’ or ‘sense’ is highly involved, modeling domain knowledge based on human designers can enhance AI-based design processes.

AI-based garment design generation technology

Recently, AI in fashion garment design has evolved from image recognition and synthesis to image generation (Anantrasirichai & Bull, 2021 ). The beginning of research on the AI-based garment design process dates back to the early 2000s. Initially, garment design studies that incorporated AI used genetic algorithms (GA) that favor the evolution of the information of the previous generation, such as the genetic phenomenon of an organism, and pass this information on to the next generation. In other words, research was conducted to combine the design attributes of fashion products that have already been released and to suggest new styles (Khajeh et al., 2016 ; Kokol et al., 2006 ). The previous researches described garment design as a process that involves making various choices by combining different design attributes. Later, some studies found the location of fashion items in photographs using computer vision. This identification was made by improving machine learning performance (Hara et al., 2016 ; Lu et al., 2022 ), determining item categories and design attributes (Akata et al., 2013 ; An et al., 2023 ; Jang et al., 2022 ; Ji et al., 2018 ; Wang et al., 2018 ), and identifying similarities among designs (Ay et al., 2019 ; Ma et al., 2020 ; Tuinhof et al., 2018 ).

GAN has recently attracted attention in the research on AI-based garment design. The GAN model is an unsupervised deep learning method that generates or edits new fake images. A GAN is composed of two neural networks, namely, a generator and a discriminator, which compete against each other to improve the generation quality (Goodfellow et al., 2014 ). First proposed by Goodfellow et al. ( 2014 ), various derivative GAN models have since been introduced, enabling the editing and easy generation or synthesis of images. Hence, various research cases have emerged in the field of design (Raffiee & Sollami, 2021 ; Rostamzadeh et al., 2018 ).

GAN is used in the fashion industry to generate new designs or modify specific parts of the design (Liu et al., 2019 ), create graphics printed on clothing (Kim et al., 2017 ; Raffiee & Sollami, 2021 ; Rostamzadeh et al., 2018 ), and achieve a fusion of mixed semantic styles (Zhu et al., 2020 ). In addition, Disco GAN technology has been developed and advanced such that AI identifies the characteristics between different object groups and learns the relationship between the two to modify the design (Kim et al., 2017 ). For Disco GAN, if the image of a handbag is designated as the input value and the image of a shoe is designated as the output value, a new shoe design can be generated by identifying the image characteristics of the handbag and applying them to the shoe (Kim et al., 2017 ). StyleGAN and StyleGAN2 are algorithms optimized for fashion image generation. They consider image composition as a combination of styles and synthesize images by applying style information to each layer of the generative model. Models utilizing StyleGAN or StyleGAN2 can control network architecture and styles while generating clothing images, thus enabling the editing of garments for specific attributes (Lewis et al., 2021 ).

This research analyzes existing AI-based garment design tools and develops a new AI-based garment development system specifically designed for the fashion industry. As shown in Table 1 , the entire process of the research was divided into three stages:

Requirement analysis and system design

We conducted a case study by collecting examples of AI-based fashion design tools that have been utilized in practical applications. As garment design plays a significant role in the process of garment developing, we determined the need to gather and analyze cases of AI-based garment design tools to build an AI-based garment development system. Therefore, we compared the cases with the garment development process of human designers as a benchmark and derived commonalities and differences. On the basis of the analysis results, we proposed a new system that fashion designers can use in their practical work. Currently, AI-based fashion design processes are not widely used in the industry, the evaluation of the level of development varies (Kim et al., 2022 ). Thus, we concluded that qualitative research on the current state of technological development must be conducted. The research procedure for the case study is as follows.

Data collection

We explored articles and papers to extract information on IT companies with AI-based technologies for garment design generation. We collected articles published since 2018 by searching keywords, including “AI-based fashion design,” “AI fashion design tool,” and “AI fashion design process,” on the web portal ‘Google ( www.google.com )’ in English and ‘Naver ( www.naver.com )’ in Korean. Naver, the largest local search engine in South Korea, is optimized for retrieving information in Korean. We used Korean keywords for searching on Naver and English keywords for searching on Google. Additionally, we searched Google Scholar using the same keywords in both Korean and English to collect papers published since 2018 that included cases of AI-based garment design tools. As a result, we collected 13 cases from a total of 28 relevant articles and two papers, excluding duplicate articles.

Next, we excluded AI-based garment design tools that are still in the development stage or have not been commercialized. Ultimately, nine AI-based garment design tools with a history of commercial utilization were selected for analysis. The selected companies (tool names) include ETRI (AI Fashion Market Platform); Designovel (style AI), LG (AI artist Tilda); Google and Zalando (Project Muze); Amazon (Lab126); Google, H&M, and Ivyrevel (Coded Couture); Stitch Fix (Hybrid Design); YNAP (8 by Yoox); and OpenAI (Dall-E2).

Coding and data analysis

Each case was analyzed using the collected data. Four doctoral-level researchers in the field of fashion examined the original texts of the collected articles and conducted a constant comparison analysis. The five-step garment development process of human fashion designers presented in previous studies was used as a comparative criterion. Through this criterion, the commonalities and differences between AI-based design processes and human designers were explored. The researchers coded and classified the design processes of nine AI-based garment design tools. In cases where the researchers’ opinions did not align, additional search processes were conducted by setting the respective tool as a search keyword, followed by coding.

System proposal

On the basis of the analysis of the case studies, a new AI-based design system was designed. The system design involved the participation of four fashion researchers and three computer engineering researchers. Through approximately six months of continuous discussions, a user-centered (designer-centered) workflow, which could be integrated with the garment development process of human fashion designers, was designed. Then, a system consisting of four modules was proposed.

System development and implementation

An AI-based garment development system based on StyleGAN2 was developed by computer engineering researchers. The StyleGAN2 algorithm has demonstrated superior diversity and image quality in the generated outputs. In view of these findings, the researchers chose to employ the StyleGAN2 algorithm as the cornerstone for their AI-based garment development system. In particular, the system model was trained using dress and skirt images, which are well suited for exploring various silhouette variations. To train the model, a dataset of 52,000 images was collected from 168 leading fashion brands obtained from international fashion retail platforms, such as Yoox ( www.yoox.com ), Net-A-Porte ( www.net-a-porte.com ), Vogue ( www.vogue.com ) and Tagwalk ( www.tag-walk.com ). Subsequently, a model was developed to learn the distribution of the training images and generate new fashion images by adding noise and generating image variations. The trained model enabled coarse style variations in silhouette elements, such as full length, sleeve length, and neckline, in the early stages and fine style variations, such as color, pattern, and print, in the later stages.

To evaluate the developed service, a pilot test was conducted with a diverse group of 8 designers in South Korea, including women’s clothing designers from small and medium-sized enterprises (SMEs) and large corporations, as well as designers from apparel vendor companies. Prior to the pilot test, a snowball sampling method was used to select designers who wished to review the service. Taking into account their expertise and company size, a final group of 8 users was chosen. They used the service for a period of 10 days in mid-December 2022. Then, the researchers conducted interviews and brief postservice surveys. The measurement items included the participants’ perception of the service quality before and after usage, evaluation of the design outcomes, and intention for continuous usage. Respondents indicated the degree to which they agreed with the statements using a five-point Likert scale (1 = “strongly disagree” to 5 = “strongly agree”).

Results and Discussion

The result of case study.

The nine AI-based garment design generation tools selected in this study are summarized in comparison with the human designers’ development process (Table 2 ). An “X” mark indicates that the tool does not include the stage of the human garment development process, and an “O” mark indicates that it includes the stage. Specific commonalities and differences are described later.

Garment designers spend extensive time in internal and external data research (Clodfelter, 2015 ; Jackson & Shaw, 2017 ; Kincade, 2010 ). The internal data analysis stage appeared in four out of nine cases. To support efficient design generation in a mass production preplanning system, the brand’s internal data must be incorporated. Most of these profiling data pertain to customers and are primarily utilized in the form of recommendation services to enhance consumer experiences. This aspect constitutes only a portion of the personalization service that “recommends” designs to consumers, thus causing difficulty in claiming that it is primarily aimed at “generating” designs for mass production. For instance, Stitch Fix’s Hybrid Design and YNAP’s 8 by Yoox analyze and incorporate customer data, such as user lifestyles, to generate personalized designs for users.

Some AI-generated design tools also provide external data research and analysis. Global fashion week data, social media fashion data, and social media influencer data can be included in external data. The AI Fashion Market Platform (ETRI), Style AI (Designovel), 8 by Yoox (YNAP), and Hybrid Design (Stitch Fix) support design generation based on external data (i.e., social media influencers` fashion data) research and analysis. The AI Fashion Market Platform (ETRI) generates garment designs in light of domestic trends reflected on social media, while YNAP’s 8 by Yoox reflects the trends by analyzing and showing clothes that social media influencers prefer (Melton, 2018 ). However, they have limitations in that they analyze and provide trends without considering the brand identity or brand concept for the season in the external data analysis.

Second, many AI-based design tools lack the stage of concept formation. The development of season concepts is reflected only in the case of Tilda. Tilda generated approximately 3000 inspiration images for the design theme presented by the human designer (LG AI Research, 2022 ). As the development of the designs is led by technology without design development knowledge, such as brand profiling, season trend analysis, and concept decisions, which affect the direction of learning and design, users may conclude that AI’s design ideation is less brand specific.

Third, models for design generation and modification were provided with a focus on image composition and text-to-image composition using GAN in half of the cases. Some technologies (e.g., Coded Couture; Dall-E2) are image-generation technologies that simply convert text into images rather than AI technologies that creatively generate garment designs (Lee, 2018 ; Oh, 2021 ). In addition, although designs that are generated on the basis of the serendipity of AI-based fashion design look creative, they remain limited because they require modification by human designers to be used as commercial designs. However, only three cases allowed modifications after the generation of garment design images. Last, not all cases included the finalization process.

In summary, compared with the garment design development of human designers, the biggest drawback of existing AI-based garment design development tools is the difficulty in accurately reflecting the designer’s intentions. Such tools that are currently being developed and used focus only on trend analysis and image generation. This limitation has led to a nonholistic view of AI-based garment design tools developed in the fashion domain and has raised the need to generate a tool that reflects knowledge in the fashion domain.

Suggestion of AI-aided design process

Garment design is a complex and cyclical process in which various thinking methods are continuously and simultaneously applied in each stage of the design process (Evans, 2014 ). However, through analyzing the cases, researchers have found that the AI-based garment design tools commercialized thus far do not cover the comprehensive process from the perspective of human designers. Therefore, this study proposes an AI-based garment development system that reflects fashion domain knowledge. We advance an AI-based garment development system that integrates the human-based design process as follows (Fig.  1 ). The system consists of four modules, integrating five stages of the garment design process. Detailed explanations for each module are provided in the following section on system development and implementation.

AI–human collaborative garment development system

Development of AI-aided design process

Module 1 and Module 2 involve the collection and analysis of internal and external data, respectively. Module 1 builds a dataset based on the brand’s internal data, while Module 2 extracts external information. In addition, Module 3 functions as a design source database, serving as a repository where users can store necessary keywords and images during the garment design process. Module 4 generates garment designs and modifies the garment designs. Considering that the garment design process is simultaneous and repetitive, the process was designed for users to organize the process freely depending on the purpose, such as changing the order of the module with key functions according to the user’s needs or removing an unnecessary module.

Module 1: building a database of the company’s internal environment

A system was designed to analyze and integrate the brand’s internal data, thus enabling the inference of brand concepts and designing intentions from the brand’s own product data. Users are prompted to upload brand-related data directly when they first start using the system. Then, users upload images related to the brand and reference images used during garment development. These images can be uploaded manually by users or automatically collected through a crawling robot by entering the website address of the shopping mall or social media platforms managed by the company. Upon uploading a product image, an automated tagging system labels the design features and automatically generates and stores product information in the database. In addition, a brief profiling survey is conducted in which users are asked to select brands similar to their own brand from domestic and global fashion brand lists. All of these processes are optional, thus allowing users to skip them without any hindrance in utilizing other modules. The collected information is utilized as a weighting factor during the generation of garment designs for user brands. Once the input of basic information regarding the brand’s internal environment is completed, the system extracts the typical design factors associated with the brand’s design and incorporates them into the garment generation process.

To implement Module 1, technology is needed to identify the design features of garment products in images and label them in text format. To accomplish this step, computer vision and natural language processing (NLP) techniques are employed to preprocess and structure internal data, encompassing extensive unstructured image and text data. Generally, internal databases contain various, large-scale, and unnormalized data, which can be obstacles to utilizing AI techniques. Before applying advanced image/text content analysis techniques, building a database can be helpful. For example, auto labeling (Cheng et al., 2018 ) techniques can extract style keywords (e.g., “casual,” “modern”) and objective attributes (e.g., “turtleneck,” “puff sleeves”) from fashion images. NLP techniques can also be utilized to process unstructured text data to reduce the incompleteness of the database.

Module 2: global runway trend extraction

Module 2 was designed to analyze fashion trends based on runway collections and provide trend keywords associated with specific seasons or design attributes. This module visualizes prominent design keywords in ready-to-wear (RTW) and haute couture based on runway shows held twice a year: Spring/Summer (S/S) and Fall/Winter (F/W). The design features of a particular runway brand serve as important design references for mass-market fashion brands (Jang et al., 2022 ). Therefore, rankings must be derived on the basis of seasons and major keywords.

Runway data can be automatically collected using the brand name on TAGWALK ( www.tag-walk.com ) or the official US website of Vogue ( www.vogue.com ). Then, the data can be saved on the trend database. The saved images are turned into labeled data with major design features through the computer vision technology mentioned in Module 1. In addition to the frequency of extracted keywords, comparisons with the same season in the previous year and the last season are provided. Keywords with high interest can be moved to the design source of Module 3. Again, global runway trend keywords are stored together with relevant images.

Module 3: design source database

The design source database is a function that can save and manage keywords and images selected by users. All keywords and images directly entered by the user in Module 1 are also stored in Module 3. Then, users can use them as needed. The users can freely organize the dashboard by season, item, or design features depending on the purpose. Moreover, the users’ convenience can be increased by separately storing the sources required for future design generation. Module 3 must further implement a feature to search associated images by selecting one or more design keywords. In addition, a user interface must be implemented to facilitate the retrieval of information (keyword or image) stored by the user according to the user’s purpose. This module allows users to gather keywords and images stored in the database on the basis of their needs. As a result, the module serves as a mood board in the garment design process and facilitates the establishment of season concepts.

Module 4: design feature combination and GAN-based garment design generation

In Module 4, users can not only upload or retrieve new images from the design source library (Module 3) to generate a new garment design but also modify their own designs or the generated designs within the available options presented by the system. Users can modify various design features, such as color, silhouette (fit, length), pattern and prints, and detail features.

Furthermore, users can repeat this process as many times as they want until they are satisfied. Then, they can obtain new inspiration from the AI-generated images. They can also use the design as it is or transform the details or colors for a better design. Moreover, users can generate or transform images by uploading their own brand in Module 1 to ensure that they obtain results that reflect their input. Among the generated images, an image selected by the user can be included in Module 3. Even when the image is not selected, the system can ask users why they did not store the image, thereby recording enhanced personalized preference results. The accumulated personalized data may be associated with the elaboration of the image generation result. The images finally generated can be shared with users and people with registered accounts related to the brands for evaluation. This step corresponds to the finalization stage of the human design process.

To implement Module 4, an image generation model, namely the GAN model called StyleGAN2, was employed (Karras et al., 2019 , 2020 , 2021 ). Once the image generation model is trained with a large set of training images, the model can generate a wide range of synthetic but photorealistic fashion images. Furthermore, the design features of the generated garment images can be finely modified, including the silhouette, color, patterns, and prints (Patashnik et al., 2021 ; Shen et al., 2020 ; Wu et al., 2021 ). Figure  2 presents an overview of the AI-based garment design framework, which utilizes the StyleGan2 model. As shown in Fig.  2 , the recent image editing technique can support various user-specified cues, such as silhouette (length), pattern, and colors. Furthermore, Fig.  3 shows an example of images generated using the AI techniques mentioned in Fig.  2 .

AI techniques for image generation and editing (all images are generated by artificial intelligence)

The example of fashion image generation and editing Note. top-left image: From Look 3 [Photography], by Jil Sander, 2022, Vogue (https://www.vogue.com/fashion-shows/resort-2022/jil-sander/slideshow/collection#3). Note. top-left image: From Look 48 [Photography], by Daniele Oberrauch, 2022, Vogue (https://www.vogue.com/fashion-shows/spring-2022-ready-to-wear/sergio-hudson/slideshow/collection#48). Note. bottom-left image: From Look 44 [Photography], by Gucci, 2022, Vogue (https://www.vogue.com/fashion-shows/spring-2022-ready-to-wear/gucci/slideshow/collection#44. Accessed 2 August 2022)

Pilot test of AI-based garment development system

The research team created a front-end program to enable fashion designers to evaluate both quantitative and qualitative performance through access to the AI-based garment development system. As a result of the quantitative performance indicators of the design generation system, the inception score (IS) was 7.40, image reality score was 3.76, response time of the design generation was 1.02 s/req, and processing rate of the design generation was 58.4 req/min. Next, following Nielsen’s usability test guidelines (Nielsen, 2012 ), a pilot test was qualitatively conducted with 8 fashion designers in this study. The results showed that the participants’ expectations for the quality of the AI-generated garment design images were rated at 2.44 before using the AI-based garment development system. However, after using the system, the satisfaction with the AI-generated outcomes increased to 3.81, thus indicating that the final design results demonstrated high completeness and exceeded the participants’ expectations.

Conclusions

Garment design undergoes the comprehensive process of building the season concept by considering both global fashion trends and brand merchandising knowledge, going through design ideation based on the above, concretizing, and ultimately creating the design (Evans, 2014 ; Lamb & Kallar, 1992 ). If even one of these processes is omitted, a design with commerciality and brand identity can be difficult to develop. Although nine AI-based design generation solutions have been advanced thus far, they have focused only on the advancement of fashion trend analysis and automatic fashion image generation technology. The lack of intermediate stages in the garment development process leads to the absence of a holistic view of garment design (Kim et al., 2022 ). Thus, this study attempted to develop and propose an ideal AI-based garment development system by comparing the design process of human designers with the AI design process. The research on developing an AI design system that reflects the perspective of fashion designers is particularly relevant and timely, given the rapidly growing importance placed on advancing generative AI technologies (Market.US, 2023 ). Furthermore, by surveying the satisfaction of fashion designers, the potential usability of the proposed system has been confirmed. On this basis, the academic and practical implications of this study are as follows.

First, in this study, computer science and fashion fields were efficiently integrated, thus leading to the implementation of an AI-based garment development system that can yield highly effective results. To ensure the practical application of technology in the industry, the technology must be closely aligned with industry-standard processes (Caruelle et al., 2022 ; Jarek et al., 2019 ). While AI cannot fully comprehend the intuition of human designers, AI design tools can assist human designers by learning domain knowledge and being designed according to the design processes commonly followed by human designers (Dubey et al., 2020 ; Song et al., 2022 ). In this way, AI systems can be incorporated into the work environment and support human designers effectively. In this sense, this study holds academic and practical significance because it analyzed existing cases of AI-based garment design tools and developed an AI-based garment development system that incorporates fashion domain knowledge. Most research in the fashion field related to AI technology has remained at the stage of case analysis. However, the current study stands out by collaborating with computer science researchers to design a new system and implement it at a practical level, thus demonstrating its academic importance. Furthermore, generative AI has currently gained significant attention. Generative AI can produce images or music that reflects the user’s intent with simple prompts (Hsu & Ching, 2023 ; McCormack et al., 2023 ). The significance of the developed system in this study lies in its ability to generate results that incorporate the user’s intent. Finally, by allowing real fashion designers to use the system and evaluating its usability, this study confirms the practical significance of the developed system and its potential for practical application.

The following limitations exist in this study, and we would like to suggest further research to supplement them. First, we developed an AI-based fashion design system using Style GAN2 as the main algorithm. However, since we did not compare and analyze image generation performance because we focused on the algorithm development process, subsequent studies need to supplement this. Second, while GAN model is a crucial technology for image generation and has been actively used in garment design generation, Rostamzadeh et al. ( 2018 ) explained that the quality of the dataset affects design image generation when creating garment designs using GAN. Therefore, the data source must be obtained in such a way that designs of various conditions can be learned. Third, the AI-based garment development system developed in this study has a limited image resolution. This limitation poses challenges for fashion designers in manipulating and utilizing the images. However, recent advancements in diffusion models have significantly improved (Li et al., 2022 ), enabling the transformation of low-resolution images into high-resolution images. By incorporating such neural networks, the system’s utility can be enhanced. Fourth, the current system focuses only on dresses and skirts. In the future, expanding the training set to include a wider range of item categories (e.g., outer, pants, etc.) will allow for the broader application of the AI-based garment development system.

Availability of data and materials

The datasets used and analyzed during the current study are available from the first author on reasonable request.

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This work was supported by the Institute of Information & Communications Technology Planning & Evaluation (IITP) full name funded by the Korean Government (2021-0-00302, AI Fashion Designer: Mega-Trend and Merchandizing Knowledge Aware AI Fashion Designer Solution).

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WJC, SJ, and HYK designed the study and developed the theoretical framework, collected and analyzed the cases of Ai driven design tools, designed the module, and wrote the manuscript. YL guided the development of the theoretical background, results, and conclusion, and revised the manuscript. SP, S-GL and HL gave advise on designed module. All authors read and approved the final manuscript.

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Choi, W., Jang, S., Kim, H.Y. et al. Developing an AI-based automated fashion design system: reflecting the work process of fashion designers. Fash Text 10 , 39 (2023). https://doi.org/10.1186/s40691-023-00360-w

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• Artificial intelligence
• AI driven garment development tool
• Automated garment design generation tool
• GAN (Generative Adversarial Networks)
• Fashion domain knowledge

Wearable Technology in Fashion

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• Sumin Koo 4 &
• Youngjin Chae 5  

Part of the book series: Palgrave Studies in Practice: Global Fashion Brand Management ((PSP:GFBM))

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As smart clothing is being increasingly recognized as a strong contender in future wearables, with its flexible and comfortable interface becoming more accessible to people, fashion also has been successfully orienting itself as the next game-changer in wearable technology through its connection to a wide range of design, lifestyle, and functionality with its scalability. This chapter introduces the currently available wearables in the fashion industry for varied symbolic, aesthetic, cultural, or functional purposes and the projects concerning smart clothing and soft wearable robot for future living with the enhanced comfort of the wearer. The chapter also discusses the future of wearable technology in the fashion industry.

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Koo, S., Chae, Y. (2022). Wearable Technology in Fashion. In: Lee, YA. (eds) Leading Edge Technologies in Fashion Innovation. Palgrave Studies in Practice: Global Fashion Brand Management . Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-030-91135-5_3

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FASHION TECHNOLOGY PARK HATIJHEEL,DHAKA

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ELSAYED AHMED ELNASHAR, Full-Professor of textiles Apparel, Kaferelsheikh University, Egypt. Born in 19 /8/1965. Have Ph.D. 2000, Msc.1995, Bsc.1989, Helwan University. Diploma1985advanced industrial textiles institute. He holds several academic administrative positions: Dean, Vice Dean, Head of Department, He has many textiles patents, Member of international scientific committees. Development of Faculties of Education, commissioned of Supreme Council of Egyptian Universities. Has design books published in Germany. Has published over 160 scientific Articles. Founder and editor two scientific journals. And Smartex Conference Egypt. Member of the editorial board of several international journals and conferences, He has made many scientific agreements with European &Africa universities

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Prof.Dr. ELSAYED AHMED ELNASHAR, Ph.D. Full-Professor of textiles & Apparel Faculty of Specific Education, Kafrelsheikh University, Egypt. Editor in chief “SmarTex Research Journal” Print: (2090-634x) , Online: (2090-6358) EDUCATION: Ph.D. (2000): University of Helwan, Cairo, Egypt. Ph.D. thesis entitles "Design of Database for Forecasting the Specification of Woven Fabric Design for Ladies Dresses". Msc. (1995): Faculty of applied arts, University of Helwan, Cairo, Egypt. Entitle: "Effect of warp-ends densities distributions on some esthetical and physical properties of multi- layers woven fabric". Bsc. (1989): With a very good, "spinning, weaving &knitting Branch", from Helwan University, faculty of applied arts, Cairo, Egypt. Diploma. (1985): of the advanced of an industrial technical five years institute, weaving department. PROFESSIONAL EXPERENCE: Vice Dean for Higher Studies and Research of faculty of Specific Education, Kaferelsheikh University, Egypt, Since August 2011 till 31July 2015. Dean & Vice Dean for Higher Studies and Research of faculty of Specific Education, Kaferelsheikh University, Egypt, since 1 August 2013 till: 16 May 2015 Vice Dean for Higher Studies and Research & Head of Home Economic Department, Faculty of Specific Education, Kafrelsheikh University, Egypt. Since August 2011 to 1 August 2013 Editor in chief “SmarTex Research Journal” Print ISSN: (2090-634X) Starting Jun 2011. Editor in chief “SmarTex Research Journal” Online ISSN: (2090-6358) starting Jun 2011 August 2010 Head of Home Economic Department, Faculty of Specific Education, Kaferelsheikh University, Egypt. 27/5/2012- present Professor of textiles & Apparel in Home Economics Department, faculty of Specific Education, Kafrelsheikh University Egypt. 27/5/2007- Associate Professor of textiles & Apparel in Home Economics Department, faculty of Specific Education, Kafrelsheikh University Egypt . 1/02-3/07 Assistant Professor Textile apparel in Home Economics Department, faculty of Specific Education, Kafrelsheikh University Egypt. 12/00-1/02Assistant Prof. Textiles in art education Department, faculty of Specific Education, Kafrelsheikh University Egypt. 1/97-12/00Assistant lecture of textiles in art education Department, faculty of Specific Education, Kafrelsheikh University Egypt.

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Journal of Art and Civilization of Orient (JACO)

Women's garment is one of the symbols of urban landscape in India. Today we can see that despite passing historical ages and colonialism of Britain and also arriving modernism in to this country, women's clothing has not been changed; this has identified and preserved the cultural landscape in India. Garment and textile industry in India have an old background. This industry is extremely money making and so many management actions have been occurred to preserve it. Although there are big malls with famous brands, we can see Indian people wear cloths which are made in their own country and also women use their traditional garments. This process is succeed by numerous management, economic and cultural practices; in parallel with government policies in India, supporting domestic industry, custom legislation for imported goods, publicity, garment export beyond country border and creativity in printing and swing technics also played a crucial role in textile industry and make Indian garment a selected option by Indian women. Now it's the question, what is the main survival secret of this garment in many passed years? What made Indian garment chosen by Indian women? This article is based on author's perception of urban landscape in India and also theoretical studies.

Elsayed Elnashar

Prof.Dr. ELSAYED AHMED ELNASHAR, Ph.D. Full-Professor of textiles & Apparel Faculty of Specific Education, Kafrelsheikh University, Egypt. EDUCATION: Ph.D. (2000): University of Helwan, Cairo, Egypt. Ph.D. thesis entitles "Design of Database for Forecasting the Specification of Woven Fabric Design for Ladies Dresses". Msc. (1995): Faculty of applied arts, University of Helwan, Cairo, Egypt. Entitle: "Effect of warp-ends densities distributions on some esthetical and physical properties of multi- layers woven fabric". Bsc. (1989): With a very good, "spinning, weaving &knitting Branch", from Helwan University, faculty of applied arts, Cairo, Egypt. Diploma. (1985): of the advanced of an industrial technical five years institute, weaving department. PROFESSIONAL EXPERENCE: Vice Dean for Higher Studies and Research of faculty of Specific Education, Kaferelsheikh University, Egypt, Since August 2011 till 31July 2015. Dean & Vice Dean for Higher Studies and Research of faculty of Specific Education, Kaferelsheikh University, Egypt, since 1 August 2013 till: 16 May 2015 Vice Dean for Higher Studies and Research & Head of Home Economic Department, Faculty of Specific Education, Kafrelsheikh University, Egypt. Since August 2011 to 1 August 2013 Editor in chief “SmarTex Research Journal” Print ISSN: (2090-634X) Starting Jun 2011. Editor in chief “SmarTex Research Journal” Online ISSN: (2090-6358) starting Jun 2011 August 2010 Head of Home Economic Department, Faculty of Specific Education, Kaferelsheikh University, Egypt. 27/5/2012- present Professor of textiles & Apparel in Home Economics Department, faculty of Specific Education, Kafrelsheikh University Egypt. 27/5/2007- Associate Professor of textiles & Apparel in Home Economics Department, faculty of Specific Education, Kafrelsheikh University Egypt . 1/02-3/07 Assistant Professor Textile apparel in Home Economics Department, faculty of Specific Education, Kafrelsheikh University Egypt. 12/00-1/02Assistant Prof. Textiles in art education Department, faculty of Specific Education, Kafrelsheikh University Egypt. 1/97-12/00Assistant lecture of textiles in art education Department, faculty of Specific Education, Kafrelsheikh University Egypt.

EDITOR IJESRR

India is a profolic of tradition and culture both in custom, art and craft. India, wrapped in mystique enhanced with romance of fable crafts has one of the finest textile traditions in the world. Since decades highly developed civilizations continue to produce remarkable, eminent and ornate textiles with its distinct patterns, designs and motifs having different placements and layouts. However, escalating demands of consumers requires modification in the fashion industry with respect to design, motifs, colour, style and technique. In design-making process, source of inspiration has a vital role, both in defining the characteristics of a new design and in informing the creation of a distinct design. The purpose of this study was to create some original and innovative designs for textiles inspired from selected Mughal monuments of Agra. Several designs were developed on Adobe Photoshop software taking inspiration from three different monuments. Thirty designs that were innovative and suitable for textile designing were screened by experts. Through this study, an attempt was made to incorporate the rich motifs and designs of the various mughal monuments into contemporary forms on textile products. The present research also contributes to documentation and digitization of the motifs of these architectural wonders. It holds incredible promise for both collections and researchers through greater access, interaction and preservation of heritage for future generations. All the developed designs were subjected to visual evaluation for ranking of best designs in each category by the panel of fifty respondents to find out the suitability of the developed designs for textile design applications.

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