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Abstractsparkles

AI

The fashion and apparel (F&A) industry, a significant contributor to global economies, faces challenges in sustainability and waste management due to overproduction and consumer dissatisfaction. This review explores the integration of artificial intelligence (AI) techniques within various stages of the F&A supply chain to create a more sustainable framework. By detailing the applications of AI technologies in processes like design, manufacturing, and retail, the review underscores the potential of AI to enhance operational efficiency and reduce environmental impact.

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Key takeawayssparkles

AI

  1. The F&A industry contributes significantly to global economies, with projected sales growth of 7.5% in Asia Pacific.
  2. AI techniques can enhance sustainability by optimizing production and reducing waste in the F&A supply chain.
  3. Most research focuses on B2B challenges, with only 8% addressing B2C issues, highlighting a critical gap.
  4. Machine learning and expert systems dominate AI applications in the F&A industry, particularly in production and distribution.
  5. A systematic literature review identified 149 relevant articles examining AI's impact in the F&A industry from 1989 to 2018.
Figures (20)arrow_back_ios
COMPETENCIES OF RESEARCHERS
COMPETENCIES OF RESEARCHERS
Figure 1. Systematic Literature Review: Research Framework Based on the research focus, the methodology adopted is shown in Figure 1. The review process commenced with collecting and preparing data from scientific databases. Subsequently, articles were selected in five phases (depicted in Figure 2), strictly adhering to the inclusion and exclusion criteria defined in table IV and V. Finally, the selected articles were considered for classification (described in section IV) and further analysis complying with the research questions. There were two researchers involved in the entire review process and one expert researcher for the validation of the classification process. The competencies of each researcher can be seen in the following Table I.
Figure 1. Systematic Literature Review: Research Framework Based on the research focus, the methodology adopted is shown in Figure 1. The review process commenced with collecting and preparing data from scientific databases. Subsequently, articles were selected in five phases (depicted in Figure 2), strictly adhering to the inclusion and exclusion criteria defined in table IV and V. Finally, the selected articles were considered for classification (described in section IV) and further analysis complying with the research questions. There were two researchers involved in the entire review process and one expert researcher for the validation of the classification process. The competencies of each researcher can be seen in the following Table I.
Similarly, considering the same inclusion and exclusion criteria in the ‘Phase 4’, the two researchers first studied and analyzed the “Full text” of the articles, and then cross- validated the analysis based on their major competencies (refer Table I). While accessing the full texts, a few conference articles were encountered having published only abstracts. Such abstracts were excluded from the study. At this point, the number of remaining articles were 123 from journals and 98 from conference proceedings (total articles 221).
Similarly, considering the same inclusion and exclusion criteria in the ‘Phase 4’, the two researchers first studied and analyzed the “Full text” of the articles, and then cross- validated the analysis based on their major competencies (refer Table I). While accessing the full texts, a few conference articles were encountered having published only abstracts. Such abstracts were excluded from the study. At this point, the number of remaining articles were 123 from journals and 98 from conference proceedings (total articles 221).
298 from journals and 137 from conference proceedings (total articles 435). EXCLUSION CRITERIA
298 from journals and 137 from conference proceedings (total articles 435). EXCLUSION CRITERIA
This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/.
This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/.
Figure 3. Article Classification based on Research Questions The article classification conforming to the research questions is represented in Figure 3. As it can be seen, RQI is focused on understanding the overall trend of AI in the F&A industry. Hence, the focus of the screening process was limited to those articles discussing the implementation and execution of AI techniques in the F&A industry. To acknowledge RQI, AI techniques were divided into five categories: Machine Learning, Decision Support System, Expert System, Optimization, and Image Recognition & Vision. The algorithms considered under each class are discussed in section IV.B. While extracting information, these classes were assigned to the articles.
Figure 3. Article Classification based on Research Questions The article classification conforming to the research questions is represented in Figure 3. As it can be seen, RQI is focused on understanding the overall trend of AI in the F&A industry. Hence, the focus of the screening process was limited to those articles discussing the implementation and execution of AI techniques in the F&A industry. To acknowledge RQI, AI techniques were divided into five categories: Machine Learning, Decision Support System, Expert System, Optimization, and Image Recognition & Vision. The algorithms considered under each class are discussed in section IV.B. While extracting information, these classes were assigned to the articles.
are considered as Business to business (B2B) as their primary customers are other businesses, while retailers are considered as business to consumer (B2C) as their primary customers are the end-users or consumers. However, in the past decade, with the advent of e-commerce, the definition of B2B and B2C has evolved [22]. Therefore, it has become important for the industry to adapt to this change and create new business strategies. It has also become vital to give a comprehensive demarcation between B2B and B2C, and how AI can help in combating problems at these segments. (e.g. garment manufacturer buying yarn), needs services for operational reasons (e.g. employing a third-party logistics service provider), re-sells goods and services produced by other businesses (e.g. a retailer buying products from manufacturer). The goal of a B2B transaction is to help their business stay profitable, competitive and successful. Table II shows the classification of the supply chain into B2B.
are considered as Business to business (B2B) as their primary customers are other businesses, while retailers are considered as business to consumer (B2C) as their primary customers are the end-users or consumers. However, in the past decade, with the advent of e-commerce, the definition of B2B and B2C has evolved [22]. Therefore, it has become important for the industry to adapt to this change and create new business strategies. It has also become vital to give a comprehensive demarcation between B2B and B2C, and how AI can help in combating problems at these segments. (e.g. garment manufacturer buying yarn), needs services for operational reasons (e.g. employing a third-party logistics service provider), re-sells goods and services produced by other businesses (e.g. a retailer buying products from manufacturer). The goal of a B2B transaction is to help their business stay profitable, competitive and successful. Table II shows the classification of the supply chain into B2B.
B2B AND B2C ACTIVITIES IN THE F&A INDUSTRY B. Proposed taxonomy of applied AI methods in F&A supply chain Artificial in elligence has already proved its capability to solve the real world problems due to its heuristic characteristics of generalizing data. In the last three decades, the F&A ind ustry has undergone a number of changes and AI has played a key role in this transformation. Currently, the F&A industry is equipped required at t he various stages o has improved the overall eff processes [9]. Application of categorized by operating proces the F&A [8]. However, t categorization of the applied A The study in [8] explains the research issues in the operating process of the apparel industry. This study found research has 45% contribution to apparel manu issues, approximately 9% to apparel forecasting and fashion recommendation. ses at a manageria hese researches in the F&A supp with advanced machines f apparel production, which ficiency of the industrial AI is well explained and level in ack the y chain. that AI facturing 4.2% to
B2B AND B2C ACTIVITIES IN THE F&A INDUSTRY B. Proposed taxonomy of applied AI methods in F&A supply chain Artificial in elligence has already proved its capability to solve the real world problems due to its heuristic characteristics of generalizing data. In the last three decades, the F&A ind ustry has undergone a number of changes and AI has played a key role in this transformation. Currently, the F&A industry is equipped required at t he various stages o has improved the overall eff processes [9]. Application of categorized by operating proces the F&A [8]. However, t categorization of the applied A The study in [8] explains the research issues in the operating process of the apparel industry. This study found research has 45% contribution to apparel manu issues, approximately 9% to apparel forecasting and fashion recommendation. ses at a manageria hese researches in the F&A supp with advanced machines f apparel production, which ficiency of the industrial AI is well explained and level in ack the y chain. that AI facturing 4.2% to
Figure 5. Classification of Al in the F&A industry Supervised Learning- is a parametric model and it has input (independent variables) and target variable (dependent variable) [36]. Supervised model performance can be improved by optimizing the model parameters through iterative processes [37]. Based on the research problem, it could be a classification or regression task and this relies on dependent variable whether it is categorical or numerical.
Figure 5. Classification of Al in the F&A industry Supervised Learning- is a parametric model and it has input (independent variables) and target variable (dependent variable) [36]. Supervised model performance can be improved by optimizing the model parameters through iterative processes [37]. Based on the research problem, it could be a classification or regression task and this relies on dependent variable whether it is categorical or numerical.
Number of Research Papers Figure 6. Decade-wise article representation of Al in The detailed trend per year of articles published in journals and conference proceedings is depicted in Figure 7. As can be seen, the overall importance of this research domain has been equal in both journals and conferences. The only downward slope is visible for conference publications between the year 2013 and 2016 as highlighted in Figure 7. Apart from this, figure 8 shows the top 10 authors and institutions contributing to the literature of AI in the F&A industry.
Number of Research Papers Figure 6. Decade-wise article representation of Al in The detailed trend per year of articles published in journals and conference proceedings is depicted in Figure 7. As can be seen, the overall importance of this research domain has been equal in both journals and conferences. The only downward slope is visible for conference publications between the year 2013 and 2016 as highlighted in Figure 7. Apart from this, figure 8 shows the top 10 authors and institutions contributing to the literature of AI in the F&A industry.
Figure 7. Overall trend of Al in F&A since 1989
Figure 7. Overall trend of Al in F&A since 1989
Figure 8. Top 10 affiliations and authors B. Distribution of articles by applied AI over time
Figure 8. Top 10 affiliations and authors B. Distribution of articles by applied AI over time
Figure 9. Total distribution of articles by Al methods applied Figure 10. Distribution of articles by applied Al over time COUNT OF APPLIED ARTIFICIAL ARTICLES IN F&A SUPPLY CHAIN
Figure 9. Total distribution of articles by Al methods applied Figure 10. Distribution of articles by applied Al over time COUNT OF APPLIED ARTIFICIAL ARTICLES IN F&A SUPPLY CHAIN
Figure 11. Distribution by supply chain processes systems in journal articles, while the focus has been on machine learning and image recognition in conference articles. In fabric production, all AI classes have been widely used with a major focus on machine learning and Expert systems. Additionally, there has been growing use of image recognition in fabric inspection, which is a process under fabric production (described in section IV.A).
Figure 11. Distribution by supply chain processes systems in journal articles, while the focus has been on machine learning and image recognition in conference articles. In fabric production, all AI classes have been widely used with a major focus on machine learning and Expert systems. Additionally, there has been growing use of image recognition in fabric inspection, which is a process under fabric production (described in section IV.A).
There were total 149 articles reviewed, out of which 122, 13 and 14 belonged to B2B, B2C and both (B2B/B2C) respectively. If we consider B2B, as shown in figure 13, substantial research has been carried out in all three decades as compared to B2C. In the case of B2C, the total number of articles published in itself is low i.e. 13. Out of this, only two were published in the second decade and rest were published in decade 3. There were no articles published in decade 1. By both, it is meant that the focus of the study is both B2B and B2C. The total number of articles, in this case, is 14, out o which ten were published in the third decade and four in the second decade. There were no articles published in decade for this as well. As a conclusion, we can say that little work has been done with the perspective of B2C and there is a need to attend to this gap. Figure 13. Decade-wise article representation of B2B, B2C and Both
There were total 149 articles reviewed, out of which 122, 13 and 14 belonged to B2B, B2C and both (B2B/B2C) respectively. If we consider B2B, as shown in figure 13, substantial research has been carried out in all three decades as compared to B2C. In the case of B2C, the total number of articles published in itself is low i.e. 13. Out of this, only two were published in the second decade and rest were published in decade 3. There were no articles published in decade 1. By both, it is meant that the focus of the study is both B2B and B2C. The total number of articles, in this case, is 14, out o which ten were published in the third decade and four in the second decade. There were no articles published in decade for this as well. As a conclusion, we can say that little work has been done with the perspective of B2C and there is a need to attend to this gap. Figure 13. Decade-wise article representation of B2B, B2C and Both
Figure 12. Distribution of articles by B2B and B2C focused on solving the issues related to B2B and there is little attention on B2C both in journal and conference articles. To get a clear picture, figure 13 shows the distribution of articles in three decades separately for B2B, B2C and both.
Figure 12. Distribution of articles by B2B and B2C focused on solving the issues related to B2B and there is little attention on B2C both in journal and conference articles. To get a clear picture, figure 13 shows the distribution of articles in three decades separately for B2B, B2C and both.
AI METHODS USED AT VARIOUS SUPPLY CHAIN STAGES AND PROCESSES IN JOURNAL ARTICLES This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/.
AI METHODS USED AT VARIOUS SUPPLY CHAIN STAGES AND PROCESSES IN JOURNAL ARTICLES This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/.
AI METHODS USED AT VARIOUS SUPPLY CHAIN STAGES AND PROCESSES IN CONFERENCE ARTICLES TABLE IX.
AI METHODS USED AT VARIOUS SUPPLY CHAIN STAGES AND PROCESSES IN CONFERENCE ARTICLES TABLE IX.
As we have noticed that fuzzy techniques and genetic algorithms are exhaustively used for expert systems, decision support systems and optimization. These techniques can be Figure 14. Word Cloud using Text Mining on Abstracts industry equip ‘FAIR’ data principle [205] in strategizing their business. combined with advanced AI techniques to enhance the computational ability of a machine learning algorithm. Similarly, if the classical forecasting model is combined with Al it can lead to better forecasting in terms of seasonality and trends. The aim of the study was to conduct a systematic literature review to address the three defined research questions (RQ1, RQ2, and RQ3). In line with the research framework, we retrieved 1019 articles published between 1989 and 2018, from two popular academic databases: Scopus and Web of Science. The article screening process was carried out in five phases (shown in figure 2), which resulted in 149 articles. To extract information from these articles and address our research questions, a taxonomy was proposed considering AI methods and F&A supply chain stages acknowledging RQ1 and RQ? respectively. To acknowledge RQ3, F&A supply chain was further classified into B2B and B2C.
As we have noticed that fuzzy techniques and genetic algorithms are exhaustively used for expert systems, decision support systems and optimization. These techniques can be Figure 14. Word Cloud using Text Mining on Abstracts industry equip ‘FAIR’ data principle [205] in strategizing their business. combined with advanced AI techniques to enhance the computational ability of a machine learning algorithm. Similarly, if the classical forecasting model is combined with Al it can lead to better forecasting in terms of seasonality and trends. The aim of the study was to conduct a systematic literature review to address the three defined research questions (RQ1, RQ2, and RQ3). In line with the research framework, we retrieved 1019 articles published between 1989 and 2018, from two popular academic databases: Scopus and Web of Science. The article screening process was carried out in five phases (shown in figure 2), which resulted in 149 articles. To extract information from these articles and address our research questions, a taxonomy was proposed considering AI methods and F&A supply chain stages acknowledging RQ1 and RQ? respectively. To acknowledge RQ3, F&A supply chain was further classified into B2B and B2C.
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FAQs

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AI

What are the most applied AI methods in the F&A industry?add

The review found that Machine Learning (42%) and Expert Systems (28%) are the most commonly applied AI methods in the F&A industry, with least applications observed in Optimization and Image Recognition.

How has the application of AI in F&A evolved over time?add

The study indicates that research on AI in the F&A industry has surged in the last decade (2009-2018), accounting for 56% of total publications, a notable increase from earlier decades.

What gaps exist in AI research within the F&A supply chain?add

The analysis identified significant gaps in addressing B2C problems, with only 8% of articles focusing on consumer-oriented solutions, indicating a need for more research in this area.

What classification framework was used for AI methods in F&A?add

A taxonomy was developed categorizing AI methods into five classes: Machine Learning, Decision Support Systems, Expert Systems, Optimization, and Image Recognition, along with their application stages in the supply chain.

Which stages of the F&A supply chain are least addressed by AI?add

The Design stage in the F&A supply chain received the least research attention, highlighting a substantial opportunity for future AI applications to enhance design processes.

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