The enormous impact of artificial intelligence has been realized in transforming the fashion and apparel industry in the past decades. However, the research in this domain is scattered and mainly focuses on one of the stages of the supply chain. Due to this, it is difficult to comprehend the work conducted in the distinct domain of the fashion and apparel industry. Therefore, this paper aims to study the impact and the significance of artificial intelligence in the fashion and apparel industry in the last decades throughout the supply chain. Following this objective, we performed a systematic literature review of research articles (journal and conference) associated with artificial intelligence in the fashion and apparel industry. Articles were retrieved from two popular databases “Scopus” and “Web of Science” and the article screening was completed in five phases resulting in 149 articles. This was followed by article categorization which was grounded on the proposed taxonomy and was completed in two steps. First, the research articles were categorized according to the artificial intelligence methods applied such as machine learning, expert systems, decision support system, optimization, and image recognition and computer vision. Second, the articles were categorized based on supply chain stages targeted such as design, fabric production, apparel production, and distribution. In addition, the supply chain stages were further classified based on business-to-business (B2B) and business-to-consumer (B2C) to give a broader outlook of the industry. As a result of the categorizations, research gaps were identified in the applications of AI techniques, at the supply chain stages and from a business (B2B/B2C) perspective. Based on these gaps, the future prospects of the AI in this domain are discussed. These can benefit the researchers in academics and industrial practitioners working in the domain of the fashion and apparel industry.

The apparel industry houses a huge amount and variety of data. At every step of the supply chain, data is collected and stored by each supply chain actor. This data, when used intelligently, can help with solving a good deal of problems for the industry. In this regard, this article is devoted to the application of data mining on the industry’s product data, i.e., data related to a garment, such as fabric, trim, print, shape, and form. The purpose of this article is to use data mining and symmetry-based learning techniques on product data to create a classification model that consists of two subsystems: (1) for predicting the garment category and (2) for predicting the garment sub-category. Classification techniques, such as Decision Trees, Naïve Bayes, Random Forest, and Bayesian Forest were applied to the ‘Deep Fashion’ open-source database. The data contain three garment categories, 50 garment sub-categories, and 1000 garment attributes. The two subsystems were first trained individually and then integrated using soft classification. It was observed that the performance of the random forest classifier was comparatively better, with an accuracy of 86%, 73%, 82%, and 90%, respectively, for the garment category, and sub-categories of upper body garment, lower body garment, and whole-body garment.

Purpose: Today, customers’ perceived value does not only depend on the products, but also on the services provided by a firm. In e-commerce, it is important to shift the focus beyond the product and discuss the value of personalized services in the context of e-commerce fulfillment. Therefore, the purpose of this paper is twofold: to develop a conceptual framework proposing satisfaction through personalized services as a middle-range theory; and to suggest foundational premises supporting the theoretical framework, which in turn shape middle-range theory within the context of apparel e-commerce fulfillment. Design/methodology/approach: In this theory-driven paper, the authors apply the scientific circle of enquiry, as it demonstrates the role of theorizing with the help of middle-range theory and empirical evidence and as such provides a methodological scaffolding that connects theory formulation and verification. The authors synthesize literature related to customer perceived value (CPV) and satisfaction, followed by abduction focusing on understanding the empirical domain as it occurred in practice from company cases. The presented case studies are based on semi-structured interviews with three Swedish online retailers within the apparel industry. The theory-driven analysis results in suggestions of foundational premises. Findings: Based on the theoretical foundations and empirical generalizations, three propositions are suggested. The premises regarding satisfaction through personalized service applied in the domain of apparel e-commerce fulfillment are: to ensure customer satisfaction requires a value co-creation perspective using data during the pre-purchase phase; to ensure customer satisfaction and retention require added-value perspective during the post-purchase phase of the shopping journey; and to ensure satisfaction and convenience require an added-value perspective at the last mile. Practical implications: The apparel firms lose a substantial amount of revenue because of poor online customer satisfaction, leading to e-commerce not reaching its full potential. To enhance customer value, online retailers need to find a resort in advanced technologies and analytics to address customer satisfaction, and it is suggested that retailers shift their focus beyond the products and find ways to improve personalized service offerings to gain market advantage, improve fulfillment, drive sales and increase CPV. Originality/value: To consider personalized services as a source for improving e-commerce fulfillment and CPV, the main contribution of this study is conceptual as it presents a theoretical model developed from general theory, middle-range theory and verified with empirical claims.

La gestion du big data par l’intelligence artificielle dans la chaîne d'approvisionnement de l'industrie textile : opportunités et défis: L’industrie de l'habillement a bénéficié, au cours de la dernière décennie, de l'application de big data et de l'intelligence artificielle pour résoudre divers problèmes commerciaux. Face à la concurrence accrue sur le marché et aux attentes des clients en matière de personnalisation, ces industriels sont en permanence à la recherche des moyens d'améliorer leurs stratégies commerciales afin d'accroître leur rapidité et leur rentabilité. A cet égard, les solutions de gestion de big data offrent aux enseignes de la distribution textile la possibilité d'explorer leur chaîne d'approvisionnement et d'identifier les ressources de données importantes. Ces ressources précieuses, rares et inimitables permettent de créer des stratégies axées sur les données (data-driven) et d'établir des capacités dynamiques à maintenir dans un environnement commercial incertain. Grâce à ces stratégies data-driven, les enseignes de prêt-à-porter sont en mesure de confectionner des vêtements de façon intelligente afin de fournir à leurs clients un article adapté à leurs besoins et, par conséquent, d'adopter des pratiques de consommation et de production durables.Dans ce contexte, la thèse étudie les avantages de l'utilisation de big data et de l'intelligence artificielle (IA) dans les entreprises de l'habillement, afin d'améliorer leurs opérations commerciales tout en recherchant des opportunités de gestion de big data à l'aide de solutions d'IA. Dans un premier temps, cette thèse identifie et classifie les techniques d'IA qui peuvent être utilisées à différents stades de la chaîne d'approvisionnement pour améliorer les opérations commerciales existantes. Dans un deuxième temps, des données relatives aux produits sont présentées afin de créer un modèle de classification et des règles de conception susceptibles de fournir des recommandations personnalisées ou une personnalisation permettant une meilleure expérience d'achat pour le client. Dans un troisième et dernier temps, la thèse s'appuie sur les évidences de l'industrie de l'habillement et la littérature existante pour suggérer des propositions qui peuvent guider les responsables dans le développement de stratégies data-driven pour améliorer la satisfaction du client par des services personnalisés. Enfin, cette thèse montre l'efficacité des solutions analytiques basées sur les données pour maintenir un avantage concurrentiel grâce aux données et aux connaissances déjà présentes dans une chaîne d'approvisionnement de l'habillement. Plus précisément, cette thèse contribue au domaine textile en identifiant des opportunités spécifiques de gestion de big data à l'aide de solutions d'intelligence artificielle. Ces opportunités peuvent être une source de référence pour d'autres travaux de recherche dans le domaine de la technologie et de la gestion.