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Big Data Management Using Artificial Intelligence in the Apparel Supply Chain: Opportunities and Challenges
University of Borås, Faculty of Textiles, Engineering and Business.ORCID iD: 0000-0001-8337-251x
2020 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Big data management using artificial intelligence in the apparel supply chain:

Opportunities and Challenges

Over the past decade, the apparel industry has seen several applications of big data and artificial intelligence (AI) in dealing with various business problems. With the increase in competition and customer demands for the personalization of products and services which can enhance their brand experience and satisfaction, supply-chain managers in apparel firms are constantly looking for ways to improve their business strategies so as to bring speed and cost efficiency to their organizations. The big data management solutions presented in this thesis highlight opportunities for apparel firms to look into their supply chains and identify big data resources that may be valuable, rare, and inimitable, and to use them to create data-driven strategies and establish dynamic capabilities to sustain their businesses in an uncertain business environment. With the help of these data-driven strategies, apparel firms can produce garments smartly to provide customers with a product that closer meets their needs, and as such drive sustainable consumption and production practices.

In this context, this thesis aims to investigate whether apparel firms can improve their business operations by employing big data and AI, and in so doing, seek big data management opportunities using AI solutions. Firstly, the thesis identifies and classifies AI techniques that can be used at various stages of the supply chain to improve existing business operations. Secondly, the thesis presents product-related data to create a classification model and design rules that can create opportunities for providing personalized recommendations or customization, enabling better shopping experiences for customers. Thirdly, this thesis draws from the evidence in the industry and existing literature to make suggestions that may guide managers in developing data-driven strategies for improving customer satisfaction through personalized services. Finally, this thesis shows the effectiveness of data-driven analytical solutions in sustaining competitive advantage via the data and knowledge already present within the apparel supply chain. More importantly, this thesis also contributes to the field by identifying specific opportunities with big data management using AI solutions. These opportunities can be a starting point for other research in the field of technology and management.

Abstract [sv]

Big Data Management och användning av artificiell intelligens i klädförsörjningskedjan:

Möjligheter och utmaningar

Under det senaste decenniet har användning av big data och artificiell intelligens använts för att hantera olika affärsproblem inom klädindustrin. I takt med den ökade konkurrensen på marknaden och kundernas efterfrågan på mer individanpassade lösningar, letar klädföretag efter nya sätt att förbättra affärsstrategier så att de kan bli snabbare och mer kostnadseffektiva. Big data management ger möjligheter för klädföretag att få kontroll över sin leverantörskedja och identifiera big data-resurser som är värdefulla, sällsynta, svåra att kopiera och kan användas för att skapa datadrivna strategier samt förstärka dynamiska förmågor i en osäker miljö. Med hjälp av dessa datadrivna strategier kan klädföretag på ett smartare sätt producera kläder för att ge kunderna en produkt som är närmare deras behov, och som sådan, driva hållbar konsumtion och produktionsmetoder.

I den här kontexten undersöker avhandlingen fördelarna för klädföretag att använda big data och artificiell intelligens för att förbättra sin affärsverksamhet och samtidigt söka möjligheter med big data management med hjälp av AI-lösningar. Först identifierar och klassificerar avhandlingen AI-tekniker som kan användas i olika delar av leveranskedjan för att förbättra den befintliga affärsverksamheten. För det andra presenterar avhandlingen produktrelaterad data för att skapa en klassificeringsmodell och designregler som kan vara till gagn för att ge personliga rekommendationer eller kundanpassningar som möjliggör en bättre shoppingupplevelse. För det tredje tar den fram förslag baserat på bevis från branschen och befintlig litteratur, som kan vägleda chefer i att utveckla datadrivna strategier för att förbättra kundnöjdheten genom individanpassade tjänster. Denna avhandling visar att effektiviteten hos datadrivna analytiska lösningar via befintlig data och kunskap kan leda till konkurrensfördelar. Framför allt bidrar denna avhandling till fältet genom att identifiera specifika möjligheter med big data management med hjälp av AI-lösningar. Dessa möjligheter kan vara en utgångspunkt för andra forskningsarbeten inom teknik och management.

Abstract [fr]

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.

Place, publisher, year, edition, pages
Borås: Högskolan i Borås, 2020.
Keywords [en]
Big data management, artificial intelligence, apparel supply chain, personalized offerings, data-driven strategies
Keywords [fr]
Gestion big data, intelligence artificielle, chaîne d'approvisionnement de l'habillement, personnalisation, stratégies basées sur les données (data-driven)
Keywords [sv]
Big data management, artificiell intelligens, klädförsörjningskedja, personifierade erbjudanden, data-drivna strategier
National Category
Business Administration
Research subject
Textiles and Fashion (General)
Identifiers
URN: urn:nbn:se:hb:diva-23771ISBN: 978-91-88838-81-0 (print)ISBN: 978-91-88838-82-7 (electronic)OAI: oai:DiVA.org:hb-23771DiVA, id: diva2:1466445
Public defence
2020-10-09, M202, 11:37 (English)
Opponent
Available from: 2020-09-18 Created: 2020-09-11 Last updated: 2020-09-16Bibliographically approved
List of papers
1. A Detailed Review of Artificial Intelligence Applied in the Fashion and Apparel Industry
Open this publication in new window or tab >>A Detailed Review of Artificial Intelligence Applied in the Fashion and Apparel Industry
2019 (English)In: IEEE Access, E-ISSN 2169-3536Article in journal (Refereed) Published
Abstract [en]

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.

Keywords
Artificial intelligence, big data analytics, machine learning, expert systems, fashion and apparel industry
National Category
Computer and Information Sciences
Research subject
Business and IT
Identifiers
urn:nbn:se:hb:diva-21847 (URN)10.1109/ACCESS.2019.2928979 (DOI)000478676600101 ()2-s2.0-85070237602 (Scopus ID)
Note

Author 1 and 2 are equal contributing authors.

Available from: 2019-10-14 Created: 2019-10-14 Last updated: 2021-09-24Bibliographically approved
2. Garment Categorization Using Data Mining Techniques
Open this publication in new window or tab >>Garment Categorization Using Data Mining Techniques
2020 (English)In: Symmetry, E-ISSN 2073-8994, no 6, article id 984Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
MDPI, 2020
Keywords
data mining, machine learning, classification, big data, decision trees, naïve bayes, bayesian forest, random forest
National Category
Computer and Information Sciences
Research subject
Textiles and Fashion (General)
Identifiers
urn:nbn:se:hb:diva-23776 (URN)10.3390/sym12060984 (DOI)000553945600001 ()2-s2.0-85089077127 (Scopus ID)
Available from: 2020-09-16 Created: 2020-09-16 Last updated: 2024-02-01Bibliographically approved
3. Modeling the knowledge of experts in the apparel industry using artificial intelligence.
Open this publication in new window or tab >>Modeling the knowledge of experts in the apparel industry using artificial intelligence.
Show others...
(English)Manuscript (preprint) (Other (popular science, discussion, etc.))
Keywords
Artificial intelligence, fuzzy logic, sensory evaluation, product development, automation, apparel industry
National Category
Computer and Information Sciences
Research subject
Textiles and Fashion (General)
Identifiers
urn:nbn:se:hb:diva-23777 (URN)
Available from: 2020-09-16 Created: 2020-09-16 Last updated: 2021-02-15Bibliographically approved
4. Toward a conceptualization of personalized services in apparel e-commerce fulfillment
Open this publication in new window or tab >>Toward a conceptualization of personalized services in apparel e-commerce fulfillment
(English)Manuscript (preprint) (Other (popular science, discussion, etc.))
National Category
Business Administration
Identifiers
urn:nbn:se:hb:diva-23778 (URN)
Available from: 2020-09-16 Created: 2020-09-16 Last updated: 2021-02-15Bibliographically approved

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