Customer models for artificial intelligence-based decision support in fashion online retail supply chains

Pereira, ArturM.; Moura, José Fábio Paulino de; Costa, Evandro de Barros; Vieira, Thales; Landim, André R.D.B.; Bazaki, Eirini; Wanick, Vanissa

doi:10.1016/j.dss.2022.113795

Cited by 40 publications

(12 citation statements)

References 30 publications

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“…Furthermore, few studies explore the role of suppliers and how their different positions impact supply chain transparency. The main studies analyze the agrifood industry [120,[124][125][126][127], fashion online retail [128], the manufacturing sector [129,130], healthcare [131], and the fuel and electronics industry [132][133][134]. The third research trend can be recognized in the influence of the choice of logistics service providers on the sustainable performance of supply chains, and, although there are some studies that explore the sustainable service quality as an attribute to evaluate logistics providers [135], there is still a fragmented and incomplete framework.…”

Section: Discussionmentioning

confidence: 99%

To Align Technological Advancement and Ethical Conduct: An Analysis of the Relationship between Digital Technologies and Sustainable Decision-Making Processes

Riso

Morrone

2023

Sustainability

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Digitalization and sustainability is widely investigated; however, only few studies have analyzed the role of sustainable decision-making processes in the business strategy field for achieving sustainable development goals: The study proposes a structured literature review (SLR), analyzing 318 documents published in the period 2019–2023. The results of the SLR bring to light that the publications on the topic mainly regard seven research areas. Green supply chains, logistics and digital management is the principal one, followed by sustainable goals, green indicators, and digital advancement. Furthermore, the analysis marks future research lines: although this document offers an overview of the main studies in literature, the major limitation is the use of only one database and a time span of 5 years. This study could contribute to generate further research on sustainable decision-making processes, promoting a different organizational approach to value creation and sustainable performance.

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Section: Discussionmentioning

confidence: 99%

To Align Technological Advancement and Ethical Conduct: An Analysis of the Relationship between Digital Technologies and Sustainable Decision-Making Processes

Riso

Morrone

2023

Sustainability

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“…Both the fashion and petroleum industries exhibit a noteworthy focus on environmental sustainability and social responsibility, reflecting their commitment to ethical practices. Supply chains within the fashion industry seek to provide customers with comprehensive information about sustainable products and sustainable practices (Pereira et al, 2022). Moreover, the industry upholds its social responsibility by preserving jobs within the supply chain.…”

Section: Discussion With Practical Implicationsmentioning

confidence: 99%

“…The humanitarianism industry places emphasis on alliance management capability, an essential aspect for efficient coordination of relief efforts (Dubey et al, 2021). Governance is a key area of focus for the retail industry, with a focus on price optimization (Simchi-Levi and Wu, 2018), cost reduction (Borade and Sweeney, 2015;Li and Li, 2022), supply-demand synchronization (Pereira et al, 2022) and improving efficiency Artificial intelligence and productivity (Mahroof, 2019). In a similar vein, the cosmetics supply chains prioritize governance for the optimization of their operational processes (Guo et al, 2011), ranging from supplier selection to performance evaluation (Vahdani et al, 2012).…”

Section: Discussion With Practical Implicationsmentioning

confidence: 99%

Artificial intelligence in supply chain decision-making: an environmental, social, and governance triggering and technological inhibiting protocol

Hao

Demir

2023

JM2

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Purpose Decision-making, reinforced by artificial intelligence (AI), is predicted to become potent tool within the domain of supply chain management. Considering the importance of this subject, the purpose of this study is to explore the triggers and technological inhibitors affecting the adoption of AI. This study also aims to identify three-dimensional triggers, notably those linked to environmental, social, and governance (ESG), as well as technological inhibitors. Design/methodology/approach Drawing upon a six-step systematic review following the preferred reporting items for systematic reviews and meta analysis (PRISMA) guidelines, a broad range of journal publications was recognized, with a thematic analysis under the lens of the ESG framework, offering a unique perspective on factors triggering and inhibiting AI adoption in the supply chain. Findings In the environmental dimension, triggers include product waste reduction and greenhouse gas emissions reduction, highlighting the potential of AI in promoting sustainability and environmental responsibility. In the social dimension, triggers encompass product security and quality, as well as social well-being, indicating how AI can contribute to ensuring safe and high-quality products and enhancing societal welfare. In the governance dimension, triggers involve agile and lean practices, cost reduction, sustainable supplier selection, circular economy initiatives, supply chain risk management, knowledge sharing and the synergy between supply and demand. The inhibitors in the technological category present challenges, encompassing the lack of regulations and rules, data security and privacy concerns, responsible and ethical AI considerations, performance and ethical assessment difficulties, poor data quality, group bias and the need to achieve synergy between AI and human decision-makers. Research limitations/implications Despite the use of PRISMA guidelines to ensure a comprehensive search and screening process, it is possible that some relevant studies in other databases and industry reports may have been missed. In light of this, the selected studies may not have fully captured the diversity of triggers and technological inhibitors. The extraction of themes from the selected papers is subjective in nature and relies on the interpretation of researchers, which may introduce bias. Originality/value The research contributes to the field by conducting a comprehensive analysis of the diverse factors that trigger or inhibit AI adoption, providing valuable insights into their impact. By incorporating the ESG protocol, the study offers a holistic evaluation of the dimensions associated with AI adoption in the supply chain, presenting valuable implications for both industry professionals and researchers. The originality lies in its in-depth examination of the multifaceted aspects of AI adoption, making it a valuable resource for advancing knowledge in this area.

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“…Machine learning systems have played a pivotal role in enabling the convenience of online clothing shopping [17]. The effectiveness of these systems heavily relies on the accuracy of clothing data and the proficiency of the machine learning algorithms employed.…”

Section: Related Workmentioning

confidence: 99%

AI-Driven Precision Clothing Classification: Revolutionizing Online Fashion Retailing with Hybrid Two-Objective Learning

Abbas,

Zhang,

Asim

et al. 2024

Information

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In the ever-expanding online fashion market, businesses in the clothing sales sector are presented with substantial growth opportunities. To utilize this potential, it is crucial to implement effective methods for accurately identifying clothing items. This entails a deep understanding of customer preferences, niche markets, tailored sales strategies, and an improved user experience. Artificial intelligence (AI) systems that can recognize and categorize clothing items play a crucial role in achieving these objectives, empowering businesses to boost sales and gain valuable customer insights. However, the challenge lies in accurately classifying diverse attire items in a rapidly evolving fashion landscape. Variations in styles, colors, and patterns make it difficult to consistently categorize clothing. Additionally, the quality of images provided by users varies widely, and background clutter can further complicate the task of accurate classification. Existing systems may struggle to provide the level of accuracy needed to meet customer expectations. To address these challenges, a meticulous dataset preparation process is essential. This includes careful data organization, the application of background removal techniques such as the GrabCut Algorithm, and resizing images for uniformity. The proposed solution involves a hybrid approach, combining the strengths of the ResNet152 and EfficientNetB7 architectures. This fusion of techniques aims to create a classification system capable of reliably distinguishing between various clothing items. The key innovation in this study is the development of a Two-Objective Learning model that leverages the capabilities of both ResNet152 and EfficientNetB7 architectures. This fusion approach enhances the accuracy of clothing item classification. The meticulously prepared dataset serves as the foundation for this model, ensuring that it can handle diverse clothing items effectively. The proposed methodology promises a novel approach to image identification and feature extraction, leading to impressive classification accuracy of 94%, coupled with stability and robustness.

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Customer models for artificial intelligence-based decision support in fashion online retail supply chains

Cited by 40 publications

References 30 publications

To Align Technological Advancement and Ethical Conduct: An Analysis of the Relationship between Digital Technologies and Sustainable Decision-Making Processes

To Align Technological Advancement and Ethical Conduct: An Analysis of the Relationship between Digital Technologies and Sustainable Decision-Making Processes

Artificial intelligence in supply chain decision-making: an environmental, social, and governance triggering and technological inhibiting protocol

AI-Driven Precision Clothing Classification: Revolutionizing Online Fashion Retailing with Hybrid Two-Objective Learning

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