Empowering Quality Assurance in Fashion Industry with Artificial Intelligence

In the realm of luxury fashion, quality stands as an immutable cornerstone, underpinning the industry's reputation and client trust. Luxury brands epitomize excellence, crafting each product with meticulous attention to detail and delivering an unparalleled customer experience. Yet, as the industry navigates the digital age, traditional practices must evolve to meet the demands of a rapidly changing landscape. This thesis explores a pioneering Artificial Intelligence (AI) project within the Quality team of Fendi, a renowned luxury brand, aiming to revolutionize defect claim management using Salesforce. By integrating AI and data analytics, the project seeks to enhance efficiency and precision in quality assurance processes. Traditional manual inspection methods, while artisanal, face challenges of scalability and subjectivity. Innovative solutions leveraging machine learning offer the potential to streamline defect classification, improve accuracy, and enhance scalability. The project's objective is to augment the efficiency and accuracy of defect claim management, bridging the gap between craftsmanship and modern technology. Through a feasibility study, the thesis analyzes current QA practices, explores machine learning's potential, evaluates effectiveness, and proposes a conceptual framework integrating machine learning with existing methodologies. This exploration reflects the luxury fashion industry's embrace of modern technologies to uphold quality while adapting to the digital age. By leveraging AI and data-driven approaches, brands like Fendi exemplify a commitment to maintaining artisanal excellence in an ever-evolving landscape. This thesis captures the intersection of tradition and innovation in luxury fashion's approach to quality assurance, showcasing how heritage brands embrace modern tools to uphold their legacy of craftsmanship.