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Digital Body Measurements and AI: Transforming Fashion and Apparel Production and Retail in Africa

Year: 2024
Type: article
Abstract: The fashion and apparel industry in Africa is yet to fully benefit from the recent transformations in the global fashion scene driven by the integration of digital body measurement technologies and artificial intelligence (AI).This paper explores the potential impact of these innovations on apparel production and retail within the region, highlighting how they will enhance efficiency, accuracy, and customization.Leveraging 3D body scanning and AI-powered data analytics, African garment manufacturers and designers can offer tailored solutions that meet diverse global consumer needs, meet orders, improve fit and reduce waste and production costs.This paper will further examine the case study of how Design Shark International will leverage machine learning and computer vision technology for mobile 3D body scanning with the introduction of Mezer™ its AI powered digital body measurement solution to address quality standards and scalability challenges in its local fashion industry.It discusses the challenges of adoption, provides strategic recommendations for stakeholders, and addresses how our solution is being applied for the development and adoption of a standardized African clothing size chart.Our results suggest that embracing digital body measurements and AI not only improves competitive advantage but also fosters sustainable growth in Africa's growing fashion industry.The integration of digital body measurements and artificial intelligence (AI) is revolutionizing the fashion and apparel industry globally.This paper explores a case study of a pioneering company in Africa that leverages machine learning and computer vision technology for mobile 3D body scanning, providing an AI-powered digital body measurement solution.The study examines the technological framework, implementation challenges, and the transformative impact on production efficiency and retail experiences.
DOI: 10.15221/24.38
Language: English
Open Access status: hybrid
OpenAlex ID: W4403164238
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