Merchandising/Marketing/Retailing: Management

Decoding Fashion Evolution: A Systematic Review of Fashion research using computational approaches

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Abstract

The fashion industry is undergoing a transformative shift with the integration of digital technologies, particularly artificial intelligence (AI), revolutionizing its operations. This paper addresses the challenges faced by fashion researchers in adapting to computational approaches due to limited training and resources. By presenting a multidisciplinary framework for digital technological applications in the fashion industry, the study aims to bridge the gap between computer science, fashion, textile, and apparel research. Employing a systematic review approach, the research identifies and analyzes 168 relevant articles published between 2020 and March 2023. The findings reveal the potential of computational approaches in various supply chain functions within the fashion industry, categorized into Detection, Analysis, Synthesis, and Recommendation. It also highlights emerging trends, such as an increasing focus on analysis and recommendation, particularly in E-commerce. The comprehensive overview serves as a valuable resource for researchers and professionals, encouraging further exploration of computational techniques in fashion research.

Keywords: Fashion, Data Science, Artificial Intelligience, Deep Learning, Supply Chain

How to Cite:

Liu, Y. J. & Zhao, L., (2024) “Decoding Fashion Evolution: A Systematic Review of Fashion research using computational approaches”, International Textile and Apparel Association Annual Conference Proceedings 80(1). doi: https://doi.org/10.31274/itaa.17240