Forecasting the demand for new fashion products in the fast fashion industry is a com-
plex task due to its dynamic nature, short product life cycles, and limited historical data.
Traditional forecasting models often fail, leading to inefficiencies such as overproduc-
tion or underproduction. This paper reviews key challenges and explores innovative
machine learning (ML) and artificial intelligence (AI)-based models to improve fore-
cast accuracy. We propose a hybrid AI-driven approach that integrates structured and
unstructured data sources, real-time monitoring, and ensemble models to address fore-
cast limitations in the fast fashion industry.
Speaker(s): Dileep,
Agenda:
5:30-5:35 pm – Introductions
5:35-6:35 pm – Presentations
6:35-7:00 pm – Questions
Virtual: https://events.vtools.ieee.org/m/476734