Dileep

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.

Biography:

With a strong foundation in Computer Science engineering from India, I honed my expertise in the realm of IT for over fourteen years. My focus and specialization lie in Oracle ERP Cloud, where I have successfully collaborated with esteemed clients like the Mayo Clinic, Boeing, and GE to revolutionize their businesses with cutting-edge cloud-based ERP technology. Through his meticulous attention to detail and strategic optimization of day-to-day operational and SCM processes, I;ve consistently helped delivered promising results. Additionally, I recently enriched my skillset by earning an MBA in Business Analytics from the prestigious Kelley School of Business.