Smarter Demand Forecasting for Fast Fashion: Hybrid Models for a Dynamic Market

Forecasting the demand for new fashion products in the fast fashion industry is a complex task. Due to its dynamic nature, short product life cycles, and limited historical data, traditional forecasting models often fail. This leads to inefficiencies such as overproduction or underproduction. IoSCM Fellow member, Dileep Rai, has created a white paper that explores hybrid models that can be utilised to enable smarter demand forecasting for the fast fashion industry.

This paper reviews key challenges and explores innovative machine learning (ML) and artificial intelligence (AI)-based models to improve forecast accuracy. Proposing a hybrid AI-driven approach that integrates structured and unstructured data sources, real-time monitoring, and ensemble models to address forecast limitations in the fast fashion industry.

Introduction

The fast fashion industry operates within an ever-changing landscape defined by rapid product introductions and brief product lifespans. With two main seasonal cycles, spring/summer and fall/winter, companies face constant pressure to anticipate and fulfil changing customer preferences. Misaligned forecasts can lead to overstocking, unsold inventory, or stock shortages. All of which have significant economic and environmental implications. The primary goal of this paper is to examine and propose effective demand forecasting models tailored to the fast fashion sector.

Download the white paper from the IoSCM Resources section of the website – https://www.ioscm.com/whitepapers/

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