Intelligent demand forecasting systems for fast fashion
Abstract Sales forecasting in the fashion industry has been a very challenging issue for decades. Recently, the concept of fast fashion has emerged as a successful strategy for retailers. In terms of sales forecasting, this concept involves new approaches to deal with specific features such as the limited amount of historical data and shortened time for the computation. The literature review of existing methods in this domain shows that many models have been proposed. They are mainly based on artificial intelligence techniques. Among these techniques, we focus on the two models that arise as references for long term and short term forecasts to develop a two stage sales forecasting system. Associated with a store replenishment model, which is inspired from a method implemented in a famous fast fashion brand, we propose to simulate our two stage forecasting system on real data to evaluate the real benefits of advanced forecasting techniques for fast fashion retailing.