A neural clustering and classification system for sales forecasting of new apparel items

The Textile-Apparel-Distribution network actors require a very accurate production and sourcing management to minimize their costs and satisfy their customers. For a such strategy, distributors rely on sales forecasting system to respond to the versatile textile market. However, the specific constraints of the textile sales (numerous and new items, short lifetime) complicate the forecasting procedure and distributors prefer to use intuitive estimation methods of the sales rather than the existing forecasting models. We propose a decision aid system, based on neural networks, which automatically performs item sales forecasting. Performances of our model are evaluated using real data from an important French textile distributor.