Fashion Forecasting Case Study

Most of fashion items are sold during only one season. Companies have to estimate the sales without any historical data: the forecasting system should be then designed for new product sales forecasting. New product forecasting is one of the most difficult forecasting problem. Indeed, forecasting methods described in 8.6 are not suitable. In this context, a two-step methodology seems emerged:

1. To cluster and to classify new products to forecast their sales profile (mid-term forecast).

2. To adapt and to readjust this profile according to the first weeks of sales (short term forecast).

If no historical data exists for the considered item, but similar products have already been sold in previous seasons. Indeed, new products usually replace old ones with almost the same style and/or functionality (i.e. T-shirt, pull over, etc.), it is thus possible to use historical data of similar products to estimate the sales profile of the new products. Thus, to forecast the sales profiles of new products such as garments with clustering and classification techniques, descriptive attributes (price, life span, sales period, style, etc.) of historical and new products should be taken into account. The point is to show the connection between historical data,

Whenever it is possible, i.e. when replenishments are possible at low cost and with reasonable lead time, companies can supply some new products in a small sample of selected stores for a short period before the selling season. The analysis of these sales gives precious information for the whole supply. In other cases, different models have been performed to extrapolate the future sales from few weeks of sales. Pre-sales data enable to cluster stores of fashion merchandise. The pre-sales data at the representative stores is then used to estimate the sales at all the other stores in the same