In the apparel industry, three prominent developments contribute to the complexity of forecasting: shortening product life-cycles, increasing product variety, and globalization of sourcing and manufacturing. The impact of each of these developments will be discussed in detail.

Ninety-?ve percent of SKUs (Stock Keeping Units) change every selling season (Gutgeld and Beyer, 1995). Because product life-cycles are short, there are no historical demand data that can be used to obtain a priori demand forecasts, i.e. before any demand has been realized. Furthermore, the number of in-season replenishment opportunities after observing demand and updating forecasts accordingly is limited and the risk of product obsolescence is high.

Due to global competition, faster product development, technological advances, increasingly ?exible manufacturing systems, and more demanding consumers, an unprecedented number and variety of products are competing for demand (Fisher et al., 1994). As a result, the volume of sales per SKU is very low (Gutgeld and Beyer, 1995), and demand for SKUs within the same product line can vary signi?cantly (Abernathy et al., 2000). Thus, even if aggregate demand can be predicted with some certainty, it is very dif?cult to predict how that demand will be distributed over the many products that are offered. The complexity of production planning and ordering increases accordingly.