Dynamic retail assortment models with demand learning for seasonal consumer goods

The main research question we explore in this dissertation is: How should a retailer modify its product assortment over time in order to maximize overall profits for a given selling season? Historically, long development, procurement, and production lead times have constrained fashion retailers to make supply and assortment decisions well in advance of the selling season, when only limited and uncertain demand information is available. As a result, many retailers are seemingly cursed with simultaneously missing sales for want of popular products, while having to use markdowns in order to sell the many unpopular products still accumulating in their stores. Recently however, a few innovative firms, such as Spain-based Zara, Mango and Japan-based World Co. (referred to as "Fast Fashion" retailers), have gone substantially further, implementing product development processes and supply chain architectures allowing them to make most product design and assortment decisions during the selling season. Remarkably, their higher flexibility and responsiveness is partly achieved through an increased reliance on more costly local production relative to the supply networks of more traditional retailers.