We describe how inventory management models utilizing Bayesian estimate of customer demand can help retailers to determine profit-maximizing stocking levels for fashion products. In the basic framework, the retailer can place two different purchasing orders for a fashion product before the selling season. The demand for the product is uncertain. The first order is placed using a coarse demand forecast. Following the gathering of new market information, relying on a more refined forecast of potential demand, a second “quick response’’ order is placed at a higher unit supply price compared to the first order. The total stocking quantity for the product equals the sum of the two orders. The retailer’s objective is to maximize its expected profit by avoiding unnecessarily high investment in inventory. We discuss finding the optimal procurement strategy in this practical business problem. We review a number of published papers exploring various issues related to this basic setting. The extensions of the basic model explored in the previous literature include uncertainty in purchase cost associated with the second order, availability of order cancellation flexibility, limited procurement budget, stocking assortment containingmultiple products with correlated demands, and price-sensitive retail demand.