Pre-season stocking and pricing decisions for fashion retailers with multiple information updating

Motivated by the industrial practice, we investigate in this paper the pre-season inventory and pricing decisions for fashion retailers. Before the selling season, a retailer can place orders for a seasonal fashion product from her supplier at two distinct stages via two different delivery modes. Market information from the sales of a pre-seasonal product is collected and used to update the demand forecast of the seasonal product at the succeeding stages by using Bayesian approach. We formulate a dynamic optimization problem and obtain the optimal stocking policy. After the ordered seasonal product has arrived and just before the start of the selling season, the retailer can determine the optimal selling price of the product with respect to the latest demand information, and the amount of product on-hand. We study the pricing policy under different objectives. Sensitivity analysis is carried out and the features of the policies are revealed. Managerial insights are generated.