Sales forecasting for new products is significantly important for fashion retailer companies because prediction with high accuracy helps the company improve management efficiency and customer satisfaction. The low inventory strategy of fashion products and the low stock level in each brick-and-mortar store lead to serious censored demand problems, making forecasting difficult. In this regard, a two layers (TLs) model is proposed in this paper to predict the total sales of new products. In the first layer, the demand is estimated by linear regression (LR). In the second layer, sales are modeled as a function of not only the demand but also the inventory. To solve the TLs model, a gradient-boosting decision tree method (GBDT) is used for feature selection. Considering the heterogeneity in products, a mixed k-mean algorithm is applied for product clustering and a genetic algorithm for parameter estimation in each cluster. The model is tested on real-world data from a Singapore company, and the experimental results show that our model is better than LR, GBDT, support vector regression (SVR) and artificial neural network (ANN) in most cases. Furthermore, two indicators are built: the average conversion rate and the marginal conversion rate, to measure products’ competitiveness and explore the optimal inventory level, respectively, which provide helpful guidance on decision-making for fashion industry managers.