A machine learning-driven sales forecastingapproach for newly launched products

Sales forecasting is one of the most challenging problems for companies operating in the fashion industry. The effects of demand volatility, trend, constrained lead times, huge product variety, and seasonality are vital to demand forecasting. Most importantly, fashion retailers are struggling with forecasting future demand because of the seasonality effect and short life cycles of the products. Even though it is a generally accepted opinion, forecasting future demand of newly launched products is even more challenging, because of the fact that new products do not have any historical sales data. When the number of organizations operating in the fashion industry is considered, satisfying customer demand becomes one of the most significant features for success and profit improvements. This study introduces a machine learning-driven sales forecasting approach for newly launched products. The proposed model applied on a fashion retailer's censured historical sales data. On the other hand, the proposed algorithm combines product-level predictions with chain-level predictions to provide better sales forecasting accuracy for fashion retailers and for newly launched products. Once the predictions are obtained from the model, they are measured with traditional accuracy metrics.