Intelligent Design Suggestion and Sales Forecasting for New Products in the Apparel Industry

Abstract This study demonstrates how algorithms can assist humans in decision-making in the apparel industry. A two-stage method including suggestions and intelligent forecasting was proposed. In the first stage, a web crawler was used to browse a B2C apparel website to identify popular products. In the second stage, machine learning methods were used to predict the sales demand for new products. Additionally, we used Google Trends to collect external information indices to adjust the demand forecasting. Our numerical study shows that the intelligent forecasting approach can effectively reduce the Mean Square Error (MSE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE) by at least 45.79, 26.35, and 26.34 %, respectively.