Demand Forecasting for the Full Life Cycle of New Electronic Products Based on KEM-QRGBT Model.

To improve the accuracy of demand forecasting for new electronic products, especially in scenarios with limited historical data, a novel forecasting model was proposed in this study which integrated K-means based on Euclidian distance, Multi-layer perceptron algorithm, and Quantile Regression with Gradient Boosted Trees (KEM-QRGBT). The model also incorporated grid search with K-fold cross-validation to enable the adaptive selection of the optimal parameters for product data. Additionally, the KEM-QRGBT model, which can balance the intricacies of learning parameter patterns with its ability to quantify demand uncertainty, exhibited proficiency in quantifying the uncertainty inherent in demand forecasting. Using a case study from a manufacturing enterprise in Turkey, the effectiveness of the model was validated. Results demonstrate that, for new electronic products with limited historical data, the KEMQRGBT model with adaptive parameter selection improves demand forecasting accuracy, outperforming benchmark methods, and other machine learning models. The proposed algorithm provides a strong evidence for the demand forecasting of new electronic products, particularly in cases where historical data is limited. [ABSTRACT FROM AUTHOR]