A combined forecasting method for intermittent demand using the automotive aftermarket data

Abstract

Intermittent demand forecasting is an important challenge in the process of smart supply chain transformation, and accurate demand forecasting can reduce costs and increase efficiency for enterprises. This study proposes an intermittent demand combination forecasting method based on internal and external data, builds intermittent demand feature engineering from the perspective of machine learning, predicts the occurrence of demand by classification model, and predicts non-zero demand quantity by regression model. Based on the strategy selection on the inventory side and the stocking needs on the replenishment side, this study focuses on the optimization of the classification problem, incorporates the internal and external data of the enterprise, and proposes two combination forecasting optimization methods on the basis of the best classification threshold searching and transfer learning, respectively. Based on the real data of auto after-sales business, these methods are evaluated and validated in multiple dimensions. Compared with other intermittent forecasting methods, the models proposed in this study have been improved significantly in terms of classification accuracy and forecasting precision, which validates the potential of combined forecasting framework for intermittent demand and provides an empirical study of the framework in industry practice. The results show that this research can further provide accurate upstream inputs for smart inventory and guarantee intelligent supply chain decision-making in terms of accuracy and efficiency.