Maintenance Spare Parts Demand Forecasting for Automobile 4S Shop Considering Weather Data

Abstract

Maintenance spare parts demand forecasting is an important foundation of maintenance spare parts inventory control, which is an essential responsibility for managers of an automobile 4S shop. Although the existence of the effect of weather conditions on maintenance spare parts demand has been verified, the study on maintenance spare parts demand forecasting for an automobile 4S shop considering weather data has not been found. In this paper, a novel method is proposed for maintenance spare parts demand forecasting for an automobile 4S shop considering weather data. By taking into account three dimensions of weather data (i.e., temperature, visibility, and slipperiness) and delayed effects of weather conditions on maintenance spare parts demand, a vector of numerical weather data with 30 weather factors is constructed to represent the related weather conditions of a given day. Then, a back propagation (BP) neural network is trained and the weights of the 30 weather factors are determined. Similar historical cases of the target case are extracted, and two forecasting models are respectively trained based on extreme learning machine (ELM) and support vector machines (SVM) using the similar historical cases. The final forecasting model is determined by comparing the fitting precisions of the two forecasting models. The experimental study is conducted based on the real data of an automobile 4S shop. The results show that weather data is critical to maintenance spare parts demand forecasting for an automobile 4S shop, and the extraction of similar historical cases is an effective approach to capture the complex effect mechanism of weather data on maintenance spare parts demand.