Inventory Management of Automobile After-sales Parts Based on Data Mining

Abstract

The inventory management of automotive aftermarket parts is of great significance to the after-sales activities of automobile dealers and the reduction of operating costs. In view of the problem of insufficient utilization of automobile after-sales service data, it is necessary to introduce data mining methods to further analyze and mine data. Taking the historical sales data of auto parts as the mining object, K-means clustering algorithm and LSTM recurrent neural network were applied, and the Python tool was used to develop the automobile after-sales parts classification model and the parts inventory prediction model. The classification results can be used to analyze whether the dealer's inventory structure is reasonable. The forecast results can predict the demand for parts in the next stage. Comprehensive classification and prediction results, the study provides reference for the auto dealer to determine the variety structure and quantity structure of the auto parts.