| dc.description.abstract | The vehicle industry is relatively closed, and the commercial vehicle industry is even more closed and less well-known. Dealers often face problems with parts inventory, leading to inventory costs and customer waiting complaints. These issues can extend to inventory management, parts costs, and compensation for customer business losses.
In the past, due to the closed nature of data in the vehicle industry, most research was limited to internal studies within vehicle manufacturers, resulting in intermittent research that could not be extended or replicated using existing data. Dealers, often traditional industries, have limited ability to apply machine learning to optimize service processes. Existing related research mostly focuses on models rather than practical applications. This study uses current machine learning techniques combined with actual vehicle maintenance records from Company S to predict the time of a customer′s ＂next＂ visit to the service center and the parts to be repaired during the ＂next＂ visit.
This study employs the attention mechanism Transformer, inputting vehicle age, mileage, model, and warranty status to predict parts replacement (a multi-class classification task) and the time of the next service visit (a regression task). The performance of other time series models is compared, and the importance of each feature for accuracy is experimented.
The results of this study demonstrate the feasibility of applying deep learning techniques in the field of vehicle after-sales service, with the expectation of improving efficiency, reducing costs, and enhancing customer satisfaction in the automotive industry. | en_US |