Sales Volume Forecast of Typical Auto Parts Based on BiGRU: A Case Study

Abstract

Inventory management is an important part of the auto parts supplier business. Accurate prediction of sales volume for different auto parts is the basis for staff to formulate marketing strategies and procurement plans. Based on the limited historical sales data of the South China, North China and East China branches of an auto parts company, some prediction models are trained and tested to determine the best model for predicting future production sales. An orthogonal experimental method is used to implement hyperparameter estimation for the prediction models on this basis. In addition, a posteriori test is used to verify the validity and accuracy of the Bi-GRU model in predicting the sales volume of typical auto parts. The results show that, compared with other models, the bidirectional gated recurrent unit (Bi-GRU) model has the highest accuracy in testing and is used to predict the future sales of typical auto parts. The posterior test proved that the validity and accuracy of the Bi-GRU model is verified. The orthogonal experiment method can effectively realize the hyperparameter estimation for each model. According to the prediction results, the sales volume of blind drive caps in South China, North China and East China will reach 18235, 17030 and 14949 pieces, respectively, after 90 days. Meanwhile, the corresponding sales volume of bolts will reach 13141, 15062 and 10253 pieces, respectively.

Maintenance Spare Parts Prediction Based on Multilevel Migration Learning CNN-ISE-Attention-BiLSTM

“…Gated recurrent unit neural network (GRU) improves the LSTM model and simplifies its budgeting process [25]. Literature [26][27][28] demonstrated that the bi-directional gated recurrent unit neural network (Bi-GRU) model performs well in predicting sales of typical automotive parts and other non-stationary data forecasts. The above methods have improved the ability to predict non-stationary maintenance spares to some extent, but they have the following shortcomings:…”