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.

Keywords

• forecasting
• sales
• deep learning
• inventory control
• parameter estimation