Research on commercial logistics inventory forecasting system based on neural network

Abstract

Logistics cost control is an important means to increase commercial logistics profits and control corporate capital risks. This paper uses BP neural network to build a prediction model to study the analysis of inventory demand and explores the complex relationship between inventory demand and each influencing factor by training the data of inventory demand influencing factors to obtain effective measures for inventory management and control. Moreover, this article chooses the way that BA optimizes the BP neural network to build a predictive model and uses the actual data to conduct a model test to verify the validity of the model. In addition, this article performs performance analysis of the prediction model of this study by setting up experiments. Through comparative experimental research, we can see that the method proposed in this paper has certain effects, can be applied to the actual forecast of logistics inventory, and can provide theoretical references for subsequent related research.