Optimization Of Automatic Pricing and Replenishment Decision Based on Time Series and BP Neural Network Model

Abstract

In major fresh food supermarkets, vegetable products often have a short shelf life and poor product quality, which affects their sales. Therefore, it is particularly important to make replenishment decisions for each vegetable category on the same day without knowing the specific individual product and purchase price. This article focuses on the problem of automatic pricing and replenishment decision-making for vegetable products. Based on the fact that vegetable products have a relatively short shelf life, the automatic pricing and replenishment strategies for vegetable products are optimized, indirectly proving the correlation between certain individual products. A mathematical model is constructed using time series and BP neural network models to obtain corresponding replenishment strategies, Finally, provide recommendations on automatic pricing and replenishment decisions for vegetable products. The "cost plus pricing" method involved in this question plays a crucial role in the pricing of vegetables. At the same time, from the perspectives of the demand side and supply side, the limitations of sales time and sales space are also particularly important.