Research on Vegetable Sales Volume and Pricing Strategy Based on GWO-LSTM Modeling

Abstract

This paper focuses on an in-depth study of the problem of sales volume and pricing strategy for vegetable categories, aiming at constructing an automatic pricing and replenishment model to maximize revenue. First, by analyzing the sales data of each category and single item of vegetables, the mean value of sales volume is calculated, and the correlation between each category of vegetables is visualized using charts and Pearson correlation coefficient heat maps, and it is found that there is a significant correlation between specific categories. Secondly, this paper adopts the cost-plus pricing method to calculate the markup rate of each individual item and combines it with linear regression analysis to reveal the relationship between markup rate and sales volume, and further predicts the sales volume and price in the coming week through the GWO-LSTM model, which provides data-driven pricing and replenishment strategies for the superstore based on the data. Finally, for the data of a specific time, this paper develops a linear programming model to formulate a replenishment and pricing scheme that meets market demand and maximizes revenue. The methodology and findings of this study have important practical applications for retail formats such as fresh food superstores.