Automatic Pricing and Replenishment Decision for Vegetable Products based on Planning Model and Regression Algorithm

Abstract

 Based on the distribution data of this superstore, this study establishes a planning model to help the superstore make replenishment and pricing decisions for vegetable goods to obtain higher profits. First, the mapping function is used to correspond the vegetable number to the vegetable name, wholesale price, and wastage rate, and the sales data are treated as outliers. Pearson correlation analysis was utilized to explore the interrelationship between the sales volume of each vegetable category and a single product, indicating a correlation between the total sales volume of different categories of vegetables. Then, based on the regression analysis to study the relationship between the total sales volume of each vegetable category and cost-plus pricing, a planning model was established to solve the total daily replenishment and pricing strategy of each vegetable category. Finally, with profit maximization as the objective function, a planning model is developed to solve the single-item replenishment quantity and pricing strategy for a single day. This study develops better replenishment and pricing strategies for superstores.