A Study of Replenishment And Pricing Strategies For Vegetable Commodities Based On Multiple Linear Regression

Abstract

Vegetables, which can provide the human body with rich nutrients such as vitamins, minerals and dietary fiber, are indispensable necessities in people's daily life. Therefore, on the one hand, in order to ensure the quality of vegetables to provide fresh vegetables, superstores need to replenish goods in a timely manner; on the other hand, in order to improve the revenue, superstores need to reasonable pricing. This paper takes 251 kinds of vegetables sold in fresh food superstores as the research object, firstly, using Pearson's correlation coefficient to explore the correlation between the sales volume of vegetable single product and category, and obtains that the strongest correlation in vegetable category is cauliflower and edible mushrooms, with a correlation coefficient of -0.9989, and that the strongest correlation in vegetable single product is Hericium erinaceus and fresh zongzi leaves, with a correlation coefficient of 0.9973. Then, this paper set up a multivariate linear regression model to solve the problem for each vegetable. Linear regression model to solve the relationship between total sales volume and cost-plus pricing for each category. Finally, for the total daily replenishment, through the total daily replenishment = total predicted sales / (1 - wastage rate) processing to get the total daily replenishment of vegetables for 7 days, to obtain the cost matrix and markup rate matrix, and then get the pricing of vegetables for 7 days. For example, the replenishment of cauliflower on Monday should be 268.7238 kg, and the pricing is $2.5179/kg.