Automatic Pricing and Replenishment Decision for Vegetable Products Based on Multiple Linear Regression

Abstract

Combining supply-side and demand-side planning with reliable market demand analysis to develop reasonable replenishment decisions and pricing decisions is a challenge for fresh food superstores. In this paper, a model is built based on the sales data of vegetable items to optimize the original sales strategy and develop a sales plan with the maximum gain. Firstly, the original data is pre-integrated to obtain the daily sales volume of each vegetable item, and then the data is analyzed with descriptive statistics. Spearman correlation analysis is performed on the integrated data to make the correlation between each vegetable category. Pre-integrate the data to obtain the average daily selling price, total sales volume, and average cost price of each category of vegetable commodities, and then carry out multiple linear regression to determine the pricing law of each category of vegetable commodities, respectively, and the multiple linear regression equation is the relationship between the total sales volume and the cost-plus pricing. The total sales volume and cost data of the six categories of vegetable commodities are analyzed by ARIMA time series analysis to predict the total sales volume and category cost for the next seven days, which is substituted into the multiple linear regression equation to find the predicted selling price of the vegetable commodities and calculate the profit. Calculate to find the sales price of six types of vegetable goods for the next seven days. Establish an optimization model with the goal of maximizing profit and determine the replenishment strategy for the coming week by considering the daily loss rate of different categories. Based on the optimization model by expanding the constraints to develop a more detailed decision-making scheme. Finally, based on the purpose of improving the replenishment and pricing strategy of vegetable products and trying to meet the market demand to maximize the profit of the supermarket, we provide optimization advice for the data collection of the supermarket from multiple angles, determine more accurate replenishment quantity, cost price and selling price of individual products for the supermarket, and maximize the profit margin of the supermarket, as well as explaining the reasonableness of the data collection for each type of data.