Analysis Of Decision-Making Mechanism for Pricing and Replenishment of Vegetable Commodities Based on Least Squares And ARIMA

Abstract

This study provides an in-depth study of automatic pricing and replenishment decision-making mechanisms for vegetable commodities based on the relevant data provided. In the research process, firstly, the data were carried out for pre-processing. Secondly, a one-way linear regression model was developed using the least squares method to investigate the relationship between sales volume and cost-plus pricing for each category. The resulting one-dimensional linear regression equations were well fitted, with goodness of fit R2 greater than 0.6. Then, based on the data characteristics of the different categories, appropriate values of q, d and p were selected and a time series (ARIMA) model was built to predict the wholesale price of each category in the coming week. With the final superstore profit as the objective function and the total daily replenishment as the decision variable, an optimization model was constructed for determining the daily replenishment and pricing strategy of each category, and a sensitivity analysis of the model was conducted.