Research On Vegetable Replenishment and Pricing Optimization Decision Based on Time Series Prediction and Monte Carlo Simulation

Abstract

For large fresh supermarket, how to reasonably specify the replenishment and pricing strategy of vegetable commodities is very important. In order to give a reasonable replenishment decision, this paper conducts correlation analysis at first, including cost plus pricing strategy and correlation test. And then analyzes the relationship between sales volume and selling price by using neural network regression and scatter plot matrix, and finds that there is no obvious functional correlation between them. The paper use time series forecasting methods, including simple seasonal analysis and Winter multiplication model, to forecast sales volume and wholesale cost. Finally, the linear programming model was used to determine the total amount of replenishment and markup rate of each category of vegetables in the next week, and the corresponding income situation was obtained. The results of comprehensive analysis show that the replenishment volume, markup rate and income of different categories have dynamic fluctuations, reflecting the changes of demand and market.