Research on vegetable replenishment and pricing strategy based on time series comprehensive model

In the fresh supermarket, the shelf life of vegetables is shorter, and the quality of vegetables deteriorates with the increase of sales time. In order to solve this problem, the decision makers of supermarkets usually make replenishment every day according to the historical sales and demand of different categories of vegetables. Therefore, reliable market demand analysis is particularly im-portant for replenishment decisions and pricing decisions. This paper studies the sales of a fresh food merchant in 2023 for more than one year. In the case of uncertain purchase price and specific product information, we establish a mathematical model by combining Spearman correlation coefficient, SARIMAX model and nonlinear fitting method, aiming to help supermarkets make accurate replenishment and price adjustment according to market demand. The research results show that the comprehensive method can effectively predict the future product demand and provide the optimal pricing strategy, so as to improve the operating efficiency and profitability of the supermarket.