Automatic Pricing and Replenishment Decision for Vegetable Products Based on ARIMA Model and Intelligent Algorithm

With the improvement of human requirements for quality of life, economic development and urbanization, the automatic pricing and replenishment decisions of vegetable categories and single products in supermarkets have more reasonable planning. To optimize the profitability of supermarkets, help them expand their market share, and determine their market position, this paper aims to explore the correlation between vegetable categories and single products, and to set a more reasonable replenishment and pricing strategy for supermarkets. Firstly, according to the distribution law and interrelationship of the sales volume of various categories and single products of vegetables, the sales volume of each category and single product was quantitatively analyzed, and because there was no normal distribution relationship in the data, the Spearman correlation mathematical model was established to obtain the monotonic relationship between vegetable categories. Then, the relationship between the total sales volume of each vegetable category and the cost-plus pricing was analyzed, and the dimensionality reduction of the data was carried out by using the calculation data to determine whether it conformed to the multivariate linear relationship, and the discount situation was added as a special for observation and fitting, and the total sales volume and the cost-plus pricing were inversely proportional. Finally, by optimizing the total number of single products sold, the minimum display quantity, and the single product replenishment number of saleable varieties, the existing conditions are fixed and compressed, and the greedy algorithm is modified to obtain a new supermarket revenue and single product replenishment planning model, and the optimal replenishment strategy and pricing method are obtained by solving the model.