Intelligent Pricing and Replenishment Strategy for Vegetables Based on ARIMA Prediction and BP Neural Networks

With the improvement of people's living standard, people pay more and more attention to nutritional balance, which brings huge commercial profits, because vegetable goods can only be sold on the same day, taking into account the special characteristics of vegetable goods, the use of past sales data for data mining, to help store managers to make the best decision from the single product, category, replenishment volume, pricing. Firstly, using the idea of clustering, the single items are clustered, and Spearman correlation analysis model is established for correlation analysis, then, the weekly sales volume, sales unit price, wholesale price are counted, and the cost-plus pricing relationship of each category is back-calculated daily through the cost-plus pricing formula, and the total amount of replenishment of the vegetable category for a week and pricing strategy are predicted using the BP neural network prediction model forecasting, and the total amount of replenishment of the vegetable category for a week is predicted. Finally, the ARIMA time series prediction model is built to solve the pricing reference strategy for a single item on a single day and determine the replenishment quantity of a single item on a single day by considering the unit price of the vegetable category, the wholesale unit price, and the net profit from the sale of the vegetable category. The proposed model has excellent forecasting ability and the results are evaluated more objectively.