Automatic Pricing and Replenishment Decisions for Vegetable Commodities

The various categories of vegetables show different sales trends with the changes of specific times and seasons. Reasonable arrangements and the formulation of pricing strategies, as well as the timely and effective replenishment of individual items, can increase the profits of supermarkets. Firstly, this article uses the 3σ principle to preprocess and visualize the sales data of six major vegetable categories, including eggplant, cauliflower, pepper, aquatic rhizome, and leafy and floral vegetables, and their corresponding individual vegetable items. The AMRIA time series model is established by using the statistical analysis software SPSS 26.0 to analyze the distribution of the sales volume of vegetable categories and individual items over time. It is found that the overall sales volume of individual items fluctuates over a long period of time, and leafy and floral vegetables, cauliflower, and aquatic rhizome vegetables show seasonal trends. Determine the correlation between various categories and individual products. through the Pearson correlation coefficient. A neural network prediction model is established and solved by using MATLAB software to predict the daily sales volume of each category in the next week. Subsequently, an optimization model is established to determine the total daily replenishment quantity and pricing strategy for vegetable categories in the next week, and a judgment matrix is constructed to determine the relevant data in different aspects required for supermarkets to formulate replenishment and pricing strategies.