Abstract
This study focuses on sales forecasting and pricing optimization of vegetable categories in fresh produce supermarkets. By analyzing the sales data, correlation analysis and clustering methods were used to reveal the correlation patterns among different vegetable categories. The ARIMA model was used to predict the daily replenishment quantity, and TOPSIS and entropy weighting methods were combined to screen out 27 high-quality individual products with high sales volume and stable price to achieve the maximum profit target. The study provides effective sales strategies and operational decision support for fresh food supermarkets, which has practical significance and reference value.
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