Research on Vegetable Sales Data Analysis and Cost Pricing Modeling

Abstract

Vegetable commodities have a shorter freshness period, many varieties, and a special time of purchase, so it is important to comprehensively analyze the automatic replenishment and pricing strategy of vegetable commodities for the profitability of fresh food superstores. First of all, in the correlation of vegetable categories and single product sales volume, Pearson correlation coefficient analysis is used, to obtain the strong correlation between chili peppers and foliage categories and various vegetable categories. Then based on correlation the best clustering results were obtained by K-means clustering analysis divided into three main categories, namely solanula; flowers and leaves and cauliflower; peppers, aquatic rhizomes, and edible fungi. Next using entropy weights TOPSIS method is used to give the weights of four indicators: total sales, average unit price, average wholesale price, and average wastage rate of each category of vegetable commodities for seven days, and the top 27 vegetable items are sorted to get the best sellable vegetables. Based on the above ranking of the individual vegetable items, Excel was used to forecast the cost pricing of the vegetable commodities to get the cost pricing of the 27 individual vegetable items for the next seven days.