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Fresh supermarkets face challenges in marketing and inventory management due to the short shelf life and high loss rate of fresh vegetables. This necessitates the immediate development of a practical replenishment plan and pricing strategy. The paper commences by analyzing the sales distribution patterns of various vegetable categories, identifying leaves and flowers as having notably higher sales compared to other categories. Subsequent examination of the sales trend over time reveals variations in the sales of different categories or individual vegetables across months or quarterly periods, indicating a degree of seasonality. This study calculates the Spearman correlation coefficient for each category and individual product sales, presenting the results in a heat map. Findings indicate a significant positive correlation among all vegetable categories except for solanaceae, implying bundled sales or a consumer tendency to purchase specific vegetable categories concurrently. Consequently, when formulating replenishment and pricing strategies, considering a rational sales mix is crucial. The paper constructs a linear regression model, solving the equation using the least squares approach, and identifies a negative association between sales volume and cost-plus pricing. To accommodate seasonal patterns, the Prophet time series model forecasts wholesale prices for various vegetable categories during July 1-7, 2023. The objective is to maximize profit by accurately predicting vegetable costs. A nonlinear programming model is formulated to achieve this objective, determining the maximum revenue from days 1 to 7 as ￥(108953.65 - K), where K represents the fixed indirect cost. The model optimizes replenishment and pricing strategies for different categories.
Jianan Huang (Thu,) studied this question.