商超蔬菜降损增利策略研究

Research on Strategies for Reducing Losses and Increasing Profits in Supermarket Vegetables

摘要: 本文针对生鲜蔬菜零售行业激烈竞争，以某商超为研究对象，基于附件数据，运用Python完成数据清洗与预处理，构建数学模型以优化蔬菜动态定价与补货策略，提升商超收益。问题一计算2021年7月~2024年6月每日蔬菜利润及利润率，分析得出花叶类利润率最高且稳定，花菜类具季节性，2023年下半年花叶类与辣椒类受事件影响利润率两次上升。问题二分析销量规律，发现品类及单品均呈季节性，除茄类外，其余五类蔬菜销量显著正相关。问题三通过Spearman相关分析得出销售额与成本加成定价负相关，拟合函数并推算7天销售额与定价，进而计算利润，为经营决策提供依据。

Abstract: This article focuses on the intense competition in the fresh vegetable retail industry. Taking a certain supermarket as the research object, based on the data in the attached file, Python is used to complete data cleaning and preprocessing, and a mathematical model is constructed to optimize the dynamic pricing and replenishment strategies of vegetables, thereby increasing the supermarket’s revenue. Question 1: Calculate the daily vegetable profits and profit rates from July 2021 to June 2024, and analyze to find that the profit rates of leafy vegetables are the highest and stable, while cauliflower vegetables have a seasonal pattern. In the second half of 2023, the profit rates of leafy vegetables and chili vegetables both rose twice due to events. Question 2: Analyze the sales patterns and find that both categories and individual items show sea sonal patterns. Except for the eggplant category, the sales of the other five types of vegetables are significantly positively correlated. Question 3: Through Spearman correlation analysis, it is concluded that sales are negatively correlated with cost-plus pricing. A fitting function is proposed and the 7-day sales and pricing are calculated, thereby calculating the profit, providing a basis for business decisions.