蔬菜类商品的分类和销售策略问题的研究

Research on the Classification and Sales Strategy of Vegetable Product

摘要: 随着越来越多的人开始追求新鲜的蔬菜食品，对于超市的运营方来说，预测各个商品的销售情况对于超市的经营至关重要。本文从销售商品的历史记录出发，对于未来各商品的销售情况进行分析与预测。根据已有的一批蔬菜商品销售数据中各商品的历史销售情况，对之后的蔬菜类商品销售情况进行预测和分析，使用spearman相关性及Pearson相关性分析法分析每年以及每季度中的不同单品之间的关系及不同品类之间的关系。使用线性回归模型预测并建立该品类的售价与其本身的批发价和销售量和与其相关性较强的品类的批发价和销售量有关的数学模型。由统计分析得出批发价在一年的周期上具有周期性，使用偏最小二乘回归(PLSR)模型预测各品类未来一周的合适定价，按此制定了补货策略和定价策略。其次我们深究了其他影响销售策略的因素，由此更加准确地制定销售策略。

Abstract: As more and more people pursue fresh vegetable food, predicting the sales of various products is crucial for supermarket operations. This article analyzes and predicts the future sales of various products based on the historical records of sales. Based on the existing sales data of a batch of vegetable products, we predict and analyze the sales of vegetable products in the future. We use Spearman correlation and Pearson correlation analysis methods to analyze the relationships between different single products and categories in each year and each season. We use a linear regression model to predict and establish a mathematical model that relates the selling price of this category to its own wholesale price and sales volume, as well as the wholesale price and sales volume of categories with strong correlation. Statistical analysis shows that wholesale prices have periodicity over a one-year cycle. Partial least squares regression (PLSR) model is used to predict the appropriate pricing for each category in the next week, and replenishment and pricing strategies are formulated based on this. Secondly, we delved into other factors that affect sales strategies, in order to develop sales strategies more accurately.