基于时间序列模型的蔬菜类商品的补货和定价策略分析

Replenishment and Pricing Strategy Analysis of Vegetable Commodities Based on Time Series Model

在生鲜商超中,蔬菜类商品一般都有着较短的保鲜期,它们的品相随销售时间的增加而变差。因此,对于商超而言,为了使蔬菜能够在当天售出,制定恰当的补货和定价策略显得格外重要。基于某商超经销的6个蔬菜品类在某段时间的相关销售数据,本文首先根据价格需求弹性的计算公式,对各品类取对数后的日销售量和日销售价格进行线性回归,得到各品类销售总量与成本加成定价的关系表达式。之后对各品类的日销售量进行时间序列分析,建立乘积ARIMA模型,预测得到各品类未来一周的日销售量,并根据损耗率计算公式得到各品类未来一周的日补货总量。最后以商超每日净收益最大为目标函数,建立数学规划模型,得到各品类未来一周的定价策略和日收益。

In the fresh supermarket, vegetable products generally have a short shelf life, and their products deteriorate with the increase of sales time. Therefore, it is particularly important for supermarkets to develop appropriate replenishment and pricing strategies in order to enable vegetables to be sold on the same day. Based on the relevant sales data of 6 vegetable categories distributed by a supermarket in a certain period of time, this paper first conducts linear regression on the daily sales volume and daily selling price of each category after taking logarithm according to the calculation formula of price demand elasticity, and obtains the relationship expression between the total sales volume of each category and the cost plus pricing. Then, the daily sales volume of each category is analyzed in time series, and the product ARIMA model is established to predict the daily sales volume of each category in the next week, and the total daily replenishment volume of each category in the next week is obtained according to the calculation formula of the depletion rate. Finally, taking the maximum daily net return of the supermarket as the objective function, a mathematical planning model is established to get the pricing strategy and daily return of each category in the next week.