Author(s)

Jingcong Zhang, Zehui Li, Jieyu Wu, Jiahao Liu, Wanhe Wu, Shiqi Li

Corresponding Author

Shiqi Li

Abstract

This study aims to provide a comprehensive analysis of the sales characteristics and pricing strategies of vegetable commodities. Considering the high demand for the freshness of vegetable commodities as well as the interrelationships and price fluctuations among different categories, superstores need to take into account a variety of factors, including historical sales volume, seasonal variations, and availability, in order to formulate comprehensive pricing and replenishment strategies. This study examines the relationship between sales volume and cost-plus pricing of each category of vegetables in the off-season and peak seasons, using the categories of vegetables as the basis for classification. A polynomial fitting method was used to obtain the functional relationship between the two and to verify the fitting effect, which showed that the mean value of accuracy exceeded 95.6%. In addition, the sales volume is forecasted by a time series model and the smoothness of the series is verified. Under the premise of maximizing the revenue of the superstore, this study uses the pricing of each category as a decision variable, establishes an optimization model, and obtains the specific pricing strategy of each category by a differential evolutionary algorithm. The finalized pricing strategies were: 6.87 yuan/kg for flowers and leaves, 9.83 yuan/kg for edible mushrooms, 10 yuan/kg for aquatic root meridians, 13.64 yuan/kg for chili peppers, 7 yuan/kg for eggplant, and 10.99 yuan/kg for cauliflower. These results provide an important decision-making basis for the superstores to make more effective pricing and replenishment strategies for vegetables to meet market demand and maximize revenue. Through in-depth analysis of the relationship between sales data and pricing, superstores can better respond to changes in different seasons and market conditions, thereby improving operational efficiency and providing consumers with better quality goods and services.

Keywords

Pricing Strategies, Polynomial Fitting Method, Time Series Model, Differential Evolutionary Algorithm