Author(s)

Ke Xu ¹, Qingyu Zhang ², Chenyou Guo ², Gong Zhang ²

Affiliation(s)

¹ College of Arts, Changchun University of Science and Technology, Changchun, 130022, China
² School of Optoelectronic Engineering, Changchun University of Science and Technology, Changchun, 130022, China

Corresponding Author

Gong Zhang

ABSTRACT

Fresh produce superstores sell a large variety of vegetables with short freshness periods, and their quality deteriorates with the increase in selling time. Therefore, fresh produce superstores' replenishment and pricing decisions are particularly important. In order to maximize the revenue of supermarkets, this paper establishes and solves Pearson's correlation coefficient model for the average selling price of vegetables and the daily sales volume and combines the polynomial regression model and linear regression to obtain the relationship between the sales volume of each vegetable category and the cost-plus pricing. In order to better predict the daily replenishment and pricing strategy of each vegetable category in the coming week, this paper establishes a time series model under the BP neural network by combining the daily sales data of each vegetable category of the supermarket in the past three years. It solves the replenishment and pricing decision of the vegetables when meeting the market demand and maximizing the supermarket's revenue through validation. The model can accurately predict and optimize the replenishment and pricing decisions of the hypermarket and provide a decision basis for the operation and revenue enhancement of the hypermarket.

KEYWORDS

Polynomial regression, neural network time series forecasting, pricing strategy

CITE THIS PAPER

Ke Xu, Qingyu Zhang, Chenyou Guo, Gong Zhang, A study on vegetable replenishment and pricing decisions based on polynomial regression and neural network prediction. Information Systems and Economics (2024) Vol. 5: 56-62. DOI: http://dx.doi.org/10.23977/infse.2024.050308.

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