Research on Pricing and Replenishment Strategy of Supermarket Fresh Food Based on Multiple Regression and Particle Swarm Optimization
DOI:
https://doi.org/10.54097/9992w083Keywords:
Multiple Regression, Particle Swarm Optimization, Fresh Pricing Strategy.Abstract
The characteristics of vegetable commodities such as short storage period, easy to wear and tear, and rapid demand for circulation time led to daily changes in their pricing. To develop a reasonable pricing and replenishment plan, this paper first analyzes the relationship between sales volume and pricing by using a multiple regression model, with pricing as the dependent variable and wholesale price and sales volume as the independent variables. The multivariate regression equation was derived by determining whether there is multicollinearity and heteroskedasticity among the independent variables through the variance inflation factor and White's test, and OLS + robust standard error regression was used to reduce the effect of heteroskedasticity. Secondly, replenishment and pricing strategies were developed for different categories, firstly, time series analysis was conducted to predict the cost, then particle swarm optimization was used to find out the optimal pricing, and based on the relationship between the pricing and the sales volume, the sales volume was projected, and finally the replenishment volume was inverted. The study provides practical suggestions and references for pricing and replenishment decisions in the supermarket fresh food industry.
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