Due to the short shelf life of vegetable items in fresh produce superstores, most varieties need to be restocked on a daily basis. In order to make reasonable pricing and replenishment decisions, this paper firstly integrates data and establishes a multiple linear regression model to find out the relationship between price and sales volume, and further optimizes it using PSO particle swarm algorithm to obtain the linear relationship equation, and then establishes an ARIMA time-series prediction model using SPSS to predict the total amount of replenishment and the pricing strategy for July 1-7 by using the data of the last 4 weeks. When considering the number of sales category constraints, this paper carries out the single-product profit ranking and selects the top 33 single-products with higher profits by combining the available varieties on the basis of meeting the minimum display quantity, and predicts the replenishment and pricing strategy on July 1 using the GWO gray prediction model. Ultimately, based on the above conclusions, the strategy for superstores to maximize returns in pricing and replenishment is derived.

Ziting Zhu, Boyu Wu, Tian Wang. Research on Vegetable Cost Pricing and Sales Forecast Based on PSO Optimization and Forecast Model. Academic Journal of Business & Management (2024) Vol. 6, Issue 5: 141-146. https://doi.org/10.25236/AJBM.2024.060519.

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