This paper presents a proposed pricing and replenishment strategy for vegetable commodities in fresh food supermarkets. The aim is to address the challenges faced by these supermarkets in making decisions regarding pricing and replenishment. The strategy is based on a time series model, which involves statistical analysis of sales volume using the average trend rejection method and Spearman coefficient. This analysis provides a descriptive statistical understanding of past sales volume patterns. A time series model is then established based on these statistical laws to predict the sales volume for the next three days. The relationship between sales volume and pricing is determined using K-mean clustering and regression fitting. Subsequently, a single-objective optimization model is formulated based on this relationship and solved using the simulated annealing algorithm to obtain the optimal pricing and replenishment strategy for the commodities. Experimental tests are conducted to validate the model, and the results demonstrate that the proposed model successfully identifies the seasonal distribution of sales volume for vegetable commodities, indicating the presence of an off-peak season. Additionally, the model reveals that while there is no significant correlation between different vegetable categories, there is a strong correlation between certain individual products within the same category. The model accurately predicts sales for the next three days and provides pricing and replenishment quantities for the commodities through the single-objective optimization model. Furthermore, the model enables the estimation of the superstore's profit based on the determined pricing and replenishment quantities. This study holds practical significance for fresh food supermarkets in terms of pricing and replenishment of vegetable commodities, as well as for maximizing the revenue of superstores.

Jiayi Chen, Yingjun Chen, Qianjun Bao. Pricing and Replenishment Strategies for Vegetable Commodities Based on Time Series Models. Academic Journal of Computing & Information Science (2023), Vol. 6, Issue 13: 154-160. https://doi.org/10.25236/AJCIS.2023.061322.

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