With the improvement of living standards and health awareness, the demand for vegetable goods in fresh food supermarkets continues to grow. However, vegetables have a short freshness period and are susceptible to environmental factors that lead to changes in character, making it more challenging for supermarkets to replenish and price. In addition, how to maximize profits within a limited selling space remains an important issue to be addressed. This paper explores a more accurate decision-making algorithm, based on sales flow detail data, combined with Spearman's correlation coefficient, linear regression model, and random forest regression model, aiming to optimize the sales prediction, replenishment amount, and pricing strategy of vegetable category. First, Spearman's correlation coefficient was used to analyze the relationship between vegetable categories and single-item sales volume, revealing the seasonal and cyclical patterns of change in sales volume. Subsequently, a linear regression model is used to explore the association between vegetable sales volume, time and pricing. provides an optimization plan for superstores in terms of replenishment volume prediction and pricing strategy, while further adjusting pricing by combining the cost-plus method. Finally, a random forest regression model is used to optimize the replenishment volume and pricing for the coming week, so as to maximize the revenue of the superstore. The experimental results show that the vegetable merchandising strategy proposed in this paper can help supermarkets make more accurate management decisions in the face of increasing demand and complex business environment.
Zhu et al. (Tue,) studied this question.
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