A study on vegetable stocking-pricing decision-making in supermarkets based on regression analysis and planning models

Abstract

Vegetable products in the superstore require high freshness and must meet market demand while maximizing profit. This study proposes a pricing strategy based on the proportion of sales for each product category. Using data from the previous four weeks, a relationship model is developed. Additionally, a BP neural network regression is used to analyze the relationship between vegetable sales and cost-plus pricing. A planning model is then established to determine the replenishment quantity and daily maximum profit, with the objective of maximizing profitability. Adjustments to the replenishment program and pricing strategy are made to accommodate changes in sales conditions. By extracting sellable goods data and using prediction results, the planning model is further refined by setting constraints such as ordering limits, historical pricing records, and quantity limits. This model enables the determination of replenishment quantities and pricing strategies for each individual product, ultimately maximizing profit for the superstore.