The Relationship Between Vegetable Sales Volume and Cost-Plus Pricing Strategies: A Multi-Method Analysis and Forecast

In today's highly competitive supermarket industry, optimizing sales strategies for perishable goods such as vegetables is crucial for maintaining profitability and market share. This study provides an analytical framework for optimizing vegetable sales strategies in supermarkets, focusing on perishability and consumer behavior. Through regression analysis, it explores the sales volume and pricing relationship, identifying the most effective model with a Mean Squared Error (MSE) criterion. Time series models forecast future needs, reflecting on historical sales' cyclical and seasonal trends. A Particle Swarm Optimization (PSO) algorithm, tailored with constraints, offers predictions on sales volumes and pricing, aiming to enhance profitability. Iterative refinements reveal that the model achieves stability and accuracy in predicting profits. The findings emphasize the critical role of data-driven strategies in the competitive supermarket sector, advocating for adaptive approaches in response to evolving market conditions.