Leveraging Machine Learning to Optimize Pricing and Restocking Strategies for Fresh Vegetables

Abstract

In the competitive and diverse retail market, restocking and pricing perishable vegetables is challenging. Using big data and machine learning to analyze sales data, this research can build predictive models to refine these strategies and predict trends[1][2]. This research analyzed sales data from 2020 to 2023, identifying key trends in sales volumes, seasonality, and consumer preferences. It incorporated these findings into a multi-objective programming model that also considered wholesale and loss rates. This research used statistical tests like the Kruskal-Wallis and Mann-Whitney U to detect significant sales differences across vegetable categories, while the Spearman correlation showed strong seasonal and culinary impacts on consumer buying behaviors. To optimize freshness and maximize sales and profits, this research utilized genetic algorithms and regression analyses, including an effective XGBoost model. This model had a Mean Absolute Percentage Error of 16.97 and an R² of 0.94, aiding in predicting daily sales and improving pricing strategies and restocking schedules. These methods helped enhance decision-making, ensuring vegetable freshness and maximizing profitability, with projected profits reaching ¥41,582.87. This study not only highlights the effectiveness of integrating big data and machine learning in the retail sector but also sets a benchmark for operational efficiency in managing perishable goods.