Research on Automated Pricing and Supplementation Decision-making of Vegetable Products

Abstract

In fresh supermarkets, the shelf life of vegetable products is usually very short, so it is crucial to replenish inventory daily based on historical sales data and product line requirements. In order to strengthen decision-making and inventory management, this article first selects representative supermarket sales data and conducts appropriate preprocessing, and then adopts a series of effective analysis methods. Firstly, using Kappa consistency testing to explore the correlation between different vegetable categories can help determine which products may be correlated in sales. In addition, in order to better formulate pricing and replenishment strategies for vegetable products, this article introduces a cost-plus pricing model. When determining the markup coefficient, multiple linear regression was used to ensure that the price-setting process is reasonable and consistent with cost and demand. In order to further confirm the accuracy and feasibility of this method, the article calculated the R-squared value of multiple linear regression, which reached 0.975, providing strong validation for the effectiveness of this method. When formulating replenishment strategies, in order to obtain sales forecasts for vegetable categories within the next 7 days, this article also uses a seasonal ARIMA model, which helps supermarkets plan inventory and supply chain more effectively. These strengthening measures have jointly improved the decision-making ability of vegetable product management in supermarkets, promoted more effective data-driven strategies, ensured the freshness and quality of vegetable products, and maximized the economic benefits of supermarkets.