Automated Pricing and Replenishment Decisions for Vegetable Products Based on Data Analysis

Abstract

This study delves into the critical factors for the successful operation of vegetable products supermarkets: efficient inventory management and accurate market demand analysis. Given the perishability of vegetables, their variety, and the complexity of supply chains, a linear regression model and the method of least squares have been developed to estimate model parameters. Additionally, the ARIMA model is utilized to forecast the unit prices of commodities. In a more comprehensive approach, considering various influencing factors, a combinatorial optimization algorithm (integrating linear regression and greedy algorithm) is employed to determine the optimal replenishment strategy. Based on this, the study forecasts the sales volume and unit prices for various categories over the next seven days, and accordingly, establishes the pricing strategy and predicted replenishment volume for each category. The research underscores the necessity for retailers to precisely analyze sales data and market trends, optimize supply chain management, and formulate effective pricing strategies to cope with seasonal variations and limited sales space. These measures are essential to satisfy consumer demand and enhance profitability.