Research on Supermarket Vegetable Product Management based on Time Series and Optimization Models

To address the diverse challenges faced by supermarkets in managing vegetable products, such as accurately predicting sales trends and formulating suitable pricing strategies, this study employs techniques like ARIMA seasonal decomposition and grey correlation analysis for sales data forecasting. These methods have the potential to enhance sales revenue, reduce inventory costs, and improve customer satisfaction for supermarkets. Furthermore, the paper establishes ARIMA-based pricing optimization models and utilizes genetic algorithms to optimize restocking plans. The implemented strategies are geared towards addressing various challenges inherent in the management of vegetable products. These challenges encompass the vital need for precise prediction of sales trends and the development of well-founded pricing strategies. The paper concludes by subjecting these strategies to a meticulous examination, conducting an in-depth analysis of the data to assess their effectiveness. Through its findings, the research significantly contributes to the enhancement of business outcomes in vegetable product management for supermarkets, thereby reinforcing their pivotal role in the contemporary economy. Supermarkets can enhance performance and adapt to changing consumer needs by employing advanced forecasting techniques and optimization algorithms in the modern marketplace.