Research on automatic pricing and replenishment forecasting of vegetable commodities based on data analysis and optimization model

In fresh supermarkets, the shelf life of vegetables is limited, and their appearance will deteriorate rapidly, which will have a negative impact on sales. In order to ensure sufficient income, supermarkets must replenish inventory according to the sales performance of vegetables to meet market demand, and formulate reasonable pricing strategies according to cost analysis. Based on the historical data of vegetable sales in recent years, this paper analyzes the sales distribution and relationship of different vegetable varieties and products, and makes a Spearman correlation analysis of these vegetable varieties and products. Vegetable pricing usually adopts the method of "cost plus pricing", and supermarkets usually discount goods with damaged transportation or poor product quality. Reliable market demand analysis is essential for effective replenishment and pricing decisions. Therefore, this study introduces the addition rate, establishes the time series prediction model of wholesale prices of different vegetable varieties, and establishes the optimization model to solve the problem of replenishment and pricing strategy of vegetable commodities. The goal is to assist supermarkets in developing the best replenishment and pricing strategy for the future. According to the obtained data and model, an optimization model with vegetable varieties as the unit and sales profit as the objective function is finally established to determine the optimal daily replenishment volume and pricing strategy for maximizing supermarket revenue. The optimization model can analyze the impact of different pricing strategies on costs and benefits, help enterprises allocate resources, and reduce inventory costs while ensuring profitability.