Research on Vegetables Sales Profit Based on Machine Learning

Abstract

The sales volume of different vegetables in supermarkets is different, and the profit is different. The research on the profit of each category of vegetables can help supermarkets predict the sales revenue more accurately to improve inventory management, purchasing plans, and sales strategy. This can help the supermarket avoid excess or insufficient inventory, improve profits, and help the supermarket identify potential sales risks. The research team obtained daily sales data for various categories of vegetables from a supermarket on the Internet for some time. First, according to the "cost plus pricing" method of vegetables, regression analysis was conducted on the total cost and total profit of various categories of vegetables, and it was found that there was a strong linear relationship, and the profit rate of various categories of vegetables was relatively fixed. They were [0.6629, 0.5915, 0.4663, 0.5121, 0.5596, 0.5381]. Since the sales volume of vegetables has seasonal characteristics, this paper established the ARIMA model to forecast the sales volume of each category of vegetables; and obtained the sales volume distribution of each category of vegetables from July 1 to 7, 2023. The BP neural network model with 22 layers of hidden layers was established, and the purchase unit price data could be obtained according to the known sales data. The R square of the model was greater than 0.7, and the goodness of fit was high. According to the known correlation, considering the impact of the loss rate on the profit and the rationality of the data, a multivariate linear programming model is established to give the replenishment volume and pricing strategy from July 1 to 7, 2023. It is concluded that the maximum profit of the supermarket in these seven days is [845.1835, 870.5028,861.6210, 858.8551, 839.4595, 864.7580, 892.9897]. Through the sensitivity analysis of the profit rate of each category of vegetables, it is concluded that the profit rate increases by 1%, the income will increase by about 1.1%, and the model is robust.