Research on the Product Automatic Pricing Model Based on Grey Relation Analysis

In this study, firstly, considering the close relationship between cost-plus pricing and cost-plus markup rate, as well as the known wholesale and retail prices of vegetable categories, we shifted the focus of analysis to the “relationship between sales volume of various vegetable categories and cost-plus markup rate.” Through grey relational analysis, we found that six vegetable categories exhibited a correlation coefficient exceeding 0.8, indicating a significant impact of cost-plus pricing on sales volume. Subsequently, utilizing ARIMA time series analysis, we successfully forecasted the sales volume and prices of foliage vegetables for the upcoming week. This aids supermarkets in better planning their supply chains and pricing strategies. Lastly, we addressed question three by conducting data processing and calculating crucial indicators such as average sales volume and unit profit for each individual product. By establishing an optimization model with the objective of “maximizing revenue” and “minimum display quantity” as a constraint, we determined the number and quantity of products that the supermarket should order. This decision was based on a balance between historical data and the minimum display quantity requirement to achieve maximum revenue. In summary, this study provides robust support for the sales strategy and supply chain management of supermarkets. It assists in gaining a deeper understanding of the impact of cost-plus pricing on sales, forecasting future sales trends, and maximizing revenue. This holds significant implications for enhancing operational efficiency.