Innovative Research on Pricing and Replenishment Decision Making of Vegetable Commodities Based on ARIMA Prediction Modeling

Abstract

The pricing of vegetables in modern fresh produce superstores is generally based on the "cost-plus pricing" method, and reliable market demand analysis is particularly important for replenishment and pricing decisions. Against this background, this paper provides a complete set of vegetable merchandising strategies for supermarkets. Firstly, we detect and analyze the purchase data, study the relationship between the sales volume, visualize the sales volume data, and calculate the correlation between the sales volume of each category and individual product by using Pearson's algorithm and Spearman's algorithm respectively. Then, a pricing model was established, taking into account the cost of goods, expected sales volume and sales profit, and providing a daily replenishment total and pricing strategy for the coming week by analyzing the relationship between total sales volume and cost-plus pricing for each vegetable category. The model forecasts market demand to maximize revenue enhancement. Finally, the replenishment volume and the proposed pricing strategy for each individual item are determined using a portfolio optimization approach to ensure that the superstore achieves optimal profitability on a future day. The proposed model fits the actual needs, can reasonably solve the proposed problems, and is characterized by high practicality and algorithmic efficiency, which makes it suitable for all types of superstores to develop future sales strategies.