Research on automatic pricing and replenishment strategy of vegetable commodities based on ARIMA algorithm

Abstract

In just a few years' time, supermarkets have transformed from a novelty into a mainstream channel that unifies the retail world. Since superstores sell vegetables with a wide variety of types, different origins, and shorter freshness periods, the incoming transaction time of vegetables is often in the early morning, so superstores usually replenish their stocks on a daily basis according to the historical sales and demand of each commodity. First, this paper visualizes the historical data of vegetable commodity purchasing and pricing in superstores in order to observe the distribution characteristics and trends, and then analyzes the correlation between categories or individual products using statistical methods such as correlation factors and covariance. To predict replenishment and pricing strategies for the coming week, this paper analyzes the relationship between overall sales and cost-plus pricing for each vegetable category, using given sales data to establish the relationship between sales volume and price for each category. Based on the ARIMA predicted sales model and given costs, the optimal replenishment quantities and pricing strategies were determined using an optimization algorithm.