Commodity Pricing and Replenishment Decision Strategy Based on the Seasonal ARIMA Model

Abstract

As a crucial component of enterprise marketing strategy, commodity pricing and replenishment strategies often play a pivotal role in determining the profit of retailers. In pursuit of profit maximization, this work delved into the realm of fresh food supermarket commodity pricing and replenishment strategies. We classified commodities into six distinct categories and proceeded to examine the relationship between the total quantity sold in these categories and cost-plus pricing through Pearson correlation analysis. Furthermore, a Seasonal ARIMA model was established for the prediction of replenishment quantities and pricing strategies for each of the categories over a seven-day period. To ensure precise data, we extended our forecasting to individual products for a single day, employing 0–1 integer programming. To align the inquiry with real-world scenarios, we took into account various factors, including refunds, waste, discounts, and the requirement that individual products fall within specific selling ranges. The results show that the profit will be maximized when the replenishment of chili is 39.874 kg and the replenishment of edible mushrooms is 43.257 kg in the future week. We assume that the residual of the model is white noise. By testing the white noise of the model, the analysis of the residual Q statistic results shows that it is not significant in level, which can prove that the model meets the requirements and the obtained results are reliable. This research provides valuable insights into the realm of commodity pricing and replenishment strategy, offering practical guidance for implementation.