Vegetable Replenishment and Pricing Planning Based on ARIMA Forecasting
DOI:
https://doi.org/10.54097/7hq45336Keywords:
Unitary Linear Regression, ARIMA, Nonlinear Programming, Replenishment and Pricing.Abstract
In fresh food supermarkets, vegetables have a short shelf life and can deteriorate quickly, necessitating daily replenishment and pricing decisions based on historical sales to maximize profits. This article, based on the historical sales data of vegetable products from a major supermarket, constructs a replenishment and pricing planning model based on ARIMA forecasting for six types of vegetables, seeking the optimal replenishment and pricing strategy for the next seven days. The results indicate that leafy and flowering vegetables, as well as eggplant-type vegetables, require more replenishment on Fridays and weekends, whereas aquatic root vegetables need more on Thursdays and Fridays. The replenishment of chili vegetables and edible mushrooms fluctuates, and there is a constraint relationship between pricing and replenishment volume. This study aims to assist merchants in making reasonable replenishment and pricing decisions to reduce costs and maximize economic benefits.
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