Automatic Pricing and Replenishment Decisions for Vegetables Based on Time Series Modeling

Yi Zhang; Jingjing Fan; Yimeng Zheng

doi:10.54097/hset.v70i.14001

Authors

Yi Zhang
Jingjing Fan
Yimeng Zheng

DOI:

https://doi.org/10.54097/hset.v70i.14001

Keywords:

Pearson Correlation Coefficient, Grey Relational Analysis, Time Series Prediction, Optimization Model.

Abstract

The market is highly competitive. Reasonable pricing and timely replenishment decisions can help enterprises to grasp the market demand and competitive situation, maximize profits. Through data-driven decision-making and technical support, supply chain efficiency and stability can be optimized, customer satisfaction can be improved, and personalized products and services can be provided. This paper uses statistics to analyze the distribution law of sales volume among categories and single products, and solves the problem of finding the correlation between different categories and different single products of vegetable commodities and the distribution law of sales volume. According to the distribution law and relationship of the sales volume of each category and single product of vegetables, the replenishment plan is made by taking the category as a unit, the relationship between the total sales volume of each vegetable category and the cost-plus pricing is analyzed, and the daily replenishment volume and pricing strategy of each vegetable category in the coming week are given, and finally the optimal daily replenishment and pricing strategy is determined, which provides a reference for supermarket operation and supply chain management.

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