Research On Supermarket Replenishment and Pricing Strategies Based On K-Means Clustering and ARIMA Time Series Model

Qi  Liu; Wenbin Cui; Xinbo Zhang

doi:10.54097/fv0a3d83

Authors

Qi Liu
Wenbin Cui
Xinbo Zhang

DOI:

https://doi.org/10.54097/fv0a3d83

Keywords:

Replenishment strategy; pricing analysis; K-means clustering; ARIMA time series model.

Abstract

At present, the shelf life of fresh supermarket vegetables is short, and the quality gradually declines with time. In order to maximize the benefits of supermarkets, this paper established a K-means clustering model, conducted cluster analysis of various categories, explored the correlation between different categories and different items of vegetables, and used ARIMA time series model to predict the pricing and replenishment volume of various commodities in supermarkets, which had important practical significance for supermarket decision-making.

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