Research on vegetable bundling decisions based on K-means cluster analysis
DOI: 10.23977/infse.2023.041017 | Downloads: 7 | Views: 576
Author(s)
Yining Zhou 1, Xingyu Zhou 1
Affiliation(s)
1 School of Government Audit, Nanjing Audit University, Nanjing, 211815, China
Corresponding Author
Yining ZhouABSTRACT
With the improvement of the quality of life, it has become a trend to buy vegetables in fresh agricultural products supermarkets. In the actual sales process, fresh produce usually increases supermarket revenue through bundling, so it is important to study the degree of association between each vegetable category and the correlation between single vegetable products for the bundling decision of supermarkets. In this paper, SPEARMAN correlation analysis and K-MEANS cluster analysis method are adopted to study the sales volume and pattern of each vegetable category and single vegetable product, and the time series analysis method is used to analyze the seasonal sales rules of single vegetable product and each vegetable category. This paper finds that the sales volume often reaches the maximum in winter. Finally, the optimal bundling decision and seasonal replenishment strategy are obtained according to the correlation between the vegetable category and each vegetable item and the maximum winter sales.
KEYWORDS
Spearman Correlation Coefficient, K-means Cluster Analysis, Contour CoefficientCITE THIS PAPER
Yining Zhou, Xingyu Zhou, Research on vegetable bundling decisions based on K-means cluster analysis. Information Systems and Economics (2023) Vol. 4: 127-133. DOI: http://dx.doi.org/10.23977/infse.2023.041017.
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