Author(s)

Yujing Zhang ¹, Hongling Qin ², Zhihan Wei ¹, Yu Dai ³, Linwen Zhang ⁴

Affiliation(s)

¹ School of Mathematics and Computer Science, Chongqing College of International Business and Economics, Chongqing, 401520, China
² Chongqing Science City Xi Yong First Primary School, Chongqing, 400030, China
³ School of Bioinformatics, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China
⁴ Faculty of Electronics and Information, Chengdu Jincheng College, Chengdu, 611731, China

Corresponding Author

Yujing Zhang

ABSTRACT

This article provides a comprehensive analysis and modelling of vegetable sales data. Firstly, the statistical analysis of the data was carried out by counting the monthly sales by category and individual item respectively and plotting the combination of sales volume and sales for each category of vegetables as well as the circle plot for individual items of vegetables so as to study the relationship and trend between them. The correlation coefficients between different vegetable categories and individual items were further calculated by Pearson's test, and strong correlations between edibles and aquatic roots and tubers, as well as correlation characteristics between chilli, cauliflower and foliar categories were found. In modelling cost-plus pricing, the article predicts the total replenishment and pricing strategy for the coming week by analysing the relationship between sales volume and cost-plus pricing and obtaining a fitted equation. For data processing, the article adopts the method of nearest neighbour interpolation to fill in missing values and data smoothing, decomposes the data into seasonal, trend and residual terms by STL decomposition, and establishes an ARIMA model to predict the sales volume in the coming week. Overall, this paper digs deeper into the relationships and trends in vegetable sales data through statistical analysis and modelling methods, which provides strong support and reference for the development of future sales strategies.

KEYWORDS

ARIMA Model, Correlation, Replenishment Strategy

CITE THIS PAPER

Yujing Zhang, Hongling Qin, Zhihan Wei, Yu Dai, Linwen Zhang, Research on replenishment strategy of superstore fresh goods based on STL-ARIMA. Information Systems and Economics (2024) Vol. 5: 131-140. DOI: http://dx.doi.org/10.23977/infse.2024.050318.

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