Author(s)

Tongao Zhang ¹, Zhongkai Zhang ¹, Yunsheng Chi ¹

Affiliation(s)

¹ College of New Energy, China University of Petroleum, Qingdao, China

Corresponding Author

Tongao Zhang

ABSTRACT

This paper centers on the problem of how superstores can accurately determine the replenishment number of several types of vegetables to ensure their freshness, and utilizes the Pearson correlation coefficient method, the time series analysis method, and the Markov prediction method to conduct the research. Firstly, the distribution pattern of sales volume of vegetable categories and their interrelationships were studied by preprocessing vegetable sales data, and correlation coefficients were calculated to assess the correlation between these categories; secondly, the relationship between the total sales volume of vegetable categories and the cost-plus pricing was analyzed, and the daily replenishment volume and pricing strategy for the coming week were predicted by using the time-series method; lastly, the combination of several factors was used to analyze the specific time period of the high total sales price individual items at a given time was analyzed, and Markov forecasting was applied to determine the optimal replenishment volume and pricing strategy. The model prediction results of this study show the practicality and significance for ensuring the freshness of vegetables and maximizing the benefits of supermarkets.

KEYWORDS

Vegetable restocking, Pearson's correlation coefficient, time series analysis, Markov forecasting

CITE THIS PAPER

Tongao Zhang, Zhongkai Zhang, Yunsheng Chi, Research on Vegetable Replenishment Strategy Based on Time Series Analysis. Information Systems and Economics (2024) Vol. 5: 74-81. DOI: http://dx.doi.org/10.23977/infse.2024.050110.

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