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Statistical Analysis of Fresh Produce Retail Data in Convenience Stores and Optimization of Pricing Strategies

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DOI: 10.23977/infse.2023.041015 | Downloads: 23 | Views: 620

Author(s)

Hanzhi Xu 1

Affiliation(s)

1 School of Mathematics Science, Shanxi University, Taiyuan, Shanxi, 237016, China

Corresponding Author

Hanzhi Xu

ABSTRACT

The sales volume of various supermarket categories is influenced by market and seasonal factors. In the short term, demand for different categories of vegetables fluctuates. Unreasonable purchasing and pricing strategies can inevitably lead to losses. To maximize supermarket profits, a comprehensive analysis of automatic replenishment and pricing strategies for vegetable products is of great significance for fresh food supermarkets. We utilize multiple linear regression to model the sales volume of each vegetable category, coupled with ARIMA-derived wholesale prices for each category in the next 7 days for planning. Finally, we employ simulated annealing to determine the optimal replenishment and pricing strategies, aiming to maximize revenue in the future 7 days.

KEYWORDS

Multiple Linear Regression, ARIMA, Simulated Annealing Algorithms

CITE THIS PAPER

Hanzhi Xu, Statistical Analysis of Fresh Produce Retail Data in Convenience Stores and Optimization of Pricing Strategies. Information Systems and Economics (2023) Vol. 4: 109-115. DOI: http://dx.doi.org/10.23977/infse.2023.041015.

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