Author(s)

Junxiao Chen ¹, Kaize Wang ¹, Yufan Li ¹

Affiliation(s)

¹ School of International, Jilin University of Finance and Economics, Changchun, China

Corresponding Author

Junxiao Chen

ABSTRACT

Fresh food superstores need to take advantage of the freshness period of vegetables and the corresponding time of day to purchase, using pricing methods and replenishment strategies based on the cost-plus pricing method. Proper pricing and replenishment strategies can maximize the supermarket's revenue. In this paper, we analyze the data of different categories of vegetables and different single products to get their distribution patterns and correlations, use ARIMA time series to predict the future wholesale price, use linear regression to predict the future demand, and then based on the Random Forest algorithm to predict the future sales volume of each category and single product, and then the final pricing of the product and the replenishment strategy is based on the premise of maximizing the revenue, and based on the optimization model. 

KEYWORDS

Pricing and Replenishment Strategy, ARIMA Time Series, Random Forest Algorithm

CITE THIS PAPER

Junxiao Chen, Kaize Wang, Yufan Li, Analysis of Vegetable Pricing and Replenishment Strategies Based on ARIMA Time Series and Random Forest Algorithm. Information Systems and Economics (2024) Vol. 5: 15-22. DOI: http://dx.doi.org/10.23977/infse.2024.050303.

REFERENCES

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