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  4. Vol 7, Issue 9, 2023

Automated Pricing and Replenishment Decisions for Supermarket Fresh Vegetables

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DOI: 10.23977/acss.2023.070909 | Downloads: 18 | Views: 327

Author(s)

Zhichao Zhang 1, Rinong Wu 2, Haolin Cui 3

Affiliation(s)

1 College of Information Engineering, Inner Mongolia University of Technology, Hohhot, China
2 College of Science, Inner Mongolia University of Technology, Hohhot, China
3 College of Aeronautics, Inner Mongolia University of Technology, Hohhot, China

Corresponding Author

Zhichao Zhang

ABSTRACT

In today's vegetable superstore market, vegetable items have a short shelf life due to their short shelf life. Supermarkets usually replenish the goods on a daily basis based on the historical sales and demand of each item. Therefore, this paper conducts a relevant research on automatic pricing and replenishment decisions for vegetable items based on the measured data of a superstore. First, the trends of different categories under different seasons are plotted. Then, Python linear regression is used to fit the functional relationship equation between sales volume and cost-plus pricing, and an optimization model is constructed with the total daily replenishment as the decision variable and the superstore's revenue as the objective function, so as to derive the predicted sales volume table and pricing strategy table for each category. Finally, the gray prediction model is used to predict and analyze the sales volume of individual items, so as to maximize the superstore's revenue under the premise of trying to meet the market demand for each category of vegetable goods. The model developed in the paper can help superstores predict demand more accurately, make replenishment plans, adjust pricing strategies, and improve market competitiveness.

KEYWORDS

Correlation Analysis; Linear Programming; Gray Forecasting; Automatic Pricing; Replenishment Decisions

CITE THIS PAPER

Zhichao Zhang, Rinong Wu, Haolin Cui, Automated Pricing and Replenishment Decisions for Supermarket Fresh Vegetables. Advances in Computer, Signals and Systems (2023) Vol. 7: 64-72. DOI: http://dx.doi.org/10.23977/acss.2023.070909.

REFERENCES

[1] Wu Lifang. A multi-stage pricing model for fresh produce considering freshness and consumer utility[J]. Comprehensive Transportation, 2022, 44(10):124-129. 
[2] Gu Sihong. A study on dynamic pricing of fresh products in H-retailers considering freshness change[D]. Donghua University, 2023. DOI:10. 27012/d. cnki. gdhuu. 2023. 001318. 
[3] Cheng Juanjuan. Empirical research on the relationship between research and teaching in colleges and universities--analysis based on Pearson's correlation coefficient [J]. Science and Technology in Chinese Colleges and Universities, 2022 (10):46-52. DOI:10. 16209/j. cnki. cust. 2022. 10. 016. 
[4] Zhang Liang, Yan Yonghong, Zhao Rong et al. Linear regression analysis and prediction of college grades based on SPSS software[J]. Computer and Digital Engineering, 2023, 51(05):1086-1090. 
[5] D. C. Li, J. F. Dong, J. W. Su et al. Prediction of hot keywords and topics development in intelligence based on gray prediction GM(1, 1) model[J]. Technology and Market, 2023, 30(08):137-142.

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