Author(s)

Jiaqiang Xie ¹, Pengzhan Niu ¹, Chenglong Chao ¹, Huanzheng Zhu ¹

Affiliation(s)

¹ School of Mechanical and Electronic Engineering, Shandong Jianzhu University, Jinan, 250101, China

Corresponding Author

Jiaqiang Xie

ABSTRACT

In modern retail, vegetables, as daily consumer goods, have strong seasonality and demand fluctuations, and at the same time have a short shelf life and are easy to wear out. Retailers need to optimize replenishment and pricing strategies to ensure adequate supply and control inventory depletion. How to maximize the profit of the supermarket on this basis has become an important problem that the retail industry needs to solve. We analyzed the relationship between the total sales volume and cost-plus pricing of each vegetable category, established the ARIMA model and the revenue maximization model, and gave the daily replying volume and pricing strategy of each vegetable category in the next week (July 1-7, 2023). According to the available varieties in the past week, under the condition that the order quantity of each item is in the range of 27 to 33, and the order quantity of each item is more than 2.5 kg, the linear programming is used to optimize the decision under the constraints to maximize the total revenue, and the replying quantity and pricing strategy of each item on July 1 are given. By optimizing pricing and replying strategies, retailers can better respond to market changes, improve their market competitiveness and profitability, and promote the development of intelligent retail industry, which has important academic significance and practical value.

KEYWORDS

Prediction Model, ARIMA Time Series Analysis, Linear Programming

CITE THIS PAPER

Jiaqiang Xie, Pengzhan Niu, Chenglong Chao, Huanzheng Zhu, Pricing and replenioring decision model of vegetable commodities based on historical data. Agricultural & Forestry Economics and Management (2025) Vol. 8: 44-54. DOI: http://dx.doi.org/10.23977/agrfem.2025.080107.

REFERENCES

[1] Jiang Y, Li X. Automatic Pricing and Replenishment Decision-Making for Vegetable Commodities Based on Bi-directional Long Short-Term Memory Recurrent Neural Networks and Markov Prediction Models[J]. Academic Journal of Science and Technology, 2023, 7(3): 69-73.
[2] Liang Y, Li Y, Chen X. Prediction and Replenishment Decision Making for Automatic Pricing of Vegetable Commodities Based on LSTM Models[J]. Academic Journal of Science and Technology, 2023, 8(1): 264-268.
[3] Wang K, Su K, Li H. Random Forest-Based Restocking and Pricing Prediction for Vegetable Items[J]. Frontiers in Business, Economics and Management, 2023, 11(2): 352-356.
[4] Lu Z, Wang Y, Liu K. Research on Pricing and Replenishment Strategies for Supermarkets Based on Revenue Maximization [J]. Journal of Education, Humanities and Social Sciences, 2024, 37: 148-157.
[5] Shin D, Vaccari S, Zeevi A. Dynamic pricing with online reviews [J]. Management Science, 2023, 69(2): 824-845.

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