Vegetable demand forecasting model based on ARIMA

Authors

  • Yongkuan Li

DOI:

https://doi.org/10.62051/1q7dp021

Keywords:

Fresh supermarket; ARIMA; Demand forecasting; Time series.

Abstract

Establishing an accurate vegetable demand forecasting model is crucial for fresh supermarkets to optimize replenishment and maximize profits amidst fluctuating market demand driven by the perishable nature of vegetables and changing consumer purchasing decisions. This study takes a certain fresh supermarket as an example, targeting the strong periodicity and seasonality of the vegetable market demand characteristics, using SPSS for data analysis and ARIMA time series modeling. Through stationary testing and white noise testing, the effectiveness and applicability of the model were verified, and an accurate forecast of the total demand for vegetables in the supermarket for the next week was made. This forecasting model provides a reliable basis for the supermarket to formulate replenishment plans, helping it maximize profits in vegetable supply, cope with market demand fluctuations, ensure timely vegetable supply, and meet the constantly changing purchasing needs of consumers.

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References

[1] Yang Jingxuan. Based on ARMI-Light... Research on model forecasting of fresh food sales volume [D]. Chengdu: Southwest University of Finance and Economics, 2023.

[2] Srikanth Sankaran. Demand forecasting of fresh vegetable product by seasonal ARIMA model [J]. International Journal of Operations Research, 2014, 20 (3): 315 - 330.

[3] Liu Haichao, Sun Haining. Optimization and simulation of joint decision model for replenishment of warehouse and supermarket based on quantity strategy [C]//The tenth National Youth System Science and Management Science, Xi 'an, Shaanxi, China, 2009: 10 - 17.

[4] PRIYADARSHI R, PANIGRAHI A, ROUTROY S. Demand forecasting at retail stage for selected vegetables: a performance analysis [J/OL]. Journal of Modelling in Management, 2019, 14 (4): 1042 - 1063.

[5] MO G, GUO Y. Vegetable sales forecasting based on nonlinear programming model [J/OL]. Journal of Education, Humanities and Social Sciences, 2024, 25: 190 - 197.

[6] Gao L. Research on Forecasting model of small batch material production demand based on ARIMA [J/OL]. Modern information technology, 2023, 7 (15): 97 - 101.

[7] Jiang Chunhai, YAN Zhenhao, Song Zhiyong. Research on Coal market demand forecast based on ARIMA and GM (1, 1) model [J]. Review of Industrial Economics (Shandong University), 2019, 18 (3): 54 - 86.

[8] Li Baoxin, Xi Qiongqiong. Analysis and prediction of Urban-Rural income gap in Hebei Province based on ARIMA model [J/OL]. Shanxi agricultural economy, 2023, 24: 24 - 28.

[9] Wang Yao. Prediction of China’s Future Population Based on ARIMA Model [J/OL]. Statistics and Application, 2022, 11 (6): 1392 - 1400.

[10] Yan Xiangxiang. The ARIMA model was used to predict the area of park green space [D]. Computer Science, 2023, 47 (S2): 531 - 534.

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Published

12-08-2024

How to Cite

Li, Y. (2024) “Vegetable demand forecasting model based on ARIMA”, Transactions on Computer Science and Intelligent Systems Research, 5, pp. 1234–1244. doi:10.62051/1q7dp021.