Forecasting the demand for cold chain logistics of agricultural products with Markov-optimised mean GM (1, 1) model—a case study of Guangxi Province, China
ISSN: 0368-492X
Article publication date: 1 November 2022
Issue publication date: 2 January 2024
Abstract
Purpose
The demand for the cold chain logistics of agricultural products was investigated through demand forecasting; targeted suggestions and countermeasures are provided. This paper aims to discuss the aforementioned statement.
Design/methodology/approach
A Markov-optimised mean GM (1, 1) model is proposed to forecast the demand for the cold chain logistics of agricultural products. The mean GM (1, 1) model was used to forecast the demand trend, and the Markov chain model was used for optimisation. Considering Guangxi province as an example, the feasibility and effectiveness of the proposed method were verified, and relevant suggestions are made.
Findings
Compared with other models, the Markov-optimised mean GM (1, 1) model can more effectively forecast the demand for the cold chain logistics of agricultural products, is closer to the actual value and has better accuracy and minor error. It shows that the demand forecast can provide specific suggestions and theoretical support for the development of cold chain logistics.
Originality/value
This study evaluated the development trend of the cold chain logistics of agricultural products based on the research horizon of demand forecasting for cold chain logistics. A Markov-optimised mean GM (1, 1) model is proposed to overcome the problem of poor prediction for series with considerable fluctuation in the modelling process, and improve the prediction accuracy. It finds a breakthrough to promote the development of cold chain logistics through empirical analysis, and give relevant suggestions based on the obtained results.
Keywords
Acknowledgements
This work was supported by the National Natural Science Foundation of China under Grant No. 71571092.
This work is also supported in part by the High-level Talent Project from “Six Peaks of Top Talents'' in Jiangsu Province under Grant No. JY-076, Young and Middle-aged Academic Leaders from “Green-Blue Project'' of Colleges and Universities in Jiangsu Province.
This work was supported by the Major Projects for Natural Science Research in Colleges and Universities of Jiangsu Province under Grant No. 19KJA520002, and the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD).
Citation
Tang, Q., Qiu, Y. and Xu, L. (2024), "Forecasting the demand for cold chain logistics of agricultural products with Markov-optimised mean GM (1, 1) model—a case study of Guangxi Province, China", Kybernetes, Vol. 53 No. 1, pp. 314-336. https://doi.org/10.1108/K-11-2021-1111
Publisher
:Emerald Publishing Limited
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