Demand forecast and influential factors of cold chain logistics based on a grey model

Xiangyang Ren; Juan Tan; Qingmin Qiao; Lifeng Wu; Liyuan Ren; Lu Meng; Xiangyang Ren; Juan Tan; Qingmin Qiao; Lifeng Wu; Liyuan Ren; Lu Meng

doi:10.3934/mbe.2022360

Mathematical Biosciences and Engineering

2022, Volume 19, Issue 8: 7669-7686. doi: 10.3934/mbe.2022360

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Demand forecast and influential factors of cold chain logistics based on a grey model

1.
School of Management Engineering and Business, Hebei University of Engineering, Handan 056038, China
2.
Handan Polytechnic College, Handan 056001, China

Academic Editor: Jeffrey Yi-Lin Forrest

Received: 21 March 2022 Revised: 10 May 2022 Accepted: 11 May 2022 Published: 24 May 2022

Due to high requirements of storage, operation and delivery conditions, it is more difficult for cold chain logistics to meet the demand with supply in the course of disruption. And, accurate demand forecasting promotes supply efficiency for cold chain logistics in a changeable environment. This paper aims to find the main influential factors of cold chain demand and presents a prediction to support the resilience operation of cold chain logistics. After analyzing the internal relevance between potential factors and regional agricultural cold chain logistics demand, the grey model GM (1, N) with fractional order accumulation is established to forecast future agricultural cold chain logistics demand in Beijing, Tianjin, and Hebei. The following outcomes have been obtained. (1) The proportion of tertiary industry, per capita disposable income indices for urban households and general price index for farm products are the first three factors influencing the cold chain logistics demand for agricultural products in both Beijing and Tianjin. The GDP, fixed asset investment in transportation and storage, and the proportion of tertiary industry are three major influential factors in Hebei. (2) Agricultural cold chain demand in Beijing and Hebei will grow sustainably in 2021–2025, while the trend in Tianjin remains stable. In conclusion, regional developmental differences should be considered when planning policies for the Beijing-Tianjin-Hebei cold chain logistics system.
- demand forecasting,
- grey relation model,
- FGM (1, N) model
Citation: Xiangyang Ren, Juan Tan, Qingmin Qiao, Lifeng Wu, Liyuan Ren, Lu Meng. Demand forecast and influential factors of cold chain logistics based on a grey model[J]. Mathematical Biosciences and Engineering, 2022, 19(8): 7669-7686. doi: 10.3934/mbe.2022360

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Abstract

Due to high requirements of storage, operation and delivery conditions, it is more difficult for cold chain logistics to meet the demand with supply in the course of disruption. And, accurate demand forecasting promotes supply efficiency for cold chain logistics in a changeable environment. This paper aims to find the main influential factors of cold chain demand and presents a prediction to support the resilience operation of cold chain logistics. After analyzing the internal relevance between potential factors and regional agricultural cold chain logistics demand, the grey model GM (1, N) with fractional order accumulation is established to forecast future agricultural cold chain logistics demand in Beijing, Tianjin, and Hebei. The following outcomes have been obtained. (1) The proportion of tertiary industry, per capita disposable income indices for urban households and general price index for farm products are the first three factors influencing the cold chain logistics demand for agricultural products in both Beijing and Tianjin. The GDP, fixed asset investment in transportation and storage, and the proportion of tertiary industry are three major influential factors in Hebei. (2) Agricultural cold chain demand in Beijing and Hebei will grow sustainably in 2021–2025, while the trend in Tianjin remains stable. In conclusion, regional developmental differences should be considered when planning policies for the Beijing-Tianjin-Hebei cold chain logistics system.

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