Title: Grey improvement model for intelligent supply chain demand forecasting

Authors: Xinghong Jia; Jun Wang; Tian Tian Ma; Qiong Wang

Addresses: College of E-commerce and Logistics Management, Henan University of Economics and Law, No. 180, Jinshui East Road, Zhengzhou, Henan, 450046, China ' College of E-commerce and Logistics Management, Henan University of Economics and Law, No. 180, Jinshui East Road, Zhengzhou, Henan, 450046, China ' College of E-commerce and Logistics Management, Henan University of Economics and Law, No. 180, Jinshui East Road, Zhengzhou, Henan, 450046, China ' College of E-commerce and Logistics Management, Henan University of Economics and Law, No. 180, Jinshui East Road, Zhengzhou, Henan, 450046, China

Abstract: This study helps to realise the balance between supply and demand of aquatic products and rational allocation of logistics resources. In previous studies, the prediction results of most models are not satisfactory for the cold chain logistics demand of aquatic products characterised by small-lot, low-quality uncertain data. In this paper, the traditional grey model and the grey BP neural network combination model are used to simulate and predict the demand for aquatic products cold chain logistics, and analysed and compared. The results show that compared with the traditional grey model, the grey BP neural network model has a reduced prediction error, an ideal ability to handle nonlinear systems and can take into account many influencing factors. Meanwhile, the robustness and generalisation ability of the model were verified by testing it on the dataset of similar scenarios. The method provides an innovative way for aquatic products cold chain logistics demand forecasting, which helps optimise the aquatic products supply chain in China and promotes the prosperous development of cold chain.

Keywords: demand forecasting; intelligent supply chain; BP neural network model.

DOI: 10.1504/IJMTM.2025.145929

International Journal of Manufacturing Technology and Management, 2025 Vol.39 No.3/4/5, pp.334 - 357

Received: 22 Jan 2024
Accepted: 03 Jun 2024
Published online: 30 Apr 2025 *

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