Grey improvement model for intelligent supply chain demand forecasting

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.