Supply chain sales forecasting based on lightGBM and LSTM combination model

Purpose: The purpose of this paper is to design a model that can accurately forecast the supply chain sales. Design/methodology/approach: This paper proposed a new model based on lightGBM and LSTM to forecast the supply chain sales. In order to verify the accuracy and efficiency of this model, three representative supply chain sales data sets are selected for experiments. Findings: The experimental results show that the combined model can forecast supply chain sales with high accuracy, efficiency and interpretability. Practical implications: With the rapid development of big data and AI, using big data analysis and algorithm technology to accurately forecast the long-term sales of goods will provide the database for the supply chain and key technical support for enterprises to establish supply chain solutions. This paper provides an effective method for supply chain sales forecasting, which can help enterprises to scientifically and reasonably forecast long-term commodity sales. Originality/value: The proposed model not only inherits the ability of LSTM model to automatically mine high-level temporal features, but also has the advantages of lightGBM model, such as high efficiency, strong interpretability, which is suitable for industrial production environment.