Enhancing Supply Chain Transparency and Risk Management Using CNN-LSTM With Transfer Learning

Yongping Zhang, Achyut Shankar

OAI: oai:igi-global.com:333472 • DOI: 10.4018/JOEUC.333472

Enhancing supply chain transparency and risk management is crucial in modern businesses. The supply chain involves multiple stages and participants, including suppliers, manufacturers, and logistics companies. However, supply chain data is often vast and complex, encompassing various types of information. Effectively analyzing and leveraging this data can help businesses identify potential risks and improvement opportunities. Therefore, a powerful method is needed to process supply chain data and provide accurate predictions and decision support. In this article, the authors approach is based on CNN-LSTM and transfer learning. By comparing with traditional methods and baseline models, this CNN-LSTM model achieved significant improvements in supply chain transparency and risk management. This model accurately predicts potential supply chain risks, providing corresponding decision support. This research is of great significance to enhance the efficiency, reliability, and transparency of the supply chain, offering valuable support for business decision-making.

End-User Computing Computer Science and Information Technology Human-Computer Interaction