A Study on Risk-Predicting Model of Supply Suspension Based on Elman Dynamic Neural Network
Junyu Zhang, Yongxiang Wu · ICCREM 2021 · 2021
Negatively affected by the nonlinear time-serial data of supply chain, the conventional assessment methods have no longer met the expectation of society, and in practice the failure to assess the risk of supply delay and suspension are more and more likely to occur. This study explores how to identify the threat of suppliers’ delay and suspension in dynamic uncertainty and creates assessment index criteria which can be used by experts for professionally measuring the risks of supply delay against dynamic circumstances. Furthermore, an Elman dynamic neural-network model based on risk assessment of supply delay and suspension is generated to examine the historical data from five vendors contracted by a certain corporation for its further learning and training to evaluate their future supply risks in the upcoming quarterly so as to prove the validity of this model.