Key Material Supply Forecasting Diagnostics with Dynamic Bayesian Network

When supply channels varied increasingly, key material supply forecasting has become indispensable to effective operations management. Rapid technological changes and an abundance of product configurations mean that the supply for key material is frequently volatile and hard to forecast. The paper describes a key material supply forecasting diagnostics tools based on Dynamic Bayesian Network (DBN). The tool was embodied parametric description of some important factors in key material supply forecasting. Furthermore, we developed this tool to pool supply patterns of little or no supply history data. Finally, we solve this reasoning problem with stochastic simulation.