A hierarchical assessment method using Bayesian network for material risk detection on green supply chain

Today's social awareness of environmental protection presents the electronic companies with an irreversible trend towards green manufacturing. It raises harsh requirement for the sourcing process and imposes unprecedented pressure to the QA system, majorly due to the risk of hazardous material. As QA procedures are becoming more complicated for coping with increasing material risk and meanwhile the time and resource available are tightly constrained, the development of an effective mechanism for material testing turns up to be a critical issue. In this study, a hierarchical material risk assessment approach is proposed based on FMEA framework. Taking into account the risk occurrence, the difficulty in detection and the severity the risk causes, it enables companies to estimate their material risks dynamically using Bayesian network. With its help, companies can assess and prioritize the material risk in a systematic and efficient manner which will drive QA towards a more high-performance process.