Development of a Bayesian Network-Driven Quality Risk Assessment System for the Aviation Manufacturing Sector

In view of the high complexity of aerospace products and strict quality and safety standards, enterprises must ensure that control measures are strictly verified and establish a comprehensive quality risk assessment model to ensure the safety, stability and sustainable development of the supply chain. Bayesian network represents the causal relationships between variables by directed acyclic graph (DAG) and quantifies these relationships by conditional probability table (CPT), which is suitable for dealing with complex quality risk problems in aviation manufacturing industry. In this paper, many factors such as design, production, supply chain and market environment are considered, 15 initial quality risk factors are identified, and 9 important factors are screened out by risk matrix method. In this paper, the quality risk Bayesian network model of aviation manufacturing industry is constructed, and some suggestions are put forward.