Multi-layer causal knowledge network for product quality problem analysis using third data.

The realization of digital intelligence is inseparable from the collection and effective application of knowledge. However, in the production process of enterprises, there is a type of “Third Data” which is rich in empirical knowledge but has not been paid attention to. This kind of data is generated from the records of the process in which enterprise employees solve quality problems and is the externalization of empirical knowledge. Because causal analysis is the key to solving quality problems, and there are many factors affecting the cause analysis and the analysis process is complex, this paper makes full use of the causal knowledge and problem contexts contained in the “Third Data”, and systematically considered the duality of cause analysis, the situation dependence of quality problems, the diversity of situations, the similarity among actual quality problems, the differences between short-term solutions and long-term solutions, then proposed the concept of Multi-layer Causal Knowledge Network(MCKN). Next, this paper analyzes the composition and expression of MCKN, and the corresponding construction process and steps are given. Finally, through the case study, the scientificity, rationality, and effectiveness of MCKN are verified. MCKN proposed in this paper provides new ideas and methods for comprehensive and in-depth analysis of the causality of quality problems and helps to improve the level of knowledge and intelligence of quality management.