A Two-Layer Causal Knowledge Network Construction Method Based on Quality Problem-Solving Data
“Cause analysis” constitutes an indispensable component in quality management systems, serving to systematically identify the causes of quality defects, thereby enabling the development of targeted improvement strategies that concurrently address surface-level manifestations and fundamental drivers....
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-02-01
|
| Series: | Systems |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2079-8954/13/3/142 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | “Cause analysis” constitutes an indispensable component in quality management systems, serving to systematically identify the causes of quality defects, thereby enabling the development of targeted improvement strategies that concurrently address surface-level manifestations and fundamental drivers. However, relying solely on personal experience makes it challenging to conduct a comprehensive and in-depth analysis of quality problems. The reason is that, when analyzing the causes of quality problems, it is essential not only to consider the specific context in which the problems occur. This enables “specific problems” to be “specifically analyzed” for the formulation of temporary containment measures. Additionally, the context of the problem needs to be stripped. This allows for a general and in-depth analysis of the “class problem” or the causal linkages underlying the problem, thereby determining the root cause of the problem and formulating a corresponding long-term program. The analysis of the causes of quality problems exhibits “duality” characteristics. Based on this, this study proposes and constructs a two-layer causal knowledge network by leveraging the causal knowledge generated and applied in the process of quality problem solving to address the “duality” characteristic of the cause analysis of quality problems. The proposed network can assist front-line employees in analyzing the quality problems of products from diverse perspectives and overcome the challenge of relying solely on personal experience to comprehensively and profoundly analyze the causal relationships of quality problems. Our method not only contributes to enhancing the efficiency of quality problem solving but also makes a valuable contribution to the advancement of theories and methods related to quality management and knowledge management. |
|---|---|
| ISSN: | 2079-8954 |