Big Data Oriented Root Cause Heuristic Identification Approach Based on FWARM for Quality Accident

Abstract

With the advent of big data era, data driven quality management decision-making has become an important means to seek new quality improvement opportunity for manufacturers. Quality accidents (QAs) can cause severely economic and reputational damage to manufacturer, accurate identification of root causes for severe quality accident is the primary task and always a big challenge for quality managers. Because of the fuzzy nature of incomplete and noisy data for big data, the fuzzy concept is proposed, and the fuzzy weighted association rule mining (FWARM) method is adopted into the root cause identification of quality accident novelly. Firstly, Quality Accident formation mechanism and modeling of big data for quality accident are defined; Secondly, root cause identification framework of quality accident based on the relevance tree is established; Then, the FWARM method is used to identify product functional defects and physical defects through big data for quality accident. Finally, a case study of root cause identification of quality accident is adopted to validate the proposed approach.