Automated digital cause-and-effect diagrams to assist causal analysis in problem-solving: a data-driven approach

Abstract

Causal analysis is an integral part of product quality problem-solving (QPS). Quality management within the manufacturing industry has generated a considerable amount of QPS data; while this implies a historical and extensive body of QPS experience, these valuable empirical data are not being fully utilised. Therefore, the current study proposes a method by which to mine know-why from historical empirical data, and it develops an approach for constructing digital cause-and-effect diagrams (CEDs). The K-means algorithm is first adopted to cluster the problems and causes. The random forest classifier is then selected to classify cause text into the main cause categories, which manifest as ‘rib branches’ in the CED. Based on the clustering and classification results, we obtain an abstract cause-and-effect diagram (ACED) and a detailed cause-and-effect diagram (DCED). We use the quality data of an automotive company to validate the method, and we additionally undertake a pilot run of the Fishbone Next system to demonstrate how users can obtain these two CEDs to support causal analysis in QPS. The results show that the proposed approach efficiently constructs a digital CED and thus provides quality management problem-solvers with decision support to derive the potential causes of problems, thereby improving the efficiency and effectiveness of their causal analysis initiatives.