Anwendung von Causal-Discovery-Algorithmen zur Root-Cause-Analyse in der Fahrzeugmontage

TL;DR

This paper explores the use of Causal Discovery Algorithms to enhance Root Cause Analysis in automotive assembly, leveraging large data sets to identify causal relationships and improve quality management.

Contribution

It demonstrates the application of various CDA methods to automotive assembly data and compares their effectiveness and runtime for quality management.

Findings

Causal structures learned by different algorithms vary in accuracy.

Some CDAs are more suitable for real-time RCA in manufacturing.

The study shows potential for data-driven RCA to improve quality control.

Abstract

Root Cause Analysis (RCA) is a quality management method that aims to systematically investigate and identify the cause-and-effect relationships of problems and their underlying causes. Traditional methods are based on the analysis of problems by subject matter experts. In modern production processes, large amounts of data are collected. For this reason, increasingly computer-aided and data-driven methods are used for RCA. One of these methods are Causal Discovery Algorithms (CDA). This publication demonstrates the application of CDA on data from the assembly of a leading automotive manufacturer. The algorithms used learn the causal structure between the characteristics of the manufactured vehicles, the ergonomics and the temporal scope of the involved assembly processes, and quality-relevant product features based on representative data. This publication compares various CDAs in terms of their suitability in the context of quality management. For this purpose, the causal structures learned by the algorithms as well as their runtime are compared. This publication provides a contribution to quality management and demonstrates how CDAs can be used for RCA in assembly processes.