On the influence of overlap in automatic root cause analysis in manufacturing

• Type: Article in International Scientific Journal
• Date: 2022

Authors

Oliveira, EE (Author) Other; Vera L. Miguéis (Author) FEUP; José Luís Moura Borges (Author) FEUP

Journal

Title: International Journal of Production Research

Vol. 60

Pages: 6491-6507

ISSN: 0020-7543

Publisher: Taylor & Francis

Indexing

ISI Web of Knowledge - 9 Citations

Scopus - 8 Citations

Other information

Authenticus ID: P-00V-MZ8

DOI: 10.1080/00207543.2021.1992680

Abstract (EN): To improve manufacturing processes, it is essential to find the root causes of occurring problems, in order to solve them permanently. Automatic Root Cause Analysis (ARCA) solutions aid analysts in finding such root causes, by using automatic data analysis to improve the digital decision. When trying to locate the root cause of a problem in a manufacturing process, a phenomenon can occur that disrupts the application of ARCA solutions. Overlap, as we denominated, is a phenomenon where local synchronicities in the manufacturing process lead to data where it is impossible to discern the influence of each location in the quality of products, which impedes automated diagnosis, especially when using classifiers. This paper identifies and defines overlap, and proposes a two-phase ARCA solution that uses factor-ranking algorithms, instead of classifiers. The proposed solution is evaluated in simulated and real case-study data. Results proved the presence of overlap in the datasets, and its negative impact on classifiers. The proposed solution has a positive performance detecting root causes even in the presence of overlap.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 17