Improved root cause analysis supporting resilient production systems
Review article, 2022

Manufacturing companies struggle to be efficient and effective when conducting root cause analyses of production disturbances; a fact which hinders them from creating and developing resilient production systems. This article aims to describe the challenges and enablers identified in current research relating to the different phases of root cause analysis. A systematic literature review was conducted, in which a total of 14 challenges and 17 enablers are identified and described. These correlate to the different phases of root cause analysis. Examples of challenges are “need for expertise”, “employee bias”, “poor data quality” and “lack of data integration”, among others. Examples of enablers are “visualisation tools”, “collaborative platforms”, “thesaurus” and “machine learning techniques”. Based on these findings, the authors also propose potential areas for further research and then design inputs for new solutions to improve root cause analysis. This article provides a theoretical contribution in that it describes the challenges and enablers of root cause analysis and their correlation to the creation of resilient production systems. The article also provides practical contributions, with an overview of current research to support practitioners in gaining insights into potential solutions to be implemented and further developed, with the aim of improving root cause analysis in production systems.

Production disturbances

Production systems

Resilience

Root cause analysis

Author

Adriana Ito

Chalmers, Industrial and Materials Science, Production Systems

Malin Hane Hagström

Chalmers, Industrial and Materials Science, Product Development

Jon Bokrantz

Chalmers, Industrial and Materials Science, Production Systems

Anders Skoogh

Chalmers, Industrial and Materials Science, Production Systems

Mario Nawcki

Kanika Gandhi

Volvo Group

Dag Henrik Bergsjö

Chalmers, Industrial and Materials Science, Product Development

Maja Bärring

Chalmers, Industrial and Materials Science, Production Systems

Journal of Manufacturing Systems

0278-6125 (ISSN)

Vol. 64 468-478

KIDSAM: Knowledge and information-sharing in digital collaborative projects

VINNOVA (2018-03966), 2018-11-01 -- 2021-11-30.

Digitala Stambanan Produktion

VINNOVA (2021-02421), 2021-07-01 -- 2024-07-01.

Subject Categories

Production Engineering, Human Work Science and Ergonomics

DOI

10.1016/j.jmsy.2022.07.015

More information

Latest update

10/14/2022