Improved root cause analysis supporting resilient production systems

Abstract

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.