Taxonomy of performance shaping factors in manufacturing: A systematic literature review

Abstract

Human error in manufacturing can have substantial consequences, including loss of life, injuries, productivity, and financial losses. Human reliability analysis (HRA) methods can be used to evaluate the likelihood of human error in manufacturing tasks and identify potential sources of error. Performance shaping factors (PSFs) are internal and external factors that influence human performance and can affect the likelihood of human reliability estimates in HRA methods. Understanding the impact of PSFs on human performance in manufacturing is essential for developing effective strategies to minimize the likelihood of human error and improve the safety and efficiency of manufacturing processes. This systematic review scrutinizes the literature on PSFs within manufacturing, highlighting HRA applications. Using the PRISMA protocol, studies from 2000 to 2024 across engineering and psychology were examined, culminating in the analysis of 35 pertinent works. The review identifies and contrasts various PSF taxonomies from established HRA methods like SPAR-H, HEART, CREAM, and THERP, revealing their diverse applications in different manufacturing settings. The review also uncovers a tendency to devise taxonomies through the lens of experts' domain knowledge, particularly tailored to discrete manufacturing contexts. A critical gap is observed in the lack of a uniform PSF framework, with the current literature reflecting a disparate understanding of PSFs' roles, definitions, and interrelations. This absence is further pronounced by the inadequate integration of human factors in the dialogue surrounding Industry 4.0. The analysis points to the necessity of harmonizing PSFs to better assess human reliability amid technological integration. The findings emphasize the need for an industry-specific PSF framework that aligns with the intricacies of manufacturing operations, thus enabling more accurate HRA outcomes and informing strategies for error reduction and process optimization.