Human error contribution to accidents in the manufacturing sector: A structured approach to evaluate the interdependence among performance shaping factors. (May 2023) - La Fata, C.M.; Adelfio, L.; Micale, R.; La Scalia, G.
Highlights: Evaluation of both the mutual influence and the relative importance of PSF when computing HEP. Proposal of a structured and replicable methodology able to consider the uncertainty and vagueness of input data. Implementation of a Fuzzy DEMATEL-based approach in a multi-group decision context. Identification of human factors on which taking priority actions to improve the human reliability. Implementation in an Italian textile company. Abstract: Since the 1970s, Human Reliability Analysis (HRA) methods have received a great interest for the quantification of the Human Error Probability (HEP) in Nuclear Power Plants (NPPs). To this purpose, the second-generation HRA methods consider contextual and cognitive factors - named Performance Shaping Factors (PSFs) - that may influence the workers' performance during tasks execution. Despite the recent extension of HRA methods to different fields, only few studies refer to the manufacturing sector. In addition, the majority of contributions assume the independence among PSFs, which may result in an over or under estimation of HEP. Therefore, the present paper focuses on the manufacturing sector to propose a Fuzzy DEMATEL (FDEMATEL) based method to support the risk analyst in the quantification of PSF interrelationships and importance, when computing HEP. As a result, the most influential human factors on which taking priority actions to improve the overall human reliability may be identified accurately. Based on a selected list of PSFs, the methodological approach is implemented in an Italian textile company, where experience and training factors are demonstrated to be the most central ones to increase the human reliability when performing the weaving process tasks. The designed approach is well structured and effortless as well as it allows at considering the uncertainty and vagueness of input data and a group decision context.