Introducing FMEA plus method for comprehensive safety risk assessment in the steel industry

Ahmad Soltanzadeh, Esmaeil Zarei, Mohsen Mahdinia, Kiana Hosseinzadeh and Mohsen Sadeghi-Yarandi

PLOS ONE, 2025, vol. 20, issue 10, 1-20

Abstract: Addressing the challenge of employing a comprehensive risk analysis approach that effectively captures and quantifies all contributing factors remains a significant endeavor in both academic research and practical field applications. This study endeavors to fill this gap by introducing a practical safety risk assessment approach, named the FMEA+ method, grounded in the conventional Failure Modes and Effects Analysis. To construct a comprehensive taxonomy that encompasses the contributing factors within each dimension of risk, a three-stage Delphi study engaged 35 Subject Matter Experts (SMEs). The Fuzzy Analytical Hierarchy Process (FAHP) was employed to acquire knowledge and assign weights to the factors and sub-factors. The validation and reliability assessment of the developed taxonomy included evaluating the Content Validity Ratio (CVR), Content Validity Index (CVI), and Cronbach’s alpha coefficient, yielding values of 0.77, 0.91, and 0.86, respectively. Independent peer reviews and reality checks further substantiated the credibility of the proposed taxonomy. The introduced safety risk assessment algorithm, FMEA + , derived from the FMEA technique, comprises three main factors and 12 sub-factors. The final normalized weights for the three factors—occurrence, severity, and detectability—were determined to be 0.337, 0.348, and 0.315, respectively. In the three factors of occurrence, severity, and detection, the most important sub-factors identified were human reliability, human injury, and technical inspection, respectively. This proposed taxonomy serves as a foundational tool for facilitating informed decision-making and the effective implementation of risk mitigation strategies. The application of this innovative approach offers a scientific alternative to traditional FMEA methods within similar industries, addressing existing challenges in a more comprehensive and nuanced manner.

Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0331748

DOI: 10.1371/journal.pone.0331748

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