ArticlePloS one2025
Introducing FMEA plus method for comprehensive safety risk assessment in the steel industry.
Article in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
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Authors and funding
5 authors.
Funding
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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.
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