Abstract Modern industrial systems are characterized as complex socio-technical systems. The behavior of such systems is emergent, making safety management challenging. Traditional risk assessment methodologies have proven insufficient due to their limitations in addressing systemic complexities. The behavior of complex socio-technical systems is more effectively explained through a functional perspective that places performance variabilities at its center. Rather than viewing variabilities as system deficiencies to be eliminated, this approach recognizes them as intrinsic characteristics that require sophisticated management. The present study leverages the FRAM with Monte Carlo simulation and a Hierarchical Fuzzy Inference Tree to identify critical functions. The study employed a fuzzy similarity aggregate method and multi-objective optimization to systematically select interventions for managing performance variabilities while respecting cost constraints. Moreover, PROMETHEE II was used to rank interventions based on effectiveness, cost, and functional significance. The methodology offers flexibility for the decision maker in intervention selection, enabling organizations to balance effectiveness with implementation costs. The approach prioritizes interventions that are highly effective, low-cost, and correspond to functions with significant variability potential. The developed methodology was applied to a case study of the anode-changing operation in an aluminum smelter. The study revealed that interventions such as developing a digital pre-operational checklist, a tool inventory management system, and visual aids for PTM operators for lowering the shovel in the pot cavity should be prioritized. The developed framework could serve as a versatile tool for analyzing and managing functional variabilities across various domains.
Kumar et al. (Thu,) studied this question.
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