Akademik Veri Yönetim Sistemi
WOOD MATERIAL SCIENCE & ENGINEERING, 2026 (SCI-Expanded, Scopus)
Human reliability is central to process safety and operational performance in industrial manufacturing, where human failures raise risk and vulnerability. In human reliability analysis (HRA), the standardized plant analysis risk-human (SPAR-H) technique captures this by modeling how performance shaping factors (PSFs), organizational, human, and task conditions, affect human-error likelihood. Building on this PSF view, this study proposed an integrated multicriteria decision-support framework combining expert judgment, Pythagorean fuzzy (PF) decision-making, and cognitive modeling for risk-informed decisions. PSFs are identified through literature review and expert evaluation, then weighted with PF-SWARA to obtain robust importance values under uncertainty. Critical tasks are prioritized with an improved PF-EDAS (IPF-EDAS), which ranks tasks by deviation from a system-representative average risk level. This average is computed in a dependency-aware manner using a PF-based fuzzy cognitive map (P-FCM) that represents interdependence and feedback among PSFs and supports scenario analysis. The framework is demonstrated in a particleboard production process in the wood industry. Results identify key risk drivers, distinguish high-risk tasks, and support targeted mitigation planning. Sensitivity and comparative analyses confirm robust rankings and show better discrimination when PSF dependencies are explicitly modeled. The framework offers a structured, dynamic basis for prioritizing interventions and allocating safety resources across tasks.