TITLE:
A Hybrid Probabilistic Framework for Managing Systemic Risks in Human-Centric Wearables-Enabled Manufacturing
AUTHORS:
Ali Karevan, Sylvie Nadeau
KEYWORDS:
Bayesian Networks, Particle Swarm Optimization, Systemic Risk, STPA, Industry 5.0, Wearables-Enabled Manufacturing
JOURNAL NAME:
Intelligent Control and Automation,
Vol.17 No.4,
November
9,
2026
ABSTRACT: This paper proposes a novel hybrid framework, STPA-BN-PSO, designed to proactively quantify and manage systemic sociotechnical risks in wearables-enabled manufacturing. The study specifically addresses the “parameterization gap” that occurs during the design and early integration phases when empirical failure data for new technologies is scarce. The methodology integrates System-Theoretic Process Analysis (STPA) to derive hierarchical control structures, Bayesian Networks (BN) for probabilistic modeling, and Particle Swarm Optimization (PSO) to calibrate weighted conditional probability tables against expert-derived profiles. The framework is validated through three distinct Industry 5.0 case studies: sequential assembly, flexible job-shop, and disassembly for recycling. This research provides a unique contribution by positioning PSO as a calibration engine for STPA-derived structures. Unlike traditional data-driven BNs, this framework allows for path-based probabilistic quantification and predictive risk assessment before a system becomes operational, bridging the gap between qualitative systems-thinking and quantitative reliability engineering. Results demonstrate that operational vulnerability is highly context-dependent, with the flexible job-shop showing the highest baseline risk (31.5%). Criticality analysis identifies shifting risk drivers: planning quality dominates sequential assembly (ΔRisk = 0.181), while digital information quality is the primary amplifier in flexible job-shops (ΔRisk = 0.286). Sensitivity analyses reveal that risk-propagation pathways reorganize significantly based on operational layout.