Developing hybrid intelligent methods to manage systemic risks in the integration of smart wearables in manufacturing

Abstract

The increasing integration of smart wearables, such as smart glasses and smart gloves, into modern manufacturing systems promises benefits in worker guidance, ergonomics, and real time monitoring. However, these technologies also introduce new systemic risks that traditional risk management methods struggle to capture. Advanced systemic approaches such as STAMP–STPA, and FRAM more accurately capture the complexity of socio-technical systems; however, they remain predominantly qualitative. This dissertation develops and validates three hybrid methods that combine systemic risk models with Particle Swarm Optimization (PSO) to enhance quantitative assessment and mitigation. First, the STPA–PSO framework extends System-Theoretic Process Analysis by quantifying control actions and supporting semi-automated mitigation strategies. Second, the FRAM–PSO framework integrates the Functional Resonance Analysis Method with PSO to model performance variability and, for the first time, embeds sustainability dimensions— economic, environmental, and social—into systemic risk mitigation. Third, the STPA-BN– PSO framework applies Bayesian Networks optimized with PSO to capture probabilistic dependencies among control actions, hazards, and losses, enabling path-based systemic risk quantification and sensitivity analysis. These frameworks were applied to three case studies in sequential assembly, job-shop assembly, and disassembly, with a focus on the introduction and integration of multiple smart wearables. The results highlight three main findings: (1) the combined use of smart glasses and smart gloves creates new couplings and dependencies that reshape the risk landscape; (2) the three hybrid frameworks provide complementary strengths—with STPA–PSO suited to early design, FRAM–PSO to variability and sustainability evaluation, and STPA-BN–PSO to operational risk monitoring; and (3) embedding sustainability principles enables not only measurable reductions in systemic risk but also alignment with broader industrial and societal objectives. In summary, this research advances the state of risk assessment by hybridizing systemic models with metaheuristic optimization. This shows that qualitative and quantitative approaches can be meaningfully combined. The work extends existing systemic methods with new hybrid frameworks and demonstrates their applicability through case studies involving multiple wearables, and provides decision-makers with practical methods to achieve safer, more sustainable, and human-centered manufacturing. Through this contribution, the thesis advances the principles of Industry 5.0, emphasizing the complementary roles of humans and intelligent technologies in shaping resilient socio-technical systems of the future.

Date	2 Feb 2026
Original language	American English
Awarding Institution	École de technologie supérieure
Supervisor	Sylvie Nadeau (Supervisor)