System Safety Causal Analysis Models Considering Risk Influence Factors (RIFs)

In accident causation, the interaction between human involvement and organizational factors plays a crucial role, often leading to deviations at the organizational level before affecting humans and equipment. Taking a proactive approach to identify and address these organizational-level deviations holds promise in preventing subsequent human and equipment failures. However, traditional risk assessment methods like PHA, HAZOP, FMEA, LOPA, and QRA, while foundational, lack the sufficient capacity to assess safety barrier performance and quantify Risk Influence Factors (RIFs). This gap results in overlooking the impact of organizational factors on the overall risk profile. This chapter aims to fill this gap by exploring models of safety incident origins explicitly designed to integrate RIFs, addressing critical gaps in root cause analysis. These methodologies, mindful of RIFs, provide a comprehensive view covering short- and long-term factors influencing technological systems and human behavior. The modeling of RIFs becomes crucial, offering insights essential for identifying risk prevention and mitigation strategies, along with relevant indicators. The utilization of these indicators for monitoring RIF states is instrumental in uncovering fluctuations directly linked to shifts in the risk scenario. The chapter examined various models and methodologies, including Barrier and Operational Risk Analysis (BORA), Causal Modeling of Air Transportation System (CATS), Hybrid Causal Logic Model (HCL), Accidental Risk Assessment Methodology for Industries (ARAMIS), Integrated Risk (I-Risk) method, Accident Causation using Hierarchical Influence Network (MACHINE), Operational Condition Safety (OTS), and Risk Modeling—Integration of Organizational, Human, and Technical factors (Risk-OMT). It also highlights hybrid methodologies, incorporating diverse tools like Bayesian networks into accident causation modeling. This exploration serves as a guide for an adaptive approach in complex sociotechnical systems. Emphasizing the pivotal role of organizational and management factors in shaping accident dynamics and risk assessments, this work offers a scholarly yet accessible insight into risk assessment methodologies considering RIFs.