A proactive operational risk identification and analysis framework based on the integration of ACAT and FRAM

Abstract

Risks in the industrial operation processes involve complex system elements such as human, machine, organization, information, as well as nonlinear coupling relationships among them. Traditional risk analysis methods focus on the cause-effect relationships between the system elements and accidents, while ignoring what the correct and proper relationships should be. For a proactive risk identification and analysis, learning from success is suggested instead of learning from post hoc accidents, which requires that risk analysis identifies the normal functions and their couplings. Therefore, system functioning has been a subject of interest in the field of risk analysis. The Functional Resonance Analysis Method (FRAM) has been an effective tool to reveal the couplings and dependent relationships among different functions. However, the functions identification and interaction analysis in the FRAM is limited because there is no consistent or explicit stop rule. For a detailed and rigorous description of functions, the Accident Causation Analysis and Taxonomy (ACAT) model is used to enrich the FRAM by generating functions based on a closed-loop control system. Two operation processes in the hazardous industries are used as illustrations. The results show that more functional constraints and deep contributing factors to accidents can be identified with the hybrid approach.