Optimum Risk Estimates, ORE

Optimum Risk Estimates, ORE

ORE starts by asking its users to model the systems using a «fractal like» tile modelling tool (we call them jokingly “risk-engineering Lego”) which helps defining external and internal hazards and focuses the user attention to the interdependent performances of each node.

Optimum Risk Estimates Example on a tailing dam

ORE value proposition can be summarized as a quantified FMEA with the systematicity of a HAZOP, without the necessity to delve into the smallest components, at first, as ORE analysis/hazard identification is SCALABLE. Within ORE FTA, ETA, numerical probabilistic models, big data algorithmic analyses can be used to evaluate rates and probabilities of events; consequence analysis can be pushed to levels of detail pertinent with the considered system.

ORE shifts the focus on the process or function of the item in terms of success criteria. ORE’s approach allow to study, from the beginning, how system’s resiliency can be increased, what can be changed to shift toward an anti-fragile state, from cradle to grave of a project.

ORE, if conducted to the same system’s detail, should identify at least as many malfunctions as HAZOP and is not blind to external hazards and systems interdependencies. It can be used to investigate what will be the minimum level of performance of a system being hit by one or more hazards, the level of mitigation necessary to maintain that level, on top of evaluating local or catastrophic failures.

Tagged with: algorithmic analyses, Big data, catastrophic failures, ETA, external and internal hazards, interdependent performances, numerical probabilistic models, ORE FTA, success criteria

Category: Consequences, Hazard, Optimum Risk Estimates, Probabilities, Risk analysis, Risk management