While the previous generation of scoring-type algorithms have served us (the industry) well, the associated technical compromises can be troublesome in today’s environment of increasing regulatory and public oversight. Risk analyses often become the centerpiece of any legal, regulatory, or public proceedings. This prompts the need for analysis techniques that can produce risk estimates anchored in absolute terms, such as “consequences per mile year”. Accordingly, a new generation of algorithms has been developed to meet today’s needs without costly re-vamping of previously collected data or increasing the costs of risk analysis. A simple re-grouping of variables into categories of “exposure”, “mitigation”, and ‘resistance’, along with a few changes in the mathematics of combining variables, transitions older scoring models into the new approach. The advantages of the new algorithms are significant since they: • are more intuitive and predictive, • better model reality, • lead to better risk management decisions by distinguishing between unmitigated exposure to a threat, mitigation effectiveness, and system resistance, • eliminate the need for unrealistic and troublesome reweighting or balancing of variables for changes such as new technologies, • offer flexibility to present results in either absolute (probabilistic) terms or relative terms, depending on the user’s needs. The challenge is to accomplish these without losing the advantages of earlier approaches. One of the intent of the new algorithms is to avoid overly-analytic techniques that often accompany more absolute quantifications of risk. This paper will showcase this new generation of algorithms to better suit the changing needs of risk analysis within the pipeline industry.
Risk Management in nursing homes: an Italian experience in 2019