Understanding and Handling Residual Risks

摘要

Risk management and risk analysis techniques have been widely discussed and utilized in many energy firms. They are pro-active tools giving decision-makers forewarnings and scientific predictions. However, a less well known concept in the arena of risk management is worth elaborating. Residual risk, a terminology not common to most people in our industry, is rarely mentioned and discussed in the literature; it still remains a poorly understood notion. It often is confused with the Secondary Risk, and easily mistaken for missed risks in risk identification process. Depicted as “leftovers”, residual risks are often neglected in risk management process as many regard such risks are well covered under ALARP (as low as reasonably possible). Moreover, residual risks are often deliberately eliminated from stochastic modelling process as many risk analysts reckon that extreme cases, beyond “normal” 80% confidence range, are not worth considering. In reality when unpleasant events occur the real culprit is more often than not either rare event driven risk or residual risk that caught people off guard. The treatment of residual risks requires both diligent prudence and well thought-through risk taking. Failure Model Effect Analysis (FMEA) is often deployed to assess the severity of such risks, hence helping management better understand the devastation in case they occur. “Normally ranged” risks would often be handled by scientific manner and decisions are relatively easy, however, there isn’t a clear path to handle residual risks, as it is not a pure mathematical exercise but demands strong incorporation of psychological judgement as well. The paper challenges decision makers to appreciate true implications of residual risks, and what they are about to bring to their businesses. The author, through the illustration of examples, proposes a residual risk handling process that combines qualitative risk assessment, psychology of risk taking and Monte Carlo simulation techniques. The process mainly applies to early phase project development and business case fruition process where large uncertainties may easily affect decision-makers, but it is also applicable to project execution phase.