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Managing and Predicting Maritime and Off-shore Risk

Duffey R. B., Saull J. W.

TransNav : International Journal on Marine Navigation and Safety of Sea Transportation

2009

Vol. 3, no. 2

181--188

We wish to predict when an accident or tragedy will occur, and reduce the probability of its occurrence. Maritime accidents, just like all the other crashes and failures, are stochastic in their occurrence. They can seemingly occur as observed outcomes at any instant, without warning. They are due to a combination of human and technological system failures, working together in totally unexpected and/or undetected ways, occurring at some random moment. Massive show the cause is due to an unexpected combination or sequence of human, management, operational, design and training mistakes. Once we know what happened, we can fix the engineering or design failures, and try to obviate the human ones. We utilize reliability theory applied to humans, and show how the events rates and probability in shipping is related to other industries and events through the human involvement. We examine and apply the learning hypothesis to shipping losses and other events at sea, including example Case Studies stretching over some 200 years of: (a) merchant and fishing vessels; (b) oil spills and injuries in off-shore facilities; and (c) insurance claims, inspection rules and premiums. These include major losses and sinkings as well as the more everyday events and injuries. By using good practices and achieving a true learning environment, we can effectively defer the chance of an accident, but not indefinitely. Moreover, by watching our experience and monitoring our rate, understand and predict when we are climbing up the curve. Comparisons of the theory to all available human error data show a reasonable level of accord with the learning hypothesis. The results clearly demonstrate that the loss (human error) probability is dynamic, and may be predicted using the learning hypothesis. The future probability estimate is derivable from its unchanged prior value, based on learning, and thus the past frequency predicts the future probability. The implications for maritime activities is discussed and related to the latest work on managing risk, and the analysis of trends and safety indicators.

Risk prediction for modern technological systems

Duffey R. B., Saull J. W.

Journal of Polish Safety and Reliability Association

2007

Vol. 1

75--81

We have already examined the worldwide trends for outcomes (measured as accidents, errors and events) using data available for large complex technological systems with human involvement. That analysis was a dissection of the basic available, published data on real and measured risks, for trends and inter-comparisons of outcome rates. We found and showed how all the data agreed with the learning theory when the accumulated experience is accounted for. Here, learning includes both positive and negative feedback, directly or indirectly, as a result of prior outcomes or experience gained, in both the organizational and individual contexts. Our purpose here and now is to try to introduce some predictability and insight into the risk or occurrence of these apparently random events. In seeking such a general risk prediction we adopt a fundamental theoretical approach that is and must be testable against the world’s existing data. Comparisons with outcome error data from the world’s commercial airlines, the two shuttle failures, and from nuclear plant operator transient control behaviour, show a reasonable level of accord. The results demonstrate that the risk is dynamic, and that it may be predicted using the MERE learning hypothesis and the minimum failure rate, and can be utilized for predictive risk analysis purposes.