It is a deductive methodology that is it involves reasoning from the general to the specific, working backwards through time to examine preceding events leading to failure. FTA is used for determining the potential causes of incidents, or for system failures more generally. The safety engineering discipline uses this method to determine failure probabilities in quantitative risk assessments. A fault tree is a graphic model that displays the various logical combinations of failures that can result in an incident, as shown in figure given below. These combinations may include equipment failures, human errors and management system failures.
The Bonhuetter-Ferguson Method: This is a Bayesian technique, meaning it incorporates the independently derived prior estimates of the overall expected losses with the estimates generated in a similar matrix as the BCL method. To use the Bonhuetter –Fergusson method one must take the expected loss ratio and expected past/future split. Use these to calculate the expected position and compare with the actual position. This method is useful when the data are unstable. The Bonhuetter-Ferguson method assumes that the expected incremental losses are proportional to the ultimate for the accident year not to the emerged to date.
FMEA concentrates in identifying the severity and criticality of failures. FMEA is a fully bottom-up approach . Risk Priority Number, which is the product of the severity, occurrence and detection ratings is calculated as RPN = S x O x D. The RPN must be calculated for each cause of failure. RPN shows the relative likelihood of a failure mode, in that the higher number, the higher the failure mode. From RPN, a critical summary
To calculate criticality each assumption is assigned a range of uncertainty: base case, best and worst case. Then, we come up with assumption for assumption; the other assumptions in base case of the NPV changes for each assumption in the worst and best case
Avalanche risk assessment must provide for a conservative estimate of loss in consideration of accurate data and evidence, for example weather and snowpack observations, as well as make predictions for uncertainty. However, the traditional view of risk characterized by probabilities and consequences does not capture the subjective and contextual factors inherent in risk assessment. In reality, making judgments regarding the probabilities and consequences of avalanche occurrences under this inherent uncertainty is guided by social, ethical, legal and economic criteria (Aven & Korte, 2003; Flynn & MacGregor, 2003; Slovic, 2001). While the search for accurate and objective probability values is a goal of the risk assessment process, the process is driven by the boundary
As it is clear that the construction industry is a growing field in Lebanon, many interested parties are attentive to ethical problems and seek ways to resolve the issues. It is also clear the ethics in construction industry is subjected to many types of problems. It is interesting to examine the most important problems and highlight real cases in the Lebanese construction industry. 1.4 Project Goals and
(2004) develop fraud prediction models using financial ratios; however, their models suffer from high misclassification rates. Skousen and Wright (2008) use logistic regression to predict fraud roughly 69 percent of the time. Fraud triangle component can’t be research directly, so there are some additional test to determine whether the significant proxy variables could actually be used in the prediction of financial statement fraud (skousen, 2009). Based on skousen’s study (2009) examining the effectiveness of the fraud risk factor framework adopted in SAS No. 99 in detection of financial statement
Armstrong (2001) identified some key principles and factors to consider in the selection of appropriate forecasting methods. The principles are listed as follows: • Use forecasting methods that contain methodical and detailed steps that can be explained and replicated. • If sufficient data is available, use quantitative rather than qualitative methods. • If large changes in the forecasts can be expected, use causal methods instead of time-series methods. • Unless considerable proof is present that a complex method will improve forecasts, use simple forecasting methods.
The deficient of the precise knowledge that enable reaching a reliable result is uncertainty. Unlike, classical logic supposes that complete and accurate knowledge always subsists 14. Uncertainty affects decision making in unwanted sides while trying to reduce uncertainty risks are accompanied with uncertainty 21.Uncertainty can be represented by three methods which are numeric where a scale with two extreme number from 0 to 100 is used; graphical where gradient bar is used to express expert's opinion in certain events and symbolic where linguistic scale and ranking complemented with numbers can be used 11.This section explores different models for dealing with uncertainty and their