Causal factor analysis determining

Events and Causal Factor Analysis identifies the time sequence of a series of tasks or actions and the surrounding conditions leading to an occurrence as well as determines the causal factors. (See Chapter 9.)... 

Risk analysis is activity of examining each identified risk to refine the description of the risk, isolate the causal factors, and determine the effects. It refines each risk in terms of its likelihood and consequence. It also involves establishing measures to control or mitigate the risk. It is essentially the risk assessment and risk control stages of the risk management process. 

Event and causal factors analysis includes charting, which depicts the logical sequence of events and conditions (causal factors that allowed the accident to occur), and the use of deductive reasoning to determine the events or conditions that contributed to the accident. 

Instead, early commercial assessments of NCEs often employ statistical analysis to produce projections of the numbers of prescriptions in an indication marketplace through time. But from a causal standpoint, forecast methodologies do not address the underlying factors that determine how prescriptions are created in the marketplace. Operationally, prescriptions are generated by patients as they... 

Once the causal factors have been identified, the factors are analyzed using a root cause analysis tool, such as 5-AVhys or predefined trees. See Chapter 9 for a more detailed discussion of Barrier Analysis (sometimes called hazard-barrier-target analysis or HBTA) and Change Analysis (also referred to as Change Evaluation/Analysis or CE/A). In essence, these tools act as a filter to limit the number of factors, which are subjected to further analysis to determine root causes. 

Checklist analysis tools can be a user-friendly means to assist investigation teams as they conduct root cause analysis.h) Each causal factor is reviewed against the checklist to determine why that factor existed at the time of the incident. The Systematic Cause Analysis Technique (SCAT)(9> is an example of a proprietary checklist tool. 

The process of evidence gathering, timeline development, scenario determination, and causal factor identification is somewhat iterative, and therefore some of the tools and quality tests previously described may assist in causal factor identification. More specifically, barrier analysis and change analysis, together with a completeness test, can ensure that all valid causal factors are identified. 

The identification of causal factors points us to the key areas that need to he examined further for why that factor existed. It acts as a filter to limit the number of areas that are subjected to further analysis to determine root causes. This critical activity must be performed diligently and systematically to identify every causal factor applicable to the specific incident. If a causal factor is missed, one or more root causes will likely be omitted as well, which could lead to similar incidents in the future. 

Once the actual incident scenario is understood and its multiple causal factors identified, this information may be used to determine the incident s root causes. One means of performing root cause analysis involves the use of ready-made, predefined trees. A predefined tree provides a systematic approach for analyzing and selecting the relevant elements of the incident scenario. It is a deductive approach, looking backward in time to examine preceding events necessary to produce the specified incident. 

Among the multivariate statistical techniques that have been used as source-receptor models, factor analysis is the most widely employed. The basic objective of factor analysis is to allow the variation within a set of data to determine the number of independent causalities, i.e. sources of particles. It also permits the combination of the measured variables into new axes for the system that can be related to specific particle sources. The principles of factor analysis are reviewed and the principal components method is illustrated by the reanalysis of aerosol composition results from Charleston, West Virginia. An alternative approach to factor analysis. Target Transformation Factor Analysis, is introduced and its application to a subset of particle composition data from the Regional Air Pollution Study (RAPS) of St. Louis, Missouri is presented. 

Principal components analysis (PCA) and factor analysis (FA) are aimed at finding and interpreting hidden complex, and possibly causally determined, relationships between features in a data set. Correlating features are converted to the so-called factors which are themselves noncorrelated. 

The purpose of application of factor analysis (FA) is the characterization of complex changes of all observed features in partial systems of the environment by determination of summarized factors which are more comprehensive and causally explicable. The method extracts the essential information from a data set. The exclusive consideration of common factors in the reduced factor analytical solution seems to be particularly promising for the analytical process. The specific variances of the observed features will be separated from the reduced factor analytical results by means of the estimation of the communalities. They do not falsify the influence of the main pollution sources (see also Tab. 7-2). The mathematical fundamentals of FA are explained in detail in Section 5.4.3 (see also [MALINOWSKI, 1991 WEBER, 1986]). 

The existence of an indeterminate number of root causes may help explain some of the frustration that is occasionally expressed with standardized incident analysis procedures and software. In spite of their structured approach, these systems are fundamentally subjective. For example, one technique helps the investigation team list many of the possible causes that led to an event. Some of these causes are then identified as causal factors which are then developed into root causes. Yet the determination as to which causes are causal factors will necessarily depend on the... 

Unfortunately, some of the observations and interpretations presented in Chapter 7 ("Microscopical Interpretation of Clinkers") do not appear to be founded in systematic experimental design or statistical analysis. Statistical measures to determine the degrees of correlation and association of the observations, and their relationship to the various physical and chemical causal factors of the production process, are essential and urgently needed for several very important reasons ... 

Setting the above knowledge and proposals together leads to integrated incident investigation procedure which is stUl based on the timeline development, causal factor identification and causal factor chart development, but which allows the determination of different Incident Cause Level for each of multiple causes. If the Incident Cause Level of specific cause is outside the internal safety management, the apph-cation of non-hnear analysis method is recommended. 

Software Fault Tree ( Soft Trees ) The soft tree technique is used to determine what software event, failure, or combination of each will result in a real or hypothetically loss event (a top event). This top-down analytical approach, which assumes a problem and then evaluates affecting conditions backward to determine causal factors, also takes into consideration any influencing environmental factors. It is concerned primarily with the analysis of any hardware-software interfaces that deal directly with the operation of mechanical components. 

Establish accurate, complete, and substantive information that can be used to support the analysis and determine the causal factors of the accident. Stress aspects of the accident that may be the foundation for judgments of needs and fntnre preventive measures. 

Change Analysis is used when the problem is obscure. It is a systematic process that is generally used for a single occurrence and focuses on elements that were planned and unplanned changes in the system and determines their significance as causal factors in an accident. (See Chapter 10.)... 

Careful and complete analysis of the data collected following an incident is critical to the accurate determination of an accident s causal factors and an important part of preventing a reoccurrence. The results of comprehensive analyses provide the basis for corrective and preventive measures. 

The analysis portion of the accident investigation is not a single, distinct part of the investigation. Instead, it is the central part of the process that includes collecting facts and determining causal factors. Well-chosen and carefully performed analysis is important because it provides results that can be used by a company to improve its safety and health performance. 

A Fault Tree Analysis (FTA) is a top-down, deductive logic model that traces the failure pathways for a predetermined, undesirable condition or event, called the TOP Event. An FTA can be carried out either quantitatively or subjectively. The FTA generates a fault tree (a symbolic logic model) entering failure probabilities for the combinations of equipment failures and human errors that can result in the accident. Each immediate causal factor is examined to determine its subordinate causal factors until the root causal factors are identified. 

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