Root cause analysis system described

The types of data required for incident reporting and root cause analysis systems are specified. Data Collection practices in the CPI are described, and a detailed specification of the types of information needed for causal analyses is provided. 

However, in the case of a root cause analysis system, a much more comprehensive evaluation of the structure of the accident is required. This is necessary to unravel the often complex chain of events and contributing causes that led to the accident occurring. A number of techniques are available to describe complex accidents. Some of these, such as STEP (Sequential Timed Event Plotting) involve the use of charting methods to track the ways in which process and human events combine to give rise to accidents. CCPS (1992d) describes many of these techniques. A case study involving a hydrocarbon leak is used to illustrate the STEP technique in Chapter 7 of this book. The STEP method and related techniques will be described in Section 6.8.3. 

In the case of root cause analysis systems, more comprehensive evaluations of PIFs will normally be carried out as part of a full-scale human factors audit. This could make use of the types of comprehensive PIF evaluation methods described in Chapter 2 (see Section 2.7.7 and Figure 2.12). 

This involves the development of data collection and root cause analysis systems as described in Chapter 6. 

The fourth type of root cause analysis discussed at the start of this chapter is Systems Analysis. Two methods are described here to illustrate this approach Why Tree Analysis and Fault Tree Analysis. 

As Pounds says, several of the behavioral tools he describes are not a part of the typical BBS process. Also, Pounds says that the concept that he puts forth can be extended to resolve a multitude of business initiatives. A list of the major captions in his program follows. It s different it s performance-oriented. No other BBS outline that has come to this author s attention is comparable, nor do others include such elements as root cause analysis, environmental impact analysis (reengineers performance systems), behavioral consequence impact analysis (evaluates job... 

This book describes the root cause identification and analysis methods developed by the staff at Systems Improvements, Inc., which has provided consulting services on these subjects for several years. 

The more popular concepts of lean operations tend to be the concepts of muda, flow and pull system. A preliminary analysis of all these methods, as we have described earlier, however, highlights the fact that all assume sufficient machine availability exists as a prerequisite. In our experience for many companies attempting a lean transformation this assumption is not true. Machine availability depends on maximizing the machine up time by eliminating the root causes of down time. The ratio of up time and planned operation time is the efficiency of the operation. Therefore, in order to make lean concepts work it is vital that the pre-condition of running the operations at a high level of efficiency should be met. The old approach of measuring labour efficiency (e.g. the ratio of standard hours and hours worked) has now shifted to the efficiency of the control or bottleneck workstation. 

The aim of this paper is to present a meta model, suitable to socio-technical systems. This model allows describing the system to analyze, the result of the risk analysis, and the required aspects of dynamical system behavior, in order to automatically perform simulation under degraded conditions. The model is an extension of the FIS model presented in (Flaus, 2011, Negrichi et at., 2012). The meta model may also be used for fault diagnosis, as it can be used to generate redundancy relation and to perform root cause search (Flaus etal.,2011). 

This research described in this paper laid the groundwork for the next stage of our analysis of cyber-physical systems software fault injection into the cyber control. The insight gained into root causes of FACTS device failure will be used to further refine the reliability models, facilitating accurate quantitative analysis of reliability of the power grid. 

We now move further to the left in the accident-analysis framework (Figure 16.1). We will look into SHE performance indicators based on information about contributing factors and root causes. The organisation and SHE management system are in focus. These indicators have many similarities to the audit methods described in Section 14.2. 

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