Root cause described

An EPA-OSHA accident investigation at Napp Technologies Inc. in Lodi, New Jersey, developed the root causes and recommendations to address the root causes. Describe the accident, and develop layered recommendations for this specific accident. See http // www.epa.gov/ceppo/pubs/lodiintr.htm. 

Incident Investigation Previous incidents related to the chemicals or equipment involved in the new toll should be considered during the PHA and must be considered if subject to PSM/RMP compliance. In addition, procedures should be in place to describe how the client will be informed and involved in the investigation. It is veiy important to ensure that action plans addressing the root cause of past incidents were implemented. 

In the shorter case studies, only the immediate causes of the errors are described. However, the more extended examples in the latter part of the appendix illustrate two important points about accident causation. First, the precondihons for errors are often created by incorrect policies in areas such as training, procedures, systems of work, communications, or design. These "root causes" underlie many of the direct causes of errors which are described in this section. Second, the more comprehensive examples illustrate the fact that incidents almost always involve more than one cause. These issues will... 

In addition to the proactive uses of the SRK model described in the two previous sections, it can also be employed retrospectively as a means of identifying the underlying causes of incidents attributed to human error. This is a particularly useful application, since causal analyses can be used to identify recurrent vmderlying problems which may be responsible for errors which at a surface level are very different. It has already been indicated in Section 2.4.1 that the same observable error can arise from a variety of alternative causes. In this section it will be shown how several of the concepts discussed up to this point can be combined to provide a powerful analytical framework that can be used to identify the root causes of incidents. 

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). 

A specific example of a causal model is the root cause tree described in Section 6.8.4 and Figure 6.8. This is a very elaborate model which includes several levels of detail for both equipment and human causes of incidents. The root causes tree is a generic causal model, and may require tailoring for application to specific plants and processes (e.g., in the offshore sector) where other error causes may need to be considered. 

When the diagram is complete, the analyst proceeds through it to identify sets of events that were critical in the accident sequence. These critical events are then subjected to a further causal analysis using other techniques such as root cause coding, described below in Section 6.8.4. 

The first case study describes the application of the sequentially timed event plotting (STEP) technique to the incident investigation of a hydrocarbon leak accident. Following the analysis of the event sequence using STEP, the critical event causes are then analyzed using the root cause tree. 

In the second case study, variation tree analysis and the events and causal factors chart/root cause analysis method are applied to an incident in a resin plant. This case study illustrates the application of retrospective analysis methods to identify the imderlying causes of an incident and to prescribe remedial actions. This approach is one of the recommended strategies in the overall error management framework described in Chapter 8. 

This case study concerns the events leading up to the hydrocarbon explosion which was the starting point for the Piper Alpha offshore disaster. It describes the investigation of the incident using the sequentially timed events plotting (STEP) technique. Based on the STEP work sheet developed, the critical events involved in the incident are identified and analyzed in order to identify their root causes. 

This case study illustrates how the methodologies described in Chapter 6 can be used to analyze plant incidents and identify the root causes of the problems. Based on this information, specific error reduction strategies can be developed to prevent similar incidents from occurring in the future. Also, the findings of such an analysis can provide the basis for more general discussions about the prevalence of similar error inducing conditions in other plant areas. 

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

Much of the variation in these time series for the past 700 kyr can be described by a combination of a 100 kyr cycle plus additional cycles with periods of 20 and 40 kyr. This result immediately suggests that the ice-age cycles are caused by variations in the amount and seasonality of solar radiation reaching the Earth (insolation), because the 20, 40, and 100 kyr periods of climate history match the periods of cyclic variations in Earth s orbit and axial tilt, line hypothesis that these factors control climate was proposed by Milutin Milankovitch in the early part of the 20th century and is widely known as "Milankovitch Theory." It is now generally accepted that the Milankovitch variations are the root cause of the important 20 and 40 kyr climate cycles. The 100 kyr cycle, however, proves to be a puzzle. The magnitude of the insolation variation at this periodicity is relatively trivial, but the 100 kyr cycle dominates the climate history of the last 700 kyr. Further,... 

Timelines alone do not identify the root causes of an incident. They should be used in conjunction with other tools, described in the following sections. 

Chapter 9 describes the use of human factors checklists in root cause analysis. 

Figure 9-1, the two flowcharts describing root cause determinations using Methods A and B, presents general frameworks for root cause determination. Method A focuses on the logic tree method using a simplified fault tree approach. Method B focuses on the predefined tree method. 

Find the facts in the main sequence on the Causal Factor Chart that describe a component failure or a human error. Ensure the fact is not describing a management system failure (i.e., ensure the fact is not a root cause, near root cause, or root cause category). The identified negative events/conditions are candidate causal factors. Any candidate causal factor that is not dependent on another candidate causal factor is a valid causal factor. 

Once all of the root causes are identified, the investigator is ready to develop the corrective actions, as described in Chapter 10. 

A timeline or sequence diagram is first developed, and then causal factors identified. Care should be taken to ensure that the checklist is not used too early. Be sure to determine what happened and how it happened before determining why it happened. Otherwise, the team will think that they have identified the right root cause(s), when in reality only one or two of several multiple causes have been determined. The causal factors are then applied one at a time to each page of the checklist(s) to identify relevant root causes. Those pages that are not relevant to the particular incident of interest are discarded. Similar quality assurance checks should be applied as those described for predefined trees. 

The following case study describes the investigation work process for a hypothetical occurrence using a logic tree based multiple root-cause systems approach. An example incident investigation report follows the work process description. The example is intended for instructive purposes only descriptions of process equipment and conditions are not intended to reflect actual operating conditions. 

Many of these parameters are quite predictable, especially when applying some of the thermodynamic concepts described earlier. The increased processing time, which brings with it increased exposure to stressful conditions (both mechanical and as a result of environmental conditions used in the process, especially when that process is aqueous based) is much more unpredictable, and is often the root cause of much angst during the preparation of early commercial batches. 

SAD Spin-alternant determinant. The VB determinant with one electron per site and with alternating spins. Other terms describing the same determinant are the quasiclassical (QC) state, and the antiferromagnetic (AF) state. In nonalternant hydrocarbons, where compete spin alternation is impossible, the determinant is called MS AD, namely, the maximum spin-alternating determinant. The SAD MSAD are the leading terms in the wave function of molecules with one electron per site, for example, conjugated hydrocarbons. In radicals (e.g., allyl radical) the SAD is the root cause of spin polarization (i.e., negative spin densities flanked by positive ones). See Chapters 7 and 8. 

An exceptional investigation report willfully explain the technical elements and issues associated with the incident. It will describe the management systems that should have prevented the event, and will detail the system root causes associated with human errors and other deficiencies involved in the incident. 

We tried to arrive at a representative picture of CCR task performance by having a series of extensive, confidential interviews (based on Flanagan s (1954) CIT) with CCR operators before implementation of the first NMMS modules had started. In each interview a different operator was asked to report on a CCR near miss during the last year and of his own choice, which had not been previously reported. The near miss was then described (as if it were a forced near miss report) in the form of an Incident Production Tree, after which all its root causes were classified according to the RAP model described earlier. After each set of five subsequent interviews the overall pattern of classification results was checked for stability it turned out that the results (i.e. the relative frequencies of classified root causes) after 30 interviews did not differ overall from those of the first 25 therefore the series of interviews was stopped after 35 operators (about two thirds of the available CCR population at the time) had participated. 

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