Root cause analysis results

ABS Consulting. Incident Investigation/Root Cause Analysis Training Results Trending and Assessment. Knoxville, TN ABS Consulting, 2001. 

Interpersonal skills are very important during audits because you are finding fault with someone else s work. It is necessary to be honest and direct when explaining areas of noncompliance. In most cases, noncompliance issues are a result of an employee not doing their job correctly because they weren t aware of the requirements. Audits rarely result in disciplinary action but do result in more training and more work to correct the noncompliances because simply identifying areas of noncompliance does not solve the actual reason for the occurrence of the noncompliance. The audit provides the basis for the unit or work site to conduct a root cause analysis in order to eliminate the noncompliance. 

A future chapter will be devoted to effective incident/accident investigations. Every incident (near miss) or accident provides a continuous improvement opportunity that should result in root cause analysis and corrective actions. Here is a chance to utilize someone with a passion for lean, and his/her problem-solving skill sets, to fill this safety team position. Eilling this role provides another opportunity to develop a future business leader. 

Root cause - the action or activity that resulted in contact with the immediate cause. Root-cause analysis involves checking on the sequence of events and decisions that led to the accident and identifying the often remote action that triggered that sequence of events. 

The performance improvement department at Memorial Hermann operated the reporting system, aggregating and analyzing the data. The decision about whether or not to conduct a root cause analysis was made jointly by the risk manager and the director of performance improvement. An internal, multidisciplinary expert panel reviewed the results of all root cause analyses on a quarterly basis and established consensus of the findings. 

At the conclusion of the root cause analysis session, underlying causes of the event were summarized. Finally, participants suggested changes that could be made in systems and processes that would reduce the risk of similar adverse events occurring in the future. Methods for monitoring the efficacy of these changes were developed by the participants, and the results of the intervention were subsequently monitored. 

The team constructed a map that identified variables at each step of the process. A root cause analysis tool. Failure Mode and Effects Analysis, was used to prioritize the variables that were most frequently associated with errors, that resulted in the most severe errors, and that were the most difficult to detect. 

A view of data transfer and function design is shown in Figure 7.5. This method is simulation based and Excel is used as an interface for data transfer with simulation while simulation is run in the back. Data are collected in Excel and pushed to simulation, while simulation sends converged data back to Excel where optimization is conducted. Results are displayed in Excel for root cause analysis, trending, and reporting. 

Snook s term practical dn/t,which he coined in his root cause analysis of the accidental shoot down of two U.S. Army Black Hawk helicopters resulting in the loss of 26 peacekeepers. Recall, practical drift is the slow, steady uncoupling of local practice from written procedures. ... 

All process deviations whether planned or unplanned, together with errors and out of specification results should be recorded with a controlled form or electronically onto a database system. Whether a paper system or an electronic system this needs to facilitate the management of the investigation stage, including root cause analysis where necessary, corrective and preventative actions and close out, as well as the data being available for trending (CAPA-system). This is an important part of any Pharmaceutical Quality System, see Sect. 35.6.15. 

As a result of the potential significance of this occurrence, a formal, detailed root cause analysis was performed. A high level of effort was expended but the effort was justified due to the consequences of a repeat occurrence. 

Finally, the team should also consider reviewing the periodic proof-test results to identify devices that have failed repeatedly during tests. For example, if the team determines that the SIF has repeatedly failed its periodic proof test due to a transmitter fault, the team should recommend that the design, installation, and maintenance practices associated with the SIF be re-evaluated. The root cause analysis may result in the selection of a different technology for the process variable measurement or in a revised installation. Until the process variable measurement has been proven reliable in this service, the defined test interval should be reduced. 

Causal factor chain. This is a cause and effect sequence where a specific action creates a condition that contributes to or results in an event. This creates new conditions that, in turn, result in another event, etc. Figure 12-1 summarizes a root cause analysis flow chart [2]. 

The principle of multiple causes indicates that accidents and near miss incidents are usually the result of multiple causes. Investigations should not cease until all the contributing causes have been identified. Once all the obvious causes are found, a root cause analysis should be conducted to delve into the root causes of the problem. Only by identifying and rectifying the root causes wiU the problem be solved. 

Repetition of this measurement step each time a new campaign is initiated is tracked and assessed for variance in the distribution, trends, and outliers in the data measurement and risk (continuous fault) analysis on the sensors or analyzers and CPUs. All or part of the measurement and sensor data may become part of a database constructed from past validation and historical data that have been previously shown through root cause analysis to be the result of known material, process, or sensor variation that has exceeded the specifications. Each manufacturing campaign becomes a potential well for new data, which in turn becomes fodder for a dynamic database for which variable data can be assessed against nominal and expected process and sensor performance, diagnosis made, and remedial action instituted, all occurring perhaps in a millisecond to a second timescale. 

The concept of root cause analysis, which is fundamentally concerned with the cause-and-effect chain of an operation, is described. Some examples of root cause analysis are presented, which demonstrate how it works, and a few simple rules, which must be followed to achieve the desired end-result, are outlined. 

For this simple example, a similar analysis could be based on a quantitative approach, namely, a dynamic model based on an unsteady-state mass balance. The level could be changing as a result of process changes or a sensor failure. However, for more complicated processes, a reasonably accurate dynamic model may not be available, and thus a qualitative approach can be used to good advantage. The diagnosis of the abnormal event could lead to a subsequent root cause analysis, where the source of the abnormality is identified and appropriate corrective actions are taken. 

SACHEM Inc. of Cleburne, Texas, manufactures various concentrations of tetramethylammo-nium hydroxide (TMAH) solutions to meet customer specifications. To ensure consistent performance, electronic industry requires very narrow concentration specifications for the solutions. In SACHEM s quality control laboratory, standardized acids such as HC1 or H2S04 are used as titrants for the TMAH solutions to check their concentrations. The performance of the assay titration is controlled by daily analysis of internal reference standards (IRSs). If the IRS results are within controlled limits, then the assay results of a product can be reported. If not, the results cannot be reported until the root cause is uncovered and eliminated. Safety glasses and gloves are worn while performing this work in the laboratory. 

Progressive companies use a more structured and comprehensive team approach to identify root causes. Scientific principles and concepts are applied to determine root causes and make recommendations to prevent recurrence. Effective investigations should use tested data analysis tools and methodologies to seek the identification of multiple causes. To be repeatable, the investigation should use a systematic approach, which may also be prescriptive. As a rule, the benefits of this systematic approach result from two actions ... 

Another type of logic tree, the event tree, is an inductive technique. Event Tree Analysis (ETA) also provides a structured method to aid in understanding and determining the causes of an incident.(i) While the fault tree starts at the undesired event and works backward to identify root causes, the event tree looks forward to display the progression of various combinations of equipment failures and human errors that result in the incident graphically. 

Causal factor identification is relatively easy to learn and apply to simple incidents. For more complex incidents with complicated timelines, one or more causal factors can easily be overlooked, however, which inevitably will result in failure to identify their root causes. There are a number of tools, such as Barrier Analysis, Change Analysis, and Fault Tree Analysis, that can assist with bridging gaps in data and the identification of causal factors. Each of these tools has merits that can assist the investigator in understanding what happened and how it happened. 

Identifying the potential hazards (PHA, process hazard analysis, or HAZOP, hazard and operability analysis) during operation must be done from a wide-angle approach dangerous situations can occur due to many root-cause situations other than those specified by, for instance, ASME or PED. Based on the results of the risk assessment, the pressure equipment can be correctly designed and the most effective safety system selected. 

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