Process deviations root causes

If the process uses a single large study node, deviations may be missed. If study nodes are small, many are needed and the HAZOP may be tedious, moreover the root cause of deviations and their potential consequences may be lost because part of the cause may be in a different nude. 

If too much of a process is included in a single study node, deviations may be missed. If too little of a process is included, the study can become tedious. In addition, root causes of deviations and their potential consequences can become separated. Too many study nodes is common for novice HAZOP study leaders. On the positive side, a study with too many nodes is less likely to miss scenarios than one with too few nodes. 

The root cause of this accident was poor operating procedures and poor process infoiv mation. The operating procedure, for example, did not cover the safety consequences of deviations from the normal operating conditions, such as the possibility of a runaway reaction and the specific steps to be taken to avoid or recover from such deviations. 

The answer to the second question was obtained by using models from organizational control theory. A deviation can re-occur due to ineffective operation of the organization s control process. A theoretical model of this control process, in which causes of precursors can be expressed in terms of ineffective control elements of the organization s control process, was derived from existing models in literature. However, as safety literature shows, there are certain conditions shaping a situation that make these control elements ineffective. These conditions, sometimes called latent conditions in safety literature are the actual root causes of precursors and possible accidents. In this thesis a classification has been developed which identifies six main types of latent conditions (these six latent conditions are context related but... 

As with any process, there will be situations that require a departure from a predetermined step. A deviation from the process must be documented exactly as it occurred, along with what caused the need for the deviation and how the deviation will impact the quality of the product. The deviation investigation and justification must be approved by both QA and production management, and the root cause of the deviation should be identified if possible. Of critical importance is a statement regarding the possible impact the deviation has on the product. 

Deviations and failure investigation data—The failure of any batch to meet any specification, including batches failing in-process, release, or finished-product (shelf life) specifications is a crucial event. Such events must be reported and captured in the APR system. The identification of the root cause and the determination of corrective... 

A common error is to limit the types of deviations reported to and evaluated by the APR system to just deviations from finished-product specifications. All deviations should be evaluated, including deviations from manufacturing procedures, in-process specifications, deviations from raw material specifications, and other expected results. Each of these occurrences could indicate changes are necessary to prevent recurrence. For example, the cause of deviations from manufacturing procedures is frequently evaluated as a lack of training. If there are several of these occurrences by different individuals, however, it is also likely that there maybe another root cause, such as unclear or insufficient batch record instructions or inadequately designed or unclear batch record data forms. 

Deviations that directly impact GxP processes, i.e., those that affect the quality, efficacy, or safety of pharmaceutical and healthcare products, will require root cause remediation. 

Process compliance fluctuates Plan compliance measures the deviations between what was planned and what was actually produced, delivered, and sold. Understanding the root causes of these deviations will help plan better and execute the plan better. 

Equation (3.19) indicates a 9.6% deviation of AEP from the OEP. Such a gap is significant, which should alert you to initiate investigations for root causes. The mere fact of determining EPI gives you an immediate indicator as to where your process unit stands in energy performance, so that you can quickly spot problematic areas. 

For all major premises or equipment instruments and devices which may have critical influence on the preparation or analytical processes, a logbook should be kept. The logbook is the history of a piece of equipment or a facility and it aims at traceability and verification. The investigation of any deviation may use the logbook as a vital source of information to enable the root cause to be traced. In addition, the logbook will include records as to whether equipment is maintained on time, if rooms are cleaned on time etcetera. 

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. 

Initiating event In hazard analysis, an event could be the occurrence of a deviation which may lead to an accident. So, the initiating events are the causes for which there is the process deviation. The initiating events may be or may not be the most basic underlying root-causes, but are the results of the root causes. According to CCPS there are three types of initiating events or causes ... 

It is Strongly reconunended that an FMEA be used to investigate further how a particular failure (which leads to a hazard) can come about. The FMEA should not be used as the primary safety analysis tool. A more appropriate application is to hazard and operability (HAZOP) a particular part of the plant. Once the safety-critical operations have been identified, then FMEA can be used very selectively to focus on how particular failure modes might lead to process deviations and thus create a hazard. The primary reason for this is that FMEAs are a very laborious effort and easy to become bogged down. But their strength is going to the piece-part level, as necessary, to determine root causes, and this of course is paramount in understanding how to control a hazard. 

The input to the accident process consists of contributing factors and root causes. Contributing factors are more stable conditions at the workplace. By changing such factors, more lasting effects will be achieved. In practice, it is sometimes difficult to separate contributing factors (input) and deviations in the accident sequence (process). 

For the usual accurate analytical method, the mean f is assumed identical with the true value, and observed errors are attributed to an indefinitely large number of small causes operating at random. The standard deviation, s, depends upon these small causes and may assume any value mean and standard deviation are wholly independent, so that an infinite number of distribution curves is conceivable. As we have seen, x-ray emission spectrography considered as a random process differs sharply from such a usual case. Under ideal conditions, the individual counts must lie upon the unique Gaussian curve for which the standard deviation is the square root of the mean. This unique Gaussian is a fluctuation curve, not an error curve in the strictest sense there is no true value of N such as that presumably corresponding to a of Section 10.1—there is only a most probable value N. 

Therefore, the measurement precision of the sensor hardware has to be very good. But, even with an idealized sensor hardware, the extinction cross sections of particles are determined with uncertainties of at least 2 % with data block sizes of 10,000 values [4]. This is caused by the statistical uncertainty, due to the finite number of values, which are used to calculate the mean value and the root mean square deviation of the transmission signal. Therefore, a determination of particle size distributions with an advanced SE-Method is fairly difficult. However, a reliable process monitoring with the SE-Method is possible, which enables the detection of relative changes of the mean particle sizes (see also measurement results in the next section). Therefore, the pinhole diameters, as well as the length of the measurement volume, have to be adjusted to realize a mean value of the transmission and a theoretical relative extinction cross section within the optimal range. 

---