Process Capability and Continual Improvement in QbD

Process capability measures the inherent variability of a stable process that is in a state of statistical control in relation to the established acceptance criteria. 

Calculations based on the inherent variability due to common cause of a stable process (i.e., in a state of statistical control) result in process capability (Cp and Cpk) indices. When the process has not been demonstrated to be in a state of statistical control, the calculation needs to be based on sample standard deviation of all individual (observed) samples taken over a longer period of time; the result is a process performance index (Pp and Ppk). 

A state of statistical control is achieved when the process exhibits no detectable patterns or trends, such that the variation seen in the data is believed to be random and inherent to the process.

When a process is not in a state of statistical control, it is because the process is subject to special cause. Special causes can give rise to short-term variability of the process or can cause long-term shifts or drifts of the process mean. Special causes can also create transient shifts or spikes in the process mean. 

In a QbD development process, the product and process understanding gained during pharmaceutical development should result in early identification and mitigation of potential sources of common cause variation via the control strategy.

The manufacturing process will move toward a state of statistical control, and, once there, the manufacturer will continue to improve process capability by reducing or removing some of the random causes present and/or adjusting the process mean towards the preferred target value to the benefit of the patient.

In a non-QbD approach, common cause variation is more likely to be discovered during commercial production and may interrupt commercial production and cause drug shortage when it will require a root cause analysis. 

Process capability can be used to measure process improvement. 

Ongoing monitoring of process data for Cpk and other measures of statistical process control will also identify when special variations occur that need to be identified and corrective and preventive actions implemented.

Continual improvements typically have five phases as follows:

1. Define the problem and the project goals, specifically

2. Measure key aspects of the current process and collect relevant data

3. Analyze the data to investigate and verify cause-and-effect relationships. 

4. Improve or optimize the current process based upon data analysis using techniques. 

5. Control the future state process to ensure that any deviations from target are corrected before they result in defects. 

Implement control systems such as statistical process control, production boards, visual workplaces, and continuously monitor the process.

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Resource Person: BARBARA PIROLA

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