Statistical Tools for Process Validation, Stage 3: Continued Process Verification
In its industry guidance to companies that manufacture drugs and biological products for people and animals, the Food and Drug Administration (FDA) recommends three stages for process validation: Process Design, Process Qualification, and Continued Process Verification. In this post, we we will focus on that third stage.
Stage 3: Continued Process Verification
Per the FDA guidelines, the goal of this third and final stage of Process Validation is to provide:
Continual assurance that the process remains in a state of control – the validated state – during commercial manufacture...the collection and evaluation of information and data about the performance of the process will allow detection of undesired process variability. Evaluating the performance of the process identifies problems and determines whether action must be taken to correct, anticipate, and prevent problems so that the process remains in control.
Example: Monitor a Process with Control Charts
Suppose you are responsible for monitoring an oral tablet manufacturing process. You need to demonstrate that hardness is stable over time, and detect if the process variation has shifted and therefore requires attention.
You also want to make sure production line operators do not overreact to minor changes in the data, which are inherent in routine variability. Avoiding overreaction prevents unnecessary process adjustments that may actually result in an unintentional increase in variability.
You sample five tablets per hour, measure their hardness, and then enter the data into Minitab Statistical Software to create an Xbar-R control chart.
This Xbar-R chart does not reveal any points flagged in red, and therefore shows that the process is in statistical control. You can conclude that you are maintaining the validated state of the process, and that there aren’t any unwanted, unusual shifts in either the process mean (per the upper Xbar chart) or variation (per the lower R chart) that have been detected.
If the control chart had revealed an out-of-control state—a process exposed to unanticipated sources of variation—then next steps would include characterizing the issue and conducting a root cause investigation. Was there a change in material characteristics? Is there an equipment maintenance or calibration issue? Or is there some other source of variability that provoked a process shift?
Failure to detect undesirable process variation can be mitigated with routine monitoring and control charting. In addition to control charts and the statistical tools commonly used for the Process Design and Process Qualification stages, there are other useful statistical techniques to support you in your process validation efforts.
For example, Minitab also includes acceptance sampling to help you calculate the number of samples to take and use a randomly drawn sample of product to determine whether to accept or reject an entire lot.
If you don’t yet have Minitab, try it free for 30 days and see for yourself all that it offers for process validation and how easy it is to use.