APPLYING CHANGE-POINT ANALYSIS TO PROCESS MONITORING AND CONTROL
In the two case studies shown here, change-point analysis using Change-Point Analyzer was used to show that a process change
correlated with an improvement in a measured output.
Manufacturers of active pharmaceutical ingredients typically monitor process recovery to ensure that the process is performing
as expected and that yield targets are being met. The historical record of recovery also provides a baseline against which
potential improvements can be measured. Change-point analysis is an effective tool to verify whether a process change has
led to measurable improvements, such as an increase in recovery.
In the first example, a process change was initiated in an attempt to improve recovery of a target protein, and a change-point
analysis was conducted on lots bracketing the time of the change (Figure 6). This analysis revealed that an increase in recovery
occurred at lot 28 (indicated by a shift upward in the green zone and from the summary table below the plot). The actual process
change was initiated at lot 24. An increase in the percent recovery was detected only a few lots after the actual process
change occurred and within the 95% confidence interval indicated in the summary table for Figure 6. The change was from 48.6%
to 53.7% and had a confidence level of 96%.
Figure 6. Change-point analysis of percent recovery for a plasma protein. The analysis was done using Change-Point Analyzer.
The data show an upward shift in the percent recovery. The shift is centered at lot 28 and represents a shift from 48.6% to
53.7%. This change has a 96% confidence level. An additional output from the software is a confidence interval, which in this
example is from lots 10 to 39. The red lines indicate control limits. The level is an indication of the importance of the
change. A level 1 change is the first change detected and the one most visually apparent in the plot. A level 2 change is
detected on a second pass through the data after the data are subdivided into two subsets.