Hi @Chitranshu,
The behaviour you're seeing is normal and can be seen with any control charts.
Since you're removing/excluding some points based on your previous control chart, it will change the dataset on which the new chart is created : the mean will be recalculated as well as the control limits, so points that were previously within the control limits before could be outside of the newly calculated and tighter control limits.
For the calculation of multivariate control limits, you can look at the equation here : Statistical Details for Individual Observations (jmp.com). You can see that the control limits are for example influenced by the number of variables p and the number of observations m, so if you remove some points, the number of observations won't be the same anymore, hence the changes you have seen.
I don't know the context of your project, but you can maybe have a look at STIPS course about quality methods, and discussions about the use of control charts : Statistical Thinking for Industrial Problem Solving | JMP You can also have a look at this free course by JMP : JMP Statistical Process Control Course
The purpose of control chart is to study process outcomes over time, not to remove outliers.
It's a graphical tool to differentiate if the variability comes from random/"normal" variation (process in control, within the range of control limits), or if it comes from extra causes of variation that have to be identified and resolved.
Other members more experienced than me could give you more info, guidance and references about process control methodologies/control chart if needed.
I hope this answer will help you,
Victor GUILLER
L'Oréal Data & Analytics
"It is not unusual for a well-designed experiment to analyze itself" (Box, Hunter and Hunter)