Have you plotted the raw data in JMP Variability Plot? That can be used to break down the many components of variation visually, and to some extent numerically using the random effects model usually. Remember your goal is process and thus product MPROVEMENT, not a Cpk number that moves up and down often.
Why do you need to recalculate Cpk for every measurement?
Is it mandatory?
You already have SPC chart to show single point excursions and trends in the process behavior.
You could report these OOC excursions as "% OOC" rather than use what I think is a misleading rolling Cpk that hides the perhaps actionable points OOC.
Cpk is an UNDERLYING capability index.
It is used to ESTIMATE the PPM failure rate based on those spec limits.
It has a "short term" metric based on subgroup standard deviation, which I find relatively useless except for focusing improvement efforts when the 'in control" behavior is still not good enough given action taken only on OOC points.
And it has a "long term" metric based on ALL variance components, including temporal variation over at least 25 parts.
Daily reports on %OOC across all SPC charts can help prioritize short term shop floor efforts, while long term Cpk can help prioritize INVESTMENT efforts that focus on the improvment of COMMON CAUSE variation that SPC charts miss on purpose.
That long term Cpk is the only one that my clients report montly or quarterly, but not updated for each SPC chart point.
And when improvement action is suggested a long-term Cpk less than 1.5 or 1.67 or whatever goal your industry has set as target, you then need to look back at the SPC chart raw data and find the VARIANCE COMPONENTS that can be attacked by CI team. You might use the JMP Variability Plot to visualize and even enumerate those VC's. Example of variance components might be part to part, variation within part, batch to batch if batch process, and temporal variations which could be hourly, shift to shift, or PM to PM, or day to day, week to week, some seasonal measure, etc. That Variability Plot done in nested format for most production subgroups, or crossed format for DOE studies, can be very helpful for CI teams. We never report Cpk's without this kind of background Capability Study.
So again, how do you plan to USE a moving Cpk? It as same problems as moving range and moving average charts, in terms of actionable interpretation of variance components which show up better on "muilti-vari" or JMP Variability plots...in my opinion. If its required, challenge that request until someone explains what action would be taken for the many ups and downs it shows often, when actually the UNDERLYING LONG TERM CAPABILITY has NOT really changed. Deal with short term excursions using SPC rules and action plans that attack SPECIAL causes of variation using CONTROL limits. Deal with long term process behavior vs SPEC limits (rather than control limits) if necessary by attacking the COMMON causes of variation which the SPC charts are designed to MISS so you can focus short term on OOC points. CI is a two-step process, get rid of special causes, then re-assess capability and if marginal attack common causes using the JMP Variability Plots for visualization, and DOE methods to study and improve those many other variance components.
This is just suggestion based on my experience, not any particular text book.
If you report Cpk too often, you will mislead management and customers who will demand that you EXPLAIN every up or down value which may only be reflection of OOC points for down shifts, but may actually be real for up shifts which you will only really know after much longer time! Metrics should be realistic and actionable and to be actionable must address EACH source of variation, not some "average" which only leads to more confusion.
But remember, I am not a SAS expert. I am simply a high volume manufacturing process engineer that has USED JMP for more than two decades to drive improvement efforts, both short term and long term where big bucks are often involved to improve common causes when spec limits are tight.
