Manufacturing lots ranking based on their impurity profile using JMP

Question

A production plant makes 1000s of lots and analytical lab analyzes samples and generates data, concentration of impurities and other test results. Impurities need to be minimized but there are certain attributes that need to be maximized. Question is, what is the best way to rank and visualize such lots based on overall data and also by adding weight to certain variables? It's like analyzing a DOE data having all the responses but no factors. Thank you very much for your suggestions.  Regards.

gzmorgan0 · Answer

@ftadesse_genoma,

@Mark_Bailey  and @statman made very good suggestions. You did not mention if each "lot" is like a vat of material or made up of smaller components like wafers in semiconductors, or boards in a solder line.  To expand @statman's suggestion, how the sample(s) from the lots are taken and the sample-to-sample variation within a lot  also should be reviewed as a time or sequence. I suggest a variability chart using Lot id and Sample id within lot  for grouping on the X-Axis vs. the measured values, creating a plot for each metric of interest.

A mutivariate control chart might highlight not only an unusual result for one metric, but also a change in the relationship if there is correlation.

It is not that you have no factors, no controlled factors,  in fact, it is likely you have many uncontrolled factors: equipment used, batches of incoming material, time of day, temperatures, gauge readouts, etc. all dependent upon the manufactiring line you described.  That is why the control chart methods, univariate or multivariate, are suggested. They can detect anomalies, unusual results, and then collect and analyze the process history, which is similar to a method popular today's current events, "contact tracing."  The goal is to answer what might have caused the anomalies.  If you have JMP  PRO there are several tools to help with that analysis as well. While collecting process history data, I suggest bringing-in people knowledgeable of the manufacturing line. Even in sophisticated, automated manufacturing, there always seems to be something that is not captured in the data alone.

Good luck.

ftadesse_genoma · Answer

Mark, Thank you. Multivariate analysis was what I was doing too. Glad to know I'm on right path. I appreciate it.

statman · Answer

The only thoughts I would add to Mark's suggestions are:

1. Question the measurement systems.  Any way to determine the measurement errors in the data sets?  Do you have any repeated measurements?

2. Look at the data using simple time series.  Since you have not provided any hypotheses as to why the impurities vary (so you would have no subgrouping strategy), you might just use a Moving Range and Individual chart to look at the data.