You pretty much have to do your own macro, since your configuration will be unique. Here are a couple of articles which may help:

How To: Record Your Own Macro
How To: Fix a Recorded Macro
How To: Assign a Macro to a Button or Shape

Darlene

This is an issue with pivot charts, in every version of Excel that has pivot charts (2000 and later). In Changing a PivotChart removes series formatting in Excel Microsoft admits it’s an issue, and their suggestion is to record a macro next time you have to fix the chart. Thereafter, all you need to do is run the macro when necessary.

Thanking you in advance for your expertise.

Darlene

Nice thoughtful stroll through the intricacies of SPC.

Regarding the normal distribution, I think it’s the central limit theorem that tells us that the sum of a bunch of independent errors, the kind which are caused by uncontrolled factors in a process and which result in the variability of the output of that process, are defined by a normal distribution. It’s kind of a catch-all fudge factor, but it helps to explain why the assumption of a normal distribution isn’t too crazy.

Regarding BI software, it seems the vendors take the quick overview, pulling out some items to display out of context, using the fanciest graphics available, not those sanctioned by the experts in effective graphical communication. The BI programmers are knowledgeable neither in human cognition nor in statistics and SPC. So they miss out on two important points.

I found articles that explained that the control limits on the XmR chart (for example) are based on the fact that the XmR chart assumes a normal distribution, and for a normal distribution, more than 99% of the points fall within 3-sigma limits….Hmmm, OK, that appears to make sense….but how does that fit in with Dr. Wheeler’s calculation of the limits? I then came across another article that explained how to calculate the 3-sigma limits approximately. It turned out that the formulas used for the approximate calculation were the same used by Dr. Wheeler! So finally, in a roundabout way, I discovered that Dr. Wheeler’s control limits were 3-sigma limits using an approximate calculation. Whew! Well, why not calculate the 3-sigma limits directly? This is of course easy to do in Excel, but the approximate formulas were around during the days of hand calculations (the two don’t provide the same results but they’re probably close enough so that the difference can be ignored for using control charts in the real world).

But wait a minute! After I thought that I had all this stuff figured out, another question began to nag me. The XmR chart was shown to be useful in everyday situations, such as examining sales data over time. The question then was; how on earth can we assume that any of these everyday situations follow a normal distribution? This wasn’t making sense to me.

Luckily, I uncovered an article by Dr. Wheeler titled, “Shewhart’s Charts and the Probability Approach.” This article explained a lot. The first thing is that Shewhart’s use of the 3-sigma limits was only loosely based on the normal distribution. The second is that, in real life, you can’t model a process exactly and third, the use of normal distribution to explain the use of a particular control chart is wrong. Shewhart’s use of 3-sigma limits, while having a basis in probability, was selected because these limits work in practice to minimize the errors of identifying a random variation for a signal and vice versa. Thus, the fact that you don’t have to assume a normal distribution (or any other kind of model), explains why the technique could work for everyday situations. Darn! Sections of this article could have been included in the Introduction chapter of Understanding Variation to put the rest of the book in its proper context.

So is that the end of the story? Well…almost. I subsequently discovered that the use of the mR chart with the individual X chart is actually highly controversial. This controversy is summarized in an article titled “Individual Charts and Additional Tests for Changes in Spread” by Albert Trip and Jaap Wieringa. Although Dr. Wheeler is firmly in the camp supporting mR charts, I haven’t seen an article by him debunking the other side of the controversy.

Ok, after the above lengthy tirade, I think that control charts work well in practice. It’s ironic that that the scorecards you see in so many BI solutions show the same out of context comparisons that Dr. Wheeler talks about in Understanding Variation. Yet, these BI products don’t provide the contextual analysis (via control charts) needed to understand the scorecard comparisons.

Sorry for the long post.

We followed up with the vendor, who was able to identify and correct a process problem and stop the trend. What I found instructive about this episode was how, simply by keeping a history of his measurements, our QA guy was able to spot a trend before any out-of-tolerance parts were made, so we never had to reject any parts.