Control Charts: Simple Elegance or Legalized Torture?

Once again, I’m beginning to understand Deming’s hatred of statistical ‘hacks’

Mon, 01/06/2014 - 09:26

For all the talk about the power of control charts, I can empathize when audiences taking mandated courses on quality tools are left puzzled. When I look at training materials or books, their tendency is to bog down heavily in the mechanics of construction without offering a clue about interpretation.

ADVERTISEMENT

Some seminars even teach all seven control charts! And then there is the inevitable torturous discussion of "special cause tests" (usually the famous eight Western Electric rules). People are then left even more confused. Does each test signal need to be individually investigated, i.e., treated as a special cause? Not to worry—most people usually investigate only the points outside the control limits. The focus tends to be on individual observations. But what if there is one underlying explanation generating many of these signals that has nothing to do with individual outliers, e.g., a step change?

Someone once presented me with the graph shown in figure 1. (Yes, the y-scale started at 0.) It almost convinces you that there is a trend, eh?

 …

Want to continue?
Log in or create a FREE account.
By logging in you agree to receive communication from Quality Digest. Privacy Policy.

Comments

Good question. I'm not sure. Actually, if I had my way, the term "trend" would be banished from corporate language unless used in the "qualitative" sense of "tendency." Whenever someone blurts out the term "trend" because it seems like the thing to do, why don't you ask them exactly what they mean -- you will get as many different answers as people whom you ask. My use in the article is to call it a "transition" to a new steady state based on changed inputs to the process.

As someone said  "if you  torture data enough , it will confess " .

Also ,  I think Dr Deming actually meant statistical 'quacks' . To me , "trend" means "fashion" as in "trendy" !! But we should check with a data sceintist for a better definition !!

Control charts are quite powerful compared to a run chart , but it all depends how one wishes to use them

The hackles on the back of my neck always go up whenever someone showing data in a meeting uses the words "trend" or "up-tick". Ugh!!! We need to ban these words from intelligent conversations... or maybe the implication that the conversation is "intelligent" assumes these two words are already banned! ;-)

Submitted by kkbari on Thu, 01/09/2014 - 11:58

I don’t have all the credentials that you do, but I concur with your real-world, practical assessment of statistics.  I have a masters in statistics and have been teaching and using statistical methods daily for almost 30 years.  I have been fortunate to work for organizations that valued statistical analysis and thinking long before Six Sigma was rolled out, so we never used SS – we were further ahead already.  Except for one area of business (not manufacturing) I have done few regressions as well.  I bristle every time someone mentions testing for normality as the first step.  I have convulsions anytime someone says we have to transform the data because it isn’t normal and they don’t know why other than the book or teacher says so. 

 

If you asked any student of mine what the first thing they should do any time they have data, they would unequivocally state “plot a run chart”.  I, too, have learned by the school of hard knocks that more information is gained by this one simple tool than anything else, and if it is not stable then “do not pass go, do not collect $200”.  Even if data is not collected serially, it’s worth the 10 seconds it takes to plot it.  Outliers, indicators of stratification, etc. will show up quickly and you don’t waste time creating graphs and statistics that are meaningless. 

 

As for trends, my concept is simple.  I tell my classes, clients and mentees, “Anyone can look at the clouds and find a rabbit.  When you look at a scatterplot of residuals or a time series plot, you are not looking for rabbits in clouds.  The patterns, trends, signals, etc. have to be clear to everyone.  Otherwise, move on.”

I have more credentials? -- I doubt it! I have the same MS in statistics that you do...and the same 30 years of hard knox! We are indeed "colleagues." BRAVO! on your approach in the real world. You have no idea how lucky you are to have supportive management. Corporate culture wore me down.

The bigger challenge:  Have you had any luck in applying similar thinking to everyday leadership? -- i.e., cutting executive meeting time where data is involved by HALF and stopping middle management from "drawing little circles" and asking the front line "Why" a number went up...or down...or was red...or yellow...? Try as I might, executive resistance to applying these ideas to daily management is still FIERCE.

ALL the best, Davis

Hi Davis,

I'd like to quote Dr. Wheeler here:

"Unfortunately, there are people who are afraid of clarity because they fear it may not seem profound."

For a species that is naturally lazy, I would think we would all want the simple easy way to get a result. But, apparently management are different: looking for complexity everywhere.

Best regards, Shrikant Kalegaonkar (LinkedIn: http://www.linkedin.com/in/shrikale/, Twitter: @shrikale)

Hi Davis,

I'm Ok with the global approch, but fundamentally is it not an error to use SPC for a not stable process? Or not expected to be stable? And a process we correct is not expected to be stable, no? In that case SPC/Control chart is not the appropriate tool and sure that run chart is more adapted.

Maybe the trainings are to blame or the visualization of the expected response. If I expect a non stable response, for any reason, so I can start to choose the correct tool.

Thank you

Luc

Careful -- one of the lingering myths about control charts is that they are to be used only on processes in control. HOW ELSE can you determine that the process is unstable? It could just be as simple as using a run chart to determine where there are step changes, as in my example. If you can't use a control chart at all, then your process could be in Dr. Wheelers state of "chaos" -- i.e., totally unpredictable. Even though at times it SEEMS as if this is true, how else can you determine it and then use it to look at the effects of your interventions? It NEVER hurts to begin by plotting some process data over time. Most statistics courses (enumerative) treat the process as if it's a "pond" and can be characterized by repeated sampling. In the real world (analytic), processes are more like whitewater rapids! You can maybe characterize one sample, but how would it compare to the next sample? And even though it might SEEM chaotic, it could still be "stable."

Submitted by HMALLE on Fri, 01/10/2014 - 08:34

Clearly written and concise. Great examples.

Too bad in my case (ISO/IEC 17025 Assessments of Environmental laboratories) there is no requirement for control charts only trend analysis.

Your examples illustrate the importance of getting the whole picture of what is going on with the process, so that you can see when the control limits need to be re-assessed.

It was good to see actual dates/times instead of run numbers on a control for a change.

Thanks,Harold

Submitted by caseyem on Fri, 01/10/2014 - 12:36

Why are you waiting a year before creating a control chart? The statistician that waits to get a year's worth of data may be a "hack" by not understanding the importance of responding to events (good or bad) as soon as possible after they occur.

The second phase is clearly seen using 2 out of 3 points beyond 2-sigma limits in the first control chart. However, if the chart had been used as a monitoring tool with signals to be responded to when they occur, the more powerful control chart would have been based on the first phase in which even one point would have been enough to signal a change.

In addtion, the article does not menton why the subgrouping used was appropriate for whatever purpose (also lacking) the control chart was intended to address.

Background: This occurred at the very end of a seminar and it was the first time I had seen the data. They wanted to use what I had just taught them for insight. Initially, I'm not sure there was any other purpose than "accounting for" their activity and using the trend to show that they had made "general improvement" -- i.e., there was no underlying process understanding or attempt to quantify the improvements. It was a passive use of a trend line to "see how we're doin'."

Regarding sub-grouping -- they had one point per week. At the end of the week, they looked at number of events and number of events that complied with a goal -- period. They had no idea about how many 'events' there were. What you suggest leads to maybe they could disaggregate by day-of-the-week to see whether certain days were special causes.

This is an example of what happens in many cases -- one has to take the data one is given and start from there to ask the questions that you pose. And the insights gained from this analysis form a good starting point for the questions. An "outsider" doesn't always have the luxury of planning a collection, but can certainly recommend a better plan. Many times, "hacks" are just concerned with applying tools and being pedantic about which ones. As Dr. Wheeler says, the purpose of analysis in insight.

Add new comment