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Someone once presented me with the graph below (Yes, the y-scale started at "0"). It almost convinces you that there is a trend, eh?
Some of you could be wondering, "What insight might a control chart give?" Here you go:
TEST 1. One point more than 3.00 standard deviations from center line.
Test Failed at points: 9, 50, 51, 52
TEST 2. 9 points in a row on same side of center line.
Test Failed at points: 9, 10
TEST 5. 2 out of 3 points more than 2 standard deviations from center line (on
one side of CL).
Test Failed at points: 4, 6, 7, 9, 15, 48, 49, 50, 51, 52
TEST 6. 4 out of 5 points more than 1 standard deviation from center line (on
one side of CL).
Test Failed at points: 5, 6, 7, 8, 9, 10, 17, 18, 20, 50, 51, 52
TEST 8. 8 points in a row more than 1 standard deviation from center line
(above and below CL).
Test Failed at points: 9, 10
YIKES: Sixteen of the 52 data points generate special cause
signals (some data points even generate multiple signals):
30 special cause signals total! Where does one begin?
Unfortunately, the way I see control charts generally taught,
"obviously," one should initially investigate, individually, the four points outside the three standard deviation limits (Observations #9 & 50-52)...NOT!
As many of you know, over the years, I have developed an increasing affection for the much-neglected run chart -- a time plot of your process data with the MEDIAN drawn in as a reference line. It is "Filter #1" for any process data and answers the question, "Did this process possibly have at least one shift during this time period?" This is generally signaled by a clump of eight consecutive points either all above or below the median or, less often, by six consecutive increases or decreases (indicating a transition to a new process level).
[Any good software package should do this analysis and let you effortlessly toggle between run charts and control charts]
If special causes are observed in the run chart, then it makes no
sense to do a control chart at this time because the average of all
this data doesn't exist -- Sort of like, "If I put my right foot in
a bucket of boiling water and my left foot in a bucket of ice water,
on the average, I'm pretty comfortable."
One of the healthiest things that a run chart can do is get you thinking in terms of "process needle(s)" -- i.e., focusing on the process's
"central tendency."
Most of the time, run charts are glossed over and taught as the
"boring" pre-requirement to learning control charts. I mean, isn't
it far more exciting to jump right to the control chart, with all its bells and whistles, of all the data, look at the special cause signals, and, as previously mentioned, try to find reasons for each individual signal?
The run chart does not find individual special cause observations
because that is not its purpose. That is the objective of the
control chart: "Filter #2" -- plotting the data incorporating the shifts detected via the run chart, which then usually reduces the number of subsequent special cause signals, resulting in a lot less confusion.
The control chart also has an additional power to detect more subtle shifts neither obvious nor detectable in the run chart (future newsletter).
So, what light might a run chart shed on the current situation?
With the y-axis scale a lot healthier and not having control limits
as a distraction, doesn't it look like the "needle" shifted
twice -- around observation #21 and observation #47? In fact, when I asked the clients about those two particular points and their corresponding dates, they looked at me like I was a magician and
asked, "How did you know?" Those dates coincided with two major interventions to improve this process.
As the chart shows, they worked -- two "needle bumps"...NOT a continuously increasing improvement "trend"! Making only those two adjustments, the correct resulting control chart is shown below...with not a special cause to be found (and a possible improvement/transition in the making as evidenced by the last four data points -- time will tell).
[For those of you who might like to reproduce this analysis, the data are contained in the attachment to this e-mail]
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