Data Sanity (Part 1 of 5): "Which of Dr. Deming's 14 Points should I start with?" Answer: "NONE of them!" (and ALL of them)

Whether you are "doing Deming" or not, the sane use of data organizationally will help any approach to improvement...and create the needed precious time

[Part 2, Part 3, Part 4, Part 5]

Sound familiar??"I'm committed to Dr. Deming's approach [or Six Sigma or Lean or TPS (it doesn't matter)], but executives don't seem to listen any more.?All they do is keep interrupting my very clear explanations?with, 'Show me?some results, then show me what?to do.' I was shocked that my red bead experiment demonstration neither awed nor convinced them – several of them even walked out during it! Help me. Which of Dr. Deming's 14 Points should I start with?to get their attention and results they want?"?

My answer would indeed be,?"None of them."??

By the way, if anyone has to ask this last question, they need to read this,?then heed the following advice from Dr. Deming himself.?Why? Because: they don't quite "get" Dr. Deming's message. From?The Best of Deming, by Ron McCoy:

1. "We are being ruined by best efforts"
2. "Judging people does not help them"
3. "If you stay in this world, you will never learn another one"
4. "Does experience help? NO! Not if we are doing the wrong things"
5. "There is nothing more costly than a hack"

Data INsanity:?"Off we go...to the Milky Way!" Yet Again!

How many of you have to endure quarterly review meetings – the dreaded "account for" results versus (usually arbitrary numerical) goal(s)?and the ensuing “What are you going to do about [insert specific negative variance here]?”?Let me suggest how you can now easily amuse yourself during these dreadful meetings, then get a ton of respect and credibility from your colleagues after the meeting.?

Missed appointments at a medical center were an ongoing costly problem.?Here are some actual data presented at a year-end review, which, as you can see, is afflicted with the current toxic plague of “red…yellow…green” stoplight data presentation, which I consider legalized tampering.?

The table below clearly defines green, yellow, or red performance:

The national standard was 20 percent, and she had been asked at the end of the previous year to stretch and set a "tough" goal.??She decided on half of the national standard – 10 percent.

On the surface, the year's performance was not too bad:?nine greens, one yellow, and two reds. Someone astutely observed, "Both reds were in the second half of the year though. After July's red, there was a nice trend down. Good work! But the trend of the last couple of months and a red December is not a good sign. Can't you do what you did in August, September, and October again?"

This red December performance cast a real pall on things. She was grilled about the disturbing trend and why it was so high so late in the year. Doubt began to creep in whether her improvement efforts were effective – they've obviously slipped.

This was reinforced when the indicator's overall yearly average performance of 10 percent was "up" when compared to the previous year's performance of 9.4 percent. Even more pointed questions resulted, and there was discussion about making the goal even "tougher" for next year.?

Deja vu?

The alternative:?Simpler than You Might Think

The data are right in front of you. You can easily read the individual monthly results and it takes only a few minutes to sketch a run chart of these past 12 months:?

Even though the data are limited, nothing looks amiss. There is neither the presence of any trends – five or six successive increases or decreases – nor a run of eight consecutive points either all above or all below the median (indicative of a shift, e.g. possible improvement).

In another five minutes, you could easily come up with the following process behavior chart (Individuals Chart) [Note to any nitpickers: I'm intentionally?not using a p-chart]:

The process has been stable the entire year, i.e., common cause – (1) all data points?within limits, (2) no special cause tests triggered. Currently, any one month's performance will randomly fluctuate between five and 15 percent, which encompasses all three traffic lights' alleged special cause endpoints. Additionally, any one month can differ from its immediate predecessor by as much as 6.3 percent.?

In other words, each data point is merely statistical variation on a process "perfectly designed" to produce, consistently, 10 percent cancellations/no shows.

The result of all her hard effort was...??And the suggestions to help her?

Oh, and the math required to create this?chart??Basic multiplication and addition and the abilities to (1) count up to 8; (2) subtract two numbers (possible advanced skill: "borrowing"); and (3) sort a list of numbers from lowest to highest.

No belt required.

Do you realize that you are "perfectly designed" to get the process results you're already getting??Unless you know this, any well-intended, but ultimately unsuccessful efforts to improve such a stable process due to treating common cause as special?now become part of both the process's inputs ("efforts to improve the process") and another component of its natural common cause variation!?

This scenario was one of several given to me when a large organization asked me to speak at a Lean Six Sigma conference. By then, it was eight months into the next year, and I was able to add this additional data to the previous year, which resulted in the following process behavior chart:?

I wasn't sure what the new goal was (it really doesn't matter), but, regardless, I saw no change from the previous year (the last moving range is not a special cause). Incorporating this additional data into the calculations hardly changed the common cause limits. The previous year's graph with its twelve data points?was a good enough initial estimate of the situation – so much for the people who insist you must have 20 to 30 data points for an accurate chart.

Then I got really curious and asked whether they had any more data. They gave me the data for the year prior to that of the first chart above, resulting in this process behavior chart for all 32 months:

Despite all her hard work, there is no convincing evidence that anything had changed?over the past 32 months.?

After presenting this to a room full of Lean and Six Sigma practitioners, I was met with a stunned silence.

What should you do?

Let me first tell you two things not to do:

• There is a widespread, naive assumption being taught that a chart showing only common cause indicates the need for a total process redesign. This is not necessarily (and usually not) true!?

You must resist any knee-jerk urge either to form a process redesign team or brainstorm a cause-and-effect diagram answering, "Why do we have cancellations or no shows?"?(Have you all made my past mistake of facilitating several "cause-and-effect diagrams from hell"?)

• And please, please tell me that you've gotten beyond the pedantic response, "Dr. Deming says that common cause means it's management's fault 94 percent of the time and it's up to them to fix it!"

Is it any wonder why leadership might easily turn a deaf ear to you? I'm ashamed to admit that I have been very guilty of this approach – over 30 years ago.?Funny thing, no executive ever said "Thank you!" Besides...

...IT'S?NOT TRUE?(and, by the way, Dr. Deming never said that)

Here is a quote from the 2500-year old timeless wisdom of Lao Tzu's classic?Tao te Ching:?Do by not doing.?

This theme is central to Lao Tzu's philosophy of life and permeates the entire?Tao te Ching?(81 paragraphs of concentrated deep wisdom – I like Red Pine's translation). This concept confused me for quite a while, but once I understood it, I began to practice its wisdom in my consulting, which has made me far more effective. A twist on something we've all no doubt heard says it best, "Don't just do something. Stand there!"

The Tao of Dr. Deming:?Ironically, by not working specifically on any of the 14 Points, you can now implicitly work?simultaneously?on all of them.?

I would highly recommend that you consider the quiet use of data sanity as a catalyst to get out of this world and learn a new one – "built-in improvement" vs. being stuck in "bolt-on quality."?You can neither build-in improvement using a bolt-on quality process nor be "a little big pregnant" about it.?But stand there!

Lao Tzu's wisdom once again applies:?The journey of 1000 miles begins with a single step.

My challenge to you. Rewind, humbly begin that journey again and take this simple first step:?temporarily stop quoting Dr. Deming "chapter and verse" at any opportunity and simply plot over time a number that makes your organization "sweat." Begin to apply some of what I have talked about to your graph and watch how the conversations and your understanding of variation change.?

?So what should one do after constructing this plot??To be continued next week...

Hint:?By not working on meeting the goal, you will meet the goal.

Confused? Rewind...

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Regardless of your improvement approach, Chapters 1 to 4 of my book Data Sanity (10 chapters, 400 pages) teach a robust, results-oriented leadership philosophy designed to catalyze transformation to a kaizen culture. [U.S. Amazon has currently discounted it 24 percent]

• Chapter 2 covers data INsanity (such as above) specifically at the executive level with 10 real examples of everyday data. Alternative analyses are shown that will free up precious time to focus activity where it should be.
• Chapters 3 and 4 are designed specifically to understand the cultural landmines that await you and what to do about them
• An ebook edition is also available from the publisher
• For UK it is available?on Amazon UK