Many talk about reducing variation to improve quality. Does that include human variation, where everyone takes a different approach to improving overall improvement processes? What would happen if this variation were reduced?

Would some of you lean folks be interested in spearheading an effort to standardize how the “implementation of improvement” is actually implemented? Lean and nonlean practitioners would no doubt thank you effusively. (I can hear you all: “Davis, what planet are you on?”)

Nah, probably not a good idea. But could we all agree that a useful initial strategy might be to address the routine organizational use of data?

Consider: By reducing daily variations in how routine data are used, we’d find that less tampering (i.e., treating common cause as special) helps boost the credibility of more formal efforts, regardless of the approach. Think how much that liberated time and reduced chaos would increase your effectiveness.

One of W. Edwards Deming’s quotes made a lasting impression on me when I first heard it more than 30 years ago: “A good organization will take five years to turn around. Most will take 10.”

So what’s the hurry?

Just curious: Do you have monthly (and/or quarterly and/or even weekly) “How’re we doin’?” meetings like the end-of-year scenario described in my November and December columns last year—about budgets, financials, never events, incidents, near misses, machine downtime, productivity, root cause analyses, returned shipments, rehospitalizations, complaints, customer satisfaction scores, and employee satisfaction scores? 

Usually these vague meetings’ agenda is to discuss:
1. Only the past month’s overall result , e.g., “Were we red, yellow, or green?” (Special cause strategy.)
2. How overall performance seems to be “trending”—using only this-month, last-month, or 12-months ago results (Ditto.)
3. Each individual incident that occurred during the month and how each could have been fixed (Ditto.)
4. Which particular events need individual root cause analyses? (Ditto.)

Those of you familiar with W. Edwards Deming know that his Funnel Experiment ultimately shows that a process in control delivers the best results if left alone. Funnel Rule No. 4, also known as a “random walk”—i.e., making, doing, or building your next iteration based on the previous one—has been influencing Deming’s philosophy for the last 35 years. Isn’t it obvious that many times any resemblance between what one observes vs. Deming’s original intent is purely coincidental?

I’ve said it before: People don’t need statistics. They need to know how to solve their problems. All that’s needed are a few simple tools and a working knowledge of variation to be able to distinguish between common and special causes.

To illustrate, let’s return to last month’s column, where we considered a quarterly review meeting at which a department supervisor was charged with accounting for results vs. meeting arbitrary numerical goals.

Client A came to me for a consultation and told me upfront his manager would allow him to run only 12 experiments. I asked for his objective. When I informed him that it would take more than 300 experiments to test his objective, he replied, “All right, I’ll run 20.”

Sigh. No, he needed either to redefine his objectives or not run the experiment at all.

I never saw him again.

Client B came to me with what he felt was a clearly defined objective. He thought he just needed a 10-mintue consult for a design template recommendation. It actually took three consults with me totaling 2 1/2 hours because I asked similar questions to those required for planning the experiment I wrote about in my column from September 2016.

During the first two consults, Client B would often say, “Oh... I didn’t think of that. I’ll need to check it out.” He eventually ran the experiment, came to me with the data, and asked, “Could you have the analysis next week?” I asked him to sit down and was able to finish the analysis (including contour plots) in about 20 minutes.