Woodside Quality Pathways

Data Homogeneity - An excerpt from "Data Analysis with Minitab"

2011-03-30T13:23:00.000-05:00

The following is an excerpt from my Data Analysis with Minitab course. I thought this was too important; it's ignored far too often. For more information, see Davis Balestracci's Data Sanity (the paper or the book), or Don Wheeler's The Six Sigma Practitioner's Guide to Data Analysis.

Shape, Center and Spread: Histograms

We have discussed simple graphical analysis in histograms. Remember, a histogram allows us visually to get a feel for shape, center and spread of a set of data. Adding the specification limits to a histogram allow us to see performance in relationship to specifications, and any outliers might show up on a histogram.

Important to note: A histogram is a snapshot in time. It shows how the data are “piled.” If the process is not stable, we can’t make any assumptions about the distribution. So, while a histogram is a very useful tool, it’s more useful when used in conjunction with some time-series plot. The following scenarios, adapted from Davis Balestracci’s Data Sanity, illustrate the importance of looking at process data over time.

These scenarios depict the percentage of calls answered within 2 minutes for three different clinics in a metropolitan area. All three sets of data were collected during the same 60-day time period.

What can you say about the performance of the clinics, based on the histograms and data summaries?

The summaries presented in the histograms all show unimodal, fairly symmetrical, bell-shaped piles of data. The p-values for the Anderson-Darling tests for normality are all high, indicating no significant departures from a normal distribution. There are no apparent outliers. The mean percentage for each clinic is a little over 85%, and the standard deviations are all around 2.5%.

The histogram, though, is a snapshot. It only reveals how the data piled up at a particular point in time. The graphic, and its associated summary statistics, can only represent what’s happening at the clinics if the data are homogeneous. These data were gathered over time: what would a picture of the data over time reveal?

The control chart for clinic A is below. Although the histogram showed the same bell-shaped pattern and high p-value for the normality test, you can easily see that the histogram can’t represent the data for clinic A; we caught it in an overall upward trend, and so a histogram of the next sixty days will no doubt look very different from the histogram of the first sixty days.

Likewise, the control chart for Clinic B…

This chart shows that what we are actually looking at is three different processes, the data for which just appear to stack up to a single, not-different-from-normal distribution. In fact, by slicing the chart at the shifts, we can see that there are three distinct time periods when the variation is in control:

The only one of the three clinics with a stable process is clinic C. Looking at Clinic C’s plot over time, we see the random pattern of variation within the control limits. We can now expect that the histogram will not change shape significantly over time, the parameters will all remain about the same, so our assumptions about distribution will be valid and useful.

Contra the 1.5-Sigma Shift

2011-03-11T07:04:00.001-06:00

I'm currently working up some simulations to try once again to put the "1.5-Sigma Shift" to bed for good. The simulations seem to prove out what I've long felt about the shift, but I have one to run yet to demonstrate effects of the shift -- and detectability -- on a high-volume operation.
My understanding of the origin of the use of the shift is this: people at Motorola apparently had some data that showed that you could have undetected shifts of up to 1.5 Sigma; this would certainly be a valid concern when you have high-volume production with low monitoring rates.
As an example of what can happen when you get shifts in high volume enterprises, I'll mention Don Wheeler's Japanese Control Chart story from Tokai Rika. They were running about 17,000 cigarette lighter sockets per day, and had found that they could detect shifts using one subgroup of four sockets per day. They selected one at 10 AM, 12 PM, 2 PM and 4 PM each day, and kept an XbarR chart on the data. The only rule they used was rule 1, (a single point outside the control limits).
Suppose they had decided to add rule 4 of the Western Electric Zone Tests (a run of eight above or below the centerline--Minitab and JMP call this rule 2 and use a run of nine). This would mean that if a shift in the mean occurred and and the first signal was a rule 4 signal, they might run 8 x 17,000 = 136,000 sockets at the changed level. This would be unlikely to result in any nonconforming product (since they were using less than half the specified tolerance), but from a Taguchi Loss perspective, it's not desirable.
So it might be prudent to study your processes and either sample more frequently; or you can "play the slice" as Motorola did, and assume that you might have undetected shifts up to 1.5 sigma on a regular basis. If you do this, you will end up only giving yourself credit for a Cpk of 1.5 when you actually have a Cpk of 2, and you end up estimating much higher proportions defective than what you actually get. As a fudge factor for setting specifications, it's sloppy but safe, I guess.
So let's talk about what Motorola might have gotten wrong.
1. My understanding is that they (much like Tokai Rika) only used rule 1. This would keep them from picking up some of the other signals. I don't have the data from the studies they based their conclusions on, but they might have used a different value than 1.5 had they had the added sensitivity lent by the rest of the Western Electric Zone Tests.
2. "Undetected shifts" are, logically, undefined. If we operationally define a shift in the mean by using some combination of the Western Electric Zone Tests, then any long run without a signal is not (by definition) an undetected shift. Logically, you can't detect an undetected shift. We can define the difference between long-term variation (dispersion characterized by the standard deviation of the entire data set) and short-term variation (dispersion characterized by rbar/d2 or sbar/c4). If you want an operational definition of "undetected shifts," the delta between those two measures of variation might be useful. It's silly to assume, however, that there are some bursts of variation that average 1.5 sigma and somehow escape detection. Not only that, but the false alarm rate itself induces false signals.
3. It's damned difficult to induce a shift in a simulation that isn't picked up within a few subgroups. In one of the simulations I've been working recently, I created 10,000 random variables from a normal distribution, with a mean of 50 and a standard deviation of .5. I cleaned up the false signals by substituting other randomly-generated numbers for those outside the control limits, and rearranging the order to kill off the rule 2, 3 and 4 signals. I then ramped up a 1.5 sigma shift in .05-sigma intervals, 50 at a time. An ImR chart caught the shift within the first 8 subgroups (and I had only shifted .05 sigma at that time). That was for a gradual shift; an abrupt 1.5 sigma shift signalled immediately.
4. The only way you get the results the process sigma calculations give you is if all the data are shifted 1.5 sigma; in other words, the mean has to shift 1.5-sigma and stay there. So you have a control chart, and the centerline is on 50, and the upper control limit is at 51.5, and you don't have any out-of-control signals...but the actual process mean is 50.75? In what world can that happen? Those are the conditions you would need, though, to actually get "3.4 defects per million opportunities" in any process showing six sigma units between the process mean and the nearest specification limit (a process sigma of six). Occasional process meandering to as far as 1.5 in either direction, if it could go undetected, would result in significantly lower DPMO than what the Process Sigma Table predicts.
I believe it was a mistake for the statistical communiy to allow this to become an informal standard. We are about quantifying uncertainty, not about arbitrarily adding large chunks of uncertainty. The "process sigma" is already counterintuitive. If you tell managers their process sigma is 3.2, the first question they always ask is, "So what does that mean?" It's much better, I think, to use DPMO...it makes sense to most people, doesn't require translation, and doesn't have require assumptions about shifts that probably don't exist. It also acts as a sort of Rosetta Stone, allowing to translate between data from counts and data from measurements. We do have to remind managers that DPMO is still just a best estimate based on current data, but it's certainly more meaningful than the "process sigma."
There is a danger that it will become more than just an informal standard soon. There is a proposal for a new ISO interational standard for DMAIC; it does include the Process Sigma, and the language in the proposed standard says we will adjust by 1.5 sigma "by convention." Anyone interested should watch for opportunities for public comment on the standard, either through TAG 69, NIST, or ISO.

"Best Practices"

2010-11-02T12:46:00.002-05:00

Whenever I hear someone talking about a "best practice," I always add the Homer Simpson modifier: "Best practice SO FAR..." What this term means is just that it's the best solution yet to some set of problems or circumstances.

My experience has been that they don't stifle creativity in creative people...they can serve as springboards for further creativity or improvement. I think they are best used just that way...as you're studying a process, and you're analyzing the cause systems that create the outputs and outcomes, you will look for aspects of the systems that can be worked on to optimize the outcomes. Looking at "best practices" is like looking at any other process...we're just starting with a process that has already been improved before (at least for this set of inputs).

The downside to "best practices" comes from leaders who hear the term "best" and decide that it must actually mean "best it could be." Managers who do this will try to force replication, without knowing what to replicate or why it worked in its original environment (and whether it will work in the new environment). In that case, it will certainly create road blocks and slow down process improvement.

A Story about Systems Thinking

2010-06-01T11:16:00.000-05:00

In a class a few years ago, we asked students to talk about quality-related projects on which they were currently working. The class comprised a number of people from several business units. At one table, a project leader stood and told us all about his project for the marketing unit: they were exploring server consolidation. They knew that only a fraction of the capacity of each of many of their servers was in use; they had a large number of servers, therefore, that could be consolidated. Because this business unit "rented" the servers from the Shared Services unit, they figured they could save $250,000 per year by consolidating servers and turning them back over to Shared Services. The class politely applauded.

Next up was a person from the Shared Services unit, who talked about his project, which was developing a new service they could "sell" to the marketing unit, which would generate over $250,000 in new revenue for Shared Services. The class again politely applauded.

I asked, "What's wrong with these stories?"

Blank stares (I'm the idiot!)

I tried to give them a hint: "How does the company benefit from these projects?"

A tentative hand, then (in a tone that indicates that surely, I AM the idiot), "Well, the company saves half a million dollars! Why wouldn't THAT be a benefit?"

I asked, "How is the company saving a half-million dollars?"

Again, incredulous stares..."You're the stats guy...maybe you should have taken accounting instead...250,000 plus 250,000...isn't that half a million?"

I pointed out that marketing was "saving" a quarter of a million by not "renting" a quarter of a million's worth of servers from Shared Services, but that Shared Services was "making" a quarter million by "selling" a quarter-million's worth of new services to marketing. So they just dipped a bucket into one end of the lake and dumped it into the other end...and some evaporated while they were transporting it, because of the cost of the project.

Eventually, we did work out that there were benefits...increased server capacity, benefits from the new service, etc.. Most of these numbers (the actual benefits) were "unknown and unknowable" numbers. None of those benefits had been discussed originally, because the "knowable" numbers were easily calculated (and wrong)...

What is "Productivity?"

2010-04-23T10:18:00.001-05:00

In one of my stats classes, a nursing student mentioned that they measure productivity at her hospital. It's measured this way:

"To get the productivity ratio; you take the total number of hours worked by nursing ( all nurses on the unit) and divide that by the total number of patients on the unit at midnight. For example if there are 4 nurses per shift and they work 12 hour shifts then that is 96 hours; then say there are 30 patients on the unit at midnight; divide 96(nursing hours worked) by 30(# of patients) = 3.2."

In my consulting practice, my clients often tell me about productivity numbers. This, to me, is one of the compelling questions for those of us in the quality profession: what is "productivity?" To keep the discussion going with my student, I posted the following, to raise some of the issues I've seen organizations struggle with over the years:

This is one problem with many of the metrics used for "productivity." By trying to boil it down to the simplest, easiest to use ratio, you leave out a lot of important information. What is productivity in nursing? Is it just being there? Clocking in and clocking out? Most of the nurses I know work pretty hard, but even the amount of work completed wouldn't necessarily reflect the value of a nurse. A number of years ago, a paradigm came out called ABC (for Activity-Based Costing) that measured productivity in terms of activity...how much were you actually doing? Seems reasonable, but it doesn't necessarily reflect value, any more than motion reflects progress.

Nursing can be a lot like being in the Military. I can't tell you how many watches I stood in 20 years...tens of thousands of hours where no one took a shot at anyone. If my job was to kill enemies, then most of the time, I was a waste of taxpayer dollars. Did that mean we didn't need to be there? Our job was not to be constantly doing something, but to be alert and vigilant so that if something did happen, we could take immediate action.
Similarly, there are nights, even in Emergency Rooms, that are slow. Would you send everyone home, to keep your productivity numbers high? Or is there value in having some knowledgeable and experienced caregivers there for the probable event of an emergency?
What is the productivity measure tied to? Can you show that a higher ratio correlates to better outcomes? Higher profits? If it's just cost-cutting, it's hardly "productivity;" it's just lack of having to pay for "non-productivity."
The point is, productivity is difficult to measure, and productivity is in the eye of the recipient. What the patient may value, the administrator may not. What the doctor may value, the HMO may not. What the nurse may value, the patient may not (one example; waking a surgical patient up every hour during the night to check vitals).

Of course, I guess the whole point boils down to value...who defines that, how you prioritize the "whos." This is where you must be able to understand something about systems thinking.

Creating a Culture of Process Improvement

2010-03-11T09:07:00.000-06:00

This morning one of the questions posed by readers of IQ Six Sigma posed the following question:

“My department is charged with creating a "culture of process improvement" within our zone. We're struggling with what that looks like once we've created this culture. Looking at the Toyota model, they challenge employees to look for PI opportunities every day. What exactly does that look like, and what measurements should we consider (i.e. number of PI suggestions with managers being held accountable for X number per quarter, etc.) I'd like some ideas.”

My "short" answer (admittedly, this answer could have--and has--filled books):

Well, one thing you for sure don't want to do is set some quota for suggestions. You may already be faced with an uphill battle, because the leadership at your organization is actually the entity that has to create that culture of process improvement. If they are just rolling it downhill like any other MBO, it suggests that they don't know what they are doing.

Toyota does challenge employees with looking for improvement ideas. One of the ways they do that is by implementing them. Most suggestion boxes go unheeded by employees because they go unheeded by management. At companies like Toyota, they use mechanisms such as Quality Function Deployment to communicate the voice of the customer to everyone in the organization. It allows people on the production line a clear line of sight to the mind of the customer and the organization's leadership.

How do you establish this culture? Well, if you have to do it locally, start by knowing that you may not be as successful as you would if your leaders were leading. Empowerment is a big piece of the pie...you have to let people know they are empowered to make changes. You have to have mechanisms in place that let changes be approved at the lowest possible level. This doesn't mean that any line worker should be empowered to make design changes that require retooling the entire line without some study, but small local changes should be able to be made and standardized locally, as long as they don't suboptimize the system.

So, start by listening to people. I once found an operator potting an assembly with epoxy, using a pneumatic syringe...one of the primary quality characteristics in this assembly was that the epoxy had to be free from air bubbles! This line worker had been telling people about it for some time, but no one would listen; after all, an engineer had designed that workstation--who was this uneducated line worker to question the engineers? So, again, listen! Your people have the answers to most of your quality problems. It may take some time before they will talk (because it's a culture change for them, too).

It's not enough just to listen, though, you have to act! If you don't act on what you hear, and act promptly and visibly, soon you won't have anything to listen to. If you listen and act, you'll soon find that you can't keep up with the suggestions for improvement. That will be the beginning of changing the culture to one of improvement.

You also have to be a champion. You have to be out there talking it up, walking the talk, aggressively and visibly removing obstacles to improvement. Align whatever passes for reward and recognition in your zone with PI, to let people know that it's important. Constantly let people know what you value; proactively seek (and take) opportunities to demonstrate those values and beliefs. Measure important process and throughput measures...use SPC so you don't make boneheaded decisions about those measures.

As to what to measure to gage progress along the cultural change path...well, there are lots of things you can measure. Probably the most important are results and employee morale. If your error rates, rework rates and scrap rates are going down and your throughput is going up, it's working. You can also measure suggestions received; but you should use that number as the basis for a perhaps more important metric: percentage of suggestions implemented. This is certainly not an exhaustive list...there are numerous things you can measure. Deming said that the most important numbers are unknown and unknowable; this is what makes measuring what we can measure so important.

Standardize, do 5S, start holding 5-10 minute meetings at every cell every day, to go over quality metrics, suggestions entered, suggestions implemented (and get ideas for implementing suggestions), recognize people for advancing continuous improvement.

Bonus Plans

2010-02-08T11:54:00.000-06:00

In one of my LinkedIn Discussion Groups, we have been going back and forth on the idea of bonus schemes for a couple of weeks now. Today, we got a thoughtful post from John, who said that "Incentives and reinforcement are part of what I design." He offered insights as to how a system might be designed. I responded to one of his ideas.
He pointed out that "bonuses have been factored into sales compensation since the dawn of time because we know that vigorous sustainted effort is required," then asked, "Why here and not in all key jobs?" One of his reasons: "Execs are unfamiliar with the ways that objective measures can be designed for staff, managers, and production people," and goes on later to suggest that "Incentives need to be based on objective measures of performance, and that "ALL incentives are ultimately individual."
While these ideas seem to make some common sense, things that we've learned over the last 30 years or so suggest that they bear some scrutiny. Here's my reply:

_________________________________________________

I think Scott points to a couple of drawbacks to many bonus schemes. There are some problems with one of his fixes, though.

Let's talk about objective criteria: sometimes they do exist, but it's not as often as we think, and it's never (an I do mean NEVER) as clear-cut as we think. Anyone who's ever seen the Red Bead Experiment can attest to that. It's also almost never possible to separate the performance of the person from the performance of the system in which they operate. So, even when we talk about "anyone who reaches the goal gets the bonus," we assume that it's possible for everyone to reach that goal, completely independent of all the factors that drive the system.

Let me illustrate with an example from my days in the Military:

An Army school convenes twice per year, and runs for 5 months. One class starts in late Fall, the other in late Spring. Each class is led and instructed by two soldiers. During a study of these classes 10-11 years back, one of these instructor teams clearly excelled, by all the “objective” criteria used to measure performance: very low dropout rates, very high academic achievement with very little remediation, almost no legal or medical problems, excellent advancement rates for graduates, etc. The other team, however, didn’t fare so well; their dropout rates were very high, most of their students struggled to pass the weekly exams (despite extensive remediation and night study), they had numerous problems reported from both base security, military police and community police, a high incidence of sick days, and most students who graduated required a lot of extra work to gain adequate proficiency, once they arrived at their units.

Of course, the team with the highest scores on all the criteria won Instructor of the Quarter/Year, Soldier of the Quarter/Year and other achievement awards given by the training command, and were consistently ranked in the top 5 by their commanders—all this, of course, led to rapid advancement for these soldiers

The low-scoring team ended up at the bottom of the heap, in the “not ranked” category, and received letters of reprimand for their poor performance.

Eventually, someone noticed that this difference in performance transcended the soldiers themselves…ALL the Fall classes were better, and ALL the Spring classes were worse. As it turned out, there was a great logical explanation for all of it.

The classes that convened in the late Fall comprised students who had come into the Army right after High School graduation, many on delayed entry programs. They had enlisted for this particular specialization. They were highly qualified and highly motivated, both for the Army and for this school. In contrast, the Spring classes were made up of people for whom the Army was something to do after they had failed to find a job, and who had been put into this class to fill a quota. Some had needed waivers to get into the Army; many had required waivers to get into the class.

Ironically, if you looked at the workloads for the instructor teams, the hardest-working and most creative teams were those for the Spring class. They had to be, just to survive. They had to conduct remedial sessions at night study, as well as before classes, lunchtimes, weekends, etc. They had to continually push the envelope to find new and better ways to get these challenged students to learn. The other team largely skated through the duty…very little extra time, no extra thought needed.

This same sorry story still happens every day in Military recruiting. Recruiters in very populous areas in more patriotic-leaning states have very few problems meeting quota. They get awards, advancements, etc. Those in rural areas work many times harder and often don't make quota, and are forced to accept low evaluations and sometimes humiliating "remedial" sessions where senior recruiters come in and yell at them like drill sergeants ...many of these are just back from Iraq or Afghanistan.

Some Problems with Conditional Probability

2010-01-11T12:58:00.000-06:00

A lot of people in my statistics classes struggle with conditional probability; you may be in the same boat. If you are, though, please don’t feel alone. A lot of people get this (and simple probability, for that matter) wrong. If you read "Innumeracy" by Poulos or "The Power of Logical Thinking" by Vos Savant, you'll see examples of how a misunderstanding or misuse of this topic has put innocent people in prison and ruined many careers. It's one of the reasons I'm passionate about statistics; it's counterintuitive for me, too. It's not easy to work out in your head, unless maybe you do it all the time. I always have to build a table.

When confronted with conditional probability, my advice is that you be completely process-driven; identify what's given, then follow the process and the formulas religiously. After a while, you can start to see it intuitively, but it does take a while. It's all about what you are given, and how you define things.

In my MBA stats class, one of the problems that always stumped the students was a conditional problem:

“Pregnancy tests, like almost all health tests, do not yield results that are 100% accurate. In clinical trials of a blood test for pregnancy, the results shown in the accompanying table were obtained for the Abbot blood test (based on data from "Specificity and Detection Limit of Ten Pregnancy Tests" by Tiitinen and Stenman, in the Scandanavian Journal of Clinical Laboratory Investigation, 53, Supplement 216). The disclaimer in the journal stated that other tests are more reliable that the test with results given in this table.

	Positive Result	Negative Result
Subject is pregnant	80	5
Subject is not pregnant	3	11

“1. Based on the results in the table, what is the probability of a woman being pregnant if the test indicates a negative result?

“2. Based on the results in the table, what is the probability of a false positive; that is, what is the probability of getting a positive result if the woman is not actually pregnant?”

Everyone would just try to look at it as though there were no conditions...they would say, 5/80 for question 1, and 3/80 for question 2. The first question, though, is asking "what is the chance of being pregnant, given a negative result?" There were 16 negative results, and of those, 5 were pregant. So the answer is 5/16, or 31.25%. For the second question, it's what is the probability of a positive, given that the woman is not pregant. In this case, there are 14 non-pregnant women, and 3 of those got a positive result. So that's about 21.42%.

These numbers, and this idea, are really important--that is, they carry real-world import. Some statisticians make their living explaining these concepts to juries. People get fired or arrested because of false positives on urinalysis and other tests, because there is a general impression that they are far more reliable than they actually are.

Let’s look at a different example. In the military, people are given random drug screenings. The test is “certified 99% accurate.” I was always told that this means that if you do drugs, and you’re tested, it will catch you 99 percent of the time. We think, “logically,” that this means there is only a one percent false negative rate…that the fact that someone who does drugs doesn’t get caught one percent of the time indicates that one percent false positive rate. Worse, we assume that if the “false negative rate” is only 1 percent, the false positive rate must also be one percent…it’s just common sense, right?

But “common sense” isn’t…it’s neither common nor truly sensical. Look at it this way…suppose we test 100,000 service members. Suppose further that .1% or 1 in a thousand service members actually do drugs. We might get this:

	Do Drugs	Don't Do Drugs
Test Positive	99	999
Test Negative	1	98,901

Tables like this are informative, but they don’t tell the whole story. You can see from this that the company is technically correct…at least in this case, of 100 people who did drugs, 99 were caught and 1 was not. But a false positive rate and a false negative rate are made up of more. To get to the whole story, it’s also good to do the marginals, or row and column totals:

	Do Drugs	Don't Do Drugs	Total in Row
Test Positive	99	999	1098
Test Negative	1	98,901	98,902
Totals	100	99,900	100,000

Numbers like this, the numbers of people tested, are very important. This helps us figure out our givens. The false negative rate is not the number of people who did drugs and tested negative. It’s the number out of all the people who tested negative who actually did drugs. In this case, the false negative rate is much better than advertised…it’s 1/98,902, or .00001, about one in 10,000 who do drugs and get tested get away with it.

The consequences, though, are on the false positive side…this is where people get turned away for employment, get fired, etc. In the case of the military, a lot of people end up in a lot of trouble with the random urinalysis program. While we want to be cautious, and we don’t want a lot of druggies flying or controlling aircraft or tanks or other deadly weapons, we should also be concerned that we might be ruining careers unnecessarily. If we look at the table, the “common sense” interpretation of the false positive rate would be 999/100000, or 0.999 percent, very close to the one percent that we assumed initially. But, as astounding as it may seem, considering the number of people that are convicted each year because of this assumption, this is entirely incorrect!

The actual false positive rate consists of the number of people incorrectly identified as drug users, or the number of non-drug users out of the total number of positives. In this case, that’s 999 out of 1,098, or 90.98%! In other words, your chance of actually being a drug user, given a positive result on this “99% accurate” test, is only 9.02%!

Yes, it’s tricky. No, it’s not easy. But it’s important. It touches lives. Juries, lab technicians, doctors and nurses, lawyers, employers, employees and patients who don’t understand this put either themselves or others in peril every day.

More on Six Sigma Metrics

2009-11-20T08:41:00.002-06:00

There is an excellent article in this month's Six Sigma Forum magazine about the process sigma. The authors have examined a lot of the literature about the metric and come to some very interesting (and, in my opinion, accurate) conclusions. Six Sigma Forum offers an email address to respond to each article; this was my response:

I was very excited to see this article. I have been questioning this for years, and had just started doing some research with a view toward writing a similar article. I heartily agree with most of the authors’ points. I think they did a great job of catching at least the high level of the controversy and their enumeration of the advantages, disadvantages and myths should be made required reading for anyone in a Six Sigma role, especially Master Black Belts and Black Belts.
An area to which I had planned to give a bit more attention is the Statistical Process Control component of the metrics equation. Before you can make any assumptions about capability or process performance over time, you must measure it over time, and it must display a reasonable degree of statistical control. Only then do you have the assumption of homogeneity of data that makes any assumptions about an underlying distribution valid.
A foundational basis of SPC also sharpens the focus of the discussion surrounding the shift, and the short-term/long-term question. While it is possible for a process in a state of statistical control to have some underlying shifts that are not detected using Shewhart charts, sustained mean shifts of up to 1.5 sigma will almost certainly be detected within 10 subgroups following the shift, if the four most common Western Electric Zone Tests are applied.
Now, if you’re taking four samples per day for your monitoring subgroup, from a high volume operation—say, 2,000 units per hour—that might mean 9 days before the signal shows up; you’d have run approximately 64,000 units from a process whose mean had shifted. In those situations, CUSUM or other schemes more sensitive to gradual sustained shifts might be more appropriate.
Having said all that, though, it’s unlikely that shifts of that sort will go completely undetected in a well-monitored process. What we are essentially saying is that some assignable-cause variation is going to show up randomly, and for time periods too short to be detected by our charts. In that case, I believe that the local measures of dispersion used for control chart factors provide a reasonable way to operationally define short-term and long-term variation, if you must. R-bar/d2 and S-bar/c4, used to calculate control limits, provide very good estimates of short-term (within-subgroup) variation. Comparing that estimate with the standard deviation for the entire set of data will reveal whether any significant shifting has taken place. This would provide a fairly unambiguous test for shifts. Whether and how you want to define and measure the magnitude of any shift detected using this method could be another discussion; the fact that this argument is taking place without a method is another source of confusion.
It seems that we not only have to discriminate between short and long-term sigma, but we have to have a “short-term sigma assuming long-term data” and “long-term sigma.” Apparently, we can also have negative process sigmas, with DPMO greater than one million! Just look at the commonly-used sigma calculator at www.isixsigma.com, and click on the link for more information about the calculations. Their explanation, that sigma is just a z-score, shows how far we have come from an understanding of capability in some of these discussions. I think most of this falls under Wheeler’s category of victories “of computation over common sense.”
I can understand if you want to gig yourself 1.5 sigma to make your process sigma align with the one in all the tables, accepting the Motorola shift. What I don’t understand is why you would then decide that, in the longer term, it’s going to shift another 1.5 sigma (this seems to be the logic behind the “benchmark Z” used in some software packages these days.) So…now six sigma is actually three sigma by default? What’s the point?
I recently taught a Six Sigma certification exam prep course for my local ASQ chapter, and the primer for that course—a popular reference used by a huge number of applicants for that ASQ certification—suggested that, given binomial data (a proportion defective), you should use a log transformation to force the data into an approximation of the Poisson. I don’t know why anyone would do this, unless you really have to be able to have that negative process sigma and a DPMO of more than one million. For years, I have been doing just the opposite; transforming Poisson data to Binomial using e-DPU to estimate DPMO.
This brings up another excellent point from your authors: we need to figure out how we are going to count units and opportunities. My own belief is that we should limit ourselves to definitions that end up providing an estimate of proportion defective. An opportunity, in that case, would be the most discrete thing we could count; in other words, there could be no more than one defect per opportunity. This would get rid of the “negative sigma” nonsense.
I strongly endorse their recommendation that we use DPMO. The procedure they outline for finding DPMO is straightforward and useful. Calculating DPMO this way would provide a reasonable estimate. If we want to err on the side of safety, we might continue to use the 1.5 sigma shift for high-volume processes, or no shift for lower-volume processes. DPMO is a more intuitive metric, and would keep people from having to go to the table to translate DPMO to process sigma, and then decode it again later for anyone who wants to know what it means. That’s unnecessary rework, something we’d all like to avoid.

Six Sigma Metrics...Why?

2009-10-14T14:35:00.002-05:00

I have a client who is in a tizzy over whether they are recording short-term or long-term process sigmas. They are not high-speed, high-volume manufacturers, so I told them just use DPMO; don't worry about process sigma. It's not an intuitive metric anyway, nor is it anything like an accurate estimate of what to expect. And once you start putting "long-term" and "short-term" stuff in, you end up with really stupid non-intuitive discussions. I have yet to see an explanation of "short-term sigma using long-term data" that helps anyone understand anything. I'm not quite in the school of the purists who believe that, if you have a process in control, you don't have any undetected shifts. I can show you any number of real data sets to prove that...even using all four of the Western Electric Zone tests. In the interests of trying to maintain some standard metric with the rest of the Six Sigma world, I have had all my clients who were interested in using process sigmas use the standard Motorola tables or the calculator at isixsigma.com. This calculator takes your number of defects and opportunities and looks up a Sigma, applying the 1.5 Sigma shift. The assumptions for the calculator say that it assumes long-term data but provides a short-term sigma. Why should we care, and how does this terminology help anyone or make sense? Long-term data are supposed to be data that come from a process that has run long enough for some shifts to have taken place. If we're going to talk long-term, what we should use for an operational definition is "data that display a significantly different overall standard deviation than that of its local dispersion statistics." What all this is intended to provide is an estimate of what we can expect from a process in the future, given a stable process. Isn't the idea derived from a capability study? Essentially a cpk of 2 (process mean six sigma units from the nearest specification limit) equated to "Six Sigma Quality." If you buy the Motorola 1.5 sigma shift, then you gig yourself a sigma and a half, so it's really 4.5 sigma; instead of 2 ppb defective, you get 3.4 ppm. Now, there are a lot of people who object to predicting parts per million non-conforming from a capability study; I'm not one of those, as long as everyone involved realizes that we can't take any of those predictions too literally or assign too much precision. If I were claiming a process sigma of 6, and a count of defectives in the next million opportunities turned out to be 5 (or even 10 or 15), I wouldn't re-assess my sigma.Another thing I've seen lately is using a transformation to transform perfectly good data to poisson data, then deriving a DPMO and sigma from that. Now there's a great example of what Don Wheeler would call "a victory of computation over common sense." If I have 100 defective parts in a run of 1000, I have 10 percent defective. Assuming that 10% is stable over time, That equates very simply to a DPMO of 100,000. This is also assuming one opportunity per unit. It does make sense to me to do the opposite...if I'm getting more than one defect per unit (so I probably have Poisson data), it makes sense to me to transform the data using e^-dpu; that gives me an approximation to the binomial that lets me estimate DPMO.My biggest question, though, is why--other than to comply with a very short-lived tradition--should we use process sigma at all? It certainly doesn't provide any more information than DPMO, and we always have to translate it into DPMO anyway. If we must continue to process sigma, can we please just can all the short-term/long term stuff and (in a nod to standardization, even if controversial), just assume that shift happens and calculate DPMO from a stable process and use the sigma table? Maybe I'm wrong, and there is some real great reason to continue doing this, but I need an explanation and justification that makes sense. It seems to me that a lot of this is arbitrary and unnecessary.

2009-09-02T11:10:00.003-05:00

During a recent discussion in the LinkedIn Deming HR group, one of the discussants posted the following link:
http://www.ted.com/talks/view/id/618
I can't recommend it too highly. It's a talk by Daniel Pink, about the science associated with rewards for performance. Anyone who was a follower of Deming, and anyone who has read Alfie Kohn, is already familiar with the concept that reward for performance can be harmful. Daniel's discussion, especially his piece about the candle problem, is an eye-opener. I would like to try that experiment at a conference or with a large class sometime.
My question or concern is the same as Daniel's: why do we continue to do, in business, what the science says is exactly the wrong thing to do? In its most public fashion, we do it on a grand scale with CEO compensation.
Daniel does a good job of pointing out that pay for performance, when it's linked to any job that requires thinking or problem solving, does more harm than good. What his talk doesn't cover, though, is the systems thinking aspect of this topic; the fact that you can't measure the performance of anyone in isolation. It's often the system that creates most of the performance we attribute to individuals.
This is certainly a worry in education these days, with many government officials pushing for performance pay for teachers. Stuck in the old carrot and stick paradigm, with nothing to go on for metrics but aggregate standardized test scores, these schemes will go a long way toward further suboptimizing our education system. Instead, put the money you might use for rewards into building systems like those built by Geoffrey Canada in the Harlem Children's Zone. Canada showed that by taking a systems approach you can improve the performance of even the most underpriveleged student populations, and put those students on an even playing field with the most priveleged.

What Happened to the Deming Philosophy?

2009-08-10T14:45:00.004-05:00

This is taken from part of a discussion on LinkedIn. Rafael Aguayo, a Consultant in Quality, Management and Strategy and Instructor at Stony Brook University, discussed this history and posed these questions today. See my response below.

Rafael's Post:

Two people in the same situation can have very different experiences. So let's consider some more objective measures of what has occurred. In the late 1980s and early 1990s Quality in the US was largely associated with Deming. Media articles regularly referred to Deming as the preeminent quality expert. Success at Ford, Harley Davidson and many other companies, that had adopted some or much of Deming's ideas, created interest and excitement in quality and other people and movements tried to position themselves as the next big thing. Specifically I would mention Reengineering and Six Sigma. At the time there were many successes accomplished under the banner of TQM and Six Sigma was a minor influence.

My assessment is that Deming-influenced quality represented 50% of the market. Yes, that is subjective, but I think anyone who was active at the time would have said that his influence and recognition was profound. Given the successes,the publicity and seeming strength of the quality movement I would have expected that by 2009 every hospital and US corporation to have been talking red beads and funnels in addition to statistical tools.

Instead, when I joined this group there were 171 members, while the Lean Six Sigma group had about 30,000. That translates to a market share of .57%. Even allowing an order of magnitude error this represents but 5.7% of the market. I have to ask what happened?
The logic of marketing is very different from formal logic. When GM discarded the Oldsmobile brand they expected those buyers to buy other GM cars. Instead they went elsewhere. If a significant amount of success were achieved under the banner of TQM and the brand is then disavowed then those successes are disavowed. At the time I observed the disappearance of some successful and respected consulting firms, such as Joiner Associates. And the interest in Deming shrank precipitously. It is possible that this outcome was inevitable. Once Jack Welch endorsed Six Sigma it may have been inevitable. Also the fiasco of Reengineering that some organizations, including ASQ, implicitly endorsed could not have helped. But whatever the causes the current reality is disappointing. While you say that has not been “your experience” your actions say something very different. By obtaining black belt certification and selling your services as such you implicitly acknowledged that Deming or SoPK is not a viable marketing brand.

It is not just that the market for a more profound understanding of quality has shrunk. Today young people who appreciate the importance of quality and process need not even once see a demonstration of the Red Beads or the Funnel. They can go out and do their best, blissfully unaware that their actions are tampering, on a massive scale. Luckily there are still many people laboring in schools and in firms with a deeper appreciation of the fundamentals.

Maybe I am hallucinating, but the reality of today is so far from what I would have expected that I must ask the question what happened? And what can be done to turn the situation around?

My response:

I'd like to add a comment to at least offer my observations in answer to Rafael's question: "What happened?"
It is, of course, not an easy thing to determine. Part of it was some admitted hubris on the part of those of us who were, or aspired to be, "Deming Disciples." One of the things we admired about Deming was his unyielding and unflinching ability to speak truth to power. He was often seen as curmudgeonly in his approach, but he never let anyone doubt that he didn't suffer fools gladly, and he was unabashed about putting anyone--including CEOs--into that category, if they offered any evidence that they belonged there. He was also very compassionate and thoughtful, and freely offered help and advice to anyone interested in learning. He just didn't have much patience for those who thought they had nothing left to learn. So he was a bitter pill for many CEOs to swallow. They did it, when they thought he could help; and, as Raphael pointed out, for a while Deming was the one person that almost everyone relied on for help.

Once he was gone, and the crisis of the 80's was over, Jack Welch and others were selling Six Sigma--not as a Quality initiative, but as a cost-cutting one--I think many of them jumped at Six Sigma because it seemed simpler, more prescriptive, more programmatic, less lofty and philosophical...maybe instant pudding. They certainly didn't have use for those Deming practitioners who (without Deming's extensive background or credibility) tried to act as Deming had. I have had Quality executives from major corporations tell me that "Deming was just a philosophy," implying that it was pie-in-the-sky, without any practical use for business. It's hard to get these people to listen to you after you explain how ignorant a statement that is...

Another thing that happened is that Six Sigma provides a roadmap that actually does work, when used well. Many companies had a lot of success with their Six Sigma projects. GE had some highly vaunted and publicized success...I will never know how much of it was real, because between making it mandatory and "firing the bottom 10%," who knows which GE numbers can be trusted? In any case, these projects can be very effective, when used as one component of an overall Quality Management System.

I think Rafael's insight about marketing is a good one. Many Deming practitioners were blindsided by Six Sigma, saw its statistical and other flaws, and concluded that it was the enemy, not worthy of consideration. We did get out-marketed, because we had no champion like Welch or Bossidy or Galvin touting huge success stories; most of the stories in Quality Progress and Quality Digest were about Six Sigma. Virtually all the mainstream business literature abandoned Quality; the only mention of it was the occasional Six Sigma story. Then Lean reared its head, and perversely became a competitor to Six Sigma.

When I joined Process Management International, they were working to develop a Deming-based Six Sigma methodology. We had people with a strong Deming foundation who had worked for Motorola and GE, and I think we were successful, with a sound methodology, presented as one set of tools in an overall tranformational approach, that took into consideration all the aspects of SoPK and the 14 points. At least we were able to continue to tell people about Deming, the SoPK and the 14 points, to show the Red Bead and the Funnel. Interestingly, during a conference that included a lot of the Deming and JUSE elite, a consensus position was developed that saw Six Sigma as a [marketing] "vehicle" for quality...a way to explain it and to act as a lever for change, a foot in the door.

Would I have been happier teaching and consuling in "pure" Deming? That's all I wanted to do when I first retired from the Navy. No one was hiring for that, though, because the jobs for consultants who did that were few and far between. In any case, Would I still do it? You bet...I do, as much as I can.

What are your thoughts?

Back Again

2009-08-06T11:34:00.002-05:00

Sorry I've been out of this loop for so long...my wife is fighting a life-threatening cancer, and dealing with that just sucks every ounce of spare energy and time out of your life.
I'm going to jump back in by recycling my latest answer in the Deming discussion group on LinkedIn. The question was about Six Sigma and Deming. Two of my favorite Deming Disciples, John Dowd and John Constantine, feel that Six Sigma is fundamentally flawed and has very little place in any discussion of Deming. This is what I wrote:

I generally agree with John Dowd and John Constantine on most things, and I agree that Six Sigma-- as taught by many of the companies consulting in it these days--has some serious problems. Some of these approaches are, indeed, incompatible with the Deming philosophy. And some of the consultants using those incompatible approaches are large enough that the argument for "as generally taught" is probably sound. My suggestion, though, is that it doesn't have to be that way. Six Sigma is just a marketing vehicle; there is no standard for it (although ASQ would like to think that their SSBOK is one). As with TQM and every other quality approach there are people who do it well, and people who don't. Unfortunately, those who don't could care less about transformation, because many of them know very little about variation, and next to nothing about systems theory, psychology, or theory of knowledge. My disagreement is in what we do about this. I have been doing what I could, through my consulting practice, any conference appearances or workshops that I am able to do, and any writing that I can get published, to bring Deming principles into Six Sigma, and to use an approach in Six Sigma that is consistent with Deming. Clients who work with me only calculate the "1.5-sigma shift" and the "process sigma" as a curiosity and a metric for communicating with those less enlightened. They see and discuss the Red Bead, the Funnel, systems theory, SoPK, and the 14 points. They learn to see and use Six Sigma projects as one tactic in an overall process management system. I think if more Deming practitioners could find it in their hearts to do something like this, we'd have more success in using Six Sigma as one tactic in our overall aim, and reach more managers and executives (and potential managers and executives) with our message. This is what Lou Schultz and William Scherkenbach taught me many years ago. As to "picking a target and pretending to improve the system by keeping centered on it," I think we need more context. It's true that arbitrary targets are harmful, but this is really the basis for world-class quality; Taguchi defined it in 1960 as "on-target with minimum variation." Getting a process centered on its specified nominal value and constantly reducing variation around it has long been the the goal of anyone trying to understand variation and use that understanding for improvement. I don't think that has changed; used properly, DMAIC projects can help a team achieve the kind of fundamental changes to a system that are needed when the process is stable but off-target or out-of-specification.

If It's Measurable (and Important) Why Aren't You Measuring It?

2009-04-01T16:15:00.001-05:00

If you're not measuring something that's important to you, why not?
I am always amazed when I talk to executives or managers--sometimes at a conference, sometimes when they've hired me to help--and find that they are not measuring the things they claim they care about, at least not in any useful or meaningful way. As an example...a couple of years ago I was talking to a manager who wanted a Six Sigma project chartered around reducing scrap for a cut-off process. He said at the outset, "If I could just do something about scrap. It's KILLING me!" I asked, "How much scrap does the process produce?" He said, "Well...last quarter it was about 23%." End of quarter had been almost 2 months prior to this conversation; I said, "OK...what was it yesterday?" "Don't know...the last number I had was from last quarter. I do know it was up from the quarter before..." I got him to agree to attend our next "Statistical Thinking for Leaders" course, and we worked out a plan to start collecting his data differently (and at a useful frequency), so we could learn enough about his scrap to make it worth chartering a project. A colleague of mine, Charles Liedtke, put it this way..."Quarterly numbers? Would you manage your checkbook that way? Using one balance per quarter?" To my scrap manager's credit, at least he was measuring something. I have seen many managers attempt to charter projects without having any data (or any way to get the data). Even given Deming's admonition that the most important numbers are unknown and unknowable, there are measurements that ARE important...if you're not measuring it, and measuring it daily, and tracking it in some useful fashion like a control chart, then why not?

More on Performance Evaluation

2009-03-27T08:45:00.002-05:00

Note: this was originally a comment in a LinkedIn Discussion. The question was "Two of the problems I currently face: 1. If work standards and numerical goals get eliminated, we have to establish a new performance monitoring system. How do we evaluate performance fairly ? Leadership is hard to measure. 2. How do we design a fair compensation / reward system after that? Any suggestion?"

I think we all wrote reams on this in the DEN several years ago.
The problems are many, but mostly they have to do with an understanding of what it means to measure, and what it means to know. I had a conversation with a young HR person a couple of years ago about the criteria they were using to put people into a training program. I asked whether the peoples' managers actually knew them well enough to be able to predict whether an individual would be successful in the program. She said, "Well, I hope they are using performance evaluations to select them...you know, some objective data instead of just a manager's opinion." I asked her where they got the "objective data" from, and (of course) she told me they came from scores and rankings assigned by the managers. Then I asked, "How is that different from manager opinions?" She looked at me as though I were an alien or an idiot or both, and said, "They assign NUMBERS!"
It's always dangerous to try to make an inherently subjective task objective, but in this case, it's impossible. Anyone with a modicum of understanding of General Systems Theory knows that you can't separate a person acting in a system's performance from the performance of the system. It's one of the primary lessons from the Red Bead. Deming usually pointed out that it's trying to solve an equation with two unknowns.
It also ignores variation theory, although in many cases, the schemes put forward by management attempt to use variation theory as an excuse for some of the bad practices. In any group of people, for any given measure, there will be a distribution of performance. Because of system effects and interactions, the distribution is mostly the result of random variation. Some people will be at the higher end in some years and the lower end in other years, due to random variation.
Some people may end up in the upper tail of the distribution, more than three sigma away from the average. They may even stay there for a few cycles. These are people who are doing better than the system, and should be studied to find out how the system could be improved. They are the ones who should probably be rewarded more. If you asked everyone in the organization "who's our number one person?" all fingers would point at that person.
Some people might end up in the lower end of the distribution...they have managed to underperform the system. If you asked anyone who should go, they would point at that person. Those people are in need of help...maybe a different job in the same organization, maybe some training or a different manager, maybe a job in another organization.
Those people in the tails are relatively easy to identify. They are also rare. Most of the rest of the people are doing their best, and their performance is a result of random variaiton. Rating and ranking them is a step away from reality, and can't be done on any sound rational basis.
If you want to try some strategies that make sense, add "Abolishing Performance Appraisals," by Tom Coens and Mary Jenkins, to your reading list. They provide a very comprehensive treatment of the downsides, but also offer some great suggestions for "what to do instead."

"Making Six Sigma Faster"

2009-03-20T18:27:00.002-05:00

I recently had a manager tell me that his company had decided that Six Sigma just “takes too long,” and that they were implementing a new mandate to “Make Six Sigma Faster.” I asked him why they thought Six Sigma takes too long. He told me that the average time to complete a project there had been 8-9 months; under the new program, Black Belts were going to be expected to complete projects (at least through the IMPROVE phase) in 90 days or less.
I couldn’t help it…I broke out laughing, and asked him “By what method?”
“Well,” he said, “we’re not sure yet, but we think if they start holding more meetings, and never go into a meeting without having the deliverable already drafted, that will help.”
I had asked Deming’s famous question for a reason. I was very familiar with this particular deployment, and I knew that the reasons their projects always ran long were many, but almost none had to do with the Black Belts or the number of meetings they held.
This organization had decided at the beginning that they just didn’t have time to do a couple of days of Champion training. They had decided instead that they could get along with a 2-hour teleconference and a required reading list. This was a big organization, and they had never bothered to set up any kind of listening posts or other pipeline-feeders, had no project portfolio management, had not coordinated with the PMO, hadn’t trained any middle managers in SPC, Six Sigma familiarization, or anything else. Black Belts were pretty much expected to find their own projects, and in many cases had to hunt down anyone willing to sign on as a Champion (sometimes, they just picked another Black Belt, because “at least I got someone who understands Six Sigma.”) Getting good data was another problem. Usually, there were no data available for even deriving a baseline, much less for stratification or for digging into cause systems to find “x’s.” Just collecting the data for a baseline might involve a couple of months’ worth of work. The organization often relied for stratification on “reason codes,” the use of which were consistently proven unreliable when tested using attribute agreement analysis.
These were the primary factors driving project lead times, but there was no plan to deal with these factors, because it meant getting leadership to change, and no one at my manager friend’s level had the ability to push that noodle uphill. So they were just going to do the usual thing…put it into the expectations for the Black Belts. All you have to do to get the variation narrower is tighten the specs, right?
This was Deming’s point; to paraphrase, “If you could cut the project time by 60 percent this year without a method, then why didn’t you do it last year? Must have been goofing off…”
Listen, executives: This is too important. Six Sigma, implemented properly and led from a systems perspective, is a proven methodology that will make your business better, your customers happier, your revenues higher, and your costs lower. But it’s not something you bolt on, walk away from, and just wait for the cash to roll in. You have to lead it, you have to be engaged, you have to remove obstacles and make Six Sigma a strategic component of a larger Quality Management System. In the end, if it fails, you can’t blame the Black Belts you didn’t support, or the culture you didn’t change, or even the consultants you didn’t listen to. It’s not that it won’t work at your company…but it certainly won’t work if you don’t lead it!

Courage a Habit?

2009-03-17T09:04:00.001-05:00

(Originally posted as a LinkedIn Change Management answer. The question involved seeing courage as a habit, and reinforcing it in our daily work.)
I'm not sure I would characterize courage as a habit, but we could probably launch a whole new discussion group just to deal with the question of what courage is and how it's defined.
In the military, I knew any number of people endowed with great physical courage: they would--without hesitation--charge into a burning compartment to save a shipmate, jump on a grenade to save the rest of the people in their platoon, or do any number of other things that might result in death or medals for valor or both.
Many of these same people, however, would not stick their necks out an inch when it came to their performance evaluations or anything else that might jeapordize their careers. Performance evaluations were pretty much the arbiters of career advancement in the military, and this gave the person that signed your evaluation--and the committee that force-ranked you against your peers--unbelievable power over your ability to excel.
As a result, leaders who valued innovation might welcome an innovator, and welcome someone who stood up to buck conventional wisdom. Since many military leaders are conventionalists and authoritarians, however, the rule at all too many commands is that "the nail that sticks up gets hammered down."
This is one of the reasons Deming was so adamant about his eighth point: "Drive out Fear." It's one of the reasons he hated performance evaluation. Besides the fact that it's unconnected to reality, performance evaluation is a carrot-and-stick approach that engenders fear, crushes joy in work, stifles innovation and keeps peers in artifical competition that sub-optimizes organizational performance.
If you want to reinforce courage in the workplace, you have to be in a workplace that values courage, that values "speaking truth to power." Developing some political tact, the ability to speak truth to power without insulting or diminishing those in power, can help, if the leadership is open to listening.
One of the things I have always used to some advantage was an agreed understanding of the leaders' goals. If they can articulate their goals up front, bringing in new ideas that advance those goals becomes easier. When they jerk knees to try to squash a new idea, a good consultant can say, "I'm sorry...I'm confused now. I thought you wanted to get [goal] accomplished. This idea is the best we've seen yet for getting there. Do you want to get there or not?"
Another important habit is that of providing evidence for your case. Bringing data, well-analyzed and presented for clear understanding and insight, is also very helpful. Even though resistance is an emotional reaction, appeals to reason are still valuable in demonstrating the superiority of a proposed idea. It provides evidence for trialability and competitive advantage. It helps make the vision clearer and more concrete, therefore more feasible.
So, for those at the top...you want innovation? Create a climate for innovation. Drive out fear, articulate and share your vision to build a vision community in which every contribution toward reaching the vision is valued, innovation is a corporate strategy, and creativity is recognized as a core value.
For those not so near the top, develop good communication skills and tact, provide good clear evidence for your ideas, and persist as long as possible. If you continue to get hammered down, find another place to work that DOES value innovation; in the end, that organization will be more competitive and you will be much happier.

More Prevaricating about Sigma

2009-03-13T15:46:00.003-05:00

The one anonymous comment I received the other day after my initial posting seemed to imply that I am somehow against Six Sigma. Nothing could be further from the truth. I am currently a Quality consultant, and a large part of my business has been (and continues to be) Six Sigma. My background goes beyond Six Sigma, though, and I do have some reservations about some of the common practices within Six Sigma.
Before I ever heard of Six Sigma, I was heavily involved in the Navy’s Total Quality Leadership initiative. I had studied all the quality gurus, consulted and written courses in Statistical Process Control and systems approaches to process improvement, taken a masters degree in Quality and applied statistics. I had been director of quality for a large overseas base and an internal consultant to the entire Department of the Navy.
From the viewpoint of statistical methods, I initially viewed Six Sigma with some suspicion. There was the matter of the “1.5 Sigma Shift” applied blindly to all the calculations. This is just For one thing, one of the first slides in the deck, in the first presentation I saw, was a direct copy of one I had seen at a Crosby presentation. It talked about what you might get in “a three-sigma world,” and listed the usual “Babies dropped on heads” and “Airplane crashes,” etc. This is a great marketing slide, especially when coupled with the one that inevitably follows it, showing the several-order-of-magnitude improvement in “a six-sigma world.” The Crosby presentation, of course, followed the initial slide with a slide showing the improvement in a “zero-defects world.”
This slide is a pretty persuasive slide. It deals with errors that we would, of course, want to get as close to zero as possible. We can’t tolerate any aircraft falling from the sky, and can’t tolerate any babies being dropped on their heads, so of course three sigma (Cpk = 1) is never going to be as good as six sigma (Cpk = 2), but it’s somewhat disingenuous, for a number of reasons:
The calculations are correct, unless you are using the common “1.5-Sigma Shift,” in which case the six-sigma world calculations are too pessimistic (more on the shift later).
The idea of three sigma and six sigma come from the world of Statistical Process Control (SPC) and Capability Studies for continuous data, where you have tolerance limits (usually two-sided), around a process average. The data in the slide are for errors, countable things, discrete data, and all the examples are about the types of errors where the only acceptable tolerance limit is the lower bound of zero. Normal distribution theory doesn’t usually apply in this situation.
I’ve never understood why three sigma was the starting point for these slides, but I’ve always suspected that it was to promote the misconception that SPC somehow “stops” at three sigma (because of the three-sigma control limits used in SPC), and so the other approaches touted in the second slide are superior. Nothing could be further from the truth; if you watch or read “The Japanese Control Chart” by Don Wheeler, you’ll see a Japanese company that gets to TWELVE sigma, just using a paper control chart.
So I don’t really like that slide, but it worked OK as a marketing tool. From the viewpoint of statistics, I worried mostly about the mixing of the methods used to assess DPMO (Defects per Million Opportunities) and the 1.5-sigma shift. An excellent paper by Roger Hoerl got me over that. He pointed out that traditional views of capability left out the idea of mistakes or defects, and made a strong case for a metric like DPMO that can translate the idea of capability across distributions.
The “1.5 Sigma Shift” is another can of worms, but not for statisticians. Put simply, statisticians pay very little attention to it. It’s just a transformation that we will probably all be stuck with until someone with enough credibility to stop a flawed practice yells “STOP!” It’s relatively harmless, anyway, and it works as a “fudge factor” or a safety factor so you generally end up beating expectations. While it is true that undetected shifts in even a well-controlled process might allow for a lot of closer-to-spec product to be produced over many days, that does not justify an arbitrary value of 1.5 sigma to be universally applied. There are a lot of reasons for this…we’ll get into it another time.

A Rookie's First Blog

2009-03-11T13:24:00.001-05:00

Welcome to my new Blog! As a new blogger, I thought I’d take the time to talk in this first post about my vision for this blog. My intention is to provoke thought-provoking, open discussion on all aspects of enterprise improvement, operational excellence, continual improvement, “Big-Q” quality, Six Sigma, Lean, “Lean Six Sigma,” or whatever label you wish to use for a systemic approach to optimizing the performance of your organization for all its stakeholders.
It might seem odd that I don’t pin anything down with a label. Although I consider myself a Quality Consultant, I have recognized that over the past 20 years many of my fellow consultants have split improvement consulting into ever-smaller competitive niches. While there is much to be said for specialization and developing depth in a particular knowledge area, there is very little to be said for promoting that one area as “the” fix to a complex system. I have seen presenters at Quality conferences tout Lean over Six Sigma, Six Sigma over Lean, DMADV over DMAIC, “Lean Six Sigma” over everything else…it’s harmful to all of us, and harmful to all our clients, and it needs to stop. Now.
When he was alive, people at Deming seminars used to ask why Dr. Deming, with all his knowledge, didn’t provide specific prescriptions and methodologies. Quality professionals tended to study not only Deming, but Juran, Crosby, Kano, Ohno, Shingo and many others (i.e., Ackoff and Senge for Systems Theory, Kohn and Maslow for psychology, Lewis and Peirce for epistemology). Good quality practitioners were expected to know project management, standardization, lean (as they came to be called) tools, Cost-of-poor-quality, SPC, QFD, “seven old tools,” “seven new tools,” statistical theory, and systems theory.
Having said all that, there was a lot of variation in the Quality world. Some people followed their Gurus and only studied the others to find targets for disdain. Some became very dogmatic. Deming said that “the most important numbers are unknown and unknowable” so some people took that to mean that you don’t worry about costs, even those that are knowable. Some decided that they could act as curmudgeonly and arrogant as the Gurus themselves. These things (and more) turned many executives off.
After Deming died, consultants found that very few hiring executives cared much for philosophical approaches. Some of this reluctance could be laid on some of the consultants themselves; some of the most dogmatic “Deming Disciples” spent much of their time quoting Deming, often arguing about “what Dr. Deming said” or “what Dr. Deming meant” like biblical scholars interpreting a prophet.
I think, though, that managers just didn’t want to deal on a conceptual basis…nothing in business school had prepared them for seeing the long view or managing a system. They just wanted simple methods they could install quickly and painlessly. Six Sigma seemed in many ways to fit that bill. It was proven at GE, Motorola, and Allied Signal. It was trainable, had a defined roadmap, a hierarchical structure that could (with a reasonably small amount of pain) be bolted on to existing structures. Importantly, it could be positioned as a cost-cutting initiative rather than a quality initiative.
In the beginning, it fell to Quality professionals to develop the initial Six Sigma training materials. Black Belts were trained, who became Master Black Belts, and trained other Black Belts, who became Master Black Belts, etc. Because many of those Black Belts had had little or no previous exposure to Quality concepts and principles, their understanding of the tools suffered. They passed their subset of knowledge on to others, and more concepts and tools were lost in each generation…classic “rule four of the funnel.”
Management, too, fell victim to the dilution. Busy executives became too busy to take the time to learn enough to effectively lead Six Sigma as a strategic initiative, and upper/middle managers became too busy to take a few days to learn to be an effective champion. Process owners received little or no training to help them understand and cope with the changes and resource needs. Project selection became less strategic, sometimes devolving into Black Belts looking for their own projects and trying to find their own project champions. Some of these Black Belts ended up laid off; they sometimes found work with consulting companies.
As the focus narrowed from optimizing systems to local improvements and cutting costs, much of the rigor was diluted, and the conceptual underpinnings were often cut or minimized in the training. Deming’s fourteen points, seven deadly diseases and System of Profound Knowledge were treated as an important historical footnote…maybe included in introductory material, maybe not. Because Black Belts were not trained in simpler, non-project or small-project approaches, and all responsibility for tools rested with the Black Belts, many opportunities for process standardization & control, continual improvement, and reduction of waste were missed.
The Toyota Production System (Americanized to “Lean Manufacturing”) came in to help fill some of the gaps in Quality Management Systems. Because lean provides a relatively simple set of tools for daily management and doesn’t require much statistical knowledge, it quickly caught on. In some organizations, it was seamlessly integrated into an overall quality system; in others, it became another flavor of the month, replacing Six Sigma. In others, the boardroom decided to let Six Sigma and Lean compete for viability. Some consulting companies began selling “Lean Six Sigma.” Even simpler approaches, like Shop Floor Standardization, were lost in many organizations.
I could go on, but it’s already been a long path to get to an explanation of why I don’t call this a “Six Sigma” blog or a “Lean” blog, or even a “Lean Six Sigma” blog, even though I hope and fully expect to be discussing all these topics in depth and detail, as time goes on. What I’m going for is the kind of reasoned and mostly respectful discourse we used to have in the Deming Electronic Network. Hopefully, we can all suspend our assumptions, bring our knowledge to the table, learn and have fun!