My mother always told me if you don't know what you are talking about stay silent.  Obvioulsy Mr Burns should have remained silent.

His article which to my utter amazement made it through the Quality Digest vetting process is riddled with untrue statements and followed on by half truths.

Stetment #1: A process cannot stay in control for a day or two .. Ridiculous

It appears Mr. Bruns is stuck in the 70's and never progressed into  the 21st century with his thinking

Mr Burns is obviously an expert in his field and whatever that is is where he should focus his article. He obviously has no clue regarding Six Sigma and the impact to the industrial world it has been.

The sigma shift is really unimportant in the six sigma world.. The DMAIC process is the key to six sigma projects. This is what made Six Sigma such a powerful entity.

I would suggest that Mr Burns actually become a certified greenbelt or blackbelt from a reputable training program and then we can discuss issue. Until then Shame on Quality Digest for publishing this artivcle.

P.S.

Has anyone checked on where Mr Bruns might have received a doctorate from ??

I have read this article about 3 or 4 times trying understand if this is a "tongue in cheek" article or is intended to be serious.

If it is "tongue in cheek", I've missed the sarcasm and wit intended, my apologies.

If this is intended to be a serious article, it misses many targets that i was taught and have learned over a few decades if process/product improvement.

First, i do agree that psychology is very important to any choice people make. 

Unfortunately, that is where my agreement stops.  The first choices Mr. Burns proposes are Quality being related to specification limits and quality is inherent to the way a product is made.  I believe the choice is obvious.  Both are incorrect.  Quality of a product is inherent in the design of the product. A lesson taught to me in engineering school and reinforced in "Six Sigma" training and "Lean" training, business school, and a few decades of experience. 

Mr. Burns continues to discuss "real product quality" and concludes, "Most buyers would consider a ...wide specification...to be worse than ... a tight spec."  I would propose that most buyers have no knowledge of specifications nor care about specifications.  Most buyers will recognize variation (either by large within lot variation or large between lot variation...generally different problems and corrections!), ability to meet their use needs, robustness, and depending on the product durability and reliability.  Most of these attributes for the product are designed in and the process to create the product may have little effect on the customers’ perception of "quality"

As i was taught in engineering school, "six sigma" school, and life, design specifications are the knowledge transfer from "user needs" to manufacturing requirements.  If we bring in Taguchi, we recognize that a production facility that is concerned with lowest possible cost strives for 2 key aspects for processes, 1. Hit the nominal and 2. do so with minimal variation.  Reducing variation alone does not accomplish everything alone.

the next pair of examples goes on about "...impossible to keep...in control..." and "good quality is only ...if...in control".  Again, I believe the answer is obvious.  Both are incorrect.  I propose Good quality is only possible if the design is robust to the manufacturing process.  Mr. Burns goes on to discuss "process drift" and "predicting outcome".  I think Mr. Burns is trying to discuss process control and capability analysis.  What I was taught about "process drift" and "capability analysis" is that all processes have common cause and special cause variation.  The challenge is to identify the special cause variation before is detrimentally impacts the process output.  To accomplish this task Deming, Wheeler, et. al. have different techniques to monitor a process and recommend establishing "rules" to identify special cause.  There is even a generally accepted started point developed by Western Electric to identify special cause variation. 

As I was taught, these are starting points. and they begin to define the amount of "drift" that a given process is able to detect.  given a process and a particular rule, there are a few statistical things happening.  First are alpha and beta risk.  We may conclude we have a special cause when there is not or vice versa.  we set the "rules" to balance the risk of a wrong decision and the risk to consumers and the risk to the business to establish the rule.  Similar to this is the "drift" (also seen as "average run length" and a few other consultantese phrases).  This concept of "drift" is the amount of deviation a process can deviate BEFORE it is identified.  Here's where we come full circle back to Motorola.  Many of the Motorola decisions regarding "six Sigma" were business decisions (1.5 standard deviation drift, 3.4 defects per million opportunities, etc.)  MANY consultants and educators have missed this point and mistakenly teach those rules as "six sigma".  each business, product, process, needs to identify those parameters based on the risk of failure.  the reference to the "drift" as a process shift before detection is critical to understand in terms of the potential number of defects created (not found...because i agree that quality is never successful when inspected) to understand the risk to customer, business, etc.  this is far less a "fudge factor" than a clearly calculated potential occurrence.  Far less a of a fairy tale or make believe than suggested.

Your example using IBM certainly seems to reinforce your point of psychology influencing "the herd".  I don’t see the connection to "Six Sigma" and would contest that there were far greater problems in that business than the details of "six sigma" if factual information was not being considered.  The lack of maturity of the company to listen to and rely on factual data violates another tenet of the "six sigma" I was taught.

Lastly, I was also taught the there is very little new to "six sigma".  the power of the philosophy is in the focus to consistently hit the target and systematically reduce variation.  to that end, the construct of the DMAIC process becomes more of a project management tool to make sure key areas are addressed (almost like a simple check sheet)  and fact based decisions are made.  Pretty much the same as TQM, Quality Circles, and any other quality fad that "educators" and consultants have rolled out to create their niche.

In the end, there is little more than good science, good engineering, good mathematics, respect for knowledge, and fact based decisions to any of the fads broadcast throughout businesses today.

I support the spirit and the message of Dr. Burn's publication in QD. He has the courage to raise a dissenting voice on the value and scientific foundations of Six Sigma. I agree that SS foundations are flawed. For example, SS pushes 3.4 ppm defect target rate that is included in numerous SS articles and books. But this figure is simply the area under the normal distribution curve that corresponds to 4.5 sigma, which is the shift of the unsubstantiated 1.5 sigma from the claimed 6 sigma.   

Many SS claims are based on properties of tails of normal distribution that is a poor model for the majority of real processes anyway because it is 2-way unbounded symmetrical distribution.

The DMAIC process that SS claims as its novel approach to structural problem solving is being used in some forms for centuries in any reputable research since Roger Bacon time, i.e. define the problem, measure the effect under study using a reliable metric system, analyze the data, suggest independent testing and control results.  

Thus, SS is a combination of naive statistics based on normal distribution and some overall slogans for voice of customers (VOC) and stakeholder management. SS cannot offer real quantitative root analysis and improvement methods. Design of experiments (DOE) included in Analysis and Improve phase are of limited use for relatively small scale experiments.

No powerful methodologies such as computer modeling and simulation, mathematical analysis and calculus are a part of SS standard 'tool box'.

Personally I am twice certified SS Black Belt (from General Electric, GE). Therefore, I know this staff from inside. I vividly remember how 'infamous' GE CEO Jack Welch pushed SS into GE by his iron hand, creating a huge new bureaucracy of poorly educated Belt Belts, Master BB, quality champions, etc. I remember how very good engineers were fired for dissenting to SS because they rightly advocated for a wide use of mathematics and modeling for design improvement rather than relying on laughable SS claims.

Anyway, I thank Dr. Burns and QD editorial for the courage and for the sobering view of the largely damaging long-term effect of SS on quality and product design.

While reading Dr. Burns article I found many of his points valid and interesting.  Nonetheless, a few comments relative to 

• Bill Smith
• Mikel Harry
• DMAIC
• Shift (1.5 standard deviations or othewise)

As I was at Motorola during the early days of Six Sigma I knew Bill Smith as we often ate lunch together and to this day I remain in touch with Dr. Harry.  Often I represented Motorola Corporate at speaking engagements (e.g., HP, IBM, Ciba Geigy now Novartis, Johnson and Johnson) discussing the Malcolm Baldrige Award pursuit by Motorola and the history of Six Sigma.  Therefore, I have different perspectives on the history of Six Sigma.

Bill Smith's main emphasis was that the amount and cost of field failures was directly related to failures in both the design and manufacturing process.  For example, the greater number of defects caught, even if the defects were corrected, in the manufacturing process the greater the likelihood that defects would escape into the field.  Mr. Smith demonstrated that the better the product design and the better the manufacturing process the fewer defects would be observed in production which would then result in fewer defects escaping to the customer resulting in significantly lower warranty / field failure costs.

As a result of his efforts Motorola emphasized tracking of all defects in a manufacturing process in order to better understand where and why they occurred with the goal of improving both the product design so that products were easier to build while also streamlining the manufacturing process.  In other words, the use of lean (often called at that time Just-in-Time and Toyota Production System) was highly important at that time.  The focus on reduction of defect counts was emphasized as it was a method to Characterize the process and ultimately Optimize the process.  Of course, a simple way to reduce the defect counts would be to simply not count defects!  

In addition, Bill Smith realized that from a engineering / statistics perspective that the Shape, Center, and Spread of the product performance and dimension data distributions would better characterize the production than merely using the specifications.  In fact, if the Shape of the distribution was uni-modal (e.g. Normal is just one such distribution) with a monotonically decreasing distribution as you move away from the mode then tightening the spread such that the distribution width (e.g., plus or minus 3 standard deviations of the production data) was 1/2 the specification width requested by the customer would essentially lead to a product that is "twice as good" as the customer demanded.  He called such as case a 6 Sigma quality level.  

Bob Galvin, Motorola Chairman, liked the phrase 6 Sigma.  Often when asked how long do we keep up this improvement effort the answer was until we get to at least 6 Sigma level of performance.  Of course, Mr. Galvin was very smart in asking for Six Sigma level of performance as the better the products and processes the more likely the customer expectations would be greater (i.e., the specifications would assumed to tighten).  

The focus was not on the absolute level of Six Sigma as customer demands would continue to be higher.  Instead the focus was on relentless pursuit in variation reduction (i.e., relentless pursuit of consistency in higher levels of performance) in the design and manufacturing processes.  In fact, in our quarterly reviews the emphasis was on maintaining a quarterly (e.g., approximately 25% reduction in defect rate per quarter) improvement rate that would result in a 10-fold improvement every 2 years, 100-fold improvement in 4 years, etc.  I recall watching a product manager presentation in front of Mort Topfer who later became Vice Chairman of Dell Computer.  Mr. Topfer was upset because the quality level of the product that was shipping was 5.2 Sigma (very good) but unfortunately the product was shipping at 5.2 Sigma the quarter before indicating no improvement had been made.  As a result a $20 million order was not shipped to the customer but instead the customer was informed that the product was belng delayed until the quality level was improved!  Mr. Topfer was the President of the Communications Sector of Motorola and made it clear that product quality improvement was expected and non-negotiable.

As for the Sigma shift, the main concern was how much product performance / process variation was inflating over the long term relative to the short term.  Even so, if a process originally in a state of statistical control had its mean shifted halfway to the established control limits and, furthermore, if  the only out of control conditions to which manufacturing would react would be to points outside the control limits then the runs rules may show that only .0668 (approximately 1 out of 16) would end up outside the control limits.  The zone of 1 to 2 standard deviations shift was called the Zone of Indifference in that manufacturing may not react quickly to such shifts.  If the process shifted further the control limits would pick up the change quickly.  Of course this assume that both control charts are used in all processes and that other runs rules (i.e., tests of special causes) are not used. In actuality, it was not that processes shifted, instead, it was that processes may become less consistent (i.e., process variation increases).  The amount of inflation in the process variation in the long term relative to short term or best case was used to detemine a strategy for addressing process optimization.  Large inflation over time in variation (i.e., probabilistically in distribution results similar to a shift of 2 or more standard deviations) would indicate that process controls are not being effectively used as process control charts would trigger such an increase in variation.  Similarly, if the process variation was consistent over time (i.e., probabilistically similar to a shift less than 1 standard deviation) and yet the product was not meeting customer requirements then a focus on product / process design was needed rather than an improvement in process control.

Having a Ph.D. in statistics I understand that the 1.5 shift is merely dead center in the Zone of Indifference as explained above and itself may not have a strong theoretical basis.  In practice the ongoing increase in process variation often was observed to be equivalent to having observed a process shifting 1 to 2 standard deviations.  When a product / process is monitored using statistical process control charts it can be explained why a shift of 1 to 2 standard deviations may be missed for a short period of time.  A greater shift is picked up quickly while a smaller shift will go on longer before being picked up.  I can provide everyday examples of this but I think emphasizing the shift and absolute Six Sigma levels of performance are missing the point.  

Motorola was emphasizing a relentless (some would say ruthless depending on where you worked) pursuit of 10-fold improvement every 2 years, 100-fold every 4 years.  Moreover, many business units had a simultaneous objective of reducing all lead times by 50% every 2 years so time-based improvement was very much part of Six Sigma in the early days of Six Sigma.  Many of us who worked at Motorola in those early years may scratch our head when we hear "Lean Six Sigma" as if Six Sigma was not focused on Lean (i.e., time-based, efficiency focus).  Motorola very much benchmarked the world class manufacturing organizations in Asia, especially Japan, so reduction in both design and product variation was integrated into reducing lead times by eliminating unnecessary processing and streamlining what was left.  No need to improve something that should not be there in the first place (e.g., those steps that add no value).

In any case, I agree with Dr. Burns as to "fantasy" of Six Sigma but in my view the fantasy is the lack of understanding of the history and evolution of Six Sigma.  Many do not realize that Design for Six Sigma was actualy taught well before DMAIC!  The emphasis was on both product design and process design both to increase product performance consistency (e.g., reduce performance variation) while reducing cost (i.e., increase product margins through faster, value-added processing and elimination of the unnecessary waste).  Again, DMAIC is not Six Sigma but rather one methodology roadmap to help others with improvement and innovation in order to drive Six Sigma performance.

Side note:  Dr. Mikel Harry often discussed Characterization and Optimization as the basis for improvement.  A few years later, well after the launch of Six Sigma, Measure and Analyze were used together to build product / process Charaterization while Improve / Innovate and Control together led to Optimization.  It is obvious that MAIC then evolved into DMAIC.  Keep in mind that DMAIC is not Six Sigma which began a few years earlier as much as it is a model for improvement that was created to facilitate Characterization and Optimization of a product / process.  Dr. Mikel Harry "codified" the Characterization and Optimization into MAIC while leading the Six Sigma Research Institute.  During the early years of Six Sigma the emphasis was on Design for Six Sigma so Characterization and Optimization worked to support that effort.  Later many realized that it is difficult to design better products and processes if it was not understood why the current products or processes were not performing well.  Hence, if one has difficulty improving existing processes, how much more difficult would it be to then design better products or processes not inherently having the flaws (defects) that already exist in the current products or process?  Therefore, MAIC and later DMAIC was further developed to help characterize (understand) and optimize (improve and control) current processes.  Nonetheless, at Motorola there was significant emphasis on Design for Six Sigma that began in the early days of Six Sigma.  Dr. Harry contributed much to both the Design for Six Sigma effort including the Characterization and Optimization emphasis that later evolved into MAIC and then DMAIC.

Hi Tony,

I see one comment questioned your Doctorate?

I copied and pasted some confusing explanations of Six Sigma (Motorola Trade and Service Mark US Patents Office 1987 term, not 6Sigma for some other folk want to do). Anyway, Tony, these excerpts may reflect the confused BoK by many.

In searching the Austomotive Industry Action Group USA, SPC Manual for the Auto suppliers, I could only find a requirement to achive high process capability and process capability indexes e.g. >cpk 2.0 - not Six Sigma level of some value if that helps your argument.

The extracts follow.

Mike

Thomas Pyzdek The Six Sigma Handbook 1999 - "A six-sigma process will produce failures at a parts-per-million or even parts-per-billion level. This contrasts with the old three sigma process which produces parts-per-thousand failures. This difference of three to six orders of magnitude is profound" page xii Preface.... "Six Sigma Quality is a statistical measure of variation from a desired result"....."Six Sigma requires the process standard deviation be no more than one twelfth of the total allowable spread" p140.

And the final piece is found in Appendix Table 18 "Process sigma Levels and equivalent PPM Quality Levels....Based on the assumption that in the long term the process could drift by plus or minus 1.5 sigma". It then shows the modified Table.

Keki Bhote The Power of Ultimate Six Sigma" 2003. p20 - "The hyped Six Sigma approach asserts that it is too difficult to hold the average, (X Bar), at the target value (i.e. design center) of a parameter's distribution because of inherent shifts in materials and processes, etc". 

And on DMAIC - page 13 "DMAIC is muddled in definition, imprecise in measurement, impotent in analysis, incapacitated in improvement, and rudderless in control. Other than that. it is okay". And he's ex-Motorola!

Pande, Neuman, Cavanagh The Six Sigma Way 2000 p28. "By factoring in opportunities for defects in the calculation, (DPMO), Motorola made it more realistic to equate performance across different processes".  Page 229 - "The 1.5 Sigma Shift is one of the key bones of contention amongst the statistical experts about how Six Sigma measures are defined.  The lucky thing is that when a convention is adopted and applied consistently, it's still valid.....the only challenge comes if you try to equate the accepted Six Sigma scoring system to strict standard deviations under a normal curve".

Congratulations to Dr. Burns on an interesting and clearly provocative article. I will qualify my response below by saying I am still relatively new in the quality field, having transitioned two years ago from my academic position as a theoretical physicist specializing in computational statistical mechanics.  Which is a fancy way of saying I have 15+ years of experience in analyzing small data sets produced by complex processes, and am still learning how to use those skills in a manufacturing environment.

To that end, my company invested in Black Belt training for me.  Not so much for the statistical training, but for the rest of the quality and team leadership toolbox. Also because the customers like it ;) So do I, frankly.  But perhaps that is because I see it as another tool in my analysis arsenal -- "tool" being the operative word.  I use it -- it doesn't use me.  I am perfectly free to pick the right bits to use in my context and leave the rest.

And that is where I think the danger lies -- when the "Six Sigma Psychology" encourages slavish adherence to metrics as opposed to thoughtful consideration of objective process data.  If the core of Six Sigma is DMAIC, then perhaps more emphasis needs to be placed in SS training that the core of DMAIC is the scientific method.  The only thing you MUST do when examining and improving your process is control the variables and objectively analyze the results as you drive to your goal. Who was it that said "Pick something you care about and plot it over time?" Everything else is window-dressing.

I work in an environment where for some of our part numbers it would take months if not years to reach what many would consider a sufficient sample size.  Does this mean I can't use a statistical framework? Of course not.  I just have to look at my problems differently -- DMAIC still works! (And for those in my situation, I highly recommend Donald Wheeler's book Short Run SPC).  I also have to listen to process experts from the shop floor on up (which we should all do anyway). And there's even some advantages -- I sure don't have to spend much time worrying about the difference between a 1% and 2% false alarm rate on my control charts.

If there is a fairy tale in the land of Six Sigma, it is that we are somehow excused from our obligations as thoughtful professionals. That we no longer have to respond adaptively to the situations we are faced with because we have some kind of "turn-the-crank" methodology that will allow us to push a button on MiniTab and save our companies millions.  If that worked we'd all be swimming in team jackets and no time to work because of all the celebratory pizza parties. 

Thanks again to Dr. Burns and all the commenters -- healthy debate is of course also in our list of professional obligations :).

Dr. Burns states, "However, 75 percent of people will answer incorrectly at least part of the time, if they are placed in a group that intentionally answers incorrectly. That is, a majority of people will stick with the group view, even when they know the group view is ridiculous."  The second sentence of that statement is an old assumption.  More recent testing using brain scans shows the facts are more unnerving, the people actually think they are answering correctly.  See a good discussion of this in an excellent book "Quiet - The Power of Introverst in a world where people can't stop talking." 

I have studied Six Sigma for quite some time. I have never heard anyone say or imply that simply widening the specification limits improves quality. No knowledgeable person would believe this and there are many sharp people involved with Six Sigma.

In addition, I believe strongly that the Six Sigma metric (3.4 ppm, 1.5 sigma shift, etc.) should be dropped entirely from Six Sigma. This metric is not useful and has become harmful. It is the framework of DMAIC and DFSS that is important and good management structure. Nothing in the process improvement efforts has to be tied back to the 3.4ppm. This metric is easily criticized and most criticisms of Six Sigma, such as those in this paper, are just criticisms of this metric.

In case anyone is interested, our views about Six Sigma are in the paper found at http://filebox.vt.edu/users/bwoodall/2008%20Six%20Sigma%20ISR.pdf

The following article "Six Sigma Psychology - Part 2" responds to comments above:https://www.linkedin.com/pulse/six-sigma-psychology-part-2-tony-burns?t…

Please read more on the utter farce of Six Sigma's 'six sigma' in part 2 on LinkedIn.  I also reply to various comments and the inevitable ad hominem attacks.

What follows the name 'Six Sigma' is even worse.  Why do people turn their back on the world's greatest process statisticians, Dr Wheeler, Dr Shewhart, Dr Taguchi, and Dr Deming, all with PhDs in statistics, and instead follow the lunacy of the psychologist who created Six Sigma?

https://www.linkedin.com/pulse/six-sigma-psychology-part-2-tony-burns/

Thank you Dr. Tony Burns for your great articles. We appreciate you for all your hard work and efforts in creating an awareness around the world!

You are one of the very rare people who dares to talk about true facts even though it goes against many famous people!