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Six Sigma

Not to be confused with Sigma 6.

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The often used six sigma symbol.

Six Sigma is a business improvement methodology, originally developed by Motorola to systematically improve processes by eliminating defects[1]. Defects are defined as unacceptable deviation from the mean or target. The objective of Six Sigma is to deliver high performance, reliability, and value to the end customer. Since it was originally developed, Six Sigma has enjoyed wide popularity as an important element of many Total Quality Management (TQM)initiatives.

The process was pioneered by Bill Smith at Motorola in 1986[2] and was originally defined[3] as a metric for measuring defects and improving quality, and a methodology to reduce defect levels below 3.4 Defects Per (one) Million Opportunities (DPMO), or put another way, a methodology of controlling a process to the point of plus or minus six sigma (standard deviations) from a centerline. Six Sigma has now grown beyond defect control.

Six Sigma is a registered service mark and trademark of Motorola, Inc[4]. Motorola has reported over US$17 billion in savings[5] from Six Sigma to date.

In addition to Motorola, companies which also adopted six sigma methodologies early-on and continue to practice it today include Honeywell International (previously known as Allied Signal), Raytheon and General Electric (introduced by Jack Welch). The three companies have reportedly saved billions of dollars thanks to the aggressive implementation and daily practice of six sigma methodologies.[citation needed]

Recent six sigma trends lies in the advancement of the methodology with integrating to TRIZ for inventive problem solving and product design [6].


Contents

Methodology

Six Sigma has two key methodologies[7]: DMAIC and DMADV. DMAIC is used to improve an existing business process. DMADV is used to create new product designs or process designs in such a way that it results in a more predictable, mature and defect free performance.

Also see DFSS (Design for Six Sigma) quality. Sometimes a DMAIC project may turn into a DFSS project because the process in question requires complete redesign to bring about the desired degree of improvement.

DMAIC

Basic methodology consists of the following five steps:

DMADV

Basic methodology consists of the following five steps:

Some people have used DMAICR (Realize). Others contend that focusing on the financial gains realized through Six Sigma is counter-productive and that said financial gains are simply byproducts of a good process improvement.

Another additional flavor of Design for Six Sigma is the DMEDI method. This process is almost exactly like the DMADV process, utilizing the same toolkit, but with a different acronym. DMEDI stands for Define, Measure, Explore, Develop, Implement.

Statistics and robustness

The core of the Six Sigma methodology is a data-driven, systematic approach to problem solving, with a focus on customer impact. Statistical tools and analysis are often useful in the process. However, it is a mistake to view the core of the Six Sigma methodology as statistics; an acceptable Six Sigma project can be started with only rudimentary statistical tools.

Still, some professional statisticians criticize Six Sigma because practitioners have highly varied levels of understanding of the statistics involved.

Six Sigma as a problem-solving approach has traditionally been used in fields such as business, engineering, and production processes, and rarely in areas such as poetry or history.

Roles required for implementation

Six Sigma identifies five key roles[8] for its successful implementation.

In many successful modern programs, Green Belts and Black Belts are empowered to initiate, expand, and lead projects in their area of responsibility. The roles as defined above, therefore, conform to the antiquated Mikel Harry/Richard Schroeder model, which is far from being universally accepted. The terms black belt and green belt are borrowed from the ranking systems in various martial arts.

Origin

Robert Galvin did not really "invent" Six Sigma in the 1980s; rather, he applied methodologies that had been available since the 1920s developed by luminaries like Shewhart, Deming, Juran, Ishikawa, Ohno, Shingo, Taguchi and Shainin. All tools used in Six Sigma programs are actually a subset of the Quality Engineering discipline and can be considered a part of the ASQ Certified Quality Engineer body of knowledge. The goal of Six Sigma, then, is to use the old tools in concert, for a greater effect than a sum-of-parts approach.

The use of "Black Belts" as itinerant change agents is controversial as it has created a cottage industry of training and certification. This relieves management of accountability for change; pre-Six Sigma implementations, exemplified by the Toyota Production System and Japan's industrial ascension, simply used the technical talent at hand—Design, Manufacturing and Quality Engineers, Toolmakers, Maintenance and Production workers—to optimize the processes.

The expansion of the various "Belts" to include "Green Belt", "Master Black Belt" and "Gold Belt" is commonly seen as a parallel to the various "Belt Factories" that exist in martial arts.

The term Six Sigma

Sigma (the lower-case Greek letter σ) is used to represent standard deviation (a measure of variation) of a population (lower-case 's', is an estimate, based on a sample). The term "six sigma process" comes from the notion that if one has six standard deviations between the mean of a process and the nearest specification limit, he will make practically no items that exceed the specifications. This is the basis of the Process Capability Study, often used by quality professionals. The term "Six Sigma" has its roots in this tool, rather than in simple process standard deviation, which is also measured in sigmas. Criticism of the tool itself, and the way that the term was derived from the tool, often spark criticism of Six Sigma.

The widely accepted definition of a six sigma process is one that produces 3.4 defective parts per million opportunities (DPMO).[9] A process that is normally distributed will have 3.4 parts per million beyond a point that is 4.5 standard deviations above or below the mean (one-sided Capability Study). This implies that 3.4 DPMO corresponds to 4.5 sigmas, not six as the process name would imply. This can be confirmed by running on Six Sigma or Minitab a Capability Study on data with a mean of 0, a standard deviation of 1, and an upper specification limit of 4.5. The 1.5 sigmas added to the name Six Sigma are arbitrary and they are called "1.5 sigma shift" (SBTI Black Belt material, ca 1998). Dr. Donald Wheeler dismisses the 1.5 sigma shift as "goofy".[10]

In a Capability Study, sigma refers to the number of standard deviations between the process mean and the nearest specification limit, rather than the standard deviation of the process, which is also measured in "sigmas". As process standard deviation goes up, or the mean of the process moves away from the center of the tolerance, the Process Capability sigma number goes down, because fewer standard deviations will then fit between the mean and the nearest specification limit (see Cpk Index). The notion that, in the long term, processes usually do not perform as well as they do in the short term is correct. That requires that that Process Capability sigma based on long term data is less than or equal to an estimate based on short term sigma. However, the original use of the 1.5 sigma shift is as shown above, and implicitly assumes the opposite.

As sample size increases, the error in the estimate of standard deviation converges much more slowly than the estimate of the mean (see confidence interval). Even with a few dozen samples, the estimate of standard deviation often drags an alarming amount of uncertainty into the Capability Study calculations. It follows that estimates of defect rates can be very greatly influenced by uncertainty in the estimate of standard deviation, and that the defective parts per million estimates produced by Capability Studies often ought not to be taken too literally.

Estimates for the number of defective parts per million produced also depends on knowing something about the shape of the distribution from which the samples are drawn. Unfortunately, there are no means for proving that data belong to any particular distribution. One can only assume normality, based on finding no evidence to the contrary. Estimating defective parts per million down into the 100s or 10s of units based on such an assumption is wishful thinking, since actual defects are often deviations from normality, which have been assumed not to exist.

The ±1.5 Sigma Drift

The ±1.5 sigma drift is the drift of a process mean, which occurs in all processes in a six sigma program. If a product being manufactured measures 100 ± 3 cm (97 – 103 cm), over time the ±1.5 sigma drift may cause the average to range up to 98.5 - 104.5 cm or down to 95.5 - 101.5 cm. This could be of significance to customers.

The ±1.5 shift was introduced by Mikel Harry. Harry referred to a paper about tolerancing, the overall error in an assembly is effected by the errors in components, written in 1975 by Evans, "Statistical Tolerancing: The State of the Art. Part 3. Shifts and Drifts". Evans refers to a paper by Bender in 1962, "Benderizing Tolerances – A Simple Practical Probability Method for Handling Tolerances for Limit Stack Ups". He looked at the classical situation with a stack of disks and how the overall error in the size of the stack, relates to errors in the individual disks. Based on "probability, approximations and experience", Bender suggests:

<math>v = 1.5 \sqrt{var(X)}</math>

Harry then took this a step further. Supposing that there is a process in which 5 samples are taken every half hour and plotted on a control chart, Harry considered the "instantaneous" initial 5 samples as being "short term" (Harry's n=5) and the samples throughout the day as being "long term" (Harry's g=50 points). Due to the random variation in the first 5 points, the mean of the initial sample is different to the overall mean. Harry derived a relationship between the short term and long term capability, using the equation above, to produce a capability shift or "Z shift" of 1.5. Over time, the original meaning of "short term" and "long term" has been changed to result in "long term" drifting means.

Harry has clung tenaciously to the "1.5" but over the years, its derivation has been modified. In a recent note from Harry "We employed the value of 1.5 since no other empirical information was available at the time of reporting." In other words, 1.5 has now become an empirical rather than theoretical value. A further softening from Harry: "... the 1.5 constant would not be needed as an approximation".

Despite this, industry has fixed on the idea that it is impossible to keep processes on target. No matter what is done, process means will drift by ±1.5 sigma. In other words, if a process has a target value of 10.0, and control limits work out to be 13.0 and 7.0, over the long term the mean will drift to 11.5 (or 8.5), with control limits changing to 14.5 and 8.5.

In truth, any process where the mean changes by 1.5 sigma, or any other amount, is not in statistical control. Such a change can often be detected by a trend on a control chart. A process that is not in control is not predictable. It may begin to produce defects, no matter where specification limits have been set.

Digital Six Sigma

In an effort to permanently minimize variation, Motorola has evolved the Six Sigma methodology to use information systems tools to make business improvements absolutely permanent. Motorola calls this effort Digital Six Sigma.

Examples of some key tools used

Software used for Six Sigma

There are generally two classes of software used to support Six Sigma: analysis tools, which are used to perform statistical or process analysis, and program management tools, used to manage and track a corporation's entire Six Sigma program. Analysis tools include statistical software such as Minitab, JMP, SigmaXL or Statgraphics as well as process analysis tools such as iGrafx. Some alternatives include Microsoft Visio, Telelogic System Architect, IBM WebSphere Business Modeler, and Proforma Corp. ProVision. For program management, tracking and reporting, the most popular tools are PowerSteering, iNexus and SixNet.

Trivia

The cartoonist Scott Adams featured Six Sigma in a Dilbert cartoon published on November 26, 2006. When the process is introduced to his company Dilbert asks "Why don't we jump on a fad that hasn't already been widely discredited?" and comments that "Fortune magazine says... blah blah... most companies that used Six Sigma have trailed the S&P 500."[1]

Fortune in fact published an article with the statement that "of 58 large companies that have announced Six Sigma programs, 91 percent have trailed the S&P 500 since." The statement is attributed to "an analysis by Charles Holland of consulting firm Qualpro (which espouses a competing quality-improvement process)."[11] The gist of the article is that Six Sigma is effective at what it is intended to do, but that it is "narrowly designed to fix an existing process" and does not help in "coming up with new products or disruptive technologies."

References

  1. ^ Motorola University - What is Six Sigma?. Retrieved on Jan 29, 2006.
  2. ^ The Inventors of Six Sigma. Retrieved on Jan 29, 2006.
  3. ^ Motorola University Six Sigma Dictionary. Retrieved on Jan 29, 2006.
  4. ^ Motorola Inc. - Motorola University. Retrieved on Jan 29, 2006.
  5. ^ About Motorola University. Retrieved on Jan 29, 2006.
  6. ^ Averboukh, Elena A.. Six Sigma Trends: Six Sigma Leadership And Innovation Using TRIZ. Retrieved on Nov 13, 2006.
  7. ^ Joseph A. De Feo & William W Barnard. JURAN Institute's Six Sigma Breakthrough and Beyond - Quality Performance Breakthrough Methods, Tata McGraw-Hill Publishing Company Limited, 2005. ISBN 0-07-059881-9
  8. ^ Mikel Harry & Richard Schroeder. Six Sigma, Random House, Inc, 2000. ISBN 0-385-49437-8
  9. ^ Tonner, Craig; Patra, Pradeep (2003-09-03). Six Sigma (English). Retrieved on 2006-11-26.
  10. ^ Wheeler, Donald J., Phd, The Six Sigma Practitioner's Guide to Data Analysis, p307, http://www.spcpress.com
  11. ^ Betsy Morris (2006-07-11). Old rule: be lean and mean. Fortune. Retrieved on 2006-11-26.

See also

Categories


Wikipedia articles needing context | Articles with unsourced statements | Business terms | Evaluation methods | General Electric | Motorola | Production and manufacturing | Quality

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