Six Sigma

Six Sigma
From Wikipedia, the free encyclopedia
Jump to: navigation, search
Not to be confused with Sigma 6.
The introduction to this article provides insufficient context for those unfamiliar with the subject matter.Please help improve the introduction to meet Wikipedia's layout standards. You can discuss the issue on the talk page.

A Wikipedian has expressed the opinion that this article or section is unbalanced.Please help improve the article by adding information and sources on neglected viewpoints.Please see the discussion on the talk page.

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 units that are not members of the intended population. 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 (for a total span of twelve sigma). 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[hide]
1 Methodology
1.1 DMAIC
1.2 DMADV
2 Statistics and robustness
3 Roles required for implementation
4 Origin
4.1 The term Six Sigma
4.2 The ±1.5 Sigma Drift
4.3 Digital Six Sigma
5 Criticism
6 Examples of some key tools used
6.1 Software used for Six Sigma
7 References
8 See also
9 External links
//

[edit] 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.

[edit] DMAIC
Basic methodology consists of the following five steps:
Define the process improvement goals that are consistent with customer demands and enterprise strategy.
Measure the current process and collect relevant data for future comparison.
Analyze to verify relationship and causality of factors. Determine what the relationship is, and attempt to ensure that all factors have been considered.
Improve or optimize the process based upon the analysis using techniques like Design of Experiments.
Control to ensure that any variances are corrected before they result in defects. Set up pilot runs to establish process capability, transition to production and thereafter continuously measure the process and institute control mechanisms.

[edit] DMADV
Basic methodology consists of the following five steps:
Define the goals of the design activity that are consistent with customer demands and enterprise strategy.
Measure and identify CTQs (critical to qualities), product capabilities, production process capability, and risk assessments.
Analyze to develop and design alternatives, create high-level design and evaluate design capability to select the best design.
Design details, optimize the design, and plan for design verification. This phase may require simulations.
Verify the design, set up pilot runs, implement production process and handover to process owners.
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.

[edit] 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.

[edit] Roles required for implementation
Six Sigma identifies five key roles[8] for its successful implementation.
Executive Leadership includes CEO and other key top management team members. They are responsible for setting up a vision for Six Sigma implementation. They also empower the other role holders with the freedom and resources to explore new ideas for breakthrough improvements.
Champions are responsible for the Six Sigma implementation across the organization in an integrated manner. The Executive Leadership draws them from the upper management. Champions also act as mentor to Black Belts. At GE this level of certification is now called "Quality Leader".
Master Black Belts, identified by champions, act as in-house expert coach for the organization on Six Sigma. They devote 100% of their time to Six Sigma. They assist champions and guide Black Belts and Green Belts. Apart from the usual rigor of statistics, their time is spent on ensuring integrated deployment of Six Sigma across various functions and departments.
Experts This level of skill is used primarily within Aerospace and Defense Business Sectors. Experts work across company boundaries, improving services, processes, and products for their suppliers, their entire campuses, and for their customers. Raytheon Incorporated was one of the first companies to introduce Experts to their organizations. At Raytheon, Experts work not only across multiple sites, but across business divisions, incorporating lessons learned throughout the company.[citation needed]
Black Belts operate under Master Black Belts to apply Six Sigma methodology to specific projects. They devote 100% of their time to Six Sigma. They primarily focus on Six Sigma project execution, whereas Champions and Master Black Belts focus on identifying projects/functions for Six Sigma.
Green Belts are the employees who take up Six Sigma implementation along with their other job responsibilities. They operate under the guidance of Black Belts and support them in achieving the overall results.
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.

[edit] 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.

[edit] 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 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.

[edit] 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 [citation needed]. 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:
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.

[edit] 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.

[edit] Criticism
The "3.4 Defects Per Million Opportunities (DPMO)" is a gross confusion of the following situation, for example: A population of 1,100,000 units is manufactured. A perfect inspection/test process removes the 100,000 defective units. The remaining 1,000,000 units are thus the intended population and contain no defective units. Approximately three to four of these measure at Short-term Mean +-4.5 Sigma or more extreme. The "3.4 per million" is thus a characteristic of the Normal Distribution that is true of the intended population, not the defects or defective units. The defects or defective units may have any measurement, as they are members of one or more populations each of which is different in some way from the intended population; This is why the "perfect inspection/test" does not exist.
Fortune has 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."

[edit] Examples of some key tools used
5 Whys
ANOVA
ANOVA Gage R&R
Axiomatic design
Catapult Exercise on variability
Cause & Effects Diagram (a.k.a. Fishbone or Ishikawa Diagram)
Chi-Square Test of Independence and Fits
Control Charts
Correlation
Cost Benefit Analysis
CTQ Tree
Customer Output Process Input Supplier Maps
Customer survey
Design of Experiments
Failure Modes Effects Analysis
General Linear Model
Histograms
Homogeneity of Variance
Process Maps
Regression
Run Charts
Stratification
Taguchi
Thought Process Map

[edit] 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. Other Six Sigma for IT Management tools include Proxima Technology Centauri, HP Mercury, BMC Remedy..
1. Six Sigma was industry specific 2. The average was very subjective in nature, it was very difficult to define average 3. There was problem in finding whether six sigma has been achieved or not.

[edit] References
^ Motorola University - What is Six Sigma?. Retrieved on Jan 29, 2006.
^ The Inventors of Six Sigma. Retrieved on Jan 29, 2006.
^ Motorola University Six Sigma Dictionary. Retrieved on Jan 29, 2006.
^ Motorola Inc. - Motorola University. Retrieved on Jan 29, 2006.
^ About Motorola University. Retrieved on Jan 29, 2006.
^ Averboukh, Elena A.. Six Sigma Trends: Six Sigma Leadership And Innovation Using TRIZ. Retrieved on Nov 13, 2006.
^ 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
^ Mikel Harry & Richard Schroeder. Six Sigma, Random House, Inc, 2000. ISBN 0-385-49437-8
^ Tonner, Craig; Patra, Pradeep (2003-09-03). Six Sigma (English). Retrieved on 2006-11-26.
^ Wheeler, Donald J., Phd, The Six Sigma Practitioner's Guide to Data Analysis, p307, http://www.spcpress.com
^ Betsy Morris (2006-07-11). Old rule: be lean and mean. Fortune. Retrieved on 2006-11-26.

[edit] See also
Business Process
Business Process Improvement
Business Process Improvement Pattern
Design for Six Sigma
Lean manufacturing
Lean Six Sigma
Process Improvement
Statistical Process Control
Corrective and Preventative Action (CAPA)