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Six Sigma is a methodology to manage process variations that cause defects and to systematically work towards managing variation to eliminate those defects•. 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. The process was pioneered by Bill Smith at Motorola in 1986• and was originally defined• 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 ± 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•. Motorola has reported over US$17 billion in savings• from Six Sigma to date. In addition to the Motorola, companies which also adopted six sigma methodologies early-on and continue to practice it today, are Honeywell International (previously know as Allied Signal) and General Electric (introduced by Jack Welch). The two companies have reported to have saved literally billions of dollars thanks to the aggressive implementation and daily practice of six sigma methodologies. Methodology Six Sigma has two key methodologies: DMAIC (Define, Measure, Analyze, Improve, Control) and DMADV (Define, Measure, Analyze, Design, Verify). 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. 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 phases: DMADV Basic methodology consists of the following five phases: Also see Design for Six Sigma quality. The most common acronym for Design for Six Sigma is DFSS. 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: Roles required for implementation Six Sigma identifies five key roles for its successful implementation. Please note that 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. Specific training programs are available to train people to take up these roles. 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. In addition some alternatives are Microsoft Visio, Telelogic System Architect, and Proforma Corp. ProVision. For program management, tracking and reporting, the popular tools include PowerSteering, iNexus and SixNet. Origin Robert Galvin did not really "invent" Six Sigma in the 1980s, but would more correctly be said to have applied methodologies that had been available since the 1920s and were developed by luminaries like Shewhart, Deming, Juran, Ishikawa, Ohno, Shingo, Taguchi and Shainin. All tools used by and for Six Sigma are actually a subset of the Quality Engineering discipline and can be considered to be 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 which arguably 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. Additionally, there is criticism from the martial arts community for the appropriation of the term "Black Belt" for a non martial arts use. This was used as a joke in the comic strip Dilbert. 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 you have six standard deviations between the mean of a process and the nearest specification limit, you 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).* 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). So 3.4 DPMO corresponds to 4.5 sigmas, not six. Anyone with access to Minitab or QuikSigma can quickly confirm this by running a Capability Study on data with a mean of 0, a standard deviation of 1, and an upper specification limit of 4.5. So, how is this truly 4.5 sigma process transformed to a 6 sigma process? By arbitrarily adding 1.5 sigmas to the calculated result, the "1.5 sigma shift" (SBTI Black Belt material, ca 1998). Dr. Donald Wheeler, one of the most respected authors on the topics of Control Charts, Capability Studies, and Designed Experiments, dismisses the 1.5 sigma shift as "goofy". 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, we have no means for proving that data belong to any particular distribution. We 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 Everyone with a Six Sigma program knows about the +/-1.5 sigma drift of a process mean, experienced by all processes. What this is saying is that if we are manufacturing a product that is 100 +/- 3 cm (97–103cm), over time, it may drift up to 98.5–104.5 or down to 95.5–101.5. That might be of concern to our customers. So where does the "+/-1.5" come from? The +/-1.5 shift was introduced by Mikel Harry. Where did he get it? Harry refers to a paper written in 1975 by Evans, "Statistical Tolerancing: The State of the Art. Part 3. Shifts and Drifts". The paper is about tolerancing. That is how the overall error in an assembly is effected by the errors in components. Evans refers to a paper by Bender in 1962, "Benderizing Tolerances – A Simple Practical Probablity 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", he suggests: v = 1.5 SQRT (var X) What has this got to do with monitoring the myriad processes that people are concerned about? Very little. Harry then takes things a step further. Imagine a process where 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). Because of 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, suppose a process has a target value of 10.0, and control limits work out to be, say, 13.0 and 7.0. "Long term" the mean will drift to 11.5 (or 8.5), with control limits changing to 14.5 and 8.5. The simple truth is that 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. World Class Quality means "on target with minimum variation". In summary, the term "Six Sigma" has its roots in a quality tool that can easily be misapplied by a naïve user and in the controversial 1.5 sigma shift. 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. Statistics and robustness Many mistakenly believe that the core of the Six Sigma methodology is statistics. This is not so. You can do a very acceptable Six Sigma project with only the most rudimentary statistical tools. The core of the Six Sigma methodology is a data-driven, systematic approach to problem solving, and focus on customer impact. Statistical tools just happen to be useful along the way. Six Sigma is not the answer to all problems. If you're writing poetry, it will probably be of little value. If you're working on business, engineering, or production processes it is practically always applicable and successful, when applied correctly. In this sense, the core methodology is remarkably general and robust. Some professional statisticians justifiably criticize Six Sigma because the quality of statistical understanding that is propagated by practitioners is highly variable. Some programs are excellent, and some are much less so. See also | |||||||
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