What is Six Sigma? Six Sigma is a business management strategy, initially implemented by Motorola, that today enjoys widespread application in many sectors of industry, although its application is not without controversy. Six Sigma seeks to improve the quality of process outputs by identifying and removing the causes of defects (errors) and variability in manufacturing and business processes. It uses a set of quality management methods, including statistical methods, and creates a special infrastructure of people within the organization ("Black Belts","Green Belts", etc.) who are experts in these methods. Each Six Sigma project carried out within an organization follows a defined sequence of steps and has quantified financial targets (cost reduction or profit increase). Historical overview Six Sigma was originally developed as a set of practices designed to improve manufacturing processes and eliminate defects, but its application was subsequently extended to other types of business processes as well. In Six Sigma, a defect is defined as anything that could lead to customer dissatisfaction. The particulars of the methodology were first formulated by Bill Smith at Motorola in 1986. Six Sigma was heavily inspired by six preceding decades of quality improvement methodologies such as quality control, TQM, and Zero Defects, based on the work of pioneers such as Shewhart, Deming, Juran, Ishikawa, Taguchi and others. Like its predecessors, Six Sigma asserts that – - Continuous efforts to achieve stable and predictable process results (i.e. reduce process variation) are of vital importance to business success. - Manufacturing and business processes have characteristics that can be measured, analyzed, improved and controlled. - Achieving sustained quality improvement requires commitment from the entire organization, particularly from top-level management. Features that set Six Sigma apart from previous quality improvement initiatives include – - A clear focus on achieving measurable and quantifiable financial returns from any Six Sigma project. - An increased emphasis on strong and passionate management leadership and support. - A special infrastructure of "Champions," "Master Black Belts," "Black Belts," etc. to lead and implement the Six Sigma approach. - A clear commitment to making decisions on the basis of verifiable data, rather than assumptions and guesswork. The term "Six Sigma" is derived from a field of statistics known as process capability studies. Originally, it referred to the ability of manufacturing processes to produce a very high proportion of output within specification. Processes that operate with "six sigma quality" over the short term are assumed to produce long-term defect levels below 3.4 defects per million opportunities (DPMO). Six Sigma's implicit goal is to improve all processes to that level of quality or better. Other early adopters of Six Sigma who achieved well-publicized success include Honeywell (previously known as AlliedSignal) and General Electric, where the method was introduced by Jack Welch. By the late 1990s, about two-thirds of the Fortune 500 organizations had begun Six Sigma initiatives with the aim of reducing costs and improving quality. In recent years, Six Sigma has sometimes been combined with lean manufacturing to yield a methodology named Lean Six Sigma. Methods Six Sigma projects follow two project methodologies inspired by Deming's Plan-Do-Check-Act Cycle. These methodologies comprise five phases each and are known by the acronyms DMAIC and DMADV. DMAIC is used for projects aimed at improving an existing business process. DMADV is used for projects aimed at creating new product or process designs. DMAIC The five phases in the DMAIC project methodology are: - Define high-level project goals and the current process. - Measure key aspects of the current process and collect relevant data. - Analyze the data to verify cause-and-effect relationships. Determine what the relationships are, and attempt to ensure that all factors have been considered. - Improve or optimize the process based upon data analysis using techniques like Design of experiments. - Control to ensure that any deviations from target are corrected before they result in defects. Set up pilot runs to establish process capability, move on to production, set up control mechanisms and continuously monitor the process. DMADV The five phases in the DMADV project methodology are: - Define design goals that are consistent with customer demands and the enterprise strategy. - Measure and identify CTQs (characteristics that are Critical To Quality), product capabilities, production process capability, and risks. - Analyze to develop and design alternatives, create a 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 the production process and hand it over to the process owners. DMADV is also known as DFSS, an abbreviation of "Design For Six Sigma".

Quality management tools and methods used in Six Sigma Within the individual phases of a DMAIC or DMADV project, Six Sigma utilizes many established quality management tools that are also used outside of Six Sigma. The following table shows an overview of the main methods used. 5 Whys Analysis of variance ANOVA Gauge R&R Axiomatic design Business Process Mapping Catapult exercise on variability Cause & effects diagram (also known as fishbone or Ishikawa diagram) Chi-square test of independence and fits Control chart Correlation Cost-benefit analysis CTQ tree Quantitative marketing research through use of Enterprise Feedback Management (EFM) systems Design of experiments Failure mode and effects analysis (FMEA) General linear model Histograms Homoscedasticity Quality Function Deployment (QFD) Pareto chart Pick chart Process capability Regression analysis Root cause analysis Run charts SIPOC analysis (Suppliers, Inputs, Process, Outputs, Customers) Stratification Taguchi methods Taguchi Loss Function Thought process map TRIZ

Origin and meaning of the term "six sigma process" The term "six sigma process" comes from the notion that if one has six standard deviations between the process mean and the nearest specification limit, as shown in the graphic, there will be practically no items that fail to meet specifications. This is based on the calculation method employed in process capability studies. In a capability study, the number of standard deviations between the process mean and the nearest specification limit is given in sigma units. As process standard deviation goes up, or the mean of the process moves away from the center of the tolerance, fewer standard deviations will fit between the mean and the nearest specification limit, decreasing the sigma number and increasing the likelihood of items outside specification.

Sigma levels The table gives long-term DPMO values corresponding to various short-term Sigma levels. Note that these figures assume that the process mean will shift by 1.5 sigma towards the side with the critical specification limit. In other words, they assume that after the initial study determining the short-term sigma level, the long-term Cpk value will turn out to be 0.5 less than the short-term Cpk value. So, for example, the DPMO figure given for 1 sigma assumes that the long-term process mean will be 0.5 sigma beyond the specification limit (Cpk = –0.17), rather than 1 sigma within it, as it was in the short-term study (Cpk = 0.33). Note that the defect percentages only indicate defects exceeding the specification limit that the process mean is nearest to. Defects beyond the far specification limit are not included in the percentages.

Role of the 1.5 sigma shift Experience has shown that in the long term, processes usually do not perform as well as they do in the short. As a result, the number of sigmas that will fit between the process mean and the nearest specification limit is likely to drop over time, compared to an initial short-term study. To account for this real-life increase in process variation over time, an empirically-based 1.5 sigma shift is introduced into the calculation. According to this idea, a process that fits six sigmas between the process mean and the nearest specification limit in a short-term study will in the long term only fit 4.5 sigmas – either because the process mean will move over time, or because the long-term standard deviation of the process will be greater than that observed in the short term, or both. Hence the widely accepted definition of a six sigma process is one that produces 3.4 defective parts per million opportunities (DPMO). This is based on the fact that 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 the 3.4 DPMO of a "Six Sigma" process in fact corresponds to 4.5 sigmas, namely 6 sigmas minus the 1.5 sigma shift introduced to account for long-term variation. This is designed to prevent underestimation of the defect levels likely to be encountered in real-life operation.