Six Sigma seeks to improve the quality of process outputs by identifying and removing the causes of defects (errors) and minimizing 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 targets.
These targets can be financial (cost reduction or profit increase) or whatever is critical to the customer of that process (cycle time, safety, delivery, etc. ). The term six sigma originated from terminology associated with manufacturing, specifically terms associated with statistical modelling of manufacturing processes. The maturity of a manufacturing process can be described by a sigma rating indicating its yield, or the percentage of defect-free products it creates. A six-sigma process is one in which 99. 99966% of the products manufactured are free of defects, compared to a one-sigma process in which only 31% are free of defects.
Motorola set a goal of “six sigmas” for all of its manufacturing operations and this goal became a byword for the management and engineering practices used to achieve it. Historical overview Six Sigma originated 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 any process output that does not meet customer specifications, or that could lead to creating an output that does not meet customer specifications.
Bill Smith first formulated the particulars of the methodology 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 doctrine 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,” “Yellow 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” comes 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.
Six Sigma is a registered service mark and trademark of Motorola Inc. As of 2006[update] Motorola reported over US$17 billion in savings from Six Sigma. Other early adopters of Six Sigma who achieved well-publicized success include Honeywell (previously known as AlliedSignal) and General Electric, where Jack Welch introduced the method. 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[update], some practitioners have combined Six Sigma ideas 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, comprised of five phases each, bear the acronyms DMAIC and DMADV. • DMAIC is used for projects aimed at improving an existing business process. DMAIC is pronounced as “duh-may-ick”. • DMADV is used for projects aimed at creating new product or process designs. DMADV is pronounced as “duh-mad-vee”. DMAIC The DMAIC project methodology has five phases: • Define the problem, the voice of the customer, and the project goals, specifically.
• Measure key aspects of the current process and collect relevant data. • Analyze the data to investigate and verify cause-and-effect relationships. Determine what the relationships are, and attempt to ensure that all factors have been considered. Seek out root cause of the defect under investigation. • Improve or optimize the current process based upon data analysis using techniques such as design of experiments, poka yoke or mistake proofing, and standard work to create a new, future state process. Set up pilot runs to establish process capability.
• Control the future state process to ensure that any deviations from target are corrected before they result in defects. Control systems are implemented such as statistical process control, production boards, and visual workplaces and the process is continuously monitored. DMADV The DMADV project methodology, also known as DFSS (“Design For Six Sigma”), features five phases: • 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 owner(s). 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 |Histograms | |Analysis of variance |Homoscedasticity | |ANOVA Gauge R&R |Quality Function Deployment (QFD) | |Axiomatic design |Pareto chart | |Business Process Mapping |Pick chart | |Catapult exercise on variability |Process capability | |Cause & effects |Quantitative marketing research through use of Enterprise | |Chi-square test of independence and fits |Feedback Management (EFM) systems | |Control chart |Regression analysis | |Correlation |Root cause analysis | |Cost-benefit analysis |Run charts | |CTQ tree |SIPOC analysis (Suppliers, Inputs, Process, Outputs, Customers)| |Design of experiments |Stratification | |Failure mode and effects analysis (FMEA) |Taguchi methods | |General linear model |Taguchi Loss Function | |TRIZ | | Implementation roles
One key innovation of Six Sigma involves the “professionalizing” of quality management functions. Prior to Six Sigma, quality management in practice was largely relegated to the production floor and to statisticians in a separate quality department. Formal Six Sigma programs borrow martial arts ranking terminology to define a hierarchy (and career path) that cuts across all business functions. Six Sigma identifies several key roles for its successful implementation. • Executive Leadership includes the CEO and other members of top management. 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 take responsibility for Six Sigma implementation across the organization in an integrated manner. The Executive Leadership draws them from upper management. Champions also act as mentors to Black Belts. • Master Black Belts, identified by champions, act as in-house coaches on Six Sigma. They devote 100% of their time to Six Sigma. They assist champions and guide Black Belts and Green Belts. Apart from statistical tasks, they spend their time on ensuring consistent application of Six Sigma across various functions and departments. • 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, the employees who take up Six Sigma implementation along with their other job responsibilities, operate under the guidance of Black Belts. • Yellow Belts, trained in the basic application of Six Sigma management tools, work with the Black Belt throughout the project stages and are often the closest to the work. Certification In the United States. Six Sigma certification for both green and black belts is offered by the Institute of Industrial Engineers and by the American Society for Quality.
Origin and meaning of the term “six sigma process” [pic] Graph of the normal distribution, which underlies the statistical assumptions of the Six Sigma model. The Greek letter ? (sigma) marks the distance on the horizontal axis between the mean, µ, and the curve’s inflection point. The greater this distance, the greater is the spread of values encountered. For the curve shown above, µ = 0 and ? = 1. The upper and lower specification limits (USL, LSL) are at a distance of 6? from the mean. Because of the properties of the normal distribution, values lying that far away from the mean are extremely unlikely. Even if the mean were to move right or left by 1. 5?
at some point in the future (1. 5 sigma shift), there is still a good safety cushion. This is why Six Sigma aims to have processes where the mean is at least 6? away from the nearest specification limit. 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 graph, practically no items will fail to meet specifications. This is based on the calculation method employed in process capability studies. Capability studies measure the number of standard deviations between the process mean and the nearest specification limit 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. 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 may well 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 as 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 takes account of special causes that may cause a deterioration in process performance over time and is designed to prevent underestimation of the defect levels likely to be encountered in real-life operation. Sigma levels [pic] A control chart depicting a process that experienced a 1. 5 sigma drift in the process mean toward the upper specification limit starting at midnight. Control charts are used to maintain 6 sigma quality by signaling when quality professionals should investigate a process to find and eliminate special-cause variation. See also: Three sigma rule The table below 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 toward 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 to which the process mean is nearest. Defects beyond the far specification limit are not included in the percentages.