Craig Gygi Bruce Williams Neil Decarlo S Popular Books

Craig Gygi Bruce Williams Neil Decarlo S Biography & Facts

Six Sigma (6σ) is a set of techniques and tools for process improvement. It was introduced by American engineer Bill Smith while working at Motorola in 1986. Six Sigma strategies seek to improve manufacturing quality by identifying and removing the causes of defects and minimizing variability in manufacturing and business processes. This is done by using empirical and statistical quality management methods and by hiring people who serve as Six Sigma experts. Each Six Sigma project follows a defined methodology and has specific value targets, such as reducing pollution or increasing customer satisfaction. The term Six Sigma originates from statistical quality control, a reference to the fraction of a normal curve that lies within six standard deviations of the mean, used to represent a defect rate. History Motorola pioneered Six Sigma, setting a "six sigma" goal for its manufacturing business. It registered Six Sigma as a service mark on June 11, 1991 (U.S. Service Mark 1,647,704); on December 28, 1993, it registered Six Sigma as a trademark. In 2005 Motorola attributed over $17 billion in savings to Six Sigma. Honeywell and General Electric were also early adopters of Six Sigma. As GE's CEO, in 1995 Jack Welch made it central to his business strategy. In 1998 GE announced $350 million in cost savings thanks to Six Sigma, which was an important factor in the spread of Six Sigma (this figure later grew to more than $1 billion). 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, some practitioners have combined Six Sigma ideas with lean manufacturing to create a methodology named Lean Six Sigma. The Lean Six Sigma methodology views lean manufacturing, which addresses process flow and waste issues, and Six Sigma, with its focus on variation and design, as complementary disciplines aimed at promoting "business and operational excellence". In 2011, the International Organization for Standardization (ISO) published the first standard "ISO 13053:2011" defining a Six Sigma process. Other standards have been created mostly by universities or companies with Six Sigma first-party certification programs. Etymology The term Six Sigma comes from statistics, specifically from the field of statistical quality control, which evaluates process capability. 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). The 3.4 dpmo is based on a "shift" of ± 1.5 sigma explained by Mikel Harry. This figure is based on the tolerance in the height of a stack of discs. Specifically, say that there are six standard deviations—represented by the Greek letter σ (sigma)—between the mean—represented by μ (mu)—and the nearest specification limit. 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. According to a calculation method employed in process capability studies, this means that practically no items will fail to meet specifications. One should also note that the calculation of sigma levels for a process data is independent of the data being normally distributed. In one of the criticisms of Six Sigma, practitioners using this approach spend a lot of time transforming data from non-normal to normal using transformation techniques. It must be said that sigma levels can be determined for process data that has evidence of non-normality. Doctrine Six Sigma asserts that: Continuous efforts to achieve stable and predictable process results (e.g., by reducing process variation) are of vital importance to business success. Manufacturing and business processes have characteristics that can be defined, 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: Focus on achieving measurable and quantifiable financial returns Emphasis on management leadership and support Commitment to making decisions on the basis of verifiable data and statistical methods rather than assumptions and guesswork In fact, lean management and Six Sigma share similar methodologies and tools, including the fact that both were influenced by Japanese business culture. However, lean management primarily focuses on eliminating waste through tools that target organizational efficiencies while integrating a performance improvement system, while Six Sigma focuses on eliminating defects and reducing variation. Both systems are driven by data, though Six Sigma is much more dependent on accurate data. Six Sigma's implicit goal is to improve all processes but not necessarily to the 3.4 DPMO level. Organizations need to determine an appropriate sigma level for each of their most important processes and strive to achieve these. As a result of this goal, it is incumbent on management of the organization to prioritize areas of improvement. Methodologies Six Sigma projects follow two project methodologies, inspired by W. Edwards Deming's Plan–Do–Study–Act Cycle, each with five phases. DMAIC ("duh-may-ick", /də.ˈmeɪ.ɪk/) is used for projects aimed at improving an existing business process DMADV ("duh-mad-vee", /də.ˈmæd.vi/) is used for projects aimed at creating new product or process designs DMAIC The DMAIC project methodology has five phases: Define the system, the voice of the customer and their requirements, and the project goals, specifically. Measure key aspects of the current process and collect relevant data; calculate the "as-is" process capability Analyze the data to investigate and verify cause and effect. Determine what the relationships are, and attempt to ensure that all factors have been considered. Seek out the 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 the target are corrected before they result in defects. Implement control systems such as statistical process control, production boards, visual workplaces, and continuously monitor the process. This process is repeated until the desired quality level is obtained. Some organizations add a Recognize step at the beginning, which is to recognize the right probl.... Discover the Craig Gygi Bruce Williams Neil Decarlo S popular books. Find the top 100 most popular Craig Gygi Bruce Williams Neil Decarlo S books.