about a process. Hence the “belt” nomenclature was born.
Within the Six Sigma hierarchy, there are several levels of Belt‐holders. Depending on the size and complexity of the organization, there may be White, Yellow, Green, and Black Belts, as well as Master Black Belts.
White Belts, who often are also Executives in the organization, are not directly involved with improvement projects but have an awareness of Six Sigma concepts and help support enterprise‐wide deployment.
Yellow Belts serve as members of Six Sigma project teams. They are familiar with Six Sigma principles and can apply basic tools.
Green Belts tend to work on projects on a part‐time basis while maintaining their current positions in the company. A Green Belt may lead a small project team or assist a Black Belt on a larger project. Green Belts know statistical techniques and can apply a large array of non‐statistical problem‐solving tools.
At larger companies, Black Belts work full‐time in their Six Sigma roles. They lead Six Sigma projects and train and mentor Green Belts. A Black Belt possesses sophisticated knowledge of statistics and data analysis tools, as well as team dynamics.
Finally, Master Black Belts work full time managing, mentoring, and training Black Belts. A Master Black Belt may also work with upper management and the steering committee to plan Six Sigma deployment efforts and to select and prioritize Six Sigma projects.
2.3 Is Six Sigma New?
The origin of quality standards can be traced back to the medieval guild system in which craftsman placed their marks on only those products that met their standards. Quality tools such as statistical process control (SPC) charts date back to Walter Shewhart in 1924, and both W. Edwards Deming and Joseph M. Juran introduced quality thinking and methods in Japan after World War II. Japanese industry continued to innovate and develop quality systems and tools, with major contributions from Ishikawa, Shingo, and Taguchi, among others. In the last 70 years, there has been no shortage of quality programs touted as definitive solutions, including Total Quality Management, Quality Circles, and Zero Defects. Given the long history of quality, then, is Six Sigma new?
On the one hand, it can be argued that Six Sigma is simply a repackaging of existing quality know‐how. It is true that Six Sigma uses existing quality tools such as the Basic seven tools: cause and effect diagrams, check sheets, control charts, histograms, Pareto charts, scatter diagrams, and stratification; and the New seven tools: affinity diagrams, arrow diagrams, interrelationship diagrams, matrix diagrams, prioritization matrices, process decision program charts, and tree diagrams. (See Section 2.4.1.) Six Sigma’s focus on team problem‐solving is taken from Japanese practices such as quality circles. Both Deming and Juran advocated data‐based decision‐making and emphasized the vital role of management in successful quality implementation.
Several features of the Six Sigma approach are unique, however. Because Six Sigma requires data‐driven decisions, it has led to the widespread use of sophisticated statistical techniques. Analysis tools once only known to high‐level industrial statisticians, such as linear regression, chi‐squared tests, and designed experiments, are now routinely employed in Six Sigma projects by non‐statisticians. The project approach, in which upper management identifies project areas based on customer requirements and the overall business strategy, is unique to Six Sigma. In addition, the emphasis on reducing the variation in a process is a relatively new concept. Earlier quality efforts may have emphasized aligning processes to desired target levels but did not necessarily reduce the variation around those targets. Taguchi’s work in the 1980s introduced the importance of reducing variability to improve quality and reduce quality costs.
Finally, the requirement that quality improvement be tied directly to the organization’s bottom line is a distinct hallmark of Six Sigma. Six Sigma projects saved GE a reported $12 billion over five years (5). Results such as these have earned Six Sigma enduring popularity with executives.
2.4 Quality Tools Used in Six Sigma
The quality tools available to Six Sigma practitioners are vast. For example, The Quality Toolbox by Nancy Tague details over 100 separate tools that can be used in project planning, idea creation, process analysis, data collection and analysis, cause analysis, and decision making. The statistical tools used in Six Sigma projects include:
Hypothesis testing
Analysis of variance (ANOVA)
Gauge repeatability and reproducibility (R&R)
Statistical control charts
Process capability
Correlation and regression analysis
Design of experiments
Multivariate analysis
Time‐series analysis
2.4.1 The Basic Seven Tools and the New Seven Tools
Six Sigma practitioners also leverage the Basic Seven and New Seven Tools. The Basic Seven tools were first brought into focus by Kaoru Ishikawa, a professor of engineering at Tokyo University and the father of “Quality Circles.” These tools can be used to collect and analyze data in the Measure, Analyze, Improve, and Control phases of a Six Sigma project:
1 Cause‐and‐effect diagram (also known as Ishikawa or fishbone diagram): A visual tool that identifies potential causes of a problem and sorts ideas into useful categories.
2 Check sheet: A structured, prepared form for collecting and analyzing the frequency of occurrences of various events.
3 Control chart: A graph used to study how a process changes over time. Comparing current data to historical control limits leads to conclusions about whether the process variation is consistent (in control) or unpredictable (out of control due to some special causes of variation).
4 Histogram: A bar chart that shows the shape of a data set using its frequency distribution, or how often each value occurs in a data set.
5 Pareto chart: A bar graph that shows the frequency of occurrence of events in descending order. The chart helps the team focus on the main drivers of a problem.
6 Scatter diagram: An X‐Y plot that shows the relationship between two quantitative variables that are measured in pairs.
7 Stratification: A technique that separates data gathered from a variety of sources so that patterns can be seen.
All of these tools are discussed at length in the next three chapters.
In 1976, the Union of Japanese Scientists and Engineers (JUSE) saw the need for tools to promote innovation, communicate information, and successfully plan major projects. A team researched and developed the New 7 Tools, often called the Seven Management Tools:
1 Affinity diagram: A visual tool that allows a team to organize a large number of ideas, opinions, or issues into their natural relationship groupings.
2 Arrow diagram: A graphical tool that shows the required order of tasks in a project or process, the best schedule for the entire project, and potential scheduling and resource problems and their solutions.
3 Interrelationship diagram: A visual tool that shows cause‐and‐effect relationships and helps analyze the natural links between various aspects of a complex situation.
4 Matrix diagram: A tool that shows the relationship between two, three, or four groups and can give information about the relationship, such as its strength and the roles played by various individuals or measurements.
5 Prioritization matrix: A decision tool that rates various options based