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Six Sigma

Six Sigma is a science topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand Six Sigma rather than just read about it. In short: 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 — main illustration
Six Sigma — illustration

Key takeaways

  • Six Sigma belongs to science; place it in that map before memorising details.
  • Learn the definition first, then one example that makes the definition concrete.
  • Connect Six Sigma to a quantity you can measure, compute or draw — that is where exam questions come from.
  • Reproduce the core statement of Six Sigma from memory before moving on to harder problems.

Reference excerpt

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 reflects the 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 (see figure caption) will fail to meet specifications. The calculation of sigma levels for a process data doesn't need normally distributed data. The common approach for handling these distributions is via transformation (Box-Cox or Johnson) to normal; in fact, a criticism of Six Sigma is that practitioners spend a lot of time transforming data from non-normal to normal. However, sigma levels can be determined for process data that has evidence of non-normality using the cumulative density function of a better-fitting distribution. The generalized lambda distribution was proposed in 2004 as a distribution suitable for this purpose.

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:

… excerpt ends here. Continue reading the full article.

Illustrations

Six Sigma: Normal distribution underlies the statistical assumptions of Six Sigma. At 0 {\textstyle 0} , μ {\textstyle \mu } (mu) marks the mean, with the horizontal axis showing distance from the mean, denoted in units of standard deviation (represented as σ {\textstyle \sigma } or sigma). The greater the standard deviation, the larger the spread of values; for the green curve, μ = 0 {\textstyle \mu =0} and σ = 1 {\textstyle \sigma =1} . The upper and lower specification limits (USL and LSL) are at a distance of 6σ from the mean. Normal distribution means that values far away from the mean are extremely unlikely—approximately 1 in a billion too low, and the same too high. Even if the mean were to move right or left by 1.5 standard deviations (also known as a 1.5 sigma shift, colored red and blue), there is still a safety cushion: approximately 3.4 in a million for the side that the distribution has moved towards, and a much smaller number (32 per quintillion) on the other.
Normal distribution underlies the statistical assumptions of Six Sigma. At 0 {\textstyle 0} , μ {\textstyle \mu } (mu) marks the mean, with the horizontal axis showing distance from the mean, denoted in units of standard deviation (represented as σ {\textstyle \sigma } or sigma). The greater the standard deviation, the larger the spread of values; for the green curve, μ = 0 {\textstyle \mu =0} and σ = 1 {\textstyle \sigma =1} . The upper and lower specification limits (USL and LSL) are at a distance of 6σ from the mean. Normal distribution means that values far away from the mean are extremely unlikely—approximately 1 in a billion too low, and the same too high. Even if the mean were to move right or left by 1.5 standard deviations (also known as a 1.5 sigma shift, colored red and blue), there is still a safety cushion: approximately 3.4 in a million for the side that the distribution has moved towards, and a much smaller number (32 per quintillion) on the other.
Six Sigma: Six Sigma symbol
Six Sigma symbol
Six Sigma: DMAIC's five steps
DMAIC's five steps
Six Sigma: DMADV's five steps
DMADV's five steps
Six Sigma: A control chart showing a process that experienced a 1.5σ drift in the process mean toward the upper specification limit starting at midnight. Control charts help identify when a process should be investigated in order to find and eliminate special-cause variation.
A control chart showing a process that experienced a 1.5σ drift in the process mean toward the upper specification limit starting at midnight. Control charts help identify when a process should be investigated in order to find and eliminate special-cause variation.

Worked examples

Example 1 — a first encounter with Six Sigma

Start with the simplest possible case. Write down what Six Sigma claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In science, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to Six Sigma before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about Six Sigma ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of Six Sigma

In research
Six Sigma appears in science research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry
Engineering practice reuses Six Sigma in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom
Six Sigma is common in secondary-school and first-year university syllabi. It links to neighbouring topics 1986 introductions, Business processes, Business terms, so understanding it makes those chapters shorter.
In everyday life
Look for Six Sigma outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.

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How to study Six Sigma in 20 minutes

  1. Read the reference excerpt below once, without taking notes.
  2. Close the page and write down what Six Sigma means in your own words.
  3. Compare your version with the excerpt and mark what you missed.
  4. Work through the three examples above with pen and paper.
  5. Explain Six Sigma out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Six Sigma in simple terms?

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.

Why does Six Sigma matter?

Because it connects several science ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study Six Sigma?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on Six Sigma.

Tags

  • 1986 introductions
  • Business processes
  • Business terms
  • Professional certification in quality management
  • Quality management
  • Six Sigma

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