Six Sigma

The often-used six sigma symbol.

Six Sigma is a system of practices originally developed by Motorola to systematically improve processes by eliminating defects. Defects are defined as units that are not members of the intended population. Since it was originally developed, Six Sigma has become an element of many Total Quality Management (TQM) initiatives.

The process was pioneered by Bill Smith at Motorola in 1986 and was originally defined as a metric for measuring defects and improving quality, and a methodology to reduce defect levels below 3.4 Defects Per (one) Million Opportunities (DPMO).

Six Sigma is a registered service mark and trademark of Motorola, Inc. Motorola has reported over US$17 billion in savings from Six Sigma as of 2006.

In addition to Motorola, companies which also adopted Six Sigma methodologies early-on and continue to practice it today include Bank of America, Caterpillar, Honeywell International (previously known as Allied Signal), Raytheon and General Electric (introduced by Jack Welch).

Recently Six Sigma has been integrated with the TRIZ methodology for problem solving and product design.

Key concepts of Six Sigma

At its core, Six Sigma revolves around a few key concepts.

Critical to Quality: Attributes most important to the customer Defect: Failing to deliver what the customer wants Process Capability: What your process can deliver Variation: What the customer sees and feels Stable Operations: Ensuring consistent, predictable processes to

improve what the customer sees and feels Design for Six Sigma: Designing to meet customer needs and

process capability

Methodology

Six Sigma has two key methodologies:[7] DMAIC and DMADV. DMAIC is used to improve an existing business process. DMADV is used

to create new product designs or process designs in such a way that it results in a more predictable, mature and defect free performance.

DMAIC

Basic methodology consists of the following five steps: Define the process improvement goals that are consistent with

customer demands and enterprise strategy. Measure the current process and collect relevant data for future

comparison. Analyze to verify relationship and causality of factors. Determine

what the relationship is, and attempt to ensure that all factors have been considered.

Improve or optimize the process based upon the analysis using techniques like Design of Experiments.

Control to ensure that any variances are corrected before they result in defects. Set up pilot runs to establish process capability, transition to production and thereafter continuously measure the process and institute control mechanisms.

DMADV

Basic methodology consists of the following five steps:

Define the goals of the design activity that are consistent with customer demands and enterprise strategy.

Measure and identify CTQs (critical to qualities), product capabilities, production process capability, and risk assessments.

Analyze to develop and design alternatives, create 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 production process and handover to process owners.

Some people have used DMAICR (Realize). Others contend that focusing on the financial gains realized through Six Sigma is counter-productive and that said financial gains are simply byproducts of a good process improvement.

Another additional flavor of Design for Six Sigma is the DMEDI method. This process is almost exactly like the DMADV process, utilizing the same toolkit, but with a different acronym. DMEDI stands for Define, Measure, Explore, Develop, Implement.

Quality approaches and models

DFSS (Design for Six Sigma) - A systematic methodology utilizing tools, training and measurements to enable us to design products and processes that meet customer expectations and can be produced at Six Sigma Quality levels.DMAIC (Define, Measure, Analyze, Improve and Control) - A process for continued improvement. It is systematic, scientific and fact based. This closed-loop process eliminates unproductive steps, often focuses on new measurements, and applies technology for improvement.

Six Sigma - A vision of quality, which equates with only 3.4 defects per million opportunities for each product or service transaction. Strives for perfection.

Quality Tools

Associates are exposed to various tools and terms related to quality. Below are just a few of them.

Control Chart - Monitors variance in a process over time and alerts the business to unexpected variance which may cause defects.

Defect Measurement - Accounting for the number or frequency of defects that cause lapses in product or service quality.

Pareto Diagram - Focuses our efforts on the problems that have the greatest potential for improvement by showing relative frequency and/or size in a descending bar graph. Based on the proven Pareto principle: 20% of the sources cause 80% of any problems.

Process Mapping - Illustrated description of how things get done, which enables participants to visualize an entire process and identify areas of strength and weaknesses. It helps reduce cycle time and defects while recognizing the value of individual contributions.

Root Cause Analysis - Study of original reason for nonconformance with a process. When the root cause is removed or corrected, the nonconformance will be eliminated.

Statistical Process Control - The application of statistical methods to analyze data, study and monitor process capability and performance.

Tree Diagram - Graphically shows any broad goal broken into different levels of detailed actions. It encourages team members to expand their thinking when creating solutions.

Quality Terms

Black Belt - Leaders of teams responsible for measuring, analyzing, improving and controlling key processes that influence customer satisfaction and/or productivity growth. Black Belts are full-time positions.

Control - The state of stability, normal variation and predictability. Process of regulating and guiding operations and processes using quantitative data.

CTQ: Critical to Quality (Critical "Y") - Element of a process or practice which has a direct impact on its perceived quality.

Customer Needs, Expectations - Needs, as defined by customers, which meet their basic requirements and standards.

Defects - Sources of customer irritation. Defects are costly to both customers and to manufacturers or service providers. Eliminating defects provides cost benefits.Green Belt - Similar to Black Belt but not a full-time position.

Master Black Belt - First and foremost teachers. They also review and mentor Black Belts. Selection criteria for Master Black Belts are quantitative skills and the ability to teach and mentor. Master Black Belts are full-time positions.

Variance - A change in a process or business practice that may alter its expected outcome.

Statistics and robustness

The core of the Six Sigma methodology is a data-driven, systematic approach to problem solving, with a focus on customer impact. Statistical tools and analysis are often useful in the process. However, it is a mistake to view the core of the Six Sigma methodology as statistics; an acceptable Six Sigma project can be started with only rudimentary statistical tools.

Still, some professional statisticians criticize Six Sigma because practitioners have highly varied levels of understanding of the statistics involved.

Six Sigma as a problem-solving approach has traditionally been used in fields such as business, engineering, and production processes.

Roles required for implementation

Six Sigma identifies five key roles for its successful implementation.

Executive Leadership includes CEO and other key top management team members. 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 are responsible for the Six Sigma implementation across the organization in an integrated manner. The Executive Leadership draws them from the upper management. Champions also act as mentor to Black Belts. At GE this level of certification is now called "Quality Leader".

Master Black Belts, identified by champions, act as in-house expert coach for the organization on Six Sigma. They devote 100% of their time to Six Sigma. They assist champions and guide Black Belts and Green Belts. Apart from the usual rigor of statistics, their time is spent on ensuring integrated deployment of Six Sigma across various functions and departments.

Experts This level of skill is used primarily within Aerospace and Defense Business Sectors. Experts work across company boundaries, improving services, processes, and products for their suppliers, their entire campuses, and for their customers. Raytheon Incorporated was one of the first companies to introduce Experts to their organizations. At Raytheon, Experts work not only across multiple sites, but across business divisions, incorporating lessons learned throughout the company.

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 are the employees who take up Six Sigma implementation along with their other job responsibilities. They operate under the guidance of Black Belts and support them in achieving the overall results.

In many successful modern programs, Green Belts and Black Belts are empowered to initiate, expand, and lead projects in their area of responsibility. The roles as defined above, therefore, conform to the antiquated Mikel Harry/Richard Schroeder model, which is far from

being universally accepted. The terms black belt and green belt are borrowed from the ranking systems in various martial arts.

The term Six Sigma

Sigma (the lower-case Greek letter σ) is used to represent standard deviation (a measure of variation) of a population (lower-case 's', is an estimate, based on a sample). The term "six sigma process" comes from the notion that if one has six standard deviations between the mean of a process and the nearest specification limit, he will make practically no items that exceed the specifications. This is the basis of the Process Capability Study, often used by quality professionals. The term "Six Sigma" has its roots in this tool, rather than in simple process standard deviation, which is also measured in sigmas. Criticism of the tool itself, and the way that the term was derived from the tool, often sparks criticism of Six Sigma.

The widely accepted definition of a six sigma process is one that produces 3.4 defective parts per million opportunities (DPMO). 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). This implies that 3.4 DPMO corresponds to 4.5 sigmas, not six as the process name would imply. This can be confirmed by running on QuikSigma or Minitab a Capability Study on data with a mean of 0, a standard deviation of 1, and an upper specification limit of 4.5. The 1.5 sigmas added to the name Six Sigma are arbitrary and they are called "1.5 sigma shift" (SBTI Black Belt material, ca 1998). Dr. Donald Wheeler dismisses the 1.5 sigma shift as "goofy".

In a Capability Study, sigma refers to the number of standard deviations between the process mean and the nearest specification limit, rather than the standard deviation of the process, which is also measured in "sigmas". As process standard deviation goes up, or the mean of the process moves away from the center of the tolerance, the Process Capability sigma number goes down, because fewer standard deviations will then fit between the mean and the nearest specification limit (see Cpk Index). The notion that, in the long term, processes usually do not perform as well as they do in the short term is correct. That requires that Process Capability sigma based on long term data is less than or equal to an estimate based on short term sigma. However, the original use of the 1.5 sigma shift is as shown above, and implicitly assumes the opposite.

As sample size increases, the error in the estimate of standard deviation converges much more slowly than the estimate of the mean

(see confidence interval). Even with a few dozen samples, the estimate of standard deviation often drags an alarming amount of uncertainty into the Capability Study calculations. It follows that estimates of defect rates can be very greatly influenced by uncertainty in the estimate of standard deviation, and that the defective parts per million estimates produced by Capability Studies often ought not to be taken too literally.

Estimates for the number of defective parts per million produced also depends on knowing something about the shape of the distribution from which the samples are drawn. Unfortunately, there are no means for proving that data belong to any particular distribution. One can only assume normality, based on finding no evidence to the contrary. Estimating defective parts per million down into the 100s or 10s of units based on such an assumption is wishful thinking, since actual defects are often deviations from normality, which have been assumed not to exist.

The ±1.5 Sigma Drift

The ±1.5 sigma drift is the drift of a process mean, which occurs in all processes in a six sigma program. If a product being manufactured measures 100 ± 3 cm (97 – 103 cm), over time the ±1.5 sigma drift may cause the average to range up to 98.5 - 104.5 cm or down to 95.5 - 101.5 cm. This could be of significance to customers.

The ±1.5 shift was introduced by Mikel Harry. Harry referred to a paper about tolerancing, the overall error in an assembly is affected by the errors in components, written in 1975 by Evans, "Statistical Tolerancing: The State of the Art. Part 3. Shifts and Drifts". Evans refers to a paper by Bender in 1962, "Benderizing Tolerances – A Simple Practical Probability Method for Handling Tolerances for Limit Stack Ups". He looked at the classical situation with a stack of disks and how the overall error in the size of the stack, relates to errors in the individual disks. Based on "probability, approximations and experience", Bender suggests:

Harry then took this a step further. Supposing that there is a process in which 5 samples are taken every half hour and plotted on a control chart, Harry considered the "instantaneous" initial 5 samples as being "short term" (Harry's n=5) and the samples throughout the day as being "long term" (Harry's g=50 points). Due to the random variation in the first 5 points, the mean of the initial sample is different

to the overall mean. Harry derived a relationship between the short term and long term capability, using the equation above, to produce a capability shift or "Z shift" of 1.5. Over time, the original meaning of "short term" and "long term" has been changed to result in "long term" drifting means.

Harry has clung tenaciously to the "1.5" but over the years, its derivation has been modified. In a recent note from Harry "We employed the value of 1.5 since no other empirical information was available at the time of reporting." In other words, 1.5 has now become an empirical rather than theoretical value. A further softening from Harry: "... the 1.5 constant would not be needed as an approximation".

Despite this, industry has fixed on the idea that it is impossible to keep processes on target. No matter what is done, process means will drift by ±1.5 sigma. In other words, if a process has a target value of 10.0, and control limits work out to be 13.0 and 7.0, over the long term the mean will drift to 11.5 (or 8.5), with control limits changing to 14.5 and 8.5.

In truth, any process where the mean changes by 1.5 sigma, or any other amount, is not in statistical control. Such a change can often be detected by a trend on a control chart. A process that is not in control is not predictable. It may begin to produce defects, no matter where specification limits have been set.

Digital Six Sigma

In an effort to permanently minimize variation, Motorola has evolved the Six Sigma methodology to use information systems tools to make business improvements absolutely permanent. Motorola calls this effort Digital Six Sigma.

Criticism

Some companies that have embraced it have done poorly

The cartoonist Scott Adams featured Six Sigma in a Dilbert cartoon published on November 26th 2006. When the process is introduced to his company Dilbert asks "Why don't we jump on a fad that hasn't already been widely discredited?" The Dilbert character states "Fortune magazine says... blah blah... most companies that used Six Sigma have trailed the S&P 500."

Dilbert was referring to an article in Fortune which stated that "of 58 large companies that have announced Six Sigma programs, 91

percent have trailed the S&P 500 since." The statement is attributed to "an analysis by Charles Holland of consulting firm Qualpro (which espouses a competing quality-improvement process)."[11] The gist of the article is that Six Sigma is effective at what it is intended to do, but that it is "narrowly designed to fix an existing process" and does not help in "coming up with new products or disruptive technologies."

Based on arbritrary standards

While 3.4 defects per million might work well for certain products/processes, it might not be ideal for others. A pacemaker might need higher standards, for example, whereas a direct mail advertising campaign might need less. The basis and justification for choosing 6 as the number of standard deviations is not clearly explained.[citation needed]

What is Six Sigma?

Six Sigma is a rigorous and disciplined methodology that uses data and statistical analysis to measure and improve a company's operational performance by identifying and eliminating "defects" in manufacturing and service-related processes. Commonly defined as 3.4 defects per million opportunities, Six Sigma can be defined and understood at three distinct levels: metric, methodology and philosophy...

The goal of Six Sigma is to increase profits by eliminating variability, defects and waste that undermine customer loyalty.

Six Sigma can be understood/perceived at three levels:

1. Metric: 3.4 Defects per Million Opportunities. DPMO allows you to take complexity of product/process into account. Rule of thumb is to consider at least three opportunities for a physical part/component - one for form, one for fit and one for function, in absence of better considerations. Also you want to be Six Sigma in the Critical to Quality characteristics and not the whole unit/characteristics.

2. Methodology: DMAIC/DFSS structured problem solving roadmap and tools.

3. Philosophy: Reduce variation in your business and take customer-focused, data driven decisions.

Six Sigma is a methodology that provides businesses with the tools to improve the capability of their business processes. This increase in performance and decrease in process variation leads to defect

reduction and vast improvement in profits, employee morale and quality of product.

The History of Six Sigma

The roots of Six Sigma as a measurement standard can be traced back to Carl Frederick Gauss (1777-1855) who introduced the concept of the normal curve. Six Sigma as a measurement standard in product variation can be traced back to the 1920's when Walter Shewhart showed that three sigma from the mean is the point where a process requires correction. Many measurement standards (Cpk, Zero Defects, etc.) later came on the scene but credit for coining the term "Six Sigma" goes to a Motorola engineer named Bill Smith. (Incidentally, "Six Sigma" is a federally registered trademark of Motorola).

In the early and mid-1980s with Chairman Bob Galvin at the helm, Motorola engineers decided that the traditional quality levels -- measuring defects in thousands of opportunities -- didn't provide enough granularities. Instead, they wanted to measure the defects per million opportunities. Motorola developed this new standard and created the methodology and needed cultural change associated with it. Six Sigma helped Motorola realize powerful bottom-line results in their organization - in fact, they documented more than $16 Billion in savings as a result of our Six Sigma efforts.

Since then, hundreds of companies around the world have adopted Six Sigma as a way of doing business. This is a direct result of many of America's leaders openly praising the benefits of Six Sigma. Leaders such as Larry Bossidy of Allied Signal (now Honeywell), and Jack Welch of General Electric Company. Rumor has it that Larry and Jack were playing golf one day and Jack bet Larry that he could implement Six Sigma faster and with greater results at GE than Larry did at Allied Signal. The results speak for themselves.Six Sigma has evolved over time. It's more than just a quality system like TQM or ISO. It's a way of doing business. As Geoff Tennant describes in his book Six Sigma: SPC and TQM in Manufacturing and Services: "Six Sigma is many things, and it would perhaps be easier to list all the things that Six Sigma quality is not. Six Sigma can be seen as: a vision; a philosophy; a symbol; a metric; a goal; a methodology."

Roles required for implementation

Six Sigma identifies five key roles for its successful implementation.

Executive Leadership includes CEO and other key top management team members. 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 are responsible for the Six Sigma implementation across the organization in an integrated manner. The Executive Leadership draws them from the upper management. Champions also act as mentor to Black Belts. At GE this level of certification is now called "Quality Leader".

Master Black Belts, identified by champions, act as in-house expert coach for the organization on Six Sigma. They devote 100% of their time to Six Sigma. They assist champions and guide Black Belts and Green Belts. Apart from the usual rigor of statistics, their time is spent on ensuring integrated deployment of Six Sigma across various functions and departments.

Experts This level of skill is used primarily within Aerospace and Defense Business Sectors. Experts work across company boundaries, improving services, processes, and products for their suppliers, their entire campuses, and for their customers. Raytheon Incorporated was one of the first companies to introduce Experts to their organizations. At Raytheon, Experts work not only across multiple sites, but across business divisions, incorporating lessons learned throughout the company.

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 are the employees who take up Six Sigma implementation along with their other job responsibilities. They operate under the guidance of Black Belts and support them in achieving the overall results.

In many successful modern programs, Green Belts and Black Belts are empowered to initiate, expand, and lead projects in their area of responsibility.The terms black belt and green belt are borrowed from the ranking systems in various martial arts.

Software used for Six Sigma

There are generally two classes of software used to support Six Sigma: analysis tools, which are used to perform statistical or process analysis, and program management tools, used to manage and track a corporation's entire Six Sigma program. Analysis tools include statistical software such as Minitab, JMP, SigmaXL, RapAnalyst or Statgraphics as well as process analysis tools such as iGrafx. Some alternatives include Microsoft Visio, Telelogic System Architect, IBM

WebSphere Business Modeler, and Proforma Corp. ProVision. For program management, tracking and reporting, the most popular tools are Instantis, PowerSteering, iNexus and SixNet. Other Six Sigma for IT Management tools include Proxima Technology Centauri, HP Mercury, BMC Remedy.

1. Six Sigma was industry specific 2. The average was very subjective in nature, it was very difficult to

define average 3. There were problems in finding whether six sigma has been

achieved or not.

Where did the name "Six Sigma" come from?

In my recollection, two recurring questions have dominated the field of six sigma. The first inquiry can be described by the global question: “Why 6s and not some other level of capability?” The second inquiry is more molecular. It can be summarized by the question: “Where does the 1.5s shift factor come from – and why 1.5 versus some other magnitude?” For details on this subject, reference: “Harry, M. J. “Resolving the Mysteries of Six Sigma: Statistical Constructs and Engineering Rationale.” First Edition 2003. Palladyne Publishing. Phoenix, Arizona. (Note: this particular publication will be available by October 2003). But until then, we will consider the following thumbnail sketch.

At the onset of six sigma in 1985, this writer was working as an engineer for the Government Electronics Group of Motorola. By chance connection, I linked up with another engineer by the name of Bill Smith (originator of the six sigma concept in 1984). At that time, he suggested Motorola should require 50 percent design margins for all of its key product performance specifications. Statistically speaking, such a "safety margin" is equivalent to a 6 sigma level of capability.

When considering the performance tolerance of a critical design feature, he believed a 25 percent “cushion” was not sufficient for absorbing a sudden shift in process centering. Bill believed the typical shift was on the order of 1.5s (relative to the target value). In other words, a four sigma level of capability would normally be considered sufficient, if centered. However, if the process center was somehow knocked off its central location (on the order of 1.5s), the initial capability of 4s would be degraded to 4.0s – 1.5s = 2.5s. Of course, this would have a consequential impact on defects. In turn, a sudden increase in defects would have an adverse effect on reliability. As should be apparent, such a domino effect would continue straight up the value chain.

Regardless of the shift magnitude, those of us working this issue fully recognized that the initial estimate of capability will often erode over time in a “very natural way” – thereby increasing the expected rate of product defects (when considering a protracted period of production). Extending beyond this, we concluded that the product defect rate was highly correlated to the long-term process capability, not the short-term capability. Of course, such conclusions were predicated on the statistical analysis of empirical data gathered on a wide array of electronic devices.

Thus, we come to understand three things. First, we recognized that the instantaneous reproducibility of a critical-to-quality characteristic is fully dependent on the “goodness of fit” between the operating bandwidth of the process and the corresponding bandwidth of the performance specification. Second, the quality of that interface can be substantively and consequentially disturbed by process centering error. Of course, both of these factors profoundly impact long-term capability. Third, we must seek to qualify our critical processes at a 6s level of short-term capability if we are to enjoy a long-term capbility of 4s.By further developing these insights through applied research, we were able to greatly extend our understanding of the many statistical connections between such things as design margin, process capability, defects, field reliability, customer satisfaction, and economic success.

Statistical Six Sigma Definition

Six Sigma at many organizations simply means a measure of quality that strives for near perfection. But the statistical implications of a Six Sigma program go well beyond the qualitative eradication of customer-perceptible defects. It's a methodology that is well rooted in mathematics and statistics.

The objective of Six Sigma Quality is to reduce process output variation so that on a long term basis, which is the customer's aggregate experience with our process over time, this will result in no more than 3.4 defect Parts Per Million (PPM) opportunities (or 3.4 Defects Per Million Opportunities – DPMO). For a process with only one specification limit (Upper or Lower), this results in six process standard deviations between the mean of the process and the customer's specification limit (hence, 6 Sigma). For a process with two specification limits (Upper and Lower), this translates to slightly more than six process standard deviations between the mean and each specification limit such that the total defect rate corresponds to equivalent of six process standard deviations.

FIGURE 3.9

Many processes are prone to being influenced by special and/or

assignable causes that impact the overall performance of the process relative to the customer's specification. That is, the overall performance of our process as the customer views it might be 3.4 DPMO (corresponding to Long Term performance of 4.5 Sigma). However, our process could indeed be capable of producing a near perfect output (Short Term capability – also known as process entitlement – of 6 Sigma). The difference between the "best" a process can be, measured by Short Term process capability, and the customer's aggregate experience (Long Term capability) is known as Shift depicted as Zshift or shift. For a "typical" process, the value of shift is 1.5; therefore, when one hears about "6 Sigma," inherent in that statement is that the short term capability of the process is 6, the long term capability is 4.5 (3.4 DPMO – what the customer sees) with an assumed shift of 1.5. Typically, when reference is given using DPMO, it denotes the Long Term capability of the process, which is the customer's experience. The role of the Six Sigma professional is to quantify the process performance (Short Term and Long Term capability) and based on the true process entitlement and process shift, establish the right strategy to reach the established performance objective

As the process sigma value increases from zero to six, the variation of the process around the mean value decreases. With a high enough value of process sigma, the process approaches zero variation and is known as 'zero defects.'

Remembering Bill Smith, Father of Six Sigma

Born in Brooklyn, New York, in 1929, Bill Smith graduated from the U.S. Naval Academy in 1952 and studied at the University of Minnesota School of Business. In 1987, after working for nearly 35 years in engineering and quality assurance, he joined Motorola, serving as vice president and senior quality assurance manager for the Land Mobile Products Sector.

In honor of Smith's talents and dedication, Northwestern University's Kellogg Graduate School of

Management established an endowed scholarship in Smith's name. Dean Donald P. Jacobs of the Kellogg School notified Motorola's Robert Galvin of the school's intention less than a month after Smith died. "Bill was an extremely effective and inspiring communicator," Jacobs wrote in his July 27, 1993, letter. "He never failed to impress his audience by the depth of his knowledge, the extent of his personal commitment, and the level of his intellectual powers." The school created the scholarship fund in recognition of Smith's "contributions to Kellogg and his dedication to the teaching and practice of quality."

It was a fitting tribute to a man who influenced business students and corporate leaders worldwide with his innovative Six Sigma strategy.

As the one who followed most closely in his footsteps, Marjorie Hook is well-positioned to speculate about Bill Smith's take on the 2003 version of Six Sigma. "Today I think people sometimes try to make Six Sigma seem complicated and overly technical," she said. "His approach was, 'If you want to improve something, involve the people who are doing the job.' He always wanted to make it simple so people would use it."

Six Sigma Costs and Savings

The financial benefits of implementing Six Sigma at your company can be significant.

Many people say that it takes money to make money. In the world of Six Sigma quality, the saying also holds true: it takes money to save money using the Six Sigma quality methodology. You can't expect to significantly reduce costs and increase sales using Six Sigma without investing in training, organizational infrastructure and culture evolution.

Sure you can reduce costs and increase sales in a localized area of a business using the Six Sigma quality methodology -- and you can probably do it inexpensively by hiring an ex-Motorola or GE Black Belt. I like to think of that scenario as a "get rich quick" application of Six Sigma

"Companies of all types and sizes are in the midst of a quality revolution. GE saved $12 billion over five years and added $1 to its earnings per share. Honeywell (AlliedSignal) recorded more than $800 million in savings."

"GE produces annual benefits of over $2.5 billion across the organization from Six Sigma."

"Motorola reduced manufacturing costs by $1.4 billion from 1987-1994."

"Six Sigma reportedly saved Motorola $15 billion over the last 11 years."

The above quotations may in fact be true, but pulling the numbers out of the context of the organization's revenues does nothing to help a company figure out if Six Sigma is right for them. For example, how much can a $10 million or $100 million company expect to save?

I investigated what the companies themselves had to say about their Six Sigma costs and savings -- I didn't believe anything that was written on third party websites, was estimated by "experts," or was written in books on the topic. I reviewed literature and only captured facts found in annual reports, website pages and presentations found on company websites.

I investigated Motorola, Allied Signal, GE and Honeywell. I choose these four companies because they are the companies that invented and refined Six Sigma -- they are the most mature in their deployments and culture changes. As the Motorola website says, they invented it in 1986. Allied Signal deployed Six Sigma in 1994, GE in 1995. Honeywell was included because Allied Signal merged with Honeywell in 1999 (they launched their own initiative in 1998). Many companies have deployed Six Sigma between the years of GE and Honeywell -- we'll leave those companies for another article.

Table 3.7: Companies And The Year They Implemented Six Sigma

Company NameYear Began Six Sigma

Motorola (NYSE:MOT) 1986

Allied Signal (Merged With Honeywell in 1999)

1994

GE (NYSE:GE) 1995

Honeywell (NYSE:HON) 1998

Ford (NYSE:F) 2000

Table 2 identifies by company, the yearly revenues, the Six Sigma costs (investment) per year, where available, and the financial benefits (savings). There are many blanks, especially where the investment is concerned. I've presented as much information as the companies have publicly disclosed.

Table 3.8: Six Sigma Cost And Savings By Company

YearRevenue

($B)Invested

($B)

% Revenue Invested

Savings ($B)

% Revenue Savings

Motorola

1986-2001

356.9(e) ND - 16 1 4.5

Allied Signal

1998 15.1 ND - 0.5 2 3.3

GE

1996 79.2 0.2 0.3 0.2 0.2

1997 90.8 0.4 0.4 1 1.1

1998 100.5 0.5 0.4 1.3 1.2

1999 111.6 0.6 0.5 2 1.8

1996-1999

382.1 1.6 0.4 4.4 3 1.2

Honeywell

1998 23.6 ND - 0.5 2.2

1999 23.7 ND - 0.6 2.5

2000 25.0 ND - 0.7 2.6

1998-2000

72.3 ND - 1.8 4 2.4

Ford

2000-2002

43.9 ND - 1 6 2.3

Key:$B = $ Billions, United States(e) = Estimated, Yearly Revenue 1986-1992 Could Not Be FoundND = Not DisclosedNote: Numbers Are Rounded To The Nearest Tenth

Although the complete picture of investment and savings by year is not present, Six Sigma savings can clearly be significant to a company. The savings as a percentage of revenue vary from 1.2% to 4.5%. And what we can see from the GE deployment is that a company shouldn't expect more than a breakeven the first year of implementation. Six Sigma is not a "get rich quick" methodology. I like to think of it like my retirement savings plan -- Six Sigma is a get rich slow methodology -- the take-away point being that you will get rich if you plan properly and execute consistently.

As GE's 1996 annual report states, "It has been estimated that less than Six Sigma quality, i.e., the three-to-four Sigma levels that are average for most U.S. companies, can cost a company as much as 10-15% of its revenues. For GE, that would mean $8-12 billion." With GE's 2001 revenue of $111.6 billion, this would translate into $11.2-16.7 billion of savings. Although $2 billion worth of savings in 1999 is impressive, it appears that even GE hasn't been able to yet capture the losses due to poor quality -- or maybe they're above the three-to-four Sigma levels that are the average for most U.S. companies?

In either case, 1.2-4.5% of revenue is significant and should catch the eye of any CEO or CFO. For a $30 million a year company, that can translate into between $360,000 and $1,350,000 in bottom-line-impacting savings per year. It takes money to make money.

Complementary Technologies

It is difficult to concisely describe the ways in which Six Sigma may be interwoven with other initiatives (or vice versa). The following paragraphs broadly capture some of the possible interrelationships between initiatives.

Six Sigma and improvement approaches such as CMM‚, CMMISM, PSPSM/TSPSM are complementary and mutually supportive. Depending on current organizational, project or individual circumstances, Six Sigma could be an enabler to launch CMM®, CMMISM, PSPSM, or TSPSM. Or, it could be a refinement toolkit/methodology within these initiatives. For instance, it might be used to select highest priority Process Areas within CMMISM or to select highest leverage metrics within PSPSM.

Examination of the Goal-Question-Metric (GQM), Initiating-Diagnosing-Establishing-Acting-Leveraging (IDEALSM), and Practical Software Measurement (PSM) paradigms, likewise, shows compatibility and consistency with Six Sigma. GQ(I)M meshes well with the Define-Measure steps of Six Sigma. IDEAL and Six Sigma share many common features, with IDEALSM being slightly more focused on change management and organizational issues and Six Sigma being more focused on tactical, data-driven analysis and decision making. PSM provides a software-tailored approach to measurement that may well serve the Six Sigma improvement framework.

Six Sigma Process' Capability

So how do you know your processes cut the mustard? With Six Sigma, it all depends on the process’ capability. Process capability is a measure of how much variation or deviation occurs from what normally happens to what is expected to happen. For example, normally after you upgrade a system, you have a working system. It you have one piece working, and a couple other piece broken, well you now have a variation from what was expected to happen. Just to elaborate a little further, there are three main characteristics of process capability.

The requirements are frequently a range of acceptable values. The process is capable, when its variation consistently falls within

that rang. The process’ sigma level is an indicator of its capability and

likelihood of meeting expectations.

Expanding on this concept a little bit, to assure we all have a clearer understanding of how this relates to us. The first characteristic states that the requirements are frequently a range of acceptable values. For example, the billing system is always to be available weekdays between the hours of 6:00 am and 6:00 pm. So our customers will be satisfied when the system is available during these timeframes. The problem really becomes with clinical systems, when the system availability needs to be 100% of the time. That is a difficult range of acceptable values.

The next characteristic of process capability is that a process is considered capable when its variation consistently falls within that range of acceptable values. Keep in mind, variation is the process doesn’t behave as anticipated. Consider the above, if the billing system is down at 1:00 pm weekly, this is outside the range of acceptable times. It is a variation, and makes the process is not considered capable.

The final characteristic of process capability is the process’ sigma level. Remember, the lower the sigma level, the greater the variation. A 1.0 sigma level indicates that the process has a good deal of variation and is not meeting requirements. In Six Sigma, A 6.0 sigma level is the goal

What Is Six Sigma and the 1.5 shift?

To quote a Motorola hand out from about 1987.

'The performance of a product is determined by how much margin exists between the design requirement of its characteristics (and those of its parts/steps), and the actual value of those characteristics. These characteristics are produced by processes in the factory, and at the suppliers.

Each process attempts to reproduce its characteristics identically from unit to unit, but within each process some variation occurs. For more processes, such as those which use real time feedback to control outcome, the variation is quite small, and for others it may be quite large.

A variation of the process is measured in Std. Dev, (Sigma) from the Mean. The normal variation, defined as process width, is +/-3 Sigma about the mean.

Approximately 2700 parts per million parts/steps will fall outside the normal variation of +/- 3 Sigma. This, by itself, does not appear disconcerting. However, when we build a product containing 1200 parts/steps, we can expect 3.24 defects per unit (1200 x .0027), on average. This would result in a rolled yield of less than 4%, which means fewer than 4 units out of every 100 would go through the entire manufacturing process without a defect. Thus, we can see that for a product to be built virtually defect-free, it must be designed to accept characteristics which are significantly more than +/- 3 sigma away from the mean.

It can be shown that a design which can accept TWICE THE NORMAL VARIATION of the process, or +/- 6 sigma, can be expected to have no more than 3.4 parts per million defective for each characteristic, even if the process mean were to shift by as much as +/- 1.5 sigma. In the same case of a product containing 1200 parts/steps, we would now expect only only 0.0041 defects per unit (1200 x 0.0000034). This would mean that 996 units out of 1000 would go through the entire manufacturing process without a defect. To quantify this, Capability Index (Cp) is used; where:

Design Specification WidthCapability Index Cp =

Process Width

A design specification width of +/- 6 Sigma and a process width of +/- 3 Sigma yields a Cp of 12/6 = 2. However, as shown in, the process mean can shift. When the process mean is shifted with respect to design mean, the Capability Index is adjusted with a factor k, and becomes Cpk. Cpk = Cp(1-k), where:

Process Shiftk Factor =

Design Specification Width

The k factor for a +/- 6 Sigma design with a 1.5 Sigma process shift .

1.5/(12/2) or 1.5/6 = 0.25and theCpk = 2(1- 0.25)=1.5

Six Sigma (6 ) is a business-driven, multi-faceted approach to process improvement, reduced costs, and increased profits. With a fundamental principle to improve customer satisfaction by reducing defects, its ultimate performance target is virtually defect-free processes and products (3.4 or fewer defective parts per million (ppm)). The Six Sigma methodology, consisting of the steps "Define - Measure - Analyze - Improve - Control," is the roadmap to achieving this goal. Within this improvement framework, it is the responsibility of the improvement team to identify the process, the definition of defect, and the corresponding measurements. This degree of flexibility

enables the Six Sigma method, along with its toolkit, to easily integrate with existing models of software process implementation.

Six Sigma originated at Motorola in the early 1980s in response to a CEO-driven challenge to achieve tenfold reduction in product-failure levels in five years. Meeting this challenge required swift and accurate root-cause analysis and correction. In the mid-1990s, Motorola divulged the details of their quality improvement framework, which has since been adopted by several large manufacturing companies.

Technical Detail

The primary goal of Six Sigma is to improve customer satisfaction, and thereby profitability, by reducing and eliminating defects. Defects may be related to any aspect of customer satisfaction: high product quality, schedule adherence, cost minimization. Underlying this goal is the Taguchi Loss Function, which shows that increasing defects leads to increased customer dissatisfaction and financial loss. Common Six Sigma metrics include defect rate (parts per million or ppm), sigma level, process capability indices, defects per unit, and yield. Many Six Sigma metrics can be mathematically related to the others.

The Six Sigma drive for defect reduction, process improvement and customer satisfaction is based on the "statistical thinking" paradigm [ASQ 00], [ASA 01]:

Everything is a process All processes have inherent variability Data is used to understand the variability and drive process

improvement decisions

As the roadmap for actualizing the statistical thinking paradigm, the key steps in the Six Sigma improvement framework are Define - Measure - Analyze - Improve - Control. Six Sigma distinguishes itself from other quality improvement programs immediately in the "Define" step. When a specific Six Sigma project is launched, the customer satisfaction goals have likely been established and decomposed into subgoals such as cycle time reduction, cost reduction, or defect reduction. (This may have been done using the Six Sigma methodology at a business/organizational level.) The Define stage for the specific project calls for baselining and benchmarking the process to be improved, decomposing the process into manageable sub-processes, further specifying goals/sub-goals and establishing infrastructure to

accomplish the goals. It also includes an assessment of the cultural/organizational change that might be needed for success.

Once an effort or project is defined, the team methodically proceeds through Measurement, Analysis, Improvement, and Control steps. A Six Sigma improvement team is responsible for identifying relevant metrics based on engineering principles and models. With data/information in hand, the team then proceeds to evaluate the data/information for trends, patterns, causal relationships and "root cause," etc. If needed, special experiments and modeling may be done to confirm hypothesized relationships or to understand the extent of leverage of factors; but many improvement projects may be accomplished with the most basic statistical and non-statistical tools. It is often necessary to iterate through the Measure-Analyze-Improve steps. When the target level of performance is achieved, control measures are then established to sustain performance. A partial list of specific tools to support each of these steps is shown in Figure.3.10

FIGURE 3.10

An important consideration throughout all the Six Sigma steps is to distinguish which process substeps significantly contribute to the end result. The defect rate of the process, service or final product is likely more sensitive to some factors than others. The analysis phase of Six Sigma can help identify the extent of improvement needed in each substep in order to achieve the target in the final product. It is important to remain mindful that six sigma performance (in terms of the ppm metric) is not required for every aspect of every process, product and service. It is the goal only where it quantitatively drives

(i.e, is a significant "control knob" for) the end result of customer satisfaction and profitability.

The current average industry runs at four sigma, which corresponds to 6210 defects per million opportunities. Depending on the exact definition of "defect" in payroll processing, for example, this sigma level could be interpreted as 6 out of every 1000 paychecks having an error. As "four sigma" is the average current performance, there are industry sectors running above and below this value. Internal Revenue Service (IRS) phone-in tax advice, for instance, runs at roughly two sigma, which corresponds to 308,537 errors per million opportunities. Again, depending on the exact definition of defect, this could be interpreted as 30 out of 100 phone calls resulting in erroneous tax advice. ("Two Sigma" performance is where many noncompetitive companies run.) On the other extreme, domestic (U.S.) airline flight fatality rates run at better than six sigma, which could be interpreted as fewer than 3.4 fatalities per million passengers - that is, fewer than 0.00034 fatalities per 100 passengers.

As just noted, flight fatality rates are "better than six sigma," where "six sigma" denotes the actual performance level rather than a reference to the overall combination of philosophy, metric, and improvement framework. Because customer demands will likely drive different performance expectations, it is useful to understand the mathematical origin of the measure and the term "six-sigma process." Conceptually, the sigma level of a process or product is where its customer-driven specifications intersect with its distribution. A centered six-sigma process has a normal distribution with mean=target and specifications placed 6 standard deviations to either side of the mean. At this point, the portions of the distribution that are beyond the specifications contain 0.002 ppm of the data (0.001 on each side). Practice has shown that most manufacturing processes experience a shift (due to drift over time) of 1.5 standard deviations so that the mean no longer equals target. When this happens in a six-sigma process, a larger portion of the distribution now extends beyond the specification limits: 3.4 ppm.

Figure depicts a 1.5 -shifted distribution with "6 " annotations. In manufacturing, this shift results from things such as mechanical wear over time and causes the six-sigma defect rate to become 3.4 ppm. The magnitude of the shift may vary, but empirical evidence indicates that 1.5 is about average. Does this shift exist in the software process? While it will take time to build sufficient data repositories to verify this assumption within the software and systems sector, it is reasonable to presume that there are factors that would contribute to such a shift. Possible examples are declining procedural adherence over time,

learning curve, and constantly changing tools and technologies (hardware and software).

FIGURE 3.11

Assumptions: Normal Distribution Process Mean Shift of 1.5

from Nominal is Likely Process Mean and Standard

Deviation are known Defects are randomly

distributed throughout units Parts and Process Steps are

Independent For this discussion, original

nominal value = target

Key= standard deviation

µ = center of the distribution(shifted 1.5 from its original , on-target location)+/-3 & +/-6 show the specifications relative to the original target

Figure : Six Sigma Process with Mean Shifted from Nominal by 1. 5

Usage Considerations

In the software and systems field, Six Sigma may be leveraged differently based on the state of the business. In an organization needing process consistency, Six Sigma can help promote the establishment of a process. For an organization striving to streamline their existing processes, Six Sigma can be used as a refinement mechanism.

In organizations at CMM® level 1-3, "defect free" may seem an overwhelming stretch. Accordingly, an effective approach would be to use the improvement framework ('Define-Measure-Analyze-Improve-Control') as a roadmap toward intermediate defect reduction goals. Level 1 and 2 organizations may find that adopting the Six Sigma philosophy and framework reinforces their efforts to launch measurement practices; whereas Level 3 organizations may be able to begin immediate use of the framework. As organizations mature to Level 4 and 5, which implies an ability to leverage established

measurement practices, accomplishment of true "six sigma" performance (as defined by ppm defect rates) becomes a relevant goal.

Many techniques in the Six Sigma toolkit are directly applicable to software and are already in use in the software industry. For instance, "Voice of the Client" and "Quality Function Deployment" are useful for developing customer requirements (and are relevant measures). There are numerous charting/calculation techniques that can be used to scrutinize cost, schedule, and quality (project-level and personal-level) data as a project proceeds. And, for technical development, there are quantitative methods for risk analysis and concept/design selection. The strength of "Six Sigma" comes from consciously and methodically deploying these tools in a way that achieves (directly or indirectly) customer satisfaction.

As with manufacturing, it is likely that Six Sigma applications in software will reach beyond "improvement of current processes/products" and extend to "design of new processes/products." Named "Design for Six Sigma" (DFSS), this extension heavily utilizes tools for customer requirements, risk analysis, design decision-making and inventive problem solving. In the software world, it would also heavily leverage re-use libraries that consist of robustly designed software.

Maturity

Six Sigma is rooted in fundamental statistical and business theory; consequently, the concepts and philosophy are very mature. Applications of Six Sigma methods in manufacturing, following on the heels of many quality improvement programs, are likewise mature. Applications of Six Sigma methods in software development and other 'upstream' (from manufacturing) processes are emerging.

Costs and Limitations

Institutionalizing Six Sigma into the fabric of a corporate culture can require significant investment in training and infrastructure. There are typically three different levels of expertise cited by companies: Green Belt, Black Belt Practitioner, Master Black Belt. Each level has increasingly greater mastery of the skill set. Roles and responsibilities also grow from each level to the next, with Black Belt Practitioners often in team/project leadership roles and Master Black Belts often in mentoring/teaching roles. The infrastructure needed to support the Six Sigma environment varies. Some companies organize their trained Green/Black Belts into a central support organization. Others deploy

Green/Black Belts into organizations based on project needs and rely on communities of practice to maintain cohesion.

Alternatives

In past years, there have been many instances and evolutions of quality improvement programs. Scrutiny of the programs will show much similarity and also clear distinctions between such programs and Six Sigma. Similarities include common tools and methods, concepts of continuous improvement, and even analogous steps in the improvement framework. Differences have been articulated as follows:

Six Sigma speaks the language of business. It specifically addresses the concept of making the business as profitable as possible.

In Six Sigma, quality is not pursued independently from business goals. Time and resources are not spent improving something that is not a lever for improving customer satisfaction.

Six Sigma focuses on achieving tangible results. Six Sigma does not include specific integration of ISO900 or

Malcolm Baldridge National Quality Award criteria. Six Sigma uses an infrastructure of highly trained employees

from many sectors of the company (not just the Quality Department). These employees are typically viewed as internal change agents.

Six Sigma raises the expectation from 3-sigma performance to 6-sigma. Yet, it does not promote "Zero Defects" which many people dismiss as "impossible."