six sigma black belt

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Inadequate for complex manufacturing

Quality expert Philip B. Crosby pointed out that the Six Sigma standard does not go far enough—customers deserve defect-free products every time. For example, under the Six Sigma standard, semiconductors, which require the flawless etching of millions of tiny circuits onto a single chip, are all defective.

Role of consultants

The use of “Black Belts” as itinerant change agents has fostered an industry of training and certification. Critics have argued there is overselling of Six Sigma by too great a number of consulting firms, many of which claim expertise in Six Sigma when they have only a rudimentary understanding of the tools and techniques involved or the markets or industries in which they are acting.

Potential negative effects

A Fortune article stated that “of 58 large companies that have announced Six Sigma programs, 91% have trailed the S&P 500 since”. The statement was attributed to “an analysis by Charles Holland of consulting firm Qualpro (which espouses a competing quality-improvement process)”. The summary 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.”

Over-reliance on statistics

More direct criticism is the “rigid” nature of Six Sigma with its over-reliance on methods and tools. In most cases, more attention is paid to reducing variation and searching for any significant factors, and less attention is paid to developing robustness in the first place (which can altogether eliminate the need for reducing variation). The extensive reliance on significance testing and use of multiple regression techniques increase the risk of making commonly unknown types of statistical errors or mistakes. A possible consequence of Six Sigma’s array of p-value misconceptions is the false belief that the probability of a conclusion being in error can be calculated from the data in a single experiment without reference to external evidence or the plausibility of the underlying mechanism. One of the most serious but all-too-common misuses of inferential statistics is to take a model that was developed through exploratory model building and subject it to the same sorts of statistical tests that are used to validate a model that was specified in advance.

Another comment refers to the oft-mentioned Transfer Function, which seems to be a flawed theory if looked at in detail. Since significance tests were first popularized many objections have been voiced by prominent and respected statisticians. The volume of criticism and rebuttal has filled books with language seldom used in the scholarly debate of a dry subject. Much of the first criticism was already published more than 40 years ago (see Statistical hypothesis testing § Criticism).

In a 2006 issue USA Army Logistician an article critical of Six Sigma noted: “The dangers of a single paradigmatic orientation (in this case, that of technical rationality) can blind us to values associated with double-loop learning and the learning organization, organization adaptability, workforce creativity and development, humanizing the workplace, cultural awareness, and strategy making.”

Nassim Nicholas Taleb considers risk managers little more than “blind users” of statistical tools and methods. He states that statistics is fundamentally incomplete as a field as it cannot predict the risk of rare events—something Six Sigma is especially concerned with. Furthermore, errors in prediction are likely to occur as a result of ignorance of or distinction between epistemic and other uncertainties. These errors are the biggest in time variant (reliability) related failures.

1.5 sigma shift

The statistician Donald J. Wheeler has dismissed the 1.5 sigma shift as “goofy” because of its arbitrary nature. Its universal applicability is seen as doubtful.

The 1.5 sigma shift has also become contentious because it results in stated “sigma levels” that reflect short-term rather than long-term performance: a process that has long-term defect levels corresponding to 4.5 sigma performance is, by Six Sigma convention, described as a “six sigma process”. The accepted Six Sigma scoring system thus cannot be equated to actual normal distribution probabilities for the stated number of standard deviations, and this has been a key bone of contention over how Six Sigma measures are defined. The fact that it is rarely explained that a “6 sigma” process will have long-term defect rates corresponding to 4.5 sigma performance rather than actual 6 sigma performance has led several commentators to express the opinion that Six Sigma is a confidence trick.

Stifling creativity in research

According to John Dodge, editor in chief of Design News, the use of Six Sigma is inappropriate in a research environment. Dodge states “excessive metrics, steps, measurements and Six Sigma’s intense focus on reducing variability water down the discovery process. Under Six Sigma, the free-wheeling nature of brainstorming and the serendipitous side of discovery is stifled.” He concludes “there’s general agreement that freedom in basic or pure research is preferable while Six Sigma works best in incremental innovation when there’s an expressed commercial goal.”

A BusinessWeek article says that James McNerney’s introduction of Six Sigma at 3M had the effect of stifling creativity and reports its removal from the research function. It cites two Wharton School professors who say that Six Sigma leads to incremental innovation at the expense of blue skies research. This phenomenon is further explored in the book Going Lean, which describes a related approach known as lean dynamics and provides data to show that Ford’s 6 Sigma program did little to change its fortunes.

Lack of documentation

One criticism voiced by Yasar Jarrar and Andy Neely from the Cranfield School of Management’s Centre for Business Performance is that while Six Sigma is a powerful approach, it can also unduly dominate an organization’s culture; and they add that much of the Six Sigma literature – in a remarkable way (six-sigma claims to be evidence, scientifically based) – lacks academic rigor:

One final criticism, probably more to the Six Sigma literature than concepts, relates to the evidence for Six Sigma’s success. So far, documented case studies using the Six Sigma methods are presented as the strongest evidence for its success. However, looking at these documented cases, and apart from a few that are detailed from the experience of leading organizations like GE and Motorola, most cases are not documented in a systemic or academic manner. In fact, the majority are case studies illustrated on websites, and are, at best, sketchy. They provide no mention of any specific Six Sigma methods that were used to resolve the problems. It has been argued that by relying on the Six Sigma criteria, management is lulled into the idea that something is being done about quality, whereas any resulting improvement is accidental (Latzko 1995). Thus, when looking at the evidence put forward for Six Sigma’s success, mostly by consultants and people with vested interests, the question that begs to be asked is: are we making a true improvement with Six Sigma methods or just getting skilled at telling stories? Everyone seems to believe that we are making true improvements, but there is some way to go to document these empirically and clarify the causal relations.

Lean Six Sigma is a method that uses a collaborative team effort to improve performance by systematically removing waste and reducing variation. It combines lean manufacturing/lean enterprise and Six Sigma to eliminate the eight kinds of waste (muda)

Lean Six Sigma’s predecessor, Six Sigma, originated from the Motorola company in the United States in 1986. Six Sigma was developed within Motorola to compete with the kaizen (or lean manufacturing) business model in Japan.

In the 1990s, Allied Signal hired Larry Bossidy and introduced Six Sigma in heavy manufacturing. A few years later, General Electric’s Jack Welch consulted Bossidy and implemented Six Sigma at the conglomerate.

During the 2000s, Lean Six Sigma forked from Six Sigma and became its own unique process. While Lean Six Sigma developed as a specific process of Six Sigma, it also incorporates ideas from lean manufacturing, which was developed as a part of the Toyota Production System in the 1950s.

2000s–2010s

The first concept of Lean Six Sigma was created in 2001 by a book titled Leaning into Six Sigma: The Path to Integration of Lean Enterprise and Six Sigma. It was developed as a guide for managers of manufacturing plants on how to combine lean manufacturing and Six Sigma to improve quality and cycle time in the plant.

In the early 2000s Six Sigma principles expanded into other sectors of the economy, such as healthcare, finance, and supply chains.

Lean Six Sigma is a synergized managerial concept of Lean and Six Sigma. Lean traditionally focuses on eliminating the eight kinds of waste (“muda”), and Six Sigma focuses on improving process output quality by identifying and removing the causes of defects (errors) and minimizing variability in (manufacturing and business) processes.

Lean Six Sigma uses the DMAIC phases similar to that of Six Sigma. The five phases used in Lean Six Sigma aim to identify the root cause of inefficiencies and work with any process, product, or service that has a large amount of data or measurable characteristics available.

The different levels of certifications are divided into belt colors. The highest level of certification is a black belt, signifying a deep knowledge of Lean Six Sigma principles. Below the black belt are the green and yellow belts. For each of these belts, level skill sets that describe which of the overall Lean Six Sigma tools are expected to be part at a certain belt level are available. The skill sets reflect elements from Six Sigma, Lean and other process improvement methods like the theory of constraints and total productive maintenance. In order to achieve any of the certification levels, a proctored exam must be passed that asks questions about Lean Six Sigma and its applications.