six sigma green belt

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Six Sigma mostly finds application in large organizations. According to industry consultants like Thomas Pyzdek and John Kullmann, companies with fewer than 500 employees are less suited to Six Sigma or need to adapt the standard approach to making it work for them. Six Sigma, however, contains a large number of tools and techniques that work well in small to mid-size organizations. The fact that an organization is not big enough to be able to afford black belts does not diminish its ability to make improvements using this set of tools and techniques. The infrastructure described as necessary to support Six Sigma is a result of the size of the organization rather than a requirement of Six Sigma itself.

Manufacturing

After its first application at Motorola in the late 1980s, other internationally recognized firms currently recorded high number of savings after applying Six Sigma. Examples include Johnson & Johnson, with $600 million of reported savings, Texas Instruments, which saved over $500 million as well as Telefónica, which reported €30 million in savings in the first 10 months; Sony and Boeing also reported successfully reducing waste.

Engineering and construction

Although companies have considered common quality control and process improvement strategies, there’s still a need for more reasonable and effective methods as all the desired standards and client satisfaction have not always been reached. There is still a need for an essential analysis that can control the factors affecting concrete cracks and slippage between concrete and steel. After conducting a case study on Tinjin Xianyi Construction Technology, it was found that construction time and construction waste were reduced by 26.2% and 67% accordingly after adopting Six Sigma. Similarly, Six Sigma implementation was studied at one of the largest engineering and construction companies in the world: Bechtel Corporation, where after an initial investment of $30 million in a Six Sigma program that included identifying and preventing rework and defects, over $200 million were saved.

Finance

Six Sigma has played an important role by improving the accuracy of allocation of cash to reduce bank charges, automatic payments, improving the accuracy of reporting, reducing documentary credit defects, reducing check collection defects, and reducing variation in collector performance.

For example, Bank of America announced in 2004 that Six Sigma had helped it increase customer satisfaction by 10.4% and decrease customer issues by 24%; similarly, American Express eliminated non-received renewal credit cards. Other financial institutions that have adopted Six Sigma include GE Capital and JPMorgan Chase, where customer satisfaction was the main objective.

Supply chain

In the supply-chain field, it is important to ensure that products are delivered to clients at the right time while preserving high-quality standards. By changing the schematic diagram for the supply chain, Six Sigma can ensure quality control on products (defect-free) and guarantee delivery deadlines, the two main issues in the supply chain.

Healthcare

This is a sector that has been highly matched with this doctrine for many years because of the nature of zero tolerance for mistakes and potential for reducing medical errors involved in healthcare. The goal of Six Sigma in healthcare is broad and includes reducing the inventory of equipment that brings extra costs, altering the process of healthcare delivery in order to make it more efficient and refining reimbursements. A study at the MD Anderson Cancer Center, which recorded an increase in examinations with no additional machines of 45% and a reduction in patients’ preparation time of 40 minutes; from 45 minutes to 5 minutes in multiple cases.

Lean Six Sigma was adopted in 2003 at Stanford hospitals and was introduced at Red Cross hospitals in 2002.

Criticism

While there are many advocates for a Six Sigma approach for the reasons stated above, more than half of projects are unsuccessful: in 2010, the Wall Street Journal reported that more than 60% of projects fail. A review of academic literature  found 34 common failure factors in 56 papers on Lean, Six Sigma, and LSS from 1995-2013. Among them are (summarized):

• Lack of top management attitude, commitment, and involvement; lack of leadership and vision
• Lack of training and education; lack of resources (financial, technical, human, etc.)
• Poor project selection and prioritization; weak link to strategic objectives of the organization
• Resistance to culture change; Poor communication; Lack of consideration of the human factors
• Lack of awareness of the benefits of Lean/Six Sigma; Lack of technical understanding of tools, techniques, and practices

Others have provided other criticisms.

Lack of originality

Quality expert Joseph M. Juran described Six Sigma as “a basic version of quality improvement”, stating that “there is nothing new there. It includes what we used to call facilitators. They’ve adopted more flamboyant terms, like belts with different colors. I think that concept has merit to set apart, to create specialists who can be very helpful. Again, that’s not a new idea. The American Society for Quality long ago established certificates, such as for reliability engineers.”

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.