Weibull analysis of failure data using the Weibull distribution is the most often used technique in reliability engineering.

The Weibull distribution can be used to model many different failure patterns and this, along with its relative ease of use, accounts for its extensive application.

The u chart is used with a varying sample size where you are counting the number of defects in the sample. If you remember, the difference between a defect and a defective is this. A defect can be found on an otherwise acceptable product; whereas, a defective means that the whole item is unacceptable. For example, you might have some minor defects in the fabrication of the windshield. The windshield passes inspection even though there are some minor defects. Defects are still undesirable, but there are an insufficient number of defects to render the windshield unusable. If, however, the windshield is cracked, the windshield would be rendered defective, and totally unusable. With the u-chart, we are looking at defects – not defectives, and remember that were looking at a varying (as opposed to constant) sample size. If it was a constant sample size and we were evaluating defects, we would instead be using a c-chart.

The Z-score table (also known as the standard normal distribution) is used for larger sample sizes, namely where the population or sample size is greater than n=30. The Z score table assumes a normally-distributed process.

The Z score table is used in various hypothesis tests including tests on single means, the difference between two means, and tests on proportions.

Among the strongest leadership traits practiced by Six Sigma professionals, 7 qualities stand out most often. For leaders moving up into Six Sigma management, developing and sharpening these characteristics could help leverage the competitive edge.

The automotive industry was an early adopter of Six Sigma and continues to lead the way in showing other manufacturers the difference that Six Sigma can make. Companies like Toyota and Ford have shown that Six Sigma can dramatically cut costs and reduce mistakes all of which are benefits for manufacturers and their staff accountants as this frees up finances for other areas of business necessity. They have demonstrated that improved quality saves money. Fewer discarded products, fewer warranty repairs and replacements, and much higher rates of customer satisfaction and retention has resulted in increased profits for both companies.

Lean manufacturing is the concept of using fewer resources to create more value for customers; in other words, maximizing the customer value and minimizing waste. This philosophy and system aims to cut out as much waste as possible during the manufacturing process.

Because processes will randomly fluctuate over time and will often deteriorate in the long run, Motorola introduced the practice of reducing their calculated sigma values by 1.5 standard deviations to provide a more conservative estimate of the long term performance of the process.

The two most widely used Six Sigma methodologies are DMAIC and DMADV. Both methods are designed so a business process will be more efficient and effective. While both of these methodologies share some important characteristics, they are not interchangeable and were developed for use in differing business processes. Before comparing these two approaches in more detail, let’s review what the acronyms stand for.

DMADV is a Six Sigma framework that is focuses primarily on the development of a new service, product or process as opposed to improving a previously existing one. This approach – Define, Measure, Analyze, Design, Verify – is especially useful when implementing new strategies and initiatives because of its basis in data, early identification of success and thorough analysis.

One of the best ways to systematically improve your business processes is to use the DMAIC (Deh-May-Ick) framework. DMAIC is a highly effective, data-driven, five-step approach to business that is one of the many useful components of the Six Sigma toolkit. The main goal of DMAIC is to eliminate expensive variation from business and manufacturing processes.