iSixSigma

Variation

Multivariate Control Charts: T2 and Generalized Variance

Multivariate analysis techniques may be useful in statistical process control (SPC) whenever there is more than one process variable. Multivariate control charting is usually helpful when the effect of multiple parameters is not independent or when some parameters are correlated. This article focuses on parameters that correlate when the Pearson correlation coefficient is greater than…

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Reduce Special-cause Variation Before Experimentation

For several years, a fully-automated plastic drinking cup production line used excessive amounts of raw materials (plastic PET pellets) due to a wide distribution in the weight of the formed cups. When process operators and engineers had tried to reduce the plastic pellet usage by reducing the average formed cup weight, many cups – because…

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Seeking the Causes of Variation

Not all variation is created equal. Planned variation, like that in an experiment, is a process improvement strategy. Unplanned variation, however, is nearly always bad. Two types of variation concern a Six Sigma team: Common cause variation – All processes have common cause variation. This variation, also known as noise, is a normal part of…

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Correlation and Variation

Six Sigma projects differ from traditional projects in one important requirement – understanding of the Y=f(x) relationship with data before developing or implementing a solution. In the DMAIC framework, we identify, test and verify the causal relationship between a potential root cause (x) and the outcome (Big Y), and then develop a solution to change…

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Specification Limits: Proceed with Caution

It’s natural for a manufacturing group to be concerned with a product’s specification limits – the upper and lower limits imposed on the process. Specification limits are sometimes designated by the producer, definitions of quality or, most frequently, a customer, in an attempt to narrow the distribution of a product’s properties. Unfortunately, to keep a…

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Brewing a Better Beer with TQM

Recently, a well-established, rapidly expanding beer company invested heavily in a modern, state-of–the-art brewing facility. The new facility dramatically improved quality and productivity, and also reduced costs through the application of new technology. As a next step, the beer company began exploring methods of achieving a further quantum jump in performance. Recognizing that technology and…

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Understanding Process Variation

It is well established that there exist eight dimensions of quality: Conformance Performance Features Reliability Durability Serviceability Aesthetics Perceived quality Each dimension can be explicitly defined and is self-exclusive from the other dimensions of quality. A customer may rate your service or product high in conformance, but low in reliability. Or they may view two…

A Simple Model of a Variance Stable Process

Most fairly accurate descriptions of equipment and/or process lifetimes assume that failure rates follow a three period I II III “bathtub-curve pattern” where failures/errors: I – Decrease during the debugging or improvement time period. II – Remain relatively constant and at their lowest levels during the normal equipment or process operating period. III – Increase…

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Variation – The Root of All Process Evil

As a customer, the worst experience I can imagine is being a casualty of process variation. ‘It doesn’t seem that bad,’ you may be thinking to yourself. Just remember back to the last time you: Went grocery shopping only to select the slowest teller in the store. Received a haircut that was shorter or longer…

What Is a Stable Process?

Process stability is one of the most important concepts of the Six Sigma methodology, or any quality improvement methodology for that matter. Stability involves achieving consistent and, ultimately, higher process yields through the application of an improvement methodology. Does a process need to be stable in order for a black belt to improve it? Try…

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Improved Forecasting with Moving Averages and Z-scores

Forecasting is an integral part of business management. The better the forecast, the better management will be able to plan for the future. Although there are many methods for making forecasts, some are better suited than others for particular situations. For short-term forecasting, Black Belts can benefit from analyzing production trends and looking for special…

Help for Practitioners Trying to Understand ANOVA Table

The analysis of variance (ANOVA) procedure is conducted during the Analyze phase of a Six Sigma project. Assessing results from an ANOVA table can present a challenge making it difficult to understand precisely what conclusions to draw. However, there is an easy way for Master Black Belts to explain to their charges the ANOVA procedure….

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Remember Common Cause Variation in Fight Against Waste

Ideally, practitioners want an error rate to be at 0 percent. But if an error rate is steadily hovering at about 2 percent, it is fair to say that the errors are due to common cause variation. If the error rate suddenly surges beyond its usual level, however, it is probably due to some change…

What You May Not Know About Adding Variances

Imagine for a moment that your Six Sigma project does not have the rigorous measurement system you would like it to have, and you have to calculate your primary metric from a few inputs instead of measuring it directly. Or perhaps you are designing a new product and want to understand how the variability in…

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Mining for Lost Gold in the Transactional Environment

Searching for lost gold in the transactional environments that are major components of all organizations is a Six Sigma imperative. A number of ways are available for successfully mining transactional processes, thereby reaping significant savings. Transactional- or service-focused Black Belts and Green Belts address problems which center on the elimination of process chaos. Transactional chaos…

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