Posts in category Terms

Use: A common practice in quality assurance or even in six sigma is to count the number of defects found. One could keep track of the defect rate, and maybe even use an attribute chart to monitor, control, and continuously improve upon defect rates. But what is missing is where the defects are located. That is where the defect location check sheet comes in handy. Draw a picture of the product. Then, every time a defect is found, the operator or the inspector puts a mark on the drawing as to where they found the defect. So let’s say that there are a cluster of markings in the upper right-hand corner of the drawing. The six sigma team could look at that defect location check sheet and look up-stream in the process to see if they can identify what in the process is causing a defect to show up in the upper right-hand corner of the products. Again, much of six sigma in lean has to do with visualizing the sources of variation, waste, or defects.

A chi-square test is a statistical tool used to test for independence or dependence (or goodness-of-fit) between random variables taken from different populations. A chi-square test could also be used for testing for goodness between an observed frequency distribution and an expected frequency distribution. With the chi-square test you are comparing a target variance with an observed variance. It is used to test the independence of two nominal variables. Remember that nominal variables are names or categories only.

Common-cause variation is where no one, or combination of factors is unduly affected the process variation (random variation). Special-cause variation is when one or more factors are affecting the process variation in a non-random way. With special-cause variation, one should be able to identify, or put their finger on the reason behind the unexpected variation.

Let’s start with what an alias is. An alias when employing the use of a designed experiments methodology is the pattern of pluses (+) and minuses (-) into columns are identical. For example, a main effect is aliased with a two-factor interaction. During the analysis, it is impossible to know whether a change is due to a main effect or due to an interaction since the columns are identical. Confounding is similar, but it doesn’t mean 100% overlap with the pattern of pluses and minuses in the columns. Perhaps the column might be 80 percent confounded, or 90 percent confounded. It would be better if there was no confounding as far as resolution is concerned.

Continuous data can be measured on a continuum. Think of it as being able to divide a measure by one half, and in half again, and in half again, – to infinity. Contrast continuous data with discrete/attribute data that is binary, or two-state — pass/fail, go/no go, good/bad, and so on.

Continuous flow is just that—the flow of value-add never stops. Ideally, a process would continue to add value without ever stopping during the production process.

Use: Lean practitioners should relentlessly strive for continuous flow through the reduction of waste. Any time a process stops, lead time is lengthened, the customer has to wait for the product or service, and it is costing more money than if the flow could be continuous in nature.

Control limits are the limits of expected variation of either ‘individuals’ data or sample (subgroup) data. Typically, the acceptable limits of variation equates to what one would expect to see in a random process 99.73% of the time. One way that a six sigma practitioner can determine whether or not they have a ‘smoking gun’ – – meaning that they have unexpected variation, is if a point goes out of control on a control chart. This is to be viewed as an opportunity for improvement.

A control chart, sometimes referred to as a process behavior chart by the Dr. Donald Wheeler, or Shewhart Charts by some practitioners named after Walter Shewhart. The control chart is meant to separate common cause variation from assignable-cause variation. A control chart is useful in knowing when to act, and when to leave the process alone.

Dependence refers to any statistical relationship between two random variables. Correlation refers to any statistical relationship involving dependence. In a correlation study the six sigma practitioner would be looking for a correlation coefficient. The probability runs from -1 to +1. In other words, if two random variables are 100% correlated, the correlation coefficient would be +1 or -1. If there is no correlation, the correlation coefficient would be zero (0).

Critical to quality (CTQ) is the quality of a product or service in the eyes of the voice of the customer (VOC).

Use: It is a good idea to identify the critical to quality parameters as they relate to what is important to the customer at large. It is this author’s opinion that two things should be monitored—1.) the end-product parameters and 2.) And the associated process parameters that determine the quality of the end-product parameters that are important to the customer. Once these parameters are identified, the six sigma practitioner will want to monitor, control, and continuously improve upon these parameters.