Posts in category Six Sigma

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).

A critical to quality tree (also known as the CTQ tree) is a tool used to translate the needs of the customer (voice of the customer) into measurable product and process quality characteristics.

Use: To generate a critical to quality tree, begin by listing the customer’s needs, preferably in the customer’s own words. We proceed to refine these needs by asking pertinent questions about them until we have arrived at quality characteristics that can be measured (product and/or process).

Customers have needs and requirements. A customer need establishes the relationship between the organization and the customer (example: I need (or want) an iPad). Requirements are those characteristics that determine whether or not the customer is happy. (Examples: a requirement is that the iPad is user-friendly, has to be fast in data storage and retrieval, etc.)

Cycle time (also known as ‘process cycle time’) is the time from the start to the end of the process STEP.

Use: Cycle time is generally value-added time, but not necessarily. There can be some waste within cycle time which needs to be eliminated, or at least reduced. What the lean practitioner will do is add up all of the cycle times found on the value stream map and that sum is compared with the sum of the overall lead time. The ratio between the two tells you how efficient you are and most processes are less than 10 percent efficient. Many processes are even less than one percent efficient. That is not said backwards. Less than 10% of the time most processes are actually adding value.

The data collection plan consists of a nine column tactical plan. 1.      Customer needs and requirements 2.      Type of measure (input, output, or process) 3.   …

A defect location check sheet (also known as a defect map or a measles chart) is a structured, prepared form for collecting and analyzing data that provides a visual image of the item being evaluated so that data can be collected visually rather than with words. 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.

Degrees of freedom is a parameter used to help select the critical value in some probability distributions such as a T-test, f-test, Analysis of Variance (ANOVA), chi-square, etc. It is beyond the scope of the course to describe exactly what degrees of freedom is, but suffice it to say that it is the number of paired comparisons necessary to attain a critical value.

Descriptive statistics describe data collected. Measures of central tendency, such as mean and median, and measures of dispersion such as standard deviation and range, are used to summarize and interpret some of the properties of a data set (e.g., sample, or subgroup) are known as descriptive statistics. Descriptive statistics can actually be verified from the data provided. Example: Of the citations for speeding issued in July by Officer Hunt, 23% were given to drivers of red cars. This can be verified by looking at Officer Hunt’s July citation record.