A test statistic is calculated. The test statistic is compared to a critical value (found in a table). If the calculated test statistic is beyond the critical table value, the null hypothesis is rejected. If the test statistic is not beyond the critical value, the null hypothesis has not been rejected (i.e., failure to reject the null hypothesis). This type of testing using a test statistic is used in the F-test, the t-test, the chi-square tests, and more.
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 as described in the Villanova courses consists of a nine column tactical plan. Failure to use the data collection plan may result in skipping a step that may come back to haunt the team at some point in the project.
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.
For the most part, Six Sigma is taking broken processes and then fixing them. Six Sigma uses the DMAIC (Define, Measure, Analyze, Improve, and Control) approach; whereas, Design for Six Sigma (DFSS) uses a DMADV (pronounced DAH MAD VEE) approach. (Define, Measure, Analyze, Design, Verify) The more progressive organizations want to build into the design processes using a Six Sigma philosophy.
Design of experiments is a strategic and tactical approach to experimentation. With much of six sigma, we are waiting for assignable-cause variation to exhibit itself. With design of experiments, we are manipulating various factors (chosen by the cross-functional team) and manipulating them at different levels to see their effect on some desired result.
