Creating a CTQ

Name. Enter a name for the Quality or to let the system assign a numeric name, check Auto Name.

Data Type. Select Continuous (CTQ is measured) or Discrete (CTQ is counted). The data type determines the metrics gathered for the CTQ.

• Continuous data is information that can be measured on a continuum or scale and can be broken into smaller parts and still have meaning. Money, temperature, and time are continuous.
• Discrete data is information that can be categorized into a classification. Discrete data is based on counts. Only a finite number of values is possible, and the values cannot be subdivided meaningfully. For example, the number of parts damaged in shipment.

Problem Statement. An explanation of the problem, when it occurs, where it occurs, and its extent.

Operational Definition. Enter a clear understandable description of what is to be observed and measured, so different people can interpret the data consistently. This includes what are you trying to measure, what the measurement is not, basic definition of the measure, and how to take the measure in detail.

Defect Definition. Definition of what counts as a defect.

Goal. The goal or target for resolving the defect.

Opportunity. An opportunity is anything that you inspect, measure, or test on a unit that provides a chance of allowing a defect.

Out of Bounds. The measurements that would be outside of the spec limits.

Constraints. Any limits or restrictions placed on the measurement.

Comments. Comments pertaining to the Quality.

Source. For the first metric that defines the values to measure against, the Source is always Goal. For subsequent metrics, you can choose Predicted, Allocated, Measured, Controlled, or Goal. See About Critical to Quality Processing for details.

DPMO. Defects Per Million Opportunities. The average number of defects per unit observed during an average production run divided by the number of opportunities to make a defect on the product under study during that run. This number is then normalized to one million.

DPU. Defects Per Unit is the average number of defects observed when sampling a population.

    DPU = Total # of Defects / Total population

Sigma. Calculated as NORMSINV(1-((Total Defects) / (Total Opportunities))) + 1.5

Originator. The creator of the CTQ.

Comments. Any comments about the CTQ.

Source. For the first metric that defines the values to measure against, the Source is always Goal. For subsequent metrics, you can choose Predicted, Allocated, Measured, Controlled, or Goal. See About Critical to Quality Processing for details.

Mean. The required mean for the end of the project. The mean is the average data point value within a data set. To calculate the mean, add all of the individual data points then divide that figure by the total number of data points.

Standard Deviation. The required standard deviation for the end of the project. Standard deviation is a statistic used to measure the variation in a distribution. Sample standard deviation is equal to the square root of (the sum of the squared deviations of the mean divided by the sample size minus 1).

Upper Spec Limit. The required upper specification limit for the end of the project, as specified by the customer.

Lower Spec Limit. The required lower specification limit for the end of the project, as specified by the customer.

Sigma. Calculated as NORMSINV(1-((Total Defects) / (Total Opportunities))) + 1.5

Originator. The creator of the CTQ.

Comments. Any comments about the CTQ.