Six Sigma Terminology - K to N


Kaizen a Japanese term that means continuous improvement, taken from the words ‘Kai‘ that means continuous and ‘zen‘ that means improvement. Some translate ‘kai‘ to mean change and ‘zen‘ to mean good, or for the better.


Kanban a Japanese term. The actual term means “signal”. It is one of the primary tools of a Just in Time (JIT) manufacturing system. It signals a cycle of replenishment for production and materials. This can be considered as a “demand” for a product from on step in the manufacturing or delivery process to the next. It maintains an orderly and efficient flow of materials throughout the entire manufacturing process with low inventory and works in process. It is usually a printed card that contains specific information such as part name, description, quantity, etc.

In a Kanban manufacturing environment, nothing is manufactured unless there is a “signal” to manufacture. This is in contrast to a push-manufacturing environment where production is continuous.

Kano Analysis

Kano analysis is a quality measurement tool used to prioritize customer requirements based on their impact on customer satisfaction.

Kano analysis is a quality measurement tool which is used to determine which requirements are important. All identified requirements may not be of equal importance to all customers. Kano analysis can help you rank requirements for different customers to determine which have the highest priority.

Kano analysis is a tool which can be used to classify and prioritize customer needs. This is useful because customer needs are not all of the same kind, not all have the same importance, and are different for different populations. The results can be used to prioritize your effort in satisfying different customers.


Key Performance Indicator (KPI) indicates any key performance that gives the actual data of that particular outcome. Examples of quality KPI - Percentage of Rework. A Number of Customer Complaints.

Lean Manufacturing

The initiative focused on eliminating all waste in manufacturing processes.

The Production System Design Laboratory (PSD), Massachusetts Institute of Technology, states that “Lean production is aimed at the elimination of waste in every area of production including customer relations, product design, supplier networks and factory management. Its goal is to incorporate less human effort, less inventory, less time to develop products, and less space to become highly responsive to customer demand while producing top quality products in the most efficient and economical manner possible.”


Levels are the different settings a factor can have. For example, if you are trying to determine how the response (speed of data transmittal) is affected by the factor (connection type), you would need to set the factor at different levels (modem and LAN)

Likert Scale

A rating scale measuring the strength of agreement with a clear statement. Often administered in the form of a questionnaire used to gauge attitudes or reactions.

Management by Knowledge

Employing data, information, human knowledge; experiences, human behavioral capabilities & intelligence, facts, intellectual skills &psychomotor skills in improving organization total performance


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.


Relating to or constituting the middle value in a distribution.

The median is the middle point of a data set; 50% of the values are below this point, and 50% are at this point. Median is the middle value when all possible values are listed in order. Median is not the same as Average (or Mean).


Things to measure to understand quality levels.

Metric means measurement. Hence the word metric is often used in an organization to understand the metrics of the matrix.


The value or item occurring most frequently in a series of observations or statistical data.

The most often occurring value in the data set. A data set may contain more than one mode, e.g., if there are exactly 2 values or items that appear in the data the same number of times, we say the data set is bi-modal.

Multi-Vari Chart

A multi-vari chart is a tool that graphically displays patterns of variation. It is used to identify possible Xs or families of variation, such as variation within a subgroup, between subgroups, or over time. 


It refers to the value that you estimate in a design process that approximate your real CTQ (Y) target value based on the design element capacity. Nominals are usually referred to as point estimate and related to y-hat model.

Nominal Data

The data related to gender, race, religious affiliation, political affiliation etc; are the examples for Nominal data. In a more general form, the data assigned with labels or names are considered as the data in Nominal scale. Since each label or name indicates a separate category in the data; this data is also called as categorical data. The only comparison that can be made between two categorical variables is that they are equal or not, these variables can not be compared with respect to the order of the labels.

Nominal Group Technique

Nominal Group Technique a tool to bring a team in conflict to a consensus on the relative importance of issues, problems, or solutions by completing individual importance ranking into a team’s final priorities.

Normal Distribution

Normal distribution is the spread of information (such as product performance or demographics) where the most frequently occurring value is in the middle of the range and other probabilities tail off symmetrically in both directions. Normal distribution is graphically categorized by a bell-shaped curve, also known as a Gaussian distribution. For normally distributed data, the mean and median are very close and may be identical.

Normality Test

A normality test is a statistical process used to determine if a sample or any group of data fits a standard normal distribution. A normality test can be performed mathematically or graphically. 

Null Hypothesis (Ho)

A null hypothesis (H0) is a stated assumption that there is no difference in parameters (mean, variance, DPMO) for two or more populations. According to the null hypothesis, any observed difference in samples is due to chance or sampling error.

The term that statisticians often use to indicate the statistical hypothesis being tested.