Variability serves both as a descriptive measure and as an important component of most inferential statistics. In the context of inferential statistics, variability provides a measure of how accurately any individual score or sample represents the entire population.

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Variability- since the human involvement in service provision means that no two services will be completely identical, they are variable. For example, returning to the same garage time and time again for a service on your car might see different levels of customer satisfaction, or speediness of work.

Common cause variation is fluctuation caused by unknown factors resulting in a steady but random distribution of output around the average of the data. Common cause variability is a source of variation caused by unknown factors that result in a steady but random distribution of output around the average of the data.

Other examples that relate to projects are inappropriate procedures, which can include the lack of clearly defined standard procedures, poor working conditions, measurement errors, normal wear and tear, computer response times, etc. These are all common cause variation.

Variability refers to how spread out a group of data is. The common measures of variability are the range, IQR, variance, and standard deviation. Data sets with similar values are said to have little variability while data sets that have values that are spread out have high variability.

There are two types of process variation:

• Common cause variation is inherent to the system. This variation can be changed only by improving the equipment or changing the work procedures; the operator has little influence over it.
• Assignable cause variation comes from sources outside of the system.

Two types of variation concern a Six Sigma team:

• Common cause variation – All processes have common cause variation. This variation, also known as noise, is a normal part of any process.
• Special cause variation – This variation is not normal to the process. It is the result of exceptions in the process environment.

In a process with a large amount of variation, the standard deviation (represented by the Greek letter sigma) is high. In statistical terms, that means that the data in a population is widely dispersed about the arithmetic mean.

The main type of chart is known as a Statistical Process Control (SPC) chart and plots your data like a run chart every week so you can see whether you are improving, if the situation is deteriorating, whether your system is likely to be capable to meet the standard, and also whether the process is reliable or variable …

Cp and Cpk, commonly referred to as process capability indices, are used to define the ability of a process to produce a product that meets requirements. Specifications: Specifications define product requirements. In other words, they define what is expected from an item for it to be usable.

The 8 Control Chart Rules

Three Types

Statistical process control (SPC) is defined as the use of statistical techniques to control a process or production method. SPC tools and procedures can help you monitor process behavior, discover issues in internal systems, and find solutions for production issues.

capable and meets specification limits

What is Cp? The Cp ratio shows how well the process spread (expressed as six standard deviations) fits into the specification range. This measurement is determined by dividing the specification limit (voice of the customer) by the process spread (voice of the process).

Sigma level table

Cpk is a process capability index used to measure what a process is capable of producing. Cpk requires the assumption that the measurements are normally distributed. The formula for the calculation of Cpk is Cpk = min(USL – μ, μ – LSL) / (3σ) where USL and LSL are the upper and lower specification limits, respectively.

greater than one

A value of less than 1 is unacceptable, with values greater than 1.33 (1.25 for one-sided specification limits) widely accepted as the minimum acceptable value, and values greater than 1.50 (1.45 for one-sided specification limits) for critical parameters (Montgomery, 2012) .

This is why the Cp is called the process potential. The process capability index, or Cpk, measures a process’s ability to create product within specification limits. In a Six Sigma process, the Cpk equals 2.0. The Cpk is inversely proportional to the standard deviation, or variability, of a process.

Pp, Ppk vs Cp, CPK Cp & Cpk use an estimate for the standard deviation using the R Bar / d2 method. Pp, Ppk are more liberal where Cp, CPK are more conservative. Use Pp & Ppk when you are initially setting up your process. Use Cp & Cpk once the process is in a state of statistical control.

Excellent, Capable

Process Capability Pp measures the process spread vs the specification spread. In other words, how distributed the outcome of your process is vs what the requirements are. If you’d like more depth including calculations, etc, see these articles: Process Capability & Performance (Pp, Ppk, Cp, Cpk) Overview.

According to Six Sigma philosophy, Cp or Pp and Cpk or Ppk should be greater than 1.50. From a technical standpoint, Six Sigma deems a process being acceptable only after achieving a maximum defect rate of 3.4 parts per million opportunities.