Process capability

This article is about the quantifiable measure of a process. For software process capability, see Capability Maturity Model and ISO/IEC 15504.

A process is a unique combination of tools, materials, methods, and people engaged in producing a measurable output; for example a manufacturing line for machine parts. All processes have inherent statistical variability which can be evaluated by statistical methods.

The process capability is a measurable property of a process to the specification, expressed as a process capability index (e.g., C_pk or C_pm) or as a process performance index (e.g., P_pk or P_pm).

Cp is the simplest way to express the character of the process, which is a measure of the potential ability of the process to ensure that the value of the reference character quality lies within the tolerance limits.

Process Capability Index, Cpk is statistics, which measures the potential for a process to generate

defective outputs relative to either upper or lower specifications.Capability critical factor Cpk takes

into account the position of the reference mark to the tolerance limits.

The output of this measurement is usually illustrated by a histogram and calculations that predict how many parts will be produced out of specification (OOS).

Two parts of process capability are: 1) measure the variability of the output of a process, and 2) compare that variability with a proposed specification or product tolerance.

Variation means extra costs, increased sales prices and less customer satisfaction. Deviations from the target cause a loss to the producer, extra work, losses mean higher costs.

When assessing the capability of the process has to be followed a procedure involving the right way how to collect data and to verify the two basic constraints: the process has to be under statistical control and distribution of a reporting quality measures must comply with a normal distribution.

Measure the process

The input of a process usually has at least one or more measurable characteristics that are used to specify outputs. These can be analyzed statistically; where the output data shows a normal distribution the process can be described by the process mean (average) and the standard deviation.

A process needs to be established with appropriate process controls in place. A control chart analysis is used to determine whether the process is "in statistical control". If the process is not in statistical control then capability has no meaning. Therefore, the process capability involves only common cause variation and not special cause variation.

A batch of data needs to be obtained from the measured output of the process. The more data that is included the more precise the result, however an estimate can be achieved with as few as 17 data points. This should include the normal variety of production conditions, materials, and people in the process. With a manufactured product, it is common to include at least three different production runs, including start-ups.

The process mean (average) and standard deviation are calculated. With a normal distribution, the "tails" can extend well beyond plus and minus three standard deviations, but this interval should contain about 99.73% of production output. Therefore, for a normal distribution of data the process capability is often described as the relationship between six standard deviations and the required specification.

Capability study

The output of a process is expected to meet customer requirements, specifications, or engineering tolerances. Engineers can conduct a process capability study to determine the extent to which the process can meet these expectations.

The ability of a process to meet specifications can be expressed as a single number using a process capability index or it can be assessed using control charts. Either case requires running the process to obtain enough measurable output so that engineering is confident that the process is stable and so that the process mean and variability can be reliably estimated. Statistical process control defines techniques to properly differentiate between stable processes, processes that are drifting (experiencing a long-term change in the mean of the output), and processes that are growing more variable. Process capability indices are only meaningful for processes that are stable (in a state of statistical control).

For improvement of process capability it was necessary to identify the causes of variability of observed characteristics. Variability of monitored quality characteristics depend on the influences in the implementation process, but is also influenced by input factors. In the manufacturing process there are a lot of input factors that may influence the output quality of the product. In the process is to appply method DOE (furthermore Design of Experiments) for detection of the factors with significant influence on output characteristics.

Using the methods DOE may determine significant factors and also provide optimal levels of input factors in order to specify the required value of output characteristics.

See also

• Process (engineering)
• Control chart
• Corrective and Preventative Action (CAPA)
• Kurtosis
• Normal distribution
• Six Sigma
• Statistical interference
• Statistical process control
• Tolerance (engineering)

References

• Pyzdek, T, "Quality Engineering Handbook", 2003, ISBN 0-8247-4614-7
• Bothe, D. R., "Measuring Process Capability", 2001, ISBN 0-07-006652-3
• Godfrey, A. B., "Juran's Quality Handbook", 1999, ISBN 007034003X
• ASTM E2281 Standard Practice for Process and Measurement Capability Indices

External links

• NIST/SEMATEK discussion of capability

• Estimating Process Capability for parts with ovality
• The Six Sigma Zone