Statistical Process Control...
SPC and Statistical Methods for Process Improvement.
In any process, the application of statistical process control (SPC) can provide significant process and product quality improvements. Many people find statistics difficult to understand, yet alone drive and optimize the introduction of statistical control into a process. However, SPC is not difficult, once you have a solid understanding of the various approaches inherent in an effective and efficient statistical control process.
SPC & Statistical Methods for Process Improvement.
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The type of information most people ask about SPC are along the following lines.
» How do you go about implementing an effective statistical process control ppt system (SPC)?
» How do you perform Statistical Analysis?
» What are the various types of SPC Charts and Tools, e.g.
Variable, Attribute, Range, Individual, X-bar, R charts,
Moving range, p and c charts.
Shewhart, CuSum, combined Shewhart-CuSum,
Exponentially weighted moving average (EWMA).
» How do you go about interpreting control charts? The relative merits of different chart types.
» What are the risks commonly experienced when implementing an SPC control system.
Etc., etc.,…
It is always helpful to have a clear understanding of the basic definitions as applied to SPC
Statistics — drawing conclusions using a scientific/mathematical approach to analyzing data.
Process — the whole combination of manpower, machines, materials, methods, measurement and environment working together to produce an output. Any work area that has identifiable, measurable output can be called a process.
Control – making something behave in a predictable consistent manner.
There are two main types of data charts associated with SPC: Attribute and Variable:
Variable data, deals with actual measurements over a continuous scale, e.g. inches, mm’s, kg’s, lbs, amps, ohms….etc.
Attribute data, is where the characteristic of the data is discrete in nature, e.g. number of defects per product, use of a go/no go gauge, color, etc., basically you have a Pass/Reject decision.
For both variable and attribute data, there are a number of possible control chart types –
Variable Control Charts
X-Bar and R Charts (Average – Range)
MX-MR Charts (Moving Average-Moving Range)
X-MR Charts (Individual – Moving Range)
X-Bar and S Charts (Average – Standard Deviation
Attribute control charts
p Charts (Percent Defective)
np Charts (Number Defective)
c Charts (Number of Defects)
u Charts (Defects per Unit)
The various control charts have been developed to suit particular situations, however, once you understand how to construct one chart type, understanding the others is a relatively easy task.
Types of Variation.
There is variation is every process, in SPC variation is split up into “Common” and “Special” causes of variation:
Common Causes of Variation – Variation inherent in a process which occurs in a random manner.
Special Causes of Variation – Variation which can be attributed to a specific source (to once off or exceptional events).
A process is “in control” when ONLY common causes are present.
SPC & Statistical Methods for Process Improvement.
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Process Capability.
Process Capability is a measure of the proportion of conforming products or services produced by a process, when the process is statistically in control.
Process capability and statistical process control (SPC) are methods of understanding, controlling and optimizing manufacturing and production processes through statistical means. The statistics themselves often seem daunting and therefore the true power of statistical control is often not achieved by many organizations. However, implementing a statistical control system and determining process capability, is a relatively straightforward task, one which can be tested in a single process, then as understanding and confidence grows, SPC control can be extended throughout the organization.
Process Capability versus Process Stability.
A process is Capable, if the products produced are predictable to be within specification.
Stability. A process is stable if it is only influenced by common causes of variation.
You don’t actually need to know the process specifications to determine process stability, but you must know the specifications to determine capability.
Capability Index. Cpk.
What a process would be capable of, if it were stable? It is a simple measure of the ability of the process to consistently provide products or services as expected. The capability index is a measure between 0 and 2.
Interpreting the Capability Index.
Capability = 2.0 “6 sigma”
Capability, 1.33 – 1.99 “Good”
Capability, 1.00-1.32 “Needs Control”
Capability < 1.00 “Not Capable”
SPC & Statistical Methods for Process Improvement.
- Process Capability. Variability Reduction. Statistical Process Control.
- Pre-Control. R&R Studies.
- Process capability indices Cp, Cpk, Cpm, Capability ratio.
- Performance indices Pp and Ppk.
- Variable Control Charts.
- Attribute Charts.
- Pareto Charts.
- Individual – X Charts.
- Histograms / Process Capability Analysis.
- Scatter Diagrams.
- Etc. … Etc. …
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