Statistical Process Control 101
Learn all about SPC for manufacturing.
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- Why Use SPC in Manufacturing?
- Statistical Process Control (SPC) Implementation
- Understanding Process Variation
- The Problem with Tampering
- Distributions
- Populations and Sampling
- Process Behavior and Control
- Specification and Control Limits
- SPC Control Charts
- SPC Charting Examples
- Capability and Cpk Manufacturing Charts
- Overcoming Obstacles to Effective SPC
- Statistical Process Control FAQs
Distributions
To begin evaluating the type of variation in a process, one must evaluate distributions of data—as Deming plotted the drop results in his Funnel Experiment. The best way to visualize the distribution of results coming from a process is through histograms. A histogram is frequency distribution that graphically shows the number of times each given measured value occurs. These histograms show basic process output information, such as the central location, the width and the shape(s) of the data spread.
Location: Measure of Central Tendency
There are three measures of histogram’s central location, or tendency:
- Mean (the arithmetic average)
- Median (the midpoint)
- Mode (the most frequent)
When compared, these measures show how data are grouped around a center, thus describing the central tendency of the data. When a distribution is exactly symmetrical, the mean, mode and median are equal.
Formula for estimating population mean
To estimate a population mean, use the following equation:
Dispersion: Spread of the Data
The two basic measures of spread are the range (the difference between the highest value and the lowest value in the sample) and the standard deviation (the average absolute distance each individual value falls from the distribution’s mean). A large range or a high standard deviation indicate more dispersion, or variation of values within the sample set.
Formula for estimating standard deviation
To estimate the standard deviation of a population, use the following equation:
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