Statistical Process Control
Using SPC to Improve a Process
As the recession bites, the businesses that survive and flourish will be the ones that have an edge over their rivals. These will be the companies that waste less of their valuable materials and labor, and that can deliver quality products to customers on time. Statistical Process Control is to product quality what Lean Manufacturing is to product delivery.
What is Statistical Process Control?
Statistical Process Control (SPC) is a method that is used to control any process that has a measurable output. Small samples are taken from each batch, measured, and some basic calculations are then made.
This is necessarily done over a period of time that is dictated by the field of application.
How Does Statistical Process Control Work?
SPC therefore has several phases. The first phase is called a Capability Study, to estimate what measured output is typical for the process in question.
The second phase is calculation of Control Limits. The third phase of SPC is the measuring or monitoring phase, that checks samples from batches regularly.
The fourth phase is Process Improvement.
Process Capability Study
A number of batches is sampled and measured. A fixed sample size, usually five, is taken from each batch, and the average of each batch is calculated. SPC makes use of the Central Limit Theorem, so sample sizes lower than five are discouraged. The number of batches sampled varies, and although twenty is considered to be sufficient, the larger the number is, the better.
Next, the overall average is calculated. This equals the average of the batch averages. The overall standard deviation is also calculated, and this is the standard deviation of the batch averages.
At this point, customer specifications are checked. These will stipulate a Lower Specification Limit and an Upper Specification Limit. The process is potentially capable if:
(USL - LSL) is larger than six times the overall process standard deviation
The reason for this is that the probability of any measurement falling outside plus or minus three standard deviations from the average is only 0.3%.
How SPC Limits are Calculated
After the process output has been characterized, it is possible to apply some statistical tests. These are all based on the Normal Distribution, or Gaussian "Bell" Curve. Every Normal Distribution has common features.
For instance, around 68% of the measurements fall within one standard deviation of the average. About 95% of the measurements fall within two standard deviations, and 99.7% of the measurements fall within three standard deviations.
SPC Chart Rules
The chart should show no patterns or trends. For example, if the process is stable and has not shifted , then it is unlikely that 8 points in a row will be above the overall mean. If this happens, it is very likely that the reason is a process shift, rather than just random measurements. This means that the process should be fixed before proceeding.
A batch of rules, collectively known as the Western Electric Rules, have become the industry standard.
Process Improvement Using SPC
When the specification limits dictated by a customer cannot be met, two actions are needed. The first is to deal with parts that are outside customer specification, for example reworking or scrapping the parts. The second, and most important action, is to find out what caused the variation, and then to reduce this variation.
This is typically the role of the Process Engineer. The engineer will use Six Sigma or other methodologies to improve the process.
Summary of Statistical Process Control
Reducing costs by minimizing scrap and rework is the main goal of SPC. This is achieved by finding out what a process produces compared to what a customer expects, and then driving improvements to the process.
By comparing the measurements of a batch sample with the measurements of typical batch samples in the past, process "over-steering" is reduced. A more important benefit is that the causes of variation can be analyzed, and this variation is then reduced by engineers.
As variation is eliminated from the process, more and more parts can be accurately produced, and so scrap and rework are eliminated.
Typical SPC Chart
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