29 March 2014
stastical process control
Statistical process control (SPC)
conforming product. The application of statistical methods to the monitoring and control of a process to ensure that it operates at its full potential to produce.
A process behaves predictably to produce as much conforming product as possible with the least possible waste.
Has been applied most frequently to controlling manufacturing lines, it applies equally well to any process with a measurable output.
Use of SPC
It lies in the ability to examine a process and the sources of variation in that process using tools that give weight to objective analysis over subjective opinions and that allow the strength of each source to be determined numerically.
Variations in the process that may affect the quality of the end product or service can be detected and corrected, thus reducing waste as well as the likelihood that problems will be passed on to the customer.
With its emphasis on early detection and prevention of problems, SPC has a distinct advantage over other quality methods, such as inspection, that apply resources to detecting and correcting problems after they have occurred.
SPC was pioneered by Walter A. Shewhart in 1920.
W.Edwards Deming later applied SPC in US.
He successfully improved the manufacturing of munitions and other products.
Dr.shewhart introduce the concept of control charts.
He drew various mathematical and statistical theories, he found out that data from physical seldom creates a normal distribution.
In 1989, the Software Engineering Institute introduced the notion that SPC can be usefully applied to non-manufacturing processes, such as software engineering processes.
Quality Measures of SPC
a product characteristic that can be evaluated with a discrete response
good – bad; yes – no
a product characteristic that is continuous and can be measured
weight – length.
SPC in Manufacturing Industry
Traditionally achieved through post-manufacturing inspection of the product; accepting or rejecting each article (or samples from a production lot) based on how well it met its design specifications.
Uses statistical tools to observe the performance of the production process in order to predict significant deviations that may later result in rejected product.
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