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Calibration /MSA/SPC

Statistical process control (SPC) involves using statistical techniques to measure and analyze the variation in processes.  Most often used for manufacturing processes, the intent of SPC is to monitor product quality and maintain processes to fixed targets.  Statistical quality control refers to using statistical techniques for measuring and improving the quality of processes and includes SPC in addition to other techniques, such as sampling plans, experimental design, variation reduction, process capability analysis, and process improvement plans.

SPC is used to monitor the consistency of processes used to manufacture a product as designed.  It aims to get and keep processes under control.  No matter how good or bad the design, SPC can ensure that the product is being manufactured as designed and intended.  Thus, SPC will not improve a poorly designed product's reliability, but can be used to maintain the consistency of how the product is made and, therefore, of the manufactured product itself and its as-designed reliability.

A primary tool used for SPC is the control chart, a graphical representation of certain descriptive statistics for specific quantitative measurements of the manufacturing process.  These descriptive statistics are displayed in the control chart in comparison to their "in-control" sampling distributions.  The comparison detects any unusual variation in the manufacturing process, which could indicate a problem with the process.  Several different descriptive statistics can be used in control charts and there are several different types of control charts that can test for different causes, such as how quickly major vs. minor shifts in process means are detected.  Control charts are also used with product measurements to analyze process capability and for continuous process improvement efforts.
 
Typical charts and analyses used to monitor and improve manufacturing process consistency and capability (produced with Minitab statistical software).

Benefits:
  • Provides surveillance and feedback for keeping processes in control
  • Signals when a problem with the process has occurred
  • Detects assignable causes of variation
  • Accomplishes process characterization
  • Reduces need for inspection
  • Monitors process quality
  • Provides mechanism to make process changes and track effects of those changes

Once a process is stable (assignable causes of variation have been eliminated), provides process capability analysis with comparison to the product tolerance

Capabilities:

  • All forms of SPC control charts
  • Variable and attribute charts
  • Average (X—    ), Range (R), standard deviation (s), Shewhart, CuSum, combined Shewhart-CuSum, exponentially weighted moving average (EWMA)
  • Selection of measures for SPC
  • Process and machine capability analysis (Cp and Cpk)
  • Process characterization
  • Variation reduction
  • Experimental design
  • Quality problem solving
  • Cause and effect diagrams


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