SPC ( Statistical Process Control )

Statistical Process Control (SPC) is an effective method of monitoring a process through the use of control charts. Control charts enable the use of objective criteria for distinguishing background variation from events of significance based on statistical techniques. Much of its power lies in the ability to monitor both process center and its variation about that center.

In addition to reducing waste, SPC can lead to a reduction in the time required to produce the product or service from end to end. As Statistical process control 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.

  1. Improve Product Quality
  2. Reduce Scrap and Rework
  3. Increase Manufacturing Yield
  4. Meet Customer Requirements

The followings are the major benefits of SPC for an organization:

  1. Provides surveillance and feedback for keeping processes in control
  2. Signals when a problem with the process has occurred
  3. Detects assignable causes of variation
  4. Accomplishes process characterization
  5. Reduces need for inspection
  6. Monitors process quality
  7. Provides mechanism to make process changes and track effects of those changes
  8. Once a process is stable (assignable causes of variation have been eliminated), provides process capability analysis with comparison to the product tolerance

Statistical Process Control (SPC) can be applied to software development processes. A process has one or more outputs, as depicted in the figure below.

These outputs, in turn, have measurable attributes. SPC is based on the idea that these attributes have two sources of variation: natural (also known as common) and assignable (also known as special) causes.

The key steps for implementing Statistical Process Control are:

  1. Identify defined processes
  2. Identify measurable attributes of the process
  3. Characterize natural variation of attributes
  4. Track process variation
  5. If the process is in control, continue to track
  6. If the process is not in control:

          – Identify assignable cause

          – Remove assignable cause

          – Return to “Track process variation”

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