Control Charts

Control charts were identified by Ishikawa for improving Quality.   Business Analyst and the Business Partner are provided by presenting data in a format which can provide useful perception into a problem or situation. Control Charts helps in understanding how systems and processes are performing over time.

Control Charts

Control Charts has the following attributes:

  • Centerline: represents a numeric average of either actual historical or desired performance for the system or process.
  • Upper and Lower Boundaries represent the limits that define acceptable, or controlled, performance. They generally have a statistical base and are frequently described in terms of the number of standard deviations (Sigma is the mathematical representation of a Standard Deviation).

A standard deviation calculates the square root of the sum of the differences between the population mean and the single instance, squared.

For example, if the mean is 20 and the first instance is 12, (20-12=8; 8 squared = 64.) Repeat this process for each item in the distribution. Add then all up, then calculate the square root; or use the formulas built into any spreadsheet.

  • Performance over time is plotted on the chart. Plot points that fall outside the acceptable or desired performance range are generally subject to further scrutiny.

Control Charts are also mentioned as

  • Run Charts
  • Line Charts
  • Shewhart Charts.

Control Charts supports to control processes in Statistical Process Control (SPC). Statistical Process Control is a  tool for moving from CMMI™13 Level 3 to Level 4.

CMMI is a trademarked product of the Software Engineering Institute (SEI.)


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