Choose Index below for a list of all words and phrases defined in this glossary.

Cause and Effect Diagram

index | Index

Cause and Effect Diagram - definitions

Cause-and-Effect Diagram : A chart in the shape of a "fishbone" used to analyse the relationship between error cause and error effect. The diagram, invented by Kaoru Ishikawa, shows a specific effect and possible causes or error. The errors are drawn in 6 categories, each a bone on the fish. The categories are : 1) Human (or Manpower), 2) Methods, 3) Machines, and 4) Materials, 5) Measurement and 6) Environment. Also called a Fishbone diagram. (Q)

[Category=Data Quality ]

Source: Larry English, External, 14-Jan-2009 14:25


Fishbone diagram

These advertisers support this free service

Cause and Effect Diagram - A tool for analyzing process dispersion. It is also referred to as the "Ishikawa diagram," because Kaoru Ishikawa developed it, and the "fishbone diagram," because the complete diagram resembles a fish skeleton. The diagram illustrates the main causes and subcauses leading to an effect (symptom). The cause and effect diagram is one of the "seven tools of quality" (see listing).

[Category=Quality ]

Source: American Society for Quality, 09 September 2010 12:15:44, External

Cause and Effect Diagram - A cause and effect diagram is a visual tool used to logically organize possible causes for a specific problem or effect by graphically displaying them in increasing detail. It helps to identify root causes and ensures common understanding of the causes. It is also called an Ishikawa diagram.

Cause and Effect relationships govern everything that happens and as such are the path to effective problem solving. By knowing the causes, we can find some that are within our control and then change or modify them to meet our goals and objectives. By understanding the nature of the cause and effect principle, we can build a diagram to help us solve everyday problems every time.  

[Category=Data Quality ]

Source: iSixSigma, 04 January 2011 09:20:20, External

Data Quality Glossary.  A free resource from GRC Data Intelligence. For comments, questions or feedback: