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Sample Ishikawa diagram shows the causes contributing to problem. The defect, or the problem to be solved, [1] is shown as the fish's head, facing to the right, with the causes extending to the left as fishbones; the ribs branch off the backbone for major causes, with sub-branches for root-causes, to as many levels as required.
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In software testing, a cause–effect graph is a directed graph that maps a set of causes to a set of effects. The causes may be thought of as the input to the program, and the effects may be thought of as the output. Usually the graph shows the nodes representing the causes on the left side and the nodes representing the effects on the right side.
There exist several forms of causal diagrams including Ishikawa diagrams, directed acyclic graphs, causal loop diagrams, [10] and why-because graphs (WBGs). The image below shows a partial why-because graph used to analyze the capsizing of the Herald of Free Enterprise. Partial Why–because graph of the capsizing of the Herald of Free Enterprise
Causal graphs can be used for communication and for inference. They are complementary to other forms of causal reasoning, for instance using causal equality notation. As communication devices, the graphs provide formal and transparent representation of the causal assumptions that researchers may wish to convey and defend.
A bow-tie diagram can be considered as a simplified, linear, and qualitative representation of a fault tree (analyzing the cause of an event) combined with an event tree (analyzing the consequences), [2] although it can maintain the quantitative, probabilistic aspects of the fault and event tree when it is used in the context of quantified risk ...
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In science and engineering, root cause analysis (RCA) is a method of problem solving used for identifying the root causes of faults or problems. [1] It is widely used in IT operations, manufacturing, telecommunications, industrial process control, accident analysis (e.g., in aviation, [2] rail transport, or nuclear plants), medical diagnosis, the healthcare industry (e.g., for epidemiology ...
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