Cause and Effect Diagram
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The 'Cause and Effect Diagram' (also known as the 'Ishikawa Diagram' or 'Fish Bone Diagram') is a popular technique for analysing problem causes in which a diagram, with the appearance of a fish bone, is constructed.
The first cause and effect diagram was developed by Professor Kaoru Ishikawa of the University of Tokyo in 1953 when he was explaining to a group of engineers at the Kawasaki Steel Works how various production factors are related and categorized.
Constructing a Cause and Effect Diagram
The construction is a four-step process: 1. The problem statement (the 'Effect') is entered in a box at one end of the 'backbone'. 2. Primary categories (1-4) of cause are written in boxes placed parallel to, and some distance from, the backbone. The boxes are connected to the backbone by slanting lines ('ribs'). 3. Potential causes are shown as small bones connecting to the ribs. 4. On the basis of evidence, identify the key causes and eliminate non-causes. Keep asking: 'Why?' to identify root causes. Look for:
- Duplications - may be an indication of a root cause.
- A concentration on any one rib
- Anything that may have changed recently that relates to any of the causes.
Problem statements usually have two parts: the 'current state' and the 'desired state'. Cause-and-effect diagrams often use an abbreviated form of problem statement by having only the desired state or the current state in the fish's 'head'.
There are two methods of identifying the primary categories. The first is to use pre-defined categories such as manfacturing's 4Ps (People-Process-Policies-Plant) or the 4Ms (Manpower-Method-Material-Machinery). The other method is to group similar causes together and see what the grouping suggests. This is the method that was used in our example.
Example Cause and Effect Diagram
This example was used to understand why employees were reacting to badly to a recently-introduced recognition and reward programme. The causes could have been identified by using brainstorming, but in this case they were identified by a questionnaire. The categories were identified by grouping similar causes together.
As you can see, the cause and effect diagram is particularly useful for analysing qualitative data. Many people use a Pareto Diagram when analysing quantitative data, but the cause and effect diagram can also be used to analyse quantitative data.
In one particularly effective example, a group of engineers were analysing why a particular component was out of alignment by 5mm. A number of potential causes were brainstormed and a cause and effect diagram was constructed. The engineering manager then allocated a number of these potential cause to members of his team who then accurately measured what contribution these causes made to the alignment problem. If a potential cause was found to have no effect on the misalignment, it was removed from the cause and effect diagram. After the first round of measurement, it was found that they had only identified causes that contributed to 3mm of the 5mm problem, so they identified a number of additional potential causes and went off to measure these. This process was repeated until they had found all the causes of the misalignment.
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