The Psychology of Quality and More

A Toolbook for Quality Improvement and Problem Solving (contents)

Relationship Diagram: How to understand it

The Quality Toolbook > Relationship Diagram > How to understand it

When to use it | How to understand it | Example | How to do it | Practical variations

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How to understand it

In many problem situations, there are multiple complex relationships between the different elements of the problem, which cannot be organized into familiar structures such as hierarchies or matrices. The Relations Diagram addresses these situations by showing relationships between items with a network of boxes and arrows, as below.

Fig. 1. Items and relationships

The most common use of the Relations Diagram is to show the relationship between one or more problems and their causes, although it can also be used to show any complex relationship between problem elements, such as information flow within a process.

The cause-effect Relations Diagram contains one or more effects and multiple causes, with arrows pointing from cause to effect. The network of arrows is built up as multiple causes interrelate. The result can be considered as a complex Cause-Effect Diagram, as in Fig. 2. Note how causes are visually differentiated from effects.

Several useful points may be identified when interpreting a cause-effect Relations Diagram:

• Arrows flowing only away from a cause indicate a root cause. Eliminating root causes can result in subsequent causes also being eliminated, giving a significant improvement for a relatively small effort.
• A cause with multiple arrows flowing into it indicates a bottleneck. This can be difficult to eliminate, due to the multiple contributory causes.
• A key cause is one which is selected to be addressed by future action. Key causes may be highlighted in some way, such as double circling.

A good cause-effect Relations Diagram has a balance of causes and relationships that describes the problem clearly and completely, without going into obscuring detail or being vague and brief.

Fig. 2. Complex causal relationships

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