An Interrelationship Diagram maps cause-and-effect links among complex issues so teams can identify key drivers, outcomes, and leverage points.
Definition
An Interrelationship Diagram is a visual planning and analysis tool that shows how ideas, causes, issues, or factors influence one another. Arrows show direction of influence, helping teams identify drivers with many outgoing arrows and outcomes with many incoming arrows.
It is useful when a problem has multiple interacting causes and a simple list or Fishbone diagram does not reveal the system dynamics.
History
The Interrelationship Diagram is commonly included among the new seven management and planning tools used in quality management. These tools support complex planning, prioritization, and cross-functional problem solving.
It became popular because quality problems often involve connected process, people, system, supplier, and policy causes rather than a single isolated root cause.
When to Use
Use it when issues are tangled, causes influence other causes, teams disagree about leverage points, or the problem crosses departments. It works well after brainstorming, affinity grouping, or Fishbone analysis.
It is not ideal for simple linear problems where a basic process map, 5 Whys, or Pareto chart is enough.
Step-by-Step
- Define the problem or question.
- List key factors, causes, barriers, or issues.
- Place items on a page or board.
- Compare each pair and decide whether one influences the other.
- Draw arrows from cause to effect.
- Count outgoing and incoming arrows.
- Identify key drivers, outcomes, and feedback loops.
- Select follow-up analysis or countermeasures for the strongest drivers.
Examples
- Delivery delay: Staffing, schedule changes, supplier misses, and rework are mapped to find the strongest drivers.
- Culture issue: Lack of coaching, unclear priorities, and metric pressure are connected to weak improvement sustainment.
- Quality escapes: Training, inspection method, design ambiguity, and supplier variation are mapped as a system.
Common Pitfalls
- Including too many vague factors.
- Arguing arrows without evidence or process knowledge.
- Confusing correlation with cause.
- No action after identifying drivers.
- Letting hierarchy dominate the map.
- Using it where simpler tools would be clearer.