Is-Is Not Analysis sharpens problem definition by comparing where, when, what, and how a problem occurs against closely related conditions where it does not occur.
Definition
Is-Is Not Analysis is a structured problem-definition and troubleshooting method. It compares what the problem is with what it is not, where it occurs with where it does not, when it occurs with when it does not, and the extent of the problem compared with similar unaffected conditions.
The contrast helps teams narrow possible causes and avoid broad, vague problem statements.
History
The method is associated with structured troubleshooting and problem-solving traditions such as Kepner-Tregoe, 8D, and root cause analysis. It became useful in quality and operations because many problems are best understood by comparing affected and unaffected conditions.
Lean Six Sigma teams use it before deeper statistical analysis or root-cause testing because it improves scope and hypothesis quality.
When to Use
Use Is-Is Not Analysis when a problem appears in some products, shifts, locations, customers, machines, materials, or time periods but not others. It is especially useful for sudden changes, intermittent problems, escapes, complaints, and troubleshooting.
It is less useful when the problem is universal across the whole process and no meaningful contrast exists.
Step-by-Step
- Write a specific problem statement.
- Describe what the problem is and what similar thing it is not.
- Describe where it occurs and where it does not.
- Describe when it occurs and when it does not.
- Describe extent: how many, how much, trend, and severity.
- Identify differences between is and is-not conditions.
- Ask what changes could explain those differences.
- Test the most plausible causes with evidence.
Examples
- Defect: Scratches appear on Product A from Line 2 after lunch, but not Product B or Line 1.
- Service: Late approvals affect new customers but not renewals.
- Equipment: Failures occur in humid weather on one machine family.
- Supplier: Defects appear only on one lot and one cavity.
Common Pitfalls
- Using broad categories that do not create contrast.
- Failing to verify the is-not condition is truly unaffected.
- Jumping to cause before listing differences.
- Ignoring recent changes.
- No data collection to test hypotheses.
- Letting assumptions replace observation.