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name: is-is-not-scoping description: >- Problem scoping with Is/Is-Not for 8D D2 problem description, CAPA investigation, or hypothesis elimination. Defines the precise boundary by contrasting what IS observed vs what IS NOT — eliminating hypotheses that don't fit the pattern. A Ford-originated automotive technique used in every structured quality investigation. license: MIT metadata: author: RBraga01 version: "1.1" iso-9001: "10.2" iatf-16949: "10.2.3" domain: quality-engineering subdomain: problem-solving industries: automotive,electronics,aerospace,medical,general status: approved created: "2026-06-01" last_updated: "2026-06-04" updated_by: migmcc reviewed_by: RBraga01 standard_edition: "ISO 9001:2015"

Is/Is-Not Problem Scoping

When to use

Use Is/Is-Not at the start of any quality investigation to precisely define what the problem IS and what it IS NOT. This scoping prevents wasted investigation effort and immediately eliminates hypotheses that cannot explain the observed IS/IS-NOT pattern.

Most effective in 8D D2 (problem description), CAPA scoping, and any situation where the defect is intermittent, batch-specific, or location-specific.

Critical rule — facts only

The IS column contains only documented, measured facts. Hypotheses, assumptions, and opinions belong in the hypothesis list, not in the matrix. If a cell contains an assumption rather than a fact, the entire analysis is compromised.

If data is not yet available: mark the cell as "Unknown — to be investigated" and identify what investigation is needed before proceeding.

Required Is/Is-Not Checklist

☐ All five dimensions completed — What, Which, Where, When, How many — or marked "Unknown — to be investigated" ☐ IS column contains only documented facts and measured values — no assumptions, no hypotheses ☐ IS NOT column completed for each dimension — this is where hypotheses get eliminated; do not leave blank ☐ Vague IS statements converted to specific facts with numbers (batch, quantity, percentage) ☐ At least three hypotheses generated and tested against the full matrix ☐ Eliminated hypotheses documented with the specific IS/IS-NOT entry that contradicts them ☐ Is/Is-Not output referenced in the NCR problem description and carried forward to Fishbone or 5-Why

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The Is/Is-Not Matrix

Work through each dimension. For each: what DO you observe? What do you specifically NOT observe?

What

IS	IS NOT
The specific defect observed (with measurement if applicable)	Similar defects that are NOT present
The affected feature, characteristic, or function	Features that are fine

Example: "IS: Connector pin bent at 15° (pin 3 only). IS NOT: Other pins bent, housing cracked, electrical failure."

Which object / part

IS	IS NOT
Specific part number, revision, configuration	Other part numbers not affected
Production batch / lot numbers affected	Batches not affected

Example: "IS: Part number 12345 rev B, batches 2026-05-10 to 2026-05-15. IS NOT: Rev A, batches before 2026-05-10."

Where (location found)

IS	IS NOT
Where in the process, plant, or geography is defect found?	Where it is NOT found
Customer location, inspection station, production line	Other lines, plants, or customers not affected

Example: "IS: Found at customer incoming inspection, Plant A. IS NOT: Found at our outgoing inspection or at Plant B customer."

When

IS	IS NOT
Time period when defect occurs	Before/after periods where it does not
Shift, time of day, after what event	Other shifts or times not affected

Example: "IS: First observed 2026-05-12, all shifts. IS NOT: Before 2026-05-10."

How many / How much (extent)

IS	IS NOT
Defect rate (parts per million, percentage)	What rate is NOT observed
Quantity affected	Quantity not affected
Trend (increasing, stable, decreasing)	No trend in unaffected population

Example: "IS: 23.5% of parts in affected batches (47/200). IS NOT: Any defects in batches before 2026-05-10 (0/500 inspected)."

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Using the matrix to eliminate hypotheses

Once the matrix is complete, generate hypotheses and test each against the IS/IS-NOT pattern:

Hypothesis: "The entire supplier lot is non-conforming."

• IS: Only batches from 2026-05-10 to 2026-05-15 → supports
• IS NOT: Batches before 2026-05-10 unaffected → supports
• IS NOT: Rev A unaffected → contradicts (if the lot included rev A parts) → Hypothesis partially consistent — investigate further

Hypothesis: "Gauge miscalibration."

• IS NOT: Not found at our outgoing inspection → contradicts (if we measure 100%, our gauge would catch it) → Hypothesis eliminated

Any hypothesis that cannot explain ALL items in the IS and IS-NOT columns is eliminated or requires modification. Document the specific contradiction for each eliminated hypothesis.

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Workflow

1. Gather data: inspection records, production records, customer complaint details
2. Fill in the matrix — only include facts, not hypotheses; mark gaps as "Unknown — to be investigated"
3. Identify the "clue" — what distinctive pattern appears (specific batch, specific location, specific time)?
4. List candidate hypotheses (minimum three)
5. Test each hypothesis against the full matrix
6. Eliminate those that contradict the data — document the contradiction
7. Carry forward surviving hypotheses into Fishbone or 5-Why
8. Reference the Is/Is-Not matrix in the NCR problem description — the IS "What" entry is the basis for the NCR non-conformance description

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Output format

A complete Is/Is-Not matrix (table format) with:

• All five dimensions completed with specific facts (or "Unknown — to be investigated")
• Surviving hypotheses listed with explanation of why they fit the full matrix
• Eliminated hypotheses listed with the specific contradiction that rules each one out

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Common mistakes

• Listing assumptions as IS — only documented, measured facts go in the matrix; assumptions generate false hypotheses
• Leaving cells empty — if you don't know, mark "Unknown — to be investigated"; empty cells mean the analysis is incomplete
• Not using IS NOT — the IS NOT column is where hypotheses get eliminated; skipping it wastes investigation time
• Vague IS statements — "some parts are bad" → must be: "23.5% of batch 2026-05-12 are non-conforming on dimension X (47/200 units measured)"
• Testing only one or two hypotheses — generate at least three before starting elimination; premature narrowing misses the real cause
• Not linking output to NCR — the Is/Is-Not "What IS" entry must match the NCR problem description exactly

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Output Format

At the start of each use, ask the user:

"How would you like to receive the output? A — Structured Markdown (formatted tables and sections, ready to copy) B — Plain tables (simplified structure for Excel or Word) C — Narrative report (flowing text for a formal document or email)

Default: A."

Adapt all output sections to the chosen format. If the platform or session context already defines a format preference, skip this question.

Changelog

Version	Date	Author	Change
1.0	2026-06-01	@RBraga01	Initial release
1.1	2026-06-04	@migmcc	Expanded Is/Is-Not table criteria and D2 integration guidance