Standards Integration Status

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5 WHYS ROOT CAUSE ANALYSIS SESSION
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✓ **Standards Database:** Connected (AIAG-VDA, ISO 26262, MIL-STD-882 available)

Root cause validation will be offered at each "Why" iteration to validate against
documented failure patterns. Use `/lookup-standard [query]` for manual queries.

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5 WHYS ROOT CAUSE ANALYSIS SESSION
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⚠️ **Standards Database:** Unavailable

5 Whys analysis will proceed using standard iterative questioning methodology.
Root cause validation available from embedded reference data:
- ✓ 5M root cause categories (Man, Machine, Material, Method, Measurement)
- ✓ Systematic vs. random failure patterns

Not available without standards database:
- ✗ Industry-specific failure pattern catalogs
- ✗ Detailed root cause validation from AIAG-VDA, ISO 26262

To enable standards integration, ensure knowledge-mcp is configured.

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5 Whys Root Cause Analysis

Overview

Workflow

Phase 1: Problem Definition

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5 WHYS ROOT CAUSE ANALYSIS
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✓ **Inherited Context** (from Problem Definition)

Problem Statement:
Connector housing P/N 12345-A, Rev C exhibited cracked locking tabs (crack
length 3mm) at final assembly station 3 during torque verification, affecting
12 of 400 units (3%), detected by visual inspection.

Severity: 7 (AIAG-VDA FMEA Handbook (2019), Table 5.1)
- Product inoperable, loss of primary function
- Customer very dissatisfied

[Expandable] Full 5W2H + IS/IS NOT analysis ▼
| Element | IS | IS NOT |
|---------|----|----- ---|
| What (Object) | Connector housing P/N 12345-A, Rev C | Other connector types |
| What (Defect) | Cracked locking tab, 3mm length | Fully severed |
| Where | Final assembly station 3 | Stations 1, 2 |
| When | Week 12 production | Prior weeks |
| How Much | 12 of 400 units (3%) | All units |

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Proceeding to iterative Why analysis with inherited severity context...

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5 WHYS ROOT CAUSE ANALYSIS
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ℹ️ **Recommendation:** Run `/problem-definition` first

No problem definition context found. For effective root cause analysis, I recommend
establishing a clear, bounded problem statement first using `/problem-definition`.

This provides:
- Structured 5W2H + IS/IS NOT analysis
- Severity classification (flows to FMEA and corrective action prioritization)
- Problem scope boundaries (what IS and IS NOT affected)
- Consistent context for root cause validation

Options:
1. Run `/problem-definition` first (recommended for formal RCCA/8D investigations)
2. Continue 5 Whys standalone (I'll elicit basic problem statement below)

Your choice:

1. What is the specific problem or deviation observed?
2. When was it first observed? When does it occur?
3. Where does it occur (location, process step, equipment)?
4. What is the magnitude/extent (how many, how much, how often)?
5. What is the expected vs. actual state?

• Specific and measurable (not vague)
• Describing a deviation from expected performance
• Free of assumed causes or solutions
• Observable and verifiable

Phase 2: Team & Evidence Gathering

1. Who has direct knowledge of this problem/process?
2. What data, logs, or evidence is available?
3. Has this problem occurred before? What was done?

Phase 3: Iterative Why Analysis

1. Ask: "Why did [previous answer/problem] occur?"
2. Record: Document the answer verbatim
3. Validate:
   • Is this answer based on fact/evidence or assumption?
   • Does this answer logically follow from the previous statement?
   • Could there be multiple causes? (If yes, branch the analysis)

Would you like me to validate this proposed cause against documented root cause patterns from industry standards (AIAG-VDA, ISO 26262)?

This can:

• Confirm if this matches known failure mechanisms
• Suggest related causes or contributing factors
• Identify if this is a symptom vs. true root cause
• Provide confidence level: High/Medium/Low
• Yes: Query standards for root cause patterns matching "[answer]"
• No: Continue to next "Why" iteration

Your choice:

• If user says yes: Execute

  knowledge_search

  with query "root cause pattern [answer from Why] failure mechanism common causes", filter by domain="rcca" or "fmea"
• If user says no: Continue, but still offer at next iteration (different causes may benefit from validation)
• If MCP unavailable: Skip this prompt entirely (banner already warned user)
• If user declines 3+ times consecutively: "I'll stop offering validation. Use

  /lookup-standard

  if you want to validate a specific cause."

✓ High confidence match

This matches documented pattern: "Inadequate process control leading to variation" (AIAG-VDA FMEA Handbook (2019), Section 4.3.2)

Common related causes from standards:

• Lack of statistical process control
• Inadequate inspection frequency
• Process capability (Cpk) below 1.33

This is a recognized root cause pattern. Consider if any related factors apply.

⚠️ Medium confidence match

Partial match to documented patterns in ISO 26262-9 Section 8.4.3:

• Systematic failures in sensor calibration
• Environmental stress factors

Consider these related factors. You may want to explore another "Why" level.

ℹ️ Low confidence match

No exact match found for "[answer]" in documented root cause patterns.

This could indicate:

• Novel root cause (not previously documented in standards)
• Opportunity to explore another "Why" level
• Need to refine the causal statement

Recommendations:

1. Consider another Why level or alternative cause paths
2. Search related terms: [suggested related queries based on answer]
3. Continue with this cause if supported by strong evidence/expertise

1. Test: Read the chain backward: "Therefore..." - does it make logical sense?

• Further "Why" produces no meaningful answer
• You've reached a process/system issue (not a person)
• Addressing this cause would plausibly prevent recurrence
• The cause is within your control to address
• Standards validation shows high confidence (recognized root cause pattern)

• Answer is still a symptom, not a root cause
• Answer blames a person rather than a process
• Answer is "it's always been that way" or similar deflection
• Standards validation shows low confidence (suggests deeper investigation needed)

Phase 4: Root Cause Verification

1. Reversal Test: Read the chain forward with "therefore" - does each link hold?
2. Prevention Test: If we fix this cause, would the problem be prevented?
3. Recurrence Test: Has this cause produced similar problems before?
4. Control Test: Is this cause within our ability to address?
5. Evidence Test: Is this cause supported by data, not just opinion?

Phase 5: Countermeasure Development

1. How will we fix this? (immediate action)
2. How will we implement it? (plan)
3. How will we verify it works? (validation)
4. How will we standardize it? (documentation/training)
5. How will we sustain it? (monitoring/audits)

Phase 6: Documentation & Report

python scripts/generate_report.py

Quality Scoring

• Overall Score: Weighted average × 20 = 0-100 points
• Passing Threshold: 70 points minimum

python scripts/score_analysis.py

Common Pitfalls

• Stopping too early (at symptoms)
• Blaming people instead of processes
• Accepting assumptions as facts
• Single-track thinking on multi-cause problems
• Failing to validate the causal chain

Examples

• Manufacturing equipment failure
• Software deployment failure
• Customer complaint investigation
• Process quality deviation

Integration with Other Tools

• Fishbone Diagram: Use to brainstorm potential causes before 5 Whys
• Pareto Analysis: Use to prioritize which problems to analyze
• 8D Process: 5 Whys fits within D4 (Root Cause Analysis)
• A3 Report: Include 5 Whys in the root cause section

Session Conduct Guidelines

1. Facilitate, don't lead: Ask questions without suggesting answers
2. Document everything: Record exact wording of each answer
3. Challenge assumptions: Ask "How do we know this?"
4. Stay process-focused: Redirect person-blame to process gaps
5. Allow branching: Multiple valid answers create parallel chains
6. Verify with evidence: "Show me" is better than "tell me"

Manual Commands

/lookup-standard

/lookup-standard [natural language query]

• /lookup-standard common root causes for sensor failures automotive systems

• /lookup-standard systematic failures ISO 26262 definition examples

• /lookup-standard how deep should 5 Whys analysis go stopping criteria

• /lookup-standard validation tests for root cause analysis AIAG-VDA

• /lookup-standard difference between symptom and root cause

• /lookup-standard 5M Man Machine Material Method Measurement root causes

• /lookup-standard process capability Cpk root cause manufacturing

## Standards Lookup: [query]

### Result 1 (92% relevant)
**Source:** AIAG-VDA FMEA Handbook (2019), Section 4.3.2

[Content excerpt with relevant context]

### Result 2 (88% relevant)
**Source:** ISO 26262-9:2018, Section 8.4.3

[Content excerpt with relevant context]

---
Showing 3 of 7 results. Say "show more" for additional results.

• Validating proposed root causes against industry-documented patterns
• Understanding systematic vs. random failure definitions
• Checking best practices for 5 Whys depth and stopping criteria
• Finding example root cause analyses from standards
• Investigating unfamiliar failure mechanisms
• Strengthening root cause analysis with standards evidence (regulatory context)

## Standards Lookup: [query]

No direct matches found for "[query]".

Did you mean:
- "root cause pattern process control"
- "common failure modes manufacturing"
- "5M root cause categories"

Try refining with specific standard names (AIAG-VDA, ISO 26262, MIL-STD) or broader terms.

Standards database not available. Use embedded reference data in

references/root-cause-patterns.md

.