D5: Quality Impact

Core Question: How does this problem affect what we deliver?

Quality impact is often the most insidious dimension — defects found by customers cost 10× more than those found internally, and safety issues can multiply by 100×.

Primary Cascade: Quality → Customer (85% of cases)

Observable Signals

Don't wait for customer complaints. Look for early warning signals in your systems:

Signal Type	Observable	Data Source	Detection Speed
Immediate	Defect rate spike	QA system	Hours-Days
Behavioral	Rework hours increase	Timesheets	Days
Customer	Complaint volume up	Support system	Days
Safety	Incident reports	EHS system	Immediate
Returns	Product returns	Logistics/RMA	Days
Process	Specification deviations	Quality audits	Weeks
Silent	Workarounds created	Tribal knowledge, interviews	Months
Inspection	Rejection rate	Manufacturing QC	Immediate

Trigger Keywords

Language patterns indicate severity. Train your team to flag these:

High Urgency (Sound = 8-10)

"recall"                "safety incident"         "injury"
"critical defect"       "system failure"          "data breach"
"contamination"         "fatality"                "major outage"

Action: Executive escalation within 1 hour. Potential regulatory notification.

Medium Urgency (Sound = 4-7)

"defect"                "rework"                  "redo"
"didn't meet spec"      "out of tolerance"        "failed inspection"
"customer complaint"    "return"                  "warranty claim"

Action: Manager review within 24 hours.

Low Urgency / Early Warning (Sound = 1-3)

"workaround"            "manual fix"              "known issue"
"tech debt"             "legacy limitation"       "needs improvement"
"minor issue"           "cosmetic defect"         "edge case"

Action: Track pattern over time. Add to backlog.

Metrics

Track both leading (predictive) and lagging (historical) indicators:

Metric Type	Metric Name	Calculation	Target	Alert Threshold
Leading	First-pass yield	Good units / Total units	>95%	<90%
Leading	Defect detection rate	Defects found internally / Total	>90%	<80%
Leading	Code coverage	% of code with tests	>80%	<70%
Leading	Technical debt ratio	Debt remediation time / Dev time	<5%	>10%
Lagging	Customer-reported defects	Count per release/period	Decreasing	Increasing trend
Lagging	Cost of quality	Prevention + Appraisal + Failure costs	<15% of revenue	>20%
Lagging	Warranty costs	Claims / Revenue	<2%	>3%

Example Dashboard Query

-- Defect detection rate alert
SELECT
  release_version,
  COUNT(CASE WHEN found_by = 'Internal' THEN 1 END) as internal_defects,
  COUNT(CASE WHEN found_by = 'Customer' THEN 1 END) as customer_defects,
  COUNT(CASE WHEN found_by = 'Internal' THEN 1 END) * 100.0 /
    NULLIF(COUNT(*), 0) as detection_rate_pct
FROM defects
WHERE created_date >= CURRENT_DATE - INTERVAL '90 days'
GROUP BY release_version
HAVING detection_rate_pct < 80  -- Alert when <80% found internally
ORDER BY created_date DESC

Cascade Pathways

Quality impact multiplies rapidly across other dimensions:

Cascade Probabilities

Cascade Path	Probability	Severity if Occurs
Quality → Customer	85%	High
Quality → Operational	75%	Medium-High
Quality → Regulatory	30%	Very High (safety-critical industries)

Why Customer Cascade is Most Common:

1. Defects directly impact user experience (functionality, reliability)
2. Quality issues erode trust (perception of brand/product)
3. Customers amplify problems (reviews, social media, word-of-mouth)
4. Competitive alternatives exist (switching is easier than tolerating defects)

Multiplier Factors

Not all quality issues cascade equally. The multiplier depends on:

Factor	Low (1.5×)	Medium (3×)	High (10×+)
Detection Point	Internal QA	Customer-found	Field failure
Safety Impact	None	Potential	Actual injury
Scope of Defect	Single unit	Batch/Lot	Systemic design
Fix Complexity	Patch/Update	Rework	Recall/Replace
Brand Sensitivity	B2B commodity	Consumer product	Premium brand

Example Calculation

Scenario: Automotive safety defect (airbag), field failure, systemic design flaw, premium brand

Multiplier factors:
- Detection point: High (10×, field failure)
- Safety impact: High (10×, actual injury)
- Scope of defect: High (10×, systemic design)
- Fix complexity: High (10×, recall required)
- Brand sensitivity: High (10×, premium brand)

Average multiplier: (10 + 10 + 10 + 10 + 10) ÷ 5 = 10×

Impact:

• Direct cost of defect: $50M (recall, parts, labor)
• Multiplied impact: $50M × 10 = $500M (total business impact)
• Plus customer cascade: 85% probability of trust erosion → lost sales
• Plus regulatory cascade: 30% probability but very high severity (NHTSA investigation)
• Total risk: $500M+ from initial defect (real-world example: Takata airbag recall = $10B+)

3D Scoring (Sound × Space × Time)

Apply the Cormorant Foraging lens to quality dimension:

Lens	Score 1-3	Score 4-6	Score 7-10
Sound (Urgency)	Cosmetic issue	Functional defect	Safety critical
Space (Scope)	One unit	Batch/Release	All production
Time (Trajectory)	Isolated incident	Recurring	Systemic/Chronic

Formula: Dimension Score = (Sound × Space × Time) ÷ 10

Example Scoring

Scenario: Critical software bug affecting all users on production, recurring in every release despite fixes

Sound = 9 (system failure, data loss risk)
Space = 9 (all production users)
Time = 7 (recurring, systemic root cause)

Quality Impact Score = (9 × 9 × 7) ÷ 10 = 56.7

Interpretation: Critical urgency (56.7 >> 30). Expect immediate cascade to Customer (churn risk), Operational (emergency fixes consuming all resources), and Revenue (refunds, lost sales) dimensions.

Detection Strategy

Automated Monitoring

Set up alerts for:

1. Defect rate spike (>20% increase week-over-week)
2. First-pass yield drop (<90% good units)
3. Customer-reported defects (>20% of total defects found by customers)
4. Rework hours (>10% of total development time)

Human Intelligence

Train your QA/engineering teams to:

1. Flag language patterns (use trigger keyword lists)
2. Report workarounds (band-aids hiding systemic issues)
3. Escalate safety concerns (near-misses are signals)
4. Track customer sentiment (quality perception vs metrics)

Real-World Example

The "Workaround" Signal:

Observable	Data Point	3D Score
Signal	"Just use this workaround" appears in 15 support tickets	Sound = 5
Context	Affects core feature, all users on specific browser	Space = 7
Trend	Workaround documented 6 months ago, still not fixed	Time = 6
Score	(5 × 7 × 6) ÷ 10 = 21	Medium urgency

Cascade Prediction:

• 85% probability → Customer impact (frustration, perception of "broken product")
• 75% probability → Operational impact (support time, tribal knowledge required)
• Multiplier: 2-3× (customer-found, multiple users, process workaround)

Action Taken:

1. Root cause analysis prioritized (within 1 week)
2. Proper fix deployed (within 2 weeks)
3. Workaround documentation removed (after fix validation)
4. Result: Support tickets dropped 40%, NPS increased 5 points

Industry Variations

B2B SaaS

• Primary metric: Bug escape rate, mean time to resolution
• Key signal: Customer-reported defects, production incidents
• Cascade risk: Quality → Customer → Revenue

Healthcare

• Primary metric: Medication errors, patient safety incidents
• Key signal: Near-miss reports, adverse events
• Cascade risk: Quality → Regulatory → Customer (Patient) → Revenue

Manufacturing

• Primary metric: First-pass yield, defect parts per million (DPPM)
• Key signal: Scrap rate, rework hours, customer returns
• Cascade risk: Quality → Customer → Revenue → Regulatory

Next Steps

📊 D1: Customer Impact — The 85% cascade from quality issues to customer dissatisfaction

🔧 D6: Operational Impact — How quality issues create rework cycles (75% cascade)

🔄 Cascade Analysis — Map how quality issues multiply

📖 Observable Properties — Complete signal catalog

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Remember: The defect you find is fixable. The defect your customer finds is expensive. The defect that causes harm is catastrophic. Find them first. 🪶