D2: Employee Impact
Core Question: How does this problem affect the people who work for us?
Employee impact is often the most overlooked dimension — yet it creates some of the most persistent cascades through quality degradation and knowledge loss.
Primary Cascade: Employee → Quality (80% of cases)
Observable Signals
Don't wait for resignations. Look for early warning signals in your systems:
| Signal Type | Observable | Data Source | Detection Speed |
|---|---|---|---|
| Immediate | Overtime hours spike | Timesheets, HRIS | Daily |
| Behavioral | Meeting no-shows | Calendar analytics | Days |
| Sentiment | Engagement score drop | Pulse surveys | Weekly |
| Productivity | Output decline | Project management tools | Weeks |
| Turnover | Resignation notices | HR system | Immediate |
| Knowledge | Bus factor = 1 | HEAT heatmap | Ongoing |
| Health | Sick day increase | HR data | Weeks |
| Silent | Reduced participation | Meeting notes, Slack activity | Weeks |
Trigger Keywords
Language patterns indicate severity. Train your team to flag these:
High Urgency (Sound = 8-10)
"I'm done" "putting in notice" "can't do this anymore"
"looking for other jobs" "burned out" "hostile environment"
"discrimination" "harassment" "unsafe"Action: Executive escalation within 1 hour.
Medium Urgency (Sound = 4-7)
"overwhelmed" "not sustainable" "need help"
"understaffed" "no work-life balance" "thankless"
"underappreciated" "dead end" "no growth"Action: Manager review within 24 hours.
Low Urgency / Early Warning (Sound = 1-3)
"just curious about" "any development opportunities" "hypothetically"
"work from home policy" "how do other teams" "is it just us"Action: Track pattern over time.
Metrics
Track both leading (predictive) and lagging (historical) indicators:
| Metric Type | Metric Name | Calculation | Target | Alert Threshold |
|---|---|---|---|---|
| Leading | Overtime hours | Hours > 40/week | <10% of team | >20% |
| Leading | Engagement pulse | Survey score (1-10) | >7.5 | <6.5 |
| Leading | Meeting attendance | Attended / Invited | >90% | <80% |
| Leading | PTO utilization | Days taken / Days available | 70-100% | <50% |
| Lagging | Voluntary turnover | Resignations / Headcount | <15% annually | >20% |
| Lagging | Time-to-fill | Days from req to hire | <45 days | >60 days |
| Lagging | Knowledge concentration | % of critical tasks with single owner | <20% | >40% |
Example Dashboard Query
-- Overtime hours alert
SELECT
employee_id,
department,
SUM(hours) - (COUNT(DISTINCT week) * 40) as overtime_hours,
COUNT(DISTINCT week) as weeks_count
FROM timesheets
WHERE date >= CURRENT_DATE - INTERVAL '12 weeks'
GROUP BY employee_id, department
HAVING SUM(hours) - (COUNT(DISTINCT week) * 40) > (COUNT(DISTINCT week) * 4) -- Alert at 10% overtime
ORDER BY overtime_hours DESCCascade Pathways
Employee impact multiplies rapidly across other dimensions:
Cascade Probabilities
| Cascade Path | Probability | Severity if Occurs |
|---|---|---|
| Employee → Quality | 80% | High |
| Employee → Operational | 70% | High |
| Employee → Revenue | 50% | Medium (via turnover costs) |
Why Quality Cascade is Most Common:
- Burned-out employees make more mistakes (attention deficit)
- They take shortcuts to manage workload (quality compromise)
- They stop caring about excellence (engagement collapse)
- They leave, taking institutional knowledge (quality inconsistency)
Multiplier Factors
Not all employee issues cascade equally. The multiplier depends on:
| Factor | Low (1.5×) | Medium (3×) | High (6×+) |
|---|---|---|---|
| Role Criticality | Easily backfilled | Specialized | Irreplaceable expertise |
| Knowledge Concentration | Well-documented | Partially documented | Tribal knowledge only |
| Team Size | Large team (redundancy) | Medium team | Small/Solo |
| Replacement Time | <30 days | 30-90 days | >90 days |
| Training Investment | Entry level | Mid-level | Senior/Specialized |
Example Calculation
Scenario: Senior engineer with 5 years at company, owns critical payment system, 90+ days to replace
Multiplier factors:
- Role criticality: High (6×)
- Knowledge concentration: High (6×)
- Team size: Small (6×)
- Replacement time: High (6×)
- Training investment: High (6×)
Average multiplier: (6 + 6 + 6 + 6 + 6) ÷ 5 = 6×Impact:
- Direct cost of resignation: $150K (recruiting, onboarding, ramp time)
- Multiplied impact: $150K × 6 = $900K
- Plus quality cascade risk: 80% probability of defects = $200K × 0.8 = $160K
- Total risk: $1.06M from a single resignation
3D Scoring (Sound × Space × Time)
Apply the Cormorant Foraging lens to employee dimension:
| Lens | Score 1-3 | Score 4-6 | Score 7-10 |
|---|---|---|---|
| Sound (Urgency) | Grumbling | Active complaints | Resignation |
| Space (Scope) | One person | One team | Cross-functional |
| Time (Trajectory) | Temporary stress | Sustained pressure | Chronic condition |
Formula: Dimension Score = (Sound × Space × Time) ÷ 10
Example Scoring
Scenario: Entire engineering team complaining about unsustainable pace, pattern continuing for 3 months, first resignations submitted
Sound = 8 (resignations submitted)
Space = 7 (entire team affected)
Time = 7 (chronic, 3+ months)
Employee Impact Score = (8 × 7 × 7) ÷ 10 = 39.2Interpretation: Critical urgency (39.2 > 30). Expect immediate cascade to Quality and Operational dimensions. Revenue impact imminent.
Detection Strategy
Automated Monitoring
Set up alerts for:
- Overtime spike (>20% of team working >10% overtime)
- Engagement drop (pulse survey score <6.5)
- Attendance decline (meeting attendance <80%)
- PTO underutilization (<50% of available days used)
Human Intelligence
Train your managers to:
- Flag language patterns (use trigger keyword lists)
- Monitor behavioral changes (withdrawal, cynicism, reduced participation)
- Track knowledge concentration (who owns what, bus factor analysis)
- Identify burnout signals (overtime patterns, weekend work, vacation avoidance)
Real-World Example
The "Inventory System is Broken" Signal:
| Observable | Data Point | 3D Score |
|---|---|---|
| Signal | "Inventory system is broken" from senior technician in team meeting | Sound = 7 |
| Context | FAA-certified mechanic, specialized role, parts delays affecting all bays | Space = 6 |
| Trend | Complaints escalating over 6 months | Time = 6.3 |
| Score | (7 × 6 × 6.3) ÷ 10 = 26.5 | High urgency |
Cascade Prediction:
- 80% probability → Quality impact (rushed repairs, maintenance delays)
- 75% probability → Operational impact (knowledge loss if technician leaves)
- Multiplier: 4.2× (specialized aviation mechanics, FAA certifications, aircraft-specific expertise)
Action Taken:
- Inventory system upgrade prioritized (within 2 weeks)
- Technician retention bonuses implemented (within 1 week)
- Cross-training program to reduce frustration from parts delays (within 1 month)
- Result: Turnover prevented, morale improved from 6.2 to 7.5, parts availability increased 40%
Industry Variations
B2B SaaS
- Primary metric: Employee NPS (eNPS)
- Key signal: Engineering velocity decline (story points/sprint)
- Cascade risk: Employee → Quality → Customer
Healthcare
- Primary metric: Nurse-to-patient ratio, shift overtime
- Key signal: Medical error rates, near-miss incidents
- Cascade risk: Employee → Quality → Regulatory → Customer (Patient)
Manufacturing
- Primary metric: Safety incident rate, absenteeism
- Key signal: Production defect rate, downtime events
- Cascade risk: Employee → Quality → Operational → Revenue
Next Steps
Remember: The employee who complains is asking for help. The employee who goes silent has already mentally quit. Track both. 🪶