Resource Capacity Planning & Demand Forecasting

TL;DR: Analyzes resource supply against demand to identify capacity gaps, over-allocations, and optimization opportunities. Produces time-phased capacity models that forecast resource needs, enabling proactive hiring, training, or reallocation decisions before bottlenecks impact delivery.

Principio Rector

La capacidad real es siempre menor que la teórica. Un recurso al 100% de utilización no tiene capacidad para absorber variabilidad — ese es el camino al retraso sistémico. La regla: planificar al 80% de capacidad, reservar 20% para variabilidad, reuniones, y trabajo no planificado. La previsibilidad nace de la holgura deliberada. [EXPLICIT]

Assumptions & Limits

• Assumes resource availability data is current and reflects actual calendars [SUPUESTO]
• Assumes demand data comes from approved project schedules, not wishful planning [PLAN]
• Breaks when resource data is >2 weeks stale — capacity model becomes fiction
• Does not account for unplanned work unless historical unplanned-work ratio is provided
• Planning at 80% utilization is a guideline; organizational policy may override [SUPUESTO]
• Multi-project capacity requires portfolio-level demand aggregation across all active projects

Usage

# Build capacity model for a project
/pm:capacity-planning $PROJECT --type=model --horizon="6-months"

# Detect over-allocations across the portfolio
/pm:capacity-planning $PROJECT --type=over-allocation --threshold=80

# Run what-if scenario for resource changes
/pm:capacity-planning $PROJECT --type=scenario --change="add-2-devs"

Parameters:

Parameter	Required	Description
$PROJECT	Yes	Project or portfolio identifier
--type	Yes	model, over-allocation, scenario, gap-analysis
--horizon	No	Planning horizon (3-months, 6-months, 12-months)
--threshold	No	Utilization threshold for over-allocation detection (default: 80)
--change	No	Scenario description for what-if modeling

Service Type Routing

{TIPO_PROYECTO}: Agile uses velocity-based capacity; Waterfall uses hours-based resource calendars; SAFe uses PI-level capacity allocation; Portfolio uses aggregate capacity across projects.

Before Planning

1. Read the resource pool data to confirm current availability and skill assignments
2. Read the project schedule to extract demand by role and time period
3. Glob skills/capacity-planning/references/*.md for capacity planning models and benchmarks
4. Grep for historical utilization data to calibrate realistic capacity assumptions

Entrada (Input Requirements)

• Resource pool with skills and availability
• Project demand (schedule + resource plan)
• Organizational calendar and leave policies
• Other project commitments per resource
• Historical utilization data

Proceso (Protocol)

1. Supply modeling — Calculate available capacity per resource (hours/FTE per period)
2. Demand modeling — Aggregate resource demand from all project activities
3. Gap analysis — Compare supply vs. demand per skill/role per time period
4. Over-allocation detection — Flag resources allocated > 80%
5. Bottleneck identification — Identify critical skill shortages and timing
6. Scenario modeling — Model what-if scenarios (delays, scope changes, resource additions)
7. Optimization recommendations — Suggest reallocation, cross-training, or hiring
8. Hiring/training plan — If gaps require new resources, define acquisition timeline
9. Dashboard creation — Build capacity heatmap (role x time period)
10. Review cadence — Establish periodic capacity review schedule

Edge Cases

1. Single point of failure resource: Flag immediately. Design cross-training plan with ≥1 backup per critical skill. Document bus factor risk in risk register. [PLAN]
2. Resource shared across >3 projects: Cap effective allocation at 60% per project to account for context-switching overhead. Document productivity loss assumption. [INFERENCIA]
3. Demand exceeds supply with no budget for hiring: Prioritize demand using WSJF or sponsor input. Present trade-off: scope reduction, timeline extension, or quality compromise. [STAKEHOLDER]
4. Resource availability data is unreliable: Use historical average availability (typically 70-75% of theoretical) as baseline. Tag as [SUPUESTO] and recommend improving data collection. [SUPUESTO]

Example: Good vs Bad

Good Capacity Plan:

Attribute	Value
Supply model	Per-resource with skills, availability, and calendar
Demand model	Aggregated from all active project schedules
Gap analysis	Per role per month with visual heatmap
Over-allocations	Flagged with ≥80% threshold, 4 resources identified
Scenarios modeled	3 what-if scenarios with impact analysis
Recommendations	Specific: cross-train 2 devs, hire 1 QA by Month 3

Bad Capacity Plan: A spreadsheet showing "we need 10 developers" without supply analysis, no time-phasing, no skill breakdown, no over-allocation detection. Fails because it provides no actionable insight about when gaps occur, which skills are short, or what trade-offs exist. [EXPLICIT]

Validation Gate

• Supply model reflects actual resource calendars, not theoretical 100% availability
• Demand aggregated from ≥1 approved project schedule with role-level granularity
• Gap analysis produced per role AND per time period — not just aggregate totals
• Every resource >80% utilization flagged with specific over-allocation periods
• ≥2 what-if scenarios modeled with quantified impact on delivery dates
• Single-point-of-failure resources identified with cross-training recommendations
• Capacity heatmap visualization produced (role x period, color-coded by gap severity)
• Hiring or training plan includes acquisition timeline aligned with demand peaks
• Resource managers can plan proactively from the capacity model [STAKEHOLDER]
• Capacity review cadence established and aligned with methodology [PLAN]

Escalation Triggers

• Critical skill gap with no available resource within 4 weeks
• Resource utilization > 100% for extended period
• Key resource departing with no succession plan
• Capacity model shows project deadline unachievable

Salida (Deliverables)

• Primary deliverable: capacity plan with resource allocation model
• All outputs tagged with evidence markers
• Mermaid diagrams where applicable
• Executive summary for stakeholder consumption

Additional Resources

Resource	When to read	Location
Body of Knowledge	Before building model to understand capacity patterns	references/body-of-knowledge.md
State of the Art	When evaluating capacity planning tools	references/state-of-the-art.md
Knowledge Graph	To link capacity to resource plan and schedule	references/knowledge-graph.mmd
Use Case Prompts	When facilitating capacity workshops	prompts/use-case-prompts.md
Metaprompts	To generate capacity model templates	prompts/metaprompts.md
Sample Output	To calibrate expected capacity plan format	examples/sample-output.md

Output Configuration

• Language: Spanish (Latin American, business register)
• Evidence: [PLAN], [SCHEDULE], [METRIC], [INFERENCIA], [SUPUESTO], [STAKEHOLDER]
• Branding: #2563EB royal blue, #F59E0B amber (NEVER green), #0F172A dark