Analyze team capacity against upcoming demand
advancedClaude SonnetOperationsOps Managementcapacity-planningworkforce-planningresource-managementoperationsplanning
Use case
Use this prompt at the start of a planning cycle, before committing to a roadmap, or when a team leader raises concerns about bandwidth. Provide the team's current capacity and upcoming demand, and get a structured analysis with options for closing gaps.
The prompt
You are a senior operations and workforce planning expert. Conduct a capacity planning analysis for the team described below and produce a clear, actionable recommendation. **Team:**{{team_name}}**Planning horizon:**{{planning_horizon}}(e.g., "Q4 2024", "next 6 months", "calendar year 2025") **Team composition:**{{team_composition}}(headcount by role, including any contractors or part-time staff) **Current team utilization:**{{current_utilization}}(approximate % of time currently committed to active work) **Planned projects and initiatives:**{{planned_work}}(with estimated effort per project) **Recurring operational work (BAU):**{{bau_work}}(approximate % of team time) **Known capacity constraints:**{{constraints}}(planned leaves, hiring gaps, tool limitations, etc.) **Business context:**{{business_context}}(growth rate, strategic priorities, budget situation) Conduct a structured capacity analysis with these sections: ## 1. Current State Capacity Summary Calculate total available capacity in the planning horizon: - Raw available hours or story points (team size × working days × utilization ceiling) - Deduct: BAU/operational work - Deduct: meetings, overhead (standard assumption: 20% unless otherwise specified) - **Net available capacity for project work** Present this as a simple table showing the math clearly. ## 2. Demand Analysis List all planned work and their estimated capacity requirements: | Project/Initiative | Priority | Effort Estimate | Duration | Owner Role | Confidence in Estimate | Calculate total demand. Flag estimates where confidence is low. ## 3. Gap Analysis - Total available capacity vs. total demand - Surplus (if demand < capacity) or deficit (if demand > capacity) - **Capacity utilization rate** (demand / available capacity × 100%) - Risk assessment: what happens if estimates are 20% too optimistic? ## 4. Bottleneck Identification Are there specific roles, skills, or people who are disproportionately overloaded? A team-level surplus can mask role-level constraints. - Identify any individual or role that is >100% utilized - Identify any single points of failure (only one person can do a critical task) - Identify any skill gaps (work is planned but the team lacks the expertise) ## 5. Scenario Analysis Model three scenarios: - **Base case:** Current plan as-is - **Risk case:** All estimates 20% over-run; one unexpected project added - **Optimistic case:** 10% productivity improvement from tooling or process changes ## 6. Options to Close the Gap (or Right-Size a Surplus) For each option, provide: description, capacity impact, cost/effort, timeline to impact, and trade-offs. Consider: hiring, contractors, deprioritization, scope reduction, timeline extension, automation, reallocation from other teams. ## 7. Recommendation State a clear recommendation: - What is the right capacity strategy for this planning horizon? - Which projects should be committed, deferred, or descoped? - What decisions need to be made, and by whom, and by when? ## 8. Assumptions and Caveats List the key assumptions underlying this analysis. Note which ones, if wrong, would materially change the recommendation.
Variables
{{{{team_name}}}}Replace with your {{team name}}{{{{planning_horizon}}}}Replace with your {{planning horizon}}{{{{team_composition}}}}Replace with your {{team composition}}{{{{current_utilization}}}}Replace with your {{current utilization}}{{{{planned_work}}}}Replace with your {{planned work}}{{{{bau_work}}}}Replace with your {{bau work}}{{{{constraints}}}}Replace with your {{constraints}}{{{{business_context}}}}Replace with your {{business context}}
Example
Input
team_name: Data Engineering planning_horizon: Q1 2025 (13 weeks) team_composition: 4 senior data engineers, 1 data engineering manager (50% hands-on), 1 contractor (ends Dec 31) current_utilization: 85% committed to active projects; contractor is 100% planned_work: | - CDP implementation (cross-functional, required): 3 engineers × 6 weeks - Analytics dashboard rebuild (high priority): 1.5 engineers × 8 weeks - Data quality framework (medium priority): 1 engineer × 4 weeks - Security audit remediation (compliance, required): 0.5 engineer × 3 weeks - 2 ad-hoc stakeholder requests (low estimate): 2 engineer-weeks total bau_work: 25% (pipeline monitoring, incident response, stakeholder data pulls) constraints: Contractor leaving Dec 31. One senior engineer on parental leave Feb 1–March 31. No approved headcount for Q1 hiring. business_context: Company growing 40% YoY. Data team is a bottleneck cited in 3 recent executive conversations. Budget is tight — no new headcount approved but contractor extensions are possible.
Output
## 1. Current State Capacity Summary | Component | Calculation | Result | |-----------|-------------|--------| | Raw headcount | 4 senior + 0.5 manager = 4.5 FTEs in Q1 (contractor ends Dec 31) | 4.5 FTEs | | Parental leave deduction | 1 senior engineer out Feb 1–Mar 31 = 0.5 FTE equivalent for 13 weeks | -0.5 FTE | | Effective headcount | | 4.0 FTEs | | Available engineer-weeks (13 weeks) | 4.0 × 13 | 52 engineer-weeks | | BAU deduction (25%) | 52 × 0.25 | -13 engineer-weeks | | Overhead/meetings (20%) | 52 × 0.20 | -10 engineer-weeks | | **Net available for project work** | | **29 engineer-weeks** | ## 2. Demand Analysis | Project | Priority | Effort | Confidence | |---------|----------|--------|------------| | CDP implementation | Required | 18 engineer-weeks | Medium | | Analytics dashboard | High | 12 engineer-weeks | Low | | Data quality framework | Medium | 4 engineer-weeks | High | | Security audit | Required | 1.5 engineer-weeks | High | | Ad-hoc requests | Low | 2 engineer-weeks | Low | | **Total demand** | | **37.5 engineer-weeks** | | ## 3. Gap Analysis - **Available:** 29 engineer-weeks - **Demanded:** 37.5 engineer-weeks - **Deficit:** 8.5 engineer-weeks (29% over capacity) - Utilization rate: 129% — unsustainable - **Risk case** (20% overruns): deficit grows to 16 engineer-weeks — 55% over capacity ## 6. Options to Close the Gap | Option | Capacity Gain | Cost | Timeline | Trade-off | |--------|--------------|------|----------|-----------| | Extend contractor 1 month | +8 engineer-weeks | ~$24K | Immediate | Solves most of the gap if approved | | Defer analytics dashboard to Q2 | +12 engineer-weeks | $0 | Immediate | Disappoints stakeholders; delays planned metrics | | Reduce CDP commitment to 2 engineers | +6 engineer-weeks | $0 | Immediate | Extends CDP timeline by 3 weeks | ## 7. Recommendation Extend the contractor through March 31 (cost: ~$24K) AND defer the analytics dashboard to Q2. This combination brings utilization to ~95% — manageable, with buffer for the inevitable unplanned work. The dashboard deferral requires stakeholder alignment; the COO should communicate this proactively. A Q1 headcount request should be prepared for Q2 planning regardless of the contractor decision.
Tips for best results
- 1Always include a 'risk case' scenario — plans built on best-case estimates consistently disappoint. Adding 20% to all estimates reveals the true capacity picture.
- 2Role-level bottlenecks hide in team-level analyses. A 75% utilized team can have one person at 150% — always check by role.
- 3Include BAU work explicitly — teams habitually undercount the operational load and then wonder why projects slip.
- 4The recommendation must include which projects get cut or deferred — a capacity analysis that doesn't result in a prioritization decision isn't finished.
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