The project pipeline meeting reveals the uncomfortable truth.
You have 6 major projects planned for Q3. Your project managers estimate resource requirements: 2,800 hours of engineering capacity, 1,600 hours of consulting delivery, 900 hours of project management.
Your finance director does the maths: "We have 2,200 engineering hours available, 1,400 consulting hours, and 800 PM hours. We're short 600 engineering hours, 200 consulting hours, and 100 PM hours."
The subsequent debate is familiar:
Sales: "We sold these projects. We can't tell clients 'sorry, we're short on capacity.'"
Delivery: "We're already working 50-hour weeks. We can't magic capacity from nowhere."
Finance: "Hiring 3 additional engineers takes 3 months and costs £180,000. By then, Q3 is over."
Welcome to the capacity planning crisis: the multi-million pound problem most mid-market firms solve with spreadsheets, gut instinct, and heroic overtime.
The Capacity Planning Failure Modes
Failure Mode #1: The Feast-or-Famine Cycle
Months 1-3: Pipeline is thin. Revenue below target. Pressure to win deals intensifies.
Month 4: Sales wins 3 major projects simultaneously (total value: £840,000). Celebration ensues.
Month 5: Delivery realises they can't execute all 3 projects with current capacity. Panic. Emergency hiring initiated. Contractors engaged at premium rates.
Months 6-8: Projects delivered late, over budget, with quality issues. Team burned out. 2 key people resign.
Months 9-10: Project pipeline dries up (sales has been too busy firefighting to sell). Team now over-resourced. Contractors released. Fixed costs unsustainable.
Month 11: Pressure to win deals intensifies...
This cycle destroys:- Profitability (premium contractor rates, inefficiency from understaffing)- Quality (rushed delivery, burned-out teams)- Culture (chronic stress, good people leave)- Customer satisfaction (missed deadlines, poor experiences)
One £42M professional services firm tracked this cycle over 3 years:- Average EBITDA margin: 8.2% (industry benchmark: 12-15%)- Annual employee turnover: 28% (industry benchmark: 18%)- Customer NPS: 22 (industry benchmark: 40+)- Root cause analysis: Capacity planning dysfunction
Failure Mode #2: The Always-On Overtime Assumption
Finance models assume 40-hour weeks (1,920 billable hours per FTE annually, accounting for holidays and admin).
Reality: Projects are planned assuming 45-50 hour weeks. The "extra" 5-10 hours is treated as free capacity.
The Mathematics of Burnout:
A 10-person engineering team working 45-hour weeks instead of 40:- Additional capacity: 2,600 hours annually (equivalent to 1.35 FTEs)- "Savings" from not hiring: £67,500 annually
Costs:- Increased attrition: 2 additional departures per year- Replacement costs: £78,000 (recruiting + ramp)- Productivity loss during burnout: ~£40,000 (reduced quality, increased rework)-Total hidden cost: £118,000
You "saved" £67,500 and spent £118,000. Net loss: £50,500.
Plus unmeasurable costs: degraded culture, reduced innovation (exhausted people don't innovate), customer impact.
Failure Mode #3: The Utilisation Trap
Most mid-market firms set utilisation targets: "Consultants should be 75% billable."
This sounds reasonable. In practice, it creates perverse incentives:
Scenario: Consultant finishes project 1 week early through exceptional efficiency.
Outcome Under Utilisation Model:- Immediately assigned to new project (can't have idle capacity!)- No time for: learning new skills, business development, process improvement, recovery- Punishment for efficiency: More work
Result: Consultants learn to never finish early. Work expands to fill available time. Real productivity drops.
The Alternative Insight:
Target effective utilisation (value delivered per hour), not time utilisation (hours worked).
A consultant billing 60 hours/month at 90% effectiveness delivers more value than one billing 70 hours/month at 65% effectiveness.
Failure Mode #4: The Resource Fungibility Delusion
"We're short on Java developers. Let's move Sarah from Python team to cover."
Treating people as interchangeable resources ignores:- Skill specialisation (Sarah's Python expertise doesn't transfer to Java)- Context switching costs (losing Python productivity, slow Java ramp)- Team dynamics (removing Sarah disrupts Python team)- Employee development (forcing skills they don't want to develop)
One £55M software firm tracked this practice:- Avg. productivity loss when shifting resources across tech stacks: 35% for first month, 20% for second month- Voluntary attrition among frequently-shifted employees: 42% annually vs. 19% overall
Failure Mode #5: The Demand Forecasting Fantasy
Most capacity planning starts with sales pipeline: "We have £2.4M in pipeline, 60% close probability, means we need capacity for £1.44M in revenue."
Problems:
Problem 1: Pipeline accuracy is terrible- Optimistic close probabilities (sales says 70%, reality is 45%)- Inconsistent deal staging (different sellers define "qualified" differently)- Timing unreliability (Q2 deal slips to Q3, Q4, or never)
Problem 2: Won deals don't translate linearly to capacity needs- £240,000 project might require 4 specialists full-time for 3 months- Hiring those specialists for 3-month project doesn't work- Using generalists reduces quality and efficiency
Problem 3: Forecast doesn't account for unplanned work- Support escalations- Rework from quality issues- Internal projects- Employee time off, training, admin
The Reality:
For most mid-market firms, demand forecasting is 40-60% accurate 3 months out, 20-30% accurate 6 months out.
Building capacity plans on these forecasts is building on sand.
The Strategic Capacity Planning Framework
Foundation Principle:
Capacity planning is not a forecasting exercise. It's a risk management and optionality exercise.
Goal: Build resilient capacity that:1. Handles baseline demand efficiently2. Flexes to accommodate variability3. Doesn't bankrupt you during slow periods4. Enables growth when opportunities arise
Phase 1: Demand Pattern Analysis (The Historical Truth)
Before forecasting future demand, understand historical patterns.
The 12-Month Capacity Audit:
For each month over past 12-24 months, capture:- Revenue delivered- Hours worked (by role/skillset)- Headcount- Utilisation rates- Overtime hours- Contractor usage- Projects delayed due to capacity- Unplanned work (support, rework, internal)
Pattern Recognition:
Seasonality:Do certain months consistently show higher/lower demand?- Professional services: Often slow in August, December- Retail tech: Peak before Christmas, Q1 planning season- Manufacturing: Industry-specific cycles
Growth Trend:Abstracting seasonality, what's underlying growth rate?- Flat: Demand stable year-over-year- Linear growth: Adding X% capacity each period- Exponential: Accelerating growth (high-growth scale-ups)
Variability:How much does demand fluctuate month-to-month?- Low variability (±10%): Predictable, can plan tightly- Moderate variability (±25%): Needs flex capacity- High variability (±50%+): Needs substantial flex mechanisms
Example: £45M Professional Services Firm
Historical Analysis:- Average monthly delivery: 3,200 billable hours- Standard deviation: 720 hours (22.5% variability)- Seasonal pattern: -15% in August/December, +12% in March/June- Growth trend: +8% year-over-year
Implications for Capacity Planning:- Build core capacity for 3,200 hours baseline- Flex capacity for ±720 hours (would need additional 9 FTEs at peak vs. trough if fixed team)- Plan for seasonal dips (don't panic-hire in July before August slowdown)- Add ~8% capacity annually to support growth
Phase 2: The Three-Tier Capacity Model
Stop thinking about capacity as "headcount." Start thinking about three distinct capacity tiers.
Tier 1: Core Capacity (60-70% of average demand)
Composition: Permanent employees on full-time contracts
Characteristics:- Fixed cost (pay them whether busy or not)- Deep institutional knowledge- Culture carriers- Long-term investment- Training and development
Sizing: Size to handle baseline demand (slowest sustained period)
For example: If minimum monthly demand is 2,400 hours, core capacity = 2,400 hours = 15 FTEs
Why undersized relative to average? Because average demand includes peaks you'll handle with flex capacity.
Tier 2: Flex Capacity (20-30% of average demand)
Composition: Mix of contractors, part-time employees, agencies, outsourcing partners
Characteristics:- Variable cost (pay only when needed)- Specialist skills (bring in for specific expertise)- Scalable (can increase/decrease with 2-4 week notice)- More expensive per hour than core capacity- Less institutional knowledge
Sizing: Sized to handle normal variability
Example: Average demand 3,200 hours, core capacity 2,400 hours, flex capacity 800 hours
Economics:- Contractor rates: 1.4-1.8x employee cost- Trade-off: Pay premium during busy periods vs. paying fixed costs during slow periods
Tier 3: Surge Capacity (10-20% for true peaks)
Composition: Emergency options for exceptional demand
Options:- Premium contractors (immediate availability, 2-2.5x employee cost)- Overtime from core team (use sparingly to avoid burnout)- Partnerships (co-deliver with partner firms)- Delay/defer work (negotiate timeline extensions)
Characteristics:- Expensive- Lower quality/efficiency- Not sustainable- Emergency valve only
The Capacity Flex Model in Practice:
Slow month (2,400 hours demand):- Core capacity: 2,400 hours (15 FTEs) → 100% utilised- Flex capacity: 0 hours → £0 cost- Surge capacity: 0 hours → £0 cost- Total cost: £180,000 (15 FTEs × £12K/month)
Average month (3,200 hours demand):- Core capacity: 2,400 hours → 100% utilised- Flex capacity: 800 hours (contractors) → £56,000 cost (£70/hour × 800)- Surge capacity: 0- Total cost: £236,000
Peak month (4,000 hours demand):- Core capacity: 2,400 hours → 100% utilised- Flex capacity: 1,200 hours → £84,000- Surge capacity: 400 hours (premium contractors + OT) → £40,000- Total cost: £304,000
Alternative: Pure Fixed Model (20 FTEs to handle peak)
Slow month:- 20 FTEs × £12K = £240,000- Utilisation: 60% (2,400 / 4,000 capacity)
Average month:- Cost: £240,000- Utilisation: 80%
Peak month:- Cost: £240,000- Utilisation: 100%
The Economics:
Flex model:- Slow month: £180K- Average month: £236K- Peak month: £304K-Annual cost (assuming 3 slow, 6 average, 3 peak): £2.82M
Fixed model:- Every month: £240K-Annual cost: £2.88M
Similar annual cost. But flex model:- Reduces risk (not locked into fixed costs if demand drops)- Better utilises core team (less idle time)- Provides optionality (can scale up/down)
Phase 3: The Skills-Based Capacity Planning
Aggregate capacity planning ("we need 20 people") fails because work isn't fungible.
The Skills Matrix:
Dimension 1: Technical Skills
For engineering team example:- Frontend (React, Vue, Angular)- Backend (Python, Java, Node.js)- Database (SQL, NoSQL, data modelling)- DevOps (AWS, Docker, Kubernetes)- Mobile (iOS, Android, React Native)
Dimension 2: Experience Level- Junior (0-2 years): Execute defined tasks- Mid (3-5 years): Work independently, solve moderate complexity- Senior (6-10 years): Lead projects, mentor, architectural decisions- Principal (10+ years): Strategic technical direction
Dimension 3: Domain Knowledge- Industry expertise (fintech, healthcare, retail, etc.)- Specific customer knowledge- Product/platform knowledge
The Capacity Planning By Skill:
Rather than: "We need 8 more engineers"
Instead: "We need 2 senior backend Python engineers with AWS experience, 3 mid-level frontend React developers, 1 principal engineer for architecture, 2 junior generalists"
This precision enables:- Targeted hiring (recruiting specific skills, not generic "developers")- Skills development (identify gaps, train current team)- Project staffing (match skills to project needs)- Contractor engagement (bring in specialists vs. generalists)
The Skills Capacity Matrix:
Build spreadsheet tracking:- Each employee/contractor- Their skills (rated 1-5 proficiency)- Availability (hours/month)- Current allocation (which projects)- Forecast availability (3-6 months out)
Example Row:
The Staffing Decision:
New project needs: Senior backend + DevOps (240 hours over 3 months)
Options:- Sarah available Nov 15 (fits backend, moderate DevOps)- Could supplement Sarah with DevOps contractor- Or wait for Sarah, start project Nov 15 vs. Oct 1
This granular visibility enables intelligent trade-offs vs. "we're short on engineers."
Phase 4: The Demand Shaping Strategy
Most firms treat demand as exogenous (it happens to them). Strategic firms shape demand.
Demand Shaping Lever #1: Pipeline Pacing
Problem: Sales wins 3 major projects in March, then nothing until August.
Solution: Sales incentives and pipeline management that smooth demand.
Tactics:- Commission accelerators for deals closing in under-utilised periods- Pipeline staging that spreads opportunities across quarters- Proactive timing negotiations with customers ("Would Q3 start work for you?")
Demand Shaping Lever #2: Project Scope Flex
Problem: Fixed-scope projects create binary capacity demands (need 4 people for 3 months or can't do project).
Solution: Modular project scoping with phased delivery.
Example:
Original: £240,000 project, 6-month timeline, requires 4 specialists full-time
Restructured:- Phase 1: Core functionality (£120,000, 3 months, 2 specialists)- Phase 2: Advanced features (£80,000, 2 months, 3 specialists)- Phase 3: Optimisation (£40,000, 1 month, 2 specialists)
Now you can:- Start Phase 1 when you have 2 specialists available- Phase 2 when you have broader capacity- Phase 3 fits into smaller capacity windows
This transforms rigid capacity demand into flexible, schedulable work.
Demand Shaping Lever #3: Pricing for Capacity
Problem: Every project priced identically regardless of capacity implications.
Solution: Dynamic pricing based on capacity availability and timing.
Pricing Model:
Standard pricing: Projects starting 8+ weeks out with normal resource requirements
Premium pricing (+15-20%): Projects requiring:- Immediate start (< 2 weeks notice)- Scarce specialists- Peak period delivery (when capacity constrained)
Discounted pricing (-10-15%): Projects with:- Flexible timing (can schedule during low-demand periods)- Skills matching available capacity- Off-peak delivery
This incentivises customers to accept timing that optimises your capacity whilst rewarding you for accepting inconvenient timing.
Phase 5: The Make/Buy/Partner Decision Framework
When you identify capacity gap, three options:
Option 1: Make (Hire)
Best When:- Sustained demand (not temporary spike)- Core competency (strategic capability you need to own)- Cultural integration important- Knowledge retention critical
Economics:- Fixed cost- 3-6 month acquisition time- Long-term commitment
Option 2: Buy (Contractors)
Best When:- Variable demand (project-based spikes)- Specialist skills needed occasionally- Speed critical (contractors available faster than hiring)- Testing new capabilities before building internal
Economics:- Variable cost- 2-4 week acquisition time- Flexible commitment (can release when no longer needed)- 1.4-1.8x employee cost per hour
Option 3: Partner (Outsource/Co-deliver)
Best When:- Non-core work- Significant capacity requirement but uncertain duration- Geographic/expertise expansion without building internal capability- Risk sharing valuable
Economics:- Variable cost- Partner markup (1.3-1.6x)- Revenue sharing (you might retain 30-40% margin as prime contractor)
The Decision Matrix:
Phase 6: The Capacity Planning Cadence
Capacity planning isn't an annual exercise. It's continuous.
Monthly Capacity Review (2 hours):
Inputs:- Pipeline update (what's sold, what's at risk, what's new)- Current project status (on track vs. delays)- Utilisation data (actual vs. planned)- Skills availability (who's rolling off projects soon)
Outputs:- Resource allocation next 90 days- Identified gaps requiring contractor engagement- Hiring triggers (when to open new roles)- Flagged constraints (upcoming capacity crunches)
Quarterly Strategic Planning (half day):
Inputs:- 6-month demand forecast- Growth targets- Strategic initiatives (new capabilities, market expansion)- Skills inventory and gaps
Outputs:- Hiring plan (roles to open next 6 months)- Contractor budget (expected flex capacity needs)- Skills development plan (training to address gaps)- Demand shaping initiatives (pricing changes, pipeline pacing)
Annual Capacity Strategy (full day):
Inputs:- Historical demand patterns- Growth trajectory- Strategic plan- Market trends (labour availability, rate changes)
Outputs:- Target core capacity (FTE plan by quarter)- Flex capacity model (contractor/partner strategy)- Skills roadmap (build vs. buy for each capability)- Make/buy/partner framework
The Capacity Planning Technology Stack
The Spreadsheet Trap:
Most mid-market firms manage capacity in Excel. This works until ~50 people, then breaks down:
Problems:- Version control chaos (which spreadsheet is current?)- No real-time visibility (data stale as soon as entered)- Manual updates (time-consuming, error-prone)- Limited scenario planning- Can't handle complexity (skills matrix, availability forecasting)
The Purpose-Built Tools:
Tier 1: Professional Services Automation (PSA) - For Services Firms
Market Leaders:- Kimble (£40-£80/user/month)- FinancialForce PSA (£50-£90/user/month)- Kantata (formerly Mavenlink) (£35-£70/user/month)
Capabilities:- Resource scheduling and allocation- Utilisation tracking and forecasting- Skills-based resource matching- Capacity planning and scenario modelling- Financial planning integration
Best For: Professional services, consulting, agencies (20+ people)
Tier 2: Resource Management Tools - For Product/Project Businesses
Market Leaders:- Runn (£8-£12/user/month)- Float (£10-£15/user/month)- Resource Guru (£4-£8/user/month)- Teamdeck (£4-£6/user/month)
Capabilities:- Visual resource allocation- Capacity forecasting- Simple utilisation tracking- Project timeline integration
Best For: Software companies, product teams, project-based businesses (10-100 people)
Tier 3: Enterprise Resource Planning (ERP) - For Larger/Complex Orgs
Market Leaders:- Workday Adaptive Planning (£50-£100/user/month)- Anaplan (Enterprise pricing)- Oracle/SAP (Enterprise pricing)
Capabilities:- Company-wide planning- Financial integration- Advanced forecasting- Multi-dimensional modelling
Best For: 200+ people, complex organisations, need full financial integration
The Realistic Investment:
For a 60-person professional services firm:
Option 1: Spreadsheet (Status Quo)- Cost: £0 for tools- Time cost: ~10 hours/month managing manually- Hidden cost: Poor decisions from lack of visibility
Option 2: Purpose-Built Tool (Recommended)- Tool cost: £60/user/month × 60 = £3,600/month = £43,200/year- Time savings: 6 hours/month freed up- Decision improvement: Better utilisation, fewer capacity crunches
ROI Calculation:
Costs: £43,200/year
Benefits:- Utilisation improvement (3 percentage points): ~£90,000 additional revenue- Reduced emergency contractor spend (fewer capacity surprises): ~£35,000- Time savings (6 hours/month × £75/hour): ~£5,400-Total: ~£130,000
Net benefit: £86,800 annually
The Realistic Capacity Planning Outcomes
What Good Looks Like:
Not perfection. Capacity planning will never eliminate all mismatches. Goals:
Target Metrics:
Utilisation:- Core team: 75-85% billable (rest is training, business development, admin)- Flex capacity: 60-75% billable (you're paying premium, use selectively)- Avoid: >90% billable (leads to burnout) or <65% billable (inefficient)
Demand Fulfillment:- 90%+ of sold projects staffed on planned timeline- <5% of projects delayed due to capacity constraints- Overtime maintained below 5% of total hours
Economic:- Flex capacity costs 15-25% of total labour costs- Emergency surge capacity <5% of labour costs- Revenue per FTE increases year-over-year (efficiency improvement)
Quality:- Project delivery on time/on budget >85%- Rework due to understaffing <10% of project hours- Customer NPS >40
People:- Voluntary attrition <20% annually- Burnout indicators (sick days, overtime) within healthy ranges- Employee satisfaction with workload balance >3.5/5
Case Study: £48M Professional Services Firm
Before Capacity Planning Discipline:- Utilisation: Wild swings (45%-95% by month)- Emergency contractor spend: £180,000/year- Projects delayed by capacity: 23% annually- Employee turnover: 31%- EBITDA margin: 7.8%
After Implementation (18 months):- Utilisation: Stabilised (72%-84%)- Emergency contractor spend: £45,000/year (saved £135,000)- Projects delayed: 6%- Employee turnover: 18%- EBITDA margin: 11.2% (expansion from better capacity management)
Investment:- PSA tool: £38,000/year- Monthly planning discipline: ~4 hours/month (£3,600/year equivalent)-Total cost: £41,600
Financial impact:- EBITDA improvement on £48M revenue: 3.4 percentage points = £1,632,000- Direct attribution to capacity planning: Conservatively 30% = £489,600
ROI: £489,600 benefit - £41,600 cost = £448,000 net benefit (10.8x return)
Making the Capacity Planning Commitment
The philosophical question: Do you manage capacity, or does capacity manage you?
Most mid-market firms are in reactive mode:- Demand spikes → Scramble for resources → Expensive fixes → Burnout- Demand drops → Idle capacity → Cost pressure → Layoffs → Morale damage- Repeat
This approach treats capacity as something that happens to you.
Strategic firms flip this:- Build resilient core capacity- Design flex mechanisms for variability- Shape demand where possible- Plan proactively based on data, not crisis
The transition requires:- Discipline (monthly planning, quarterly strategy)- Investment (tools, time, possibly consultants to set up framework)- Uncomfortable trade-offs (saying no to projects when capacity-constrained)- Cultural shift (from "we'll figure it out" to "we plan for it")
The costs of poor capacity planning compound:
Year 1: Reduced profitability, some employee burnoutYear 2: Talent attrition, reputation damage from delivery issuesYear 3: Revenue constraints (can't take good projects, lose good people)Year 4: Strategic disadvantage becomes insurmountable
The benefits of disciplined capacity planning compound:
Year 1: Improved utilisation, reduced emergency costsYear 2: Better talent retention, delivery reliability attracts better clientsYear 3: Profit margins enable investment in growth capabilitiesYear 4: Competitive advantage in ability to scale sustainably
Which trajectory are you on?