Forecast Capacity for New Projects Using Your Pipeline

Most A&E firms forecast capacity by feel. The principal looks at the calendar, looks at the team, and decides whether the next pursuit is a yes or a no. That works at five people. It breaks at fifteen.

The fix is not a complicated software stack. It is a simple capacity forecast model that reads from the pipeline you already track. This guide shows the math, the data inputs, and the cadence that turns guesswork into a real planning tool.

The capacity forecast in one formula

The headline question is simple. Across the next 90 days, do we have enough hours in the team to deliver the projects in the pipeline if they all win.

The formula is also simple.

Capacity gap = (Pipeline hours demanded × stage probability) − (Team hours available × target utilization)

Negative number means you have spare capacity. Positive means you are about to overcommit. Both numbers are decisions, not just data points.

The five inputs that drive the model

1. Pipeline hours demanded

For each lead in the pipeline, you need an estimated hours figure. Most firms have this from their fee build up. If you do not, a rough hours per fee dollar ratio works. Most A&E firms run between 4 and 7 dollars of fee per direct hour.

2. Stage probability

The historical win probability at each pipeline stage. Use real data if you have a year of history. If not, start with these defaults: Inquiry 5 percent, Qualified 20 percent, Proposal Sent 40 percent, Shortlist 65 percent, Won 100 percent.

3. Expected start date

When the work would actually begin if the lead is won. This is rarely the contract date. It is usually two to four weeks after.

4. Team hours available

Total billable hours your team can deliver in the forecast window. Pull this from your staffing plan. Account for vacation, holidays, and any planned non billable time.

5. Target utilization

The utilization rate you are willing to run at to absorb the work. Most A&E firms target 65 to 70 percent. Above 80 percent and you are setting the team up for burnout.

A worked example

A 12 person firm running this calculation for the next 90 days.

• Pipeline hours demanded: 4,200 (across 11 leads in various stages).
• Weighted by stage probability: 1,820 hours.
• Team hours available in 90 days: 12 people × 480 working hours = 5,760 hours.
• Target utilization 68 percent: 5,760 × 0.68 = 3,917 billable hours available.
• Hours already committed to active projects: 2,300.
• Free capacity: 3,917 − 2,300 = 1,617 billable hours.
• Capacity gap: 1,820 weighted demand − 1,617 free = +203 hours short.

Translation: if the pipeline lands as forecast, the firm is roughly 200 hours short over 90 days. Not catastrophic, but enough that the principal should know now, not after the wins land.

The four decisions the forecast unlocks

The forecast is only useful if it drives action. Four decisions cluster around the capacity gap number.

1. Hire or contract. A 200 hour gap is roughly one full time hire for a quarter, or contract help.
2. Slow the pipeline. Defer pursuing new leads until the existing ones resolve.
3. Re sequence project starts. Negotiate later kickoffs on lower priority pursuits.
4. Push utilization temporarily. Acceptable for short stretches. Dangerous as a default.

The cadence that keeps the model alive

A forecast updated quarterly is theater. A forecast updated weekly is a planning tool.

Build the calculation into your existing weekly pipeline meeting. The data inputs are already there: stage moves, fee estimates, expected start dates. Adding the capacity calculation takes 10 minutes a week and changes the tone of every staffing conversation.

The accuracy improvement loop

The first version of the forecast will be wrong. That is fine. Track forecast accuracy quarterly.

• What did we forecast 90 days ago.
• What actually happened.
• Were the stage probabilities accurate.
• Were the hours estimates per project accurate.
• Did expected start dates land where we predicted.

After two quarters, your stage probabilities reflect your actual win rates and your hours estimates reflect your real productivity. The forecast becomes meaningfully accurate.

Where the model does not work

Capacity forecasting from pipeline assumes a few things that do not always hold.

• It assumes hours are fungible. A senior structural engineer is not interchangeable with a junior architect. Use role specific forecasts when the gap matters.
• It assumes the pipeline is real. Garbage in, garbage out. If your pipeline is full of stale leads, the forecast is fiction.
• It assumes start dates are accurate. Clients delay. Build a 30 day buffer into start date assumptions.

Pair it with revenue forecasting

Capacity is one half of pipeline planning. Revenue is the other. Revenue forecasting from pipeline answers whether the same wins will support payroll. Together, the two forecasts are the operating dashboard for an A&E firm above 10 people.

What changes when this is real

Firms that run this model weekly stop being surprised. Hiring decisions get made before crunches. Pursuit decisions reflect real capacity, not optimism. Burnout falls because the firm chooses fewer fights and wins more of them.

The forecast is not magic. It is arithmetic, applied weekly to data you already have. Most firms simply have not done it.