Glossary
Resource forecasting
Resource forecasting is projecting the people, skills, and hours a team will need over the coming weeks or months, based on work that is likely but not yet signed. It differs from capacity planning, which checks whether the team can deliver what is already committed right now. Forecasting looks further out and works with less certain inputs: pipeline deals, renewal patterns, and planned initiatives instead of confirmed statements of work.
How to forecast resource needs
Compare two numbers for each person, role, or skill: hours already committed, and hours available. Committed hours come from confirmed allocations plus a weighted share of pipeline deals likely to close. Available hours come from contracted hours minus approved leave and any standing non-billable commitments.
Weighting the pipeline is what keeps the forecast honest. A deal at 30% likelihood contributes 30% of its hours, not the full amount and not zero. Treating every pipeline deal as certain overstates demand; counting only signed work understates it.
Forecasted Demand = Confirmed Hours + (Pipeline Hours × Close Probability)
Example: a deal needing 40 hours of a given skill, sitting at 50% likelihood to close, adds 20 hours to that skill's forecasted demand.
Resource forecasting vs capacity planning
Capacity planning answers whether the team can deliver what is already committed, over the next few weeks. Resource forecasting answers whether the team will have the right people and skills for what is coming, over the next quarter or two. Capacity planning catches an overload this month; forecasting catches a gap before it becomes one.
Agencies that only run one tend to assume they have covered the other. A team can be perfectly balanced on confirmed work this month and badly short next quarter, because nobody checked the pipeline against available skills.
Reading the forecast by skill, not headcount
A firm can look fully staffed in aggregate while one discipline is heavily over-committed and another sits idle. Breaking the comparison down by skill or role surfaces that gap; an aggregate headcount number hides it.
The earliest useful signal is a skill where committed hours climb across two consecutive monthly reviews, even while the team-wide number still looks fine. That gives enough lead time to hire, redeploy, or raise the gap in a pipeline conversation before it turns into a scheduling emergency.
Example
A 30-person consulting firm reviews its pipeline for backend engineering work. Three deals need that skill: one confirmed (160 hours), one at 70% likelihood (120 hours), and one at 20% likelihood (200 hours). Forecasted demand is 160 + (120 × 0.7) + (200 × 0.2) = 284 hours. Against two backend engineers with 320 available hours over the same period, the firm has about 36 hours of spare capacity, not the 160 hours a confirmed-only view would have suggested.
