Capacity planning

Capacity planning balances available capacity (FTEs by role and seniority) against forecast demand over a 3-12 month horizon, combining signed projects, weighted pipeline and expected renewals.

The output is a plan flagging gaps (e.g.: 3 senior data engineers short from September) or excesses (5 juniors expected on bench in August), driving hiring plans, reskilling or commercial reshuffles. A modern PSA enables what-if scenarios: what happens if we close deal X? What if we lose client Y? Capacity planning is essential to avoid both burnout from crunch and the cost of prolonged bench.

Plans fail less often because the data is wrong than because they claim a certainty nobody has. A project that may start in October or in January is not planned by picking one date: it is planned by showing both and seeing which people are contested in each case, then acting on the decisions that hold either way — which is usually where the hiring or subcontracting call actually lives.

The hardest constraint to model, and the one most worth modelling, is concentration. When three probable projects all need the same scarce specialist, the plan looks balanced in aggregate and is impossible in detail. Flagging the individuals on whom multiple engagements depend turns an invisible risk into a decision: hire, subcontract, train a second person, or negotiate a different start date while there is still time.