What is demand forecasting in clinical trials?
In clinical trials, demand forecasting is the process of projecting how much investigational product will be needed, where, and when, over the life of a study. The forecast is driven by patient enrollment projections, site activation timing, treatment arm assignments, and each arm's dosing schedule — not by sales history, which is what distinguishes it from commercial demand planning.
A clinical demand forecast starts with an enrollment model: how many patients each site or country will enroll per month. Enrolled patients flow into treatment arms according to the randomization ratio, and each arm's dosing schedule converts patients into dispensing events — kits or units of drug required per month at each location.
Because enrollment rarely goes exactly to plan, the forecast has to be a living document. Sites activate late, screen failures run higher than expected, and protocol amendments change dosing. A forecast that was accurate at study start can be badly wrong six months in, which is why mature teams re-forecast on a monthly cadence and track forecast accuracy against actual enrollment.
The cost of getting it wrong is asymmetric. Under-forecasting risks stockouts that can delay dosing or force sites to turn patients away; over-forecasting wastes expensive drug product through overproduction and expiry. Good forecasting practice is about managing that asymmetry deliberately rather than padding every number.
How TrialSupply handles this
TrialSupply generates the demand forecast from structured inputs — enrollment projections, site activation counts, treatment arms, and dosing schedules — in a spreadsheet-style interface planners already know. When an input changes, the forecast recalculates automatically, and every forecast is snapshotted so teams can compare forecast vs. actual enrollment and measure accuracy over time.