From Static to Strategic: How Parameterized Forecasts Improve Decision-Making

Forecasting sales in drug development is challenging, especially when considering uncertainties in project timelines, costs, and commercial potential. Here's a structured way to model these factors, starting with a standard project template that makes it easy to set up new projects.

In the table on the left (see image), key inputs include development timelines, PTRS, costs, ramp-up time, peak sales, and time to peak sales. These parameters drive the sales forecast seen in the upper-right chart. Instead of using a fixed time series, we use a parameterized forecast, which means the sales curve adjusts dynamically when launch timing changes. This approach keeps forecasts aligned with evolving project realities.

The lower-right chart adds another layer, showing how uncertainties in timelines and commercial performance translate into variability in expected sales. Understanding this range is crucial, as individual project risks ultimately shape portfolio-level outcomes.

By incorporating uncertainty into forecasting, we gain a more realistic view of potential outcomes—supporting better decision-making at both the project and portfolio levels.

How do you approach forecasting in an uncertain environment?