Sales Forecast - Static vs Dynamic

In most pharma or biotech organizations, sales forecasts are created in Excel. These spreadsheets assume a certain launch date, drug efficacy, and competitive landscape and output a time series of sales figures that are then imported into the portfolio management system.

Salesmodels.pdf

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That setup has a few downsides:

⏳ It's time-consuming to maintain.

🔁 It requires manual data transfers between systems.

🧱 And most importantly—it’s static.

If the project gets delayed, or if assumptions change, we often need to go back to commercial to request a new forecast. It slows down decision-making and limits what we can simulate.

In Captario SUM, we’ve taken a different approach. Instead of importing a static forecast, we can build the sales forecast directly into the project model. That means the sales curve automatically adapts if the launch date changes. It also means we can run a much wider range of scenarios—without waiting for a new spreadsheet.

As we help companies implement Portfolio Management capabilities, one of the key considerations is this:

👉 Do we want to model sales as static data or dynamic logic?

Time-series models are aligned with your current budgeting processes and are quick to implement. But they’re rigid and don’t allow much flexibility for scenario planning.

Dynamic models, on the other hand, are built around a few core assumptions—time to peak, peak sales, LOE, residual sales. They are easy to update and great for scenario analysis. And more advanced dynamic models can incorporate patient numbers, pricing, and competitive share, enabling a much richer commercial view.

The tradeoff is about choosing a model that supports how you want to make decisions.

Which one fits your decision-making style?