Forecasting and resource planning for Örnsköldsviks municipality
As part of a project team, I developed a forecasting model to support school
facility planning for Örnsköldsviks municipality. Using demographic data
(birth rates, migration, and mortality), we simulated future student inflows,
created scenario analyses for different school programs, and translated forecasts
into facility space requirements with external benchmarks.
Datadriven decision making
The municipality faced significant uncertainty in predicting future student flows due to demographic changes, migration patterns, and varying birth rates. This made school facility planning difficult, with a risk of either overinvesting in unused capacity or underestimating demand.
To address this, we applied a data-driven approach using demographic data and advanced forecasting methods in R. By analyzing historical trends and running scenario-based simulations, we projected student inflows up to 2038 and translated them into concrete facility requirements such as classroom space and resource allocation.
The result was a scalable, updateable decision-support tool that gave Örnsköldsviks municipality a clear basis for deciding whether to invest in a merged school, what capacity would be needed, and how to balance short- and long-term planning. With transparent forecasts and scenario outputs, the model reduced uncertainty, built confidence in the investment decision, and supported proactive, cost-efficient school planning.