Forecasting and capacity planning
Demand, capacity and FTE forecasting for operational functions, with the Power BI reporting that turns a forecast into a plan leadership will act on.
A forecast is only useful if someone changes a decision because of it. The modelling matters, but so does the interval around it, the assumptions underneath it, and whether the people who own the roster can read it on a Monday morning.
I spent five years building the forecasting and reporting infrastructure that a large operational function in New Zealand government relied on: demand, capacity and workforce models covering more than three thousand FTE, with automated pipelines that replaced manual processes.
What you get
Delivered, not recommended
- Demand, capacity and FTE models with stated assumptions and prediction intervals, not a single line.
- Scenario views: what changes if demand shifts, if attrition rises, if a policy lands early.
- Automated forecasting pipelines that refresh on a schedule and flag when reality departs from the forecast.
- Reporting in Power BI built for the people who make the roster and the budget, reviewed with them until it is used.
From the work
3,000+
FTE covered by the planning models
Capacity and FTE planning for a 3,000+ FTE operation
New Zealand government · large operational function
A large operational function inside a government organisation, with a workforce of more than three thousand FTE, needed capacity and operational planning it could rely on rather than rebuild by hand each cycle.
Capacity and operational planning for the function ran on these models, covering a workforce of more than three thousand FTE.
Read the noteQuestions
Asked before the first call
What methods do you use?
Whatever the data and the decision justify. That ranges from robust seasonal baselines to hierarchical and causal models. The method is chosen for the decision it supports and is explained in plain language alongside the numbers.
How do you communicate uncertainty?
Every forecast ships with an interval and the assumptions behind it. Planning against a range is what makes a forecast usable; a single line invites false precision.
Can this work with the data we already have?
Usually. Discovery starts with what exists: historical volumes, roster data, and the systems they live in. The forecast is designed around that reality, and data improvements are sequenced rather than treated as a prerequisite.