Leave forecasting uses historical data and known patterns to predict future leave demand — giving you time to plan coverage, manage budgets, and avoid surprises. Instead of reacting to leave requests as they come in, forecasting helps you see the peaks coming months in advance.
Key Takeaways
- Leave forecasting uses historical data (past leave patterns) plus known events (school holidays, public holidays) to predict demand.
- A simple forecast based on last year’s data + 10-15% is surprisingly accurate for most businesses.
- Seasonal patterns are predictable — summer, Christmas, and school holidays create recurring peaks.
- Forecasting helps with cash flow planning for leave liability and staffing budgets.
- Leave management software with forecasting tools makes the process automatic.
What Leave Forecasting Tells You
A good leave forecast answers these questions:
- When will leave demand be highest? — Which weeks will have the most leave requests?
- When will leave demand be lowest? — Which periods are good for scheduling training or projects?
- Who is at risk of losing leave? — Which employees have use-it-or-lose-it caps approaching?
- What is the leave liability trend? — Is the total accrued leave balance growing or shrinking?
- Which teams will be most affected? — Are specific teams more likely to have coverage gaps?
Building a Simple Forecast
You do not need complex algorithms to build a useful leave forecast. A three-step approach works for most businesses:
Step 1: Gather historical data. Pull the last 2-3 years of leave records. For each month, calculate total leave days taken. If you do not have clean data, 12 months is enough to start.
Step 2: Add known events. Overlay public holidays, school holidays, and known business peaks onto the historical data. These create predictable patterns: leave demand spikes before and after public holidays, drops during busy periods, and peaks during school holidays.
Step 3: Apply a growth factor. If your team has grown or your leave policy has changed, adjust the historical data. A 10-15% uplift is a reasonable starting point for growing teams.
Result: a monthly or weekly forecast showing expected leave days per period. Compare this against your staffing levels to identify potential coverage gaps.
Using Forecasts for Planning
Once you have a forecast, use it for:
- Coverage planning — if you know August will have 30% of the team on leave at any time, start planning cross-training and hiring temporary cover in June
- Budgeting — leave liability affects the balance sheet. A forecast showing growing liability lets you plan cash flow
- Policy adjustments — if the forecast shows unsustainable demand in certain periods, consider blackout dates or additional hiring
- Employee communication — share the forecast with the team so they can plan their leave around predictable peaks
Limitations of Forecasting
Leave forecasting is not perfect. Limitations include:
- Unplanned absence — sick leave, carer’s leave, and emergency leave are not predictable
- Behavioural changes — if you introduce a new policy, historical patterns may shift
- External shocks — a pandemic, economic downturn, or regulatory change can disrupt patterns
- Small teams — with fewer than 10 people, individual behaviour creates too much noise for reliable forecasting
Despite these limitations, even an imperfect forecast is better than no forecast.
FAQ
How far ahead should I forecast?
6-12 months is the sweet spot. Anything less than 3 months is not really forecasting — it is just looking at the immediate pipeline. Anything beyond 18 months is too uncertain to be useful.
What data do I need to start forecasting?
At minimum: leave records for the past 12 months (by date, employee, and leave type). Ideally: 2-3 years of data, plus public holiday calendars and school term dates.
Can I forecast sick leave?
Predicting individual sick leave is unreliable. But at a team or company level, sick leave patterns are surprisingly stable — you can forecast total sick days per month within a reasonable margin of error using historical averages.
How does leave forecasting help with liability?
Forecasting accrued leave balances helps you predict cash flow needs. If the forecast shows growing liability, you can encourage leave usage or plan for a payout spike.
What tools support leave forecasting?
Specialised leave management systems like Leave Balance include forecasting tools. Spreadsheets work for small teams. For larger organisations, HR analytics platforms offer more sophisticated modelling.