Rolling Forecast Builder

When to Use

• Monthly or quarterly when updating financial projections
• When replacing static annual budgets with continuous planning
• When management needs a forward-looking 12–18 month view at all times
• When actuals deviate significantly and re-forecasting is required

Procedure

Step 1 — Lock Actuals

1. Import actuals for completed periods from the ERP or accounting system
2. Freeze these periods — no further edits allowed
3. Calculate YTD variance vs prior forecast for each line item

Step 2 — Reforecast Remaining Periods

1. For each P&L line, update the driver assumptions:
   • Revenue: units × price, or pipeline × conversion rate
   • COGS: volume × unit cost, or % of revenue
   • OpEx: headcount × avg cost, or run-rate with known changes
2. Apply known one-time items (restructuring, capex, etc.)
3. Document every assumption change vs prior forecast

Step 3 — Extend Horizon

1. Add new periods to maintain the 12–18 month rolling window
2. Use trailing actuals + seasonality patterns to seed new periods
3. Flag any new periods with lower confidence level

Step 4 — Sensitize Scenarios

1. Apply Base / Bull / Bear assumptions to key drivers
2. Show range around the forecast (not a single number)

Step 5 — Generate Waterfall Bridge

1. Start with Prior Forecast for the full period
2. Add variance buckets: Volume | Price/Mix | Timing | Cost | FX | One-offs
3. Arrive at New Forecast
4. Summarize the top 5 variance drivers in narrative form

Inputs

Input	Required	Format
Actuals YTD	Yes	P&L by month
Prior forecast	Yes	P&L by month for forecast period
Driver assumptions updates	Yes	Line-by-line changes with rationale
Seasonality pattern	Recommended	Historical % distribution by month

Output

## Rolling Forecast — [Period] Update

### P&L Summary (in $000s)

| Line | YTD Actual | Remaining Forecast | Full Year | vs Prior | Δ% |
|------|-----------|-------------------|-----------|---------|-----|
| Revenue | 12,400 | 19,200 | 31,600 | +1,600 | +5.3% |
| COGS | (5,200) | (8,100) | (13,300) | (400) | +3.1% |
| Gross Profit | 7,200 | 11,100 | 18,300 | +1,200 | +7.0% |
| OpEx | (4,800) | (7,600) | (12,400) | (200) | +1.6% |
| EBITDA | 2,400 | 3,500 | 5,900 | +1,000 | +20.4% |

### Waterfall Bridge (Revenue)

Prior Forecast: $30,000
  + Volume: +$800 (higher unit sales in Q3)
  + Price/Mix: +$500 (premium tier adoption)
  + Timing: +$300 (deal pulled forward)
  = New Forecast: $31,600

### Assumption Log

| Driver | Prior | New | Rationale |
|--------|-------|-----|-----------|
| Q3 unit sales | 1,200 | 1,350 | Pipeline confirmed |
| Premium mix | 15% | 18% | Q2 trend extrapolated |
| Headcount | 45 | 47 | 2 new hires approved |

### Confidence Level: HIGH (months 1–6) / MEDIUM (months 7–12) / LOW (months 13–18)

Definition of Done

• Actuals locked and frozen for completed periods
• Remaining periods reforecast with updated assumptions
• Horizon extended to maintain 12–18 month window
• Waterfall bridge shows prior → new with variance buckets
• Assumption log documents every change with rationale
• Confidence level assigned by time horizon

Examples

Prompt

We are in Month 6 of FY2026. Here are our YTD actuals: [paste P&L]
Prior forecast for the full year was: [paste prior forecast]
Key changes: Q3 pipeline is 15% stronger, we approved 2 new hires, raw material cost increased 3%.
Generate a rolling forecast update with waterfall bridge.

Quality Criteria

• Data sources and assumptions are explicitly stated
• Calculations are reproducible from provided inputs
• Visualizations or tables have clear labels, units, and time ranges
• Caveats and confidence levels are documented for estimates

Verification (4C)

Check	Question
Correctness	Are formulas, aggregations, and statistical methods applied correctly?
Completeness	Does the analysis cover all requested metrics and time ranges?
Context-fit	Are the chosen metrics relevant to the business question being answered?
Consequence	If this data were used for a decision today, what blind spots remain?

Edge Cases

• Missing or incomplete data — Document gaps and their potential impact on conclusions. Provide ranges instead of point estimates.
• Outliers skewing results — Report with and without outliers. Document the decision to include or exclude.
• Changing data definitions mid-period — Split analysis at the change boundary and note the schema difference.

Changelog

• v1.0.0 — Initial release