Forecast Override Governance: Track Manager Judgment Against the Baseline

A forecast model cannot represent every customer conversation or delivery constraint. Managers may adjust its output using current evidence, but an override can also hide model weaknesses or subjective optimism. Preserve both the baseline and the adjustment so teams can evaluate judgment instead of overwriting history.

Separate the baseline from the override

Store the original forecast value, the adjusted value, the adjustment owner, and the time it was made. Define the level of override—deal, team, segment, or total—and avoid mixing them in one field. A later report should be able to reconstruct what the model indicated before human judgment.

Use explicit categories for the reason, such as a confirmed buyer event, a known data issue, a delivery constraint, or a manager’s risk assessment. Keep free-text context for nuance, but do not rely on narrative alone for reporting.

• Retain the model output and forecast snapshot.
• Record amount, direction, date, and author of each override.
• Link to observable evidence where appropriate.

Set approval and timing rules

Determine which adjustments require review based on size, level, or timing. A last-minute change to a large forecast may need a second approver; a small correction to a stale record may not. Apply the same criteria across comparable teams.

Close the override window before the forecast is finalized and retain the snapshot. If a new buyer event occurs later, record it as a new change rather than editing a previously submitted forecast without a trace.

• Define who may override and who may approve.
• Set a forecast cut-off and preserve the submitted version.
• Use consistent rules across periods and business units.

Review calibration, not just outcomes

Compare baseline and overridden forecasts with actual outcomes after a suitable maturity period. Assess error by reason, manager, segment, and forecast horizon. One good or poor quarter does not establish skill; look for repeated patterns and note changes in pipeline composition.

Use the review to improve definitions, data quality, and judgment. If overrides consistently correct a specific model blind spot, update the model or input process. If a category adds no predictive value, revise the rule rather than defending the adjustment by anecdote.

• Measure both baseline and adjusted forecast error.
• Keep open deals and mature outcomes distinct.
• Use findings to refine the process and training.