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Selective justification

As explained in Demand Forecasting Best Practices

Selective justification is an asymmetric requirement or acceptance of explanations for forecast adjustments or forecast errors.

When forecast adjustments or errors require explanation, ask whether the same standard applies to good and bad news. Selective justification creates bias when one direction receives more scrutiny than the other. section 16.2.3

The direction of scrutiny can shape planner behavior:

  • Demanding detailed explanations for downward forecasts while accepting upward adjustments easily encourages over-forecasting. section 16.2.3
  • Requiring justification for every historical over-forecast can make planners increasingly conservative. section 16.2.3
  • Reduce the bias by aligning incentives, discussing assumptions and demand drivers, and monitoring forecast value added. section 16.4

Two different planning climates

Suppose senior management challenges every reduction in expected demand but rarely questions increases. Planners learn to defend optimistic edits and the process produces frequent over-forecasts. Reverse the pressure by questioning only increases, and planners may instead avoid optimistic adjustments altogether. section 16.2.3

How Demand Forecasting Best Practices covers it

How deep it goes
Explains the mechanism through contrasting management behaviors and links it to planner responses, incentives, and forecast governance.
Examples
Uses asymmetric questioning of downward versus upward forecasts and contrasting pressure on over- versus under-forecasts.
What it assumes
Assumes familiarity with forecasts, forecast adjustments, forecast errors, and demand-planning decisions.
What you can do after
Diagnose directional pressure by comparing explanation requirements and align review practices around assumptions and demand drivers.
How it connects
Connects selective justification with biased forecasting processes, forecast adjustments, forecast errors, incentives, S&OP, and forecast value added.
Cover of Demand Forecasting Best Practices
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