Assumption-based discussion

As explained in Demand Forecasting Best Practices

An assumption-based discussion examines the underlying beliefs and demand drivers behind a forecast rather than focusing first on its final numerical value.

Assumption-based discussion improves collaborative demand forecasting by examining the beliefs and demand drivers behind each forecast instead of debating final numbers first. Participants vote independently, reveal forecasts simultaneously, and then discuss how they interpreted promotions, product launches, marketing budgets, competition, pricing, and other leading indicators. section 16.3.2

Use the discussion to preserve independent judgment and uncover the reasoning behind different forecasts:

• Prepare numbers independently and share them simultaneously before debate. section 16.3.2
• Discuss assumptions and their expected impact on demand rather than averaging final predictions. section 16.3.2
• Ask open questions such as “How did you take this promotion into account?” rather than closed questions that invite a simple yes or no. section 16.3.2
• Let the highest-paid participant speak last to reduce anchoring, social pressure, and groupthink. section 16.3.2
• Include diverse perspectives and align objectives so participants contribute useful information without intentionally skewing the forecast. section 16.4

Why averaging can hide useful information

Suppose colleagues independently forecast between 1,150 and 1,250 units. Averaging produces roughly 1,200 units, but three colleagues may each have identified a different valid reason to increase the baseline. Adding their separate uplift assumptions produces 1,700 units, preserving insights that averaging would erase. section 16.3.2

Figure 16.9 Collaborative forecast based on assumptions

Use the assumption-based collaborative forecast figure to compare a forecast built from explicit demand-driver uplifts with one built by averaging individual predictions. section 16.3.2

How Demand Forecasting Best Practices covers it

How deep it goes

Explains meeting preparation, questioning, participation order, and bias reduction, but does not specify a formal calculation procedure.

Examples

Uses a 1,150–1,250 forecast range, a roughly 1,200 average, and additive uplifts reaching 1,700 units.

What it assumes

Assumes a shared baseline forecast and multiple informed contributors, while teaching how to examine their reasoning from basic meeting practice.

What you can do after

Apply it via independent forecast preparation, simultaneous sharing, open questioning, and explicit discussion of demand-driver impacts.

How it connects

Connects judgmental forecasting with demand forecasting, groupthink, anchoring bias, mindset diversity, and forecast value added.

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Related concepts

• wisdom of crowdscontrasts with · Wisdom of crowds is the phenomenon in which aggregating diverse, independent judgments produces a more accurate collective estimate than most individual judgments.
• leading indicatoruses · A leading indicator is a measurable signal that provides early evidence about a likely later outcome, condition, or performance.
• groupthinkaddresses · Groupthink is a group decision-making phenomenon in which pressure for consensus, harmony, or conformity suppresses dissent and independent evaluation.

This explainer was generated by Manning's knowledge graph from the text of Demand Forecasting Best Practices. The section links point to the parts of the book it draws on.