The Forecast Model Doesn't Have a Sales Target

A statistical forecast can be wrong. It can miss a structural change, fail to anticipate a promotion or inherit bias from the historical data on which it is based. What it does not have is an organizational reason to push the forecast higher or lower. Sales teams work toward commercial targets, operations teams have capacity constraints, finance has a budget, and management has commitments made to the business.

These different perspectives are exactly why the S&OP or IBP demand review is valuable. Commercial teams and planners can bring information to the forecast that a statistical model does not yet know, such as a confirmed customer order, a promotion, a distribution change or an expected market event. At the same time, the review creates a risk that expectations, targets and operational constraints gradually become mixed into the demand forecast itself.

Recent research suggests that this distinction deserves more attention. A 2025 study in the International Journal of Forecasting analyzed around 147,000 forecasts across six company datasets and examined what happened when people adjusted system-generated forecasts. Judgmental adjustments improved both accuracy and bias for only slightly more than half of the SKUs, while upward adjustments were particularly likely to worsen forecast performance.

The finding does not suggest that human judgment should disappear from demand planning. It raises a more useful question for the demand review: when someone changes the statistical baseline, are they adding information about expected demand or expressing what they would like the number to be?

The baseline gives the discussion an independent starting point

Research into judgmental forecasting has shown that organizational roles and incentives can influence forecasting behavior. That makes the statistical baseline valuable for a reason that goes beyond the quality of the algorithm. It gives everyone in the demand review the same independent starting point before commercial ambition, capacity constraints and management expectations enter the discussion.

That baseline should remain visible even after an adjustment has been made. If Sales expects higher demand because a customer has confirmed additional volume, the review should capture both the original forecast and the adjustment, together with the reason behind it. Replacing the baseline with a new consensus number makes it much harder to determine afterward whether the intervention actually added value.

Watch for signals that the forecast is becoming a negotiation

There is no problem with challenging a statistical forecast. The concern starts when the reasons for changing it become disconnected from new information about demand. A few situations deserve particular attention during the demand review:

• The forecast repeatedly moves toward the budget or sales target. A commercial objective may be achievable, but the target itself is not evidence that customers will buy more.

• Capacity constraints lead to a lower demand forecast. Limited production may affect what the company can supply, but it does not necessarily change underlying customer demand.

• Overrides are explained with confidence rather than evidence. Statements such as “Sales believes the market will be stronger” are difficult to evaluate unless they are connected to a specific customer, promotion, channel or market event.

• The same direction of override appears repeatedly. Consistently increasing or decreasing the baseline is worth investigating, particularly when the adjustments do not improve forecast performance over time.

These are not reasons to reject human input. They are reasons to make the source of that input more explicit.

An override should earn its place

The recent International Journal of Forecasting research is particularly relevant here because it uses Forecast Value Added to examine whether judgmental interventions actually improve the forecast. Rather than evaluating only the final forecast, planning teams can compare the statistical baseline with the adjusted forecast and determine whether the intervention reduced or increased error and bias.

This creates a useful feedback loop for the demand review. A team may discover that adjustments around confirmed promotions consistently improve the forecast, while broad commercial uplifts tend to make it worse. Another business may find that local market knowledge adds significant value for certain products but contributes little for mature, stable categories. Human judgment becomes more useful when the organization learns where it has historically added information that the model was missing.

Keep the target visible without turning it into the forecast

Commercial targets still belong in S&OP and IBP. In fact, the difference between expected demand and commercial ambition can create one of the most useful discussions in the process. If the statistical and market-informed demand forecast says 100 while the business wants to sell 110, that ten-unit gap tells management something important.

Changing the demand forecast to 110 because the target is 110 removes that information. Keeping the two numbers separate allows the conversation to move toward the actions required to close the gap, such as additional promotions, pricing changes, distribution expansion or commercial activity.

The statistical forecast therefore does not need to win every argument in the demand review. Its value is that it provides a neutral reference point before organizational incentives enter the process. Human judgment can then focus on adding information the model could not reasonably know, while the planning team retains enough transparency to determine afterward whether that judgment actually improved the forecast.