Forecast refinement combines the statistical forecast generated from prepared demand history with current business knowledge.

The process follows three stages:

The Computer Forecast provides the statistical baseline. Forecast Promotions, Forecast Adjustments, Forecast Overrides, and changes to the forecast engine shape the Working Forecast. Once planning is complete, the Working Forecast is approved as the Final Forecast for use throughout the app.

Initially, the Computer Forecast, Working Forecast, and Final Forecast contain the same values. As refinements are made, the Working Forecast begins to differ from the original Computer Forecast. Finalizing then copies the approved Working Forecast into the Final Forecast.

The Working Forecast is represented by the dark green dotted line.

The Final Forecast is represented by the olive green measure in the Planning table and graph.

The forecast row can be expanded to display the individual measures that contribute to the Working Forecast.

To display the forecast measures:

The table displays the available forecast measures, including:

Changes made within the editable forecast measures update the Working Forecast.

Forecast measures determine how refinements interact with the Working Forecast.

Forecast Promotions are additive.

Values entered in the Forecast Promotions measure are added to or subtracted from the existing Working Forecast. Promotions are intended for temporary demand changes associated with promotional activity.

Saved Forecast Promotions can also influence future Computer Forecast calculations.

Forecast Adjustments are additive.

They increase or decrease the Working Forecast for the periods in which the adjustment is entered. Forecast Adjustments are appropriate when expected demand requires correction without replacing the forecast value completely.

Forecast Overrides replace the Working Forecast value.

When an Override is entered, demand is set explicitly for the selected period. Other forecast measures do not influence the resulting value.

Overrides are appropriate when expected demand must be defined directly.

The Computer Forecast is generated from prepared demand history and provides the starting point for planning.

When sales history changes, an update is processed to the Computer Forecast for the current hierarchy level. A top-down Computer Forecast can also be performed to carry changes to the child members beneath that level.

To open the Adjust Computer Forecast panel:

This panel changes how the forecast engine calculates the Computer Forecast. It does not directly edit individual forecast values.

The Forecast Method determines which algorithm the forecast engine uses to generate the Computer Forecast, allowing the method to be matched to the applicable demand pattern.

In most cases, the default Expert method is recommended. It evaluates multiple forecasting approaches and automatically selects the most appropriate method.

➜ For more on this topic, read: Demand Types & Sales Forecast Generation Explained

The History Basis determines which historical measure the forecast engine uses when calculating the Computer Forecast.

Available options may include:

History Final is selected by default.

Changing the History Basis does not immediately update the forecast preview. Re-forecast must be selected before the effect can be reviewed.

Reactivity determines how quickly the Computer Forecast responds to recent changes in demand.

A higher Reactivity setting places greater emphasis on recent demand. The forecast, therefore, responds more quickly to recent increases or decreases.

A lower Reactivity setting places greater emphasis on longer-term historical patterns.

➜ For more on this topic, read: Forecast - Forecast - Reactivity

Maximum Annual Increase limits forecast growth relative to historical demand.

Maximum Annual Decrease limits forecast decline relative to historical demand.

These controls work with Reactivity. A highly reactive forecast responds more strongly to recent demand, while the annual limits restrict how far the forecast may increase or decrease.

Seasonality determines the threshold used to identify seasonal demand.

A higher setting results in more forecasts being identified as seasonal. Increased sensitivity to seasonal patterns may, however, reduce forecast accuracy in some circumstances.

➜ For more on this topic, read: Forecast - Forecast - Seasonality

Use Period to Date determines whether demand from the current incomplete period is included in the forecast calculation.

This option is deselected by default.

Period to Date may be useful for new items with very limited history where current sales need to influence the forecast as soon as possible.

Auto-Age controls how much historical demand is included in the forecast calculation.

When Auto Age is selected, all available history is used and the Periods field is unavailable.

When Auto Age is deselected, the number of historical periods used by the forecast engine can be entered manually.

A restricted historical window may be appropriate when older demand no longer reflects current demand behavior.

The Level and Slope controls visually adjust the calculated forecast.

The preview updates immediately when either control is changed. The corresponding numerical changes are also displayed in the panel for reference.

Level and Slope behave differently from the other Computer Forecast settings:

Selecting Re-forecast again recalculates the forecast from the underlying settings and resets any Level and Slope changes.

To recalculate the Computer Forecast:

Re-forecast updates only the preview.

Apply commits the recalculated Computer Forecast and updates the Working Forecast. This allows different forecast settings to be evaluated before the result is committed.

Forecast Profiles allow demand patterns to be saved, edited, and reused across products, locations, or hierarchy members.

Profiles use percentage distributions to represent how demand is spread across forecast periods. They are particularly useful when historical demand is limited or when an existing demand pattern better reflects expected future sales.

Common uses include:

Forecast Profiles use percentage weightings to distribute demand across individual forecast periods.

These percentages represent the proportion of demand allocated to each period, allowing seasonal peaks, quieter periods, and promotional patterns to be reflected in the forecast.

For example, a seasonal product may have higher percentage allocations during its peak selling months and lower allocations during quieter periods.

The percentage distribution can be reviewed and adjusted to better reflect expected demand.

When period values are entered or adjusted, the app automatically calculates their percentage weightings, ensuring the total distribution equals 100%.

To create and save a Forecast Profile:

The saved profile becomes available for future use and can be applied, edited, or deleted as required.

To access existing Forecast Profiles:

From here, you can edit, delete, or apply an existing saved profile.

Applying a profile distributes demand across the forecast horizon according to its percentage weightings.

To apply an existing Forecast Profile:

To edit an existing Forecast Profile:

💡 Pro Tip: Saving an edited profile does not automatically apply it to the forecast. To use the updated profile, follow the steps under Applying a Forecast Profile.

To remove a saved profile that is no longer required, select Delete Profile on the relevant profile.

Forecast values can be adjusted individually or across multiple selected cells using the Forecast Adjustment panel.

The effect of the change depends on the selected measure:

➜ For detailed instructions, read: How To: Adjust Values in Demand Planning

💡 Pro Tip: In some cases, a saved Forecast Override may be recorded correctly while the Working Forecast temporarily appears to display the Computer Forecast value. Refresh the browser to update the display and show the saved Override.

Forecast changes can be distributed across time using a curve instead of assigning the same pattern to every selected period.

Available curve types include:

Available curve behaviors include:

The Curve Visualizer previews how demand will change across the selected periods, allowing the planned pattern to be reviewed before it is adjusted.

After selecting the required curve type and behavior, select Adjust to update the Working Forecast.

Forecast refinement can take place at any hierarchy level.

Higher levels support broader planning decisions, while lower levels support more targeted changes.

When a forecast is adjusted at a higher level, Demand Planning distributes the change to the more detailed hierarchy members beneath it. The hierarchy structure and proportional distribution remain intact.

Forecasts can therefore be refined where business insight is strongest without editing every item individually.

The Multi view can then be used to review and refine how the higher-level change has been distributed across the next hierarchy level.

Spreadsheet Collaborator supports larger forecast updates by allowing planning data to be downloaded, edited in a spreadsheet application, and uploaded back into Demand Planning.

The feature is accessed from the Multi view.

Before downloading:

The downloaded spreadsheet may be edited in the preferred spreadsheet application.

The following fields must remain unchanged:

After the changes are complete, save or export the file before uploading it.

When the completed spreadsheet is uploaded, the app validates the file by cross-checking the System tab and checking for issues such as:

Once validation succeeds, select the measure that should receive the uploaded values.

The destination does not need to be the same measure from which the data was downloaded. Values can be mapped to another applicable forecast measure or Reference Measure.

After processing, the uploaded values are automatically distributed proportionally (prorated) to the lowest hierarchy level.

Historical demand is not always an appropriate basis for future demand.

A new product with no sales history will not yet have a forecast. A product approaching the end of its lifecycle may also require demand to decrease intentionally rather than follow its existing historical pattern.

Forecast Overrides and lifecycle curves allow these demand patterns to be defined directly.

To model demand for a new product:

Available growth curve behaviors include:

The curve builds demand gradually toward the expected steady-state level rather than entering the full value immediately.

To model declining demand:

The curve reduces demand gradually rather than causing an immediate drop.

Forecast Risk, Forecast Bias, the Analysis view, and Comments can be used to identify where planning attention is most valuable and record the reasoning behind forecast decisions.

➜ For more on this topic, read: Demand Planning: Forecasting Effectively

Once all forecast changes have been saved and the Working Forecast reflects expected demand, it can be finalized.

Finalizing copies the Working Forecast into the Final Forecast. The Final Forecast is then used throughout the app for downstream planning.

To finalize the forecast:

The Working Forecast is copied into the Final Forecast.

If additional refinements are made later, select Finalize again to replace the Final Forecast with the latest Working Forecast.

The Working Forecast remains visible as a separate measure, allowing the planning version and approved Final Forecast to be compared.

➜ Read: How To: Adjust Values in Demand Planning

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