If you have to deal with many forecasts, you can use our dedicated tool.

It is thus possible to edit several forecasts at the lowest level, or at multi-level.

This editor also allows you to have a representation of your forecasts at an aggregated level by selecting the fields to perform this aggregation.

How to aggregate fields

Start by clicking on Aggregation fields

Then select the fields to be aggregated. The selection order will determine the display.

Click Confirm

The tool adds up all the data for each period to present them in the selected format.

The display is limited to the first 5,000 items to reduce your waiting time if you manage a large number of forecasts. You must use the Filters to narrow down this list.

Configuring the display

You can switch the display of the table from "units" to "financial data" if you have populated this information for each item. Note that currently you can only modify values when "units" is selected.

You can sort each column by clicking on the corresponding field.

You can see your historical sales in the blue section (or hide it with a checkbox), and the associated forecasts in the red section.

Cells highlighted in red represent forecasts that you have adjusted (different from the forecast baseline).

Toggling the button below, you can easily switch the display of the table to choose between the baseline forecasts (before your modifications), or your adjusted forecasts.

Changing forecast values

You can click anywhere in the red section to edit the forecast values, and adjust them in 2 ways.

  • By directly entering the new value on the keyboard and confirming the entry.
  • By entering a percentage, for example "120%" to increase the initial value by 20%.

Note that if after one or more value changes, you re-enter the initial value of the forecast baseline, you will go back to a normal display of the cell, as if it had never been changed.

However, if you have made multiple changes and taking into account the distribution rule below, it is possible that the underlying forecasts are not the initial ones. You can check the adjustments at a lower level to verify that this is the case, by invalidating the aggregation function, and possibly by making a filter to view only the articles that interest you.

One way to return to the baseline forecast data at all levels is to erase all adjustments by clicking on the rounded arrow at the beginning of the line on which you are working. However, you will lose the adjustments for all the periods on this line.

Functioning and impact of the allocation on the underlying forecasts

When you modify a forecast value, the platform automatically distributes the new value as follows

  • The weight of each underlying forecast for this period is calculated. By underlying forecast is meant the baseline value calculated by SKU Science, possibly replaced by a former adjustment if it exists at the forecast level.
  • Each underlying forecast retrieves the percentage of the value entered in the aggregated cell, according to its weight in the total of the forecast previously displayed. This value is rounded.
  • Depending on the values โ€‹โ€‹entered and due to rounding, this distribution does not necessarily fall fairly according to the underlying data that compose it. As a consequence, the rest of the distribution is added to the SKU of highest weight in the distribution. If this method does not completely satisfy you, you can distribute this balance manually on the SKUs that interest you.

Here is an example to understand.

Family 1 includes 4 items. For the month of June, the values โ€‹โ€‹are 57, 28, 466, 463.

At this stage, the values โ€‹โ€‹are the ones from the forecast baseline, therefore not adjusted.

By aggregating by family, we get 1,014 in the June column.

By entering 130% at the location of 1,014 and confirming with "Enter", we obtain 1,318 in the cell. Now the cell is highlighted in red.

If we go back to the level of the 4 articles, here is what we see.

Each June value has indeed been increased by 30% and the total is now 1,318.

If now, we replace the 74 by 80 for the article 022-1 and that we aggregate again by family, we obtain 1,324, that is to say 6 more than previously.

Now it's your turn to play. ๐Ÿ˜€

You can use the demo database and make the changes you want. You can always return to the default demo by clicking on Restore demo.

Send us your comments or suggestions directly in the tool interface.

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