2892538 - The Purpose and Implication of the 'Ignore Leading Zero' Indicator in Demand Planning

You are creating a Demand Plan and have maintained a Statistical Forecasting Model. When you select Forecasting Model Variant, you can see the field 'Ignore Leading Zero(s)' under the 'Model Variant Settings' section. Hovering over the question mark beside function with your mouse, you get a popup window with some information. However, you find this information is not sufficient, and so you would like to have detailed information regarding the purpose and implication of the function on the Statistical Forecast.

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1. Navigate to the Demand Planning work center.
2. Go to the Demand Plans view.
3. Find and select the relevant Demand Plan.
4. Click the Edit button.
5. Go to the Process Settings tab and the sub-tab "Forecasting Models".
6. Select the relevant Variant ID under the Forecasting Model Variants section.
7. In the sub-section 'Model Variant Settings', you can see the box 'Ignore Leading Zero(s)'.
8. If you hover over the Question Mark beside the said function above, the following information pops up in a window: 'Select if statistical forecasting should ignore leading zeros in the history horizon for products that were added recently'.
9. Now you would like to know the impact of activating this function on the Demand Plan/Statistics.

Ignore Leading Zero(s) is primarily to handle a scenario where certain new launches of products might not have history. For example, if your Historical Horizon is one year: Let us say from February 2019 to January 2020, and the product got launched in August 2019; its obvious that you will not have historical Forecast for the Product from February 2019 to August 2019. In the system, this will be captured as zero, but does not mean that the product is not sold. It's just that the product is not available. Hence, in Statistical Forecasting, there is an option where the user can decide whether to consider leading zeros as a part of historical data or not.

If the leading zeros are indeed considered, then the statistical Forecast can be biased.

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