Navigate to: Settings > Configuration > Forecast
Definition
Definition
Each location will attract a share of the total forecast relative to its contribution to sales over the specified time frame.
A shorter time span results in a more reactive disaggregation, while a longer one is more stable. The time frame may automatically extend for products with slow-moving or irregular demand.
Use case
Use case
This parameter forms part of the Advanced Forecasting Module. It is an add-on module worth exploring if your business operates across multiple locations and managing forecasts at a location level is challenging.
➜ Be sure to read the Advanced Forecast Feature article if this applies to your business.
For businesses selling items with varying sales patterns across locations, this may not be the ideal approach.
Imagine having locations in both the northern and southern hemispheres, where the item in question is a snow shovel. Forecast disaggregation could reduce accuracy compared to forecasting at an individual location level.
This module will also not benefit businesses already achieving accurate forecasting at a location level.
However, for the average business, this feature can be very useful and is worth testing.
When setting these parameters:
Weight by the sales history of the product if it has at least X months of history at the region:
A safe value is between 3 and 6 months. This ensures that the item has sufficient sales history to determine its contribution and forecast percentage split.
If an item only has one month of sales, that data may be unrepresentative — too high (e.g., a new iPhone launch) or too low (e.g., a slow product introduction). Using insufficient data could cause inaccurate splits not reflective of future sales.Otherwise weight by the combined sales history of similar products based on this group:
This defines the fallback group. If an item has less than X months of sales, which Group’s sales should be used to calculate its contribution and forecast percentage split?Example:
Item: Pink sandals
Group 1: Apparel
Group 2: Summer wear
Group 3: Shoes
Group 4: SandalsSuppose Pink sandals lack sufficient sales history to be included in forecast disaggregation.
Setting this parameter to Group 1 (Apparel) could result in more sandals being sent to snowy regions due to higher winter apparel sales.
Conversely, setting it to Group 4 (Sandals) might be too narrow, providing too few data points and skewing results.
It is important to find the balance between accuracy and data sufficiency.
Weight the forecast using the last X months of sales history:
Use 12 months for a more stable split or 3 months for more dynamic adjustments.
Explanation
Explanation
➜ Refer to this article for a detailed explanation of Forecast Disaggregation (Advanced Forecasting Feature).
FAQs
FAQs
Question: What happens when “Weight the forecast using the last X months of sales history” is set to 12 months, but “Weight by the sales history of the product if it has at least X months of history at the region” is set to 3 months? Does this mean the new item with 3 months of history must compete with the one that has 12 months of data?
Answer: No. The system considers the average sales over the months available for each item.
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