Quick Summary: Forecast Disaggregation aggregates sales history into a single regional forecast before distributing reliable data back to individual stores. This process ensures better forecasts and easier management for multi-location businesses.
Please note: This feature is only available in the advanced forecasting feature.
How It Works
The forecast disaggregation feature works by creating a "region" that consists of multiple stores or locations. The process then follows these key steps:
Aggregation of Sales History: The system combines the sales history of all stores within the designated region.
Creation of a Reliable Forecast: A more reliable forecast is created for the entire region based on the aggregated sales data. Because the data set is larger, it is more likely to be stable and predictable, and the app can more accurately identify a seasonal profile if one exists.
Distribution of the Forecast: The regional forecast is then distributed back down to the individual stores or locations. The distribution is based on each location's historical proportion of the total regional sales.
➜ For more on this topic, read: Regional Forecasting Options (Sum, Disaggregate, Recalculate) Explained
Viewing Disaggregated Forecasts
When a forecast is managed this way, the Demand type for the item at the location level will be shown as Disaggregated on the Stock Inquiry screen.
Clicking the Disaggregated hyperlink provides visibility into the calculation.
It displays the average monthly forecast generated at the regional level.
It shows the share of that average forecast that each location was allocated, providing a clear breakdown of the distribution logic.
You can also click the information button to see the basis of the disaggregation, such as that it was based on the sales history over the last 12 months.
The location link opens the Inquiry screen for the item in that specific location, so you can drill down into the details.
Mastering Regions
➜ Read: Mastering Regions
⚠️ Watchouts
Inappropriate Grouping: Make sure to group locations that have truly similar demand patterns. Incorrectly grouping dissimilar locations (e.g., a downtown store with a rural warehouse) will lead to an inaccurate regional forecast.
Impact of Anomalies: If the sales history of the region contains a major anomaly (like a one-off event or a stockout), this will distort the regional forecast and the subsequent distribution to all locations.
Frozen Forecasts: Items with a frozen forecast will not be affected by the disaggregation process. If you want a frozen item to be included, you must first unfreeze its forecast.
Static Distribution: Once disaggregation is complete, the proportions are set. If you manually adjust one item, the total for the group will change, and the other items in the group will not automatically be rebalanced.
Reprocess Data: A Data Reprocess is required when forecasts are adjusted at a regional level. This allows the regional forecast to disaggregate correctly to the individual locations.
💡 Tips
Clean Your History: If you know your sales history contains disruptions, use the Event Correction feature to clean the data before creating or adjusting a regional forecast.
Validate the Distribution: Always review the disaggregation results to ensure that the proportions allocated to each location seem reasonable and align with your business knowledge.
Use for Young Locations: This feature is particularly useful for new or young locations that don't have enough history to generate a reliable forecast on their own.
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