> ## Documentation Index
> Fetch the complete documentation index at: https://docs.simcel.io/llms.txt
> Use this file to discover all available pages before exploring further.
# Forecast Settings
> Configure forecast granularity and review demand history for products or customers.
## Accessing the forecast settings
Click **+ Plan** to open the SIMCEL plan menu.
To open the forecast settings, click **Open Forecast Settings** at the bottom of the pop-up.
This screen gives you access to the different settings for the forecaster in SIMCEL.
## Forecast aggregation
**Forecast aggregation** is the granularity at which forecasts are generated. Choosing it involves a strategic balance between data detail, forecast precision, accuracy, and computational efficiency. As a rule of thumb:
* Higher levels of data aggregation speed up the forecasting process, because the data representation is denser. However, this often comes at the cost of reduced precision, as nuances and specific patterns may be lost.
* Finer data granularity enhances the detail level of the forecast, potentially increasing precision. This detailed approach requires more computational resources, because the data volume is larger and more complex to analyze.
Accurate forecasting requires identifying the ideal level of data granularity. Excessively aggregated data can obscure vital trends and patterns, diminishing precision and utility. Overly detailed data can create discontinuities in the forecast model, with insufficient data points in each segment to discern trends and patterns. The goal is to strike a balance that preserves critical information while ensuring computational feasibility and forecast reliability.
SIMCEL lets you select the granularity of 3 dimensions: **Product**, **Customer/Location**, and **Time**.
## Explore products and customers
This feature lets you efficiently review the actual demand for the selected product or customer over the most recent year.
Sometimes a customer or product is removed from the active list but its status is not updated in the master records. Use this feature to quickly visualize their historical demand and decide whether they should be delisted.
