Simplifying the forecasting process by pooling demand forecasts for a product group and then disaggregating demand based on historical proportions is an effective approach. Here's how it works:

Aggregate Forecasting: Start by forecasting the total demand for the entire product group, which tends to be more accurate than forecasting for individual items due to the law of large numbers.

Historical Proportions: Use historical sales data to determine the proportion of total demand attributed to each item within the group.

Disaggregation: Apply these historical proportions to the aggregate forecast to estimate the demand for each individual item.

Adjustments: This approach can be fine-tuned based on recent trends, market conditions, or changes in customer preferences.

By leveraging historical data to disaggregate demand, the process becomes more manageable and can improve the accuracy of item-level forecasts.