JLS, I tried out your code and it works like a champ! Thank you for taking the time to construct the code. I am very interested in testing it out against a larger dataset.

Jeff, I appreciate the reference to the article on hierarchy-based aggregations. We have a lot of code that does WTD, MTD, QTD and YTD aggregations against employee and organizational hierarchies that then gets rolled up against a goals hierarchy. The combination results in millions of "summary" records in the course of a year for a large organization. The real pain point is when it comes to reprocessing sales numbers from the beginning of a quarter or year after a customer fixes a problem in their source data or makes a change to which products a goal is tied to. In fact, my search for a more efficient way of aggregating data was precipitated by a credit union that wants to rerun their entire year's numbers. Each day of processing takes 3 1/2 minutes. You can imagine multiplying that by 365. We calculate each sale for the day, then aggregate the totals using cursors up the organizational hierarchy, followed aggregating up the goals hierarchy. After that, we add to the WTD, MTD, QTD and YTD numbers by using a bunch of sums in a series of cursors, done via a separate stored procedure. For a credit union with 5 branches and 40 employees, the processing takes 30 seconds a day. For one with 30 branches and 400 employees, it takes 10 times that amount, which causes a scalability problem in our multi-customer database. Long story short, we need to rewrite our aggregation routines, and it looks like you're pointing us in the right direction.