Part of the complexity is that you have time series, and errors over time within a product may well be highly autocorrelated, because some products are just harder to forecast than others. Or not. It absolutely depends on your situation.
In the extreme case where forecast errors are constant over time for all products, the same 1% of products will trigger every single day.
If autocorrelation is lower, you may get different products triggering every time. Worst case, each day of your 14 days completely different products trigger, so now 14% of your products trigger at some day during your two weeks evaluation period.
You can't get more precise than "somewhere between 1% and 14%" in general without digging into your actual data. (Yes, complete independence means that your expected percentage can be calculated a bit more precisely, but I don't think that adds a lot of enlightenment over "look at your actual data".)