Hello s40racer,
The forecast cockpit is evaluating the accuracy of the quantile 95 with respect to the past sales. In other words, it is measuring the percentage of time that the sales where over the quantile 95.
In a perfect forecast where we have the exact distribution of demand, this percentage should be equal to 5%: 95% of the time, the sales should be under the quantile 95 and 5% of the time sales should be over it.
In the example 11635178 - (Above: 9.62% - At: 0% - Below: 90.38%), it means that for the Reference 11635178, 9.62% of the time the sales were above the quantile 95 and 90.38% of the time there were below. In particular, this means that the forecast is a little underestimating the Demand for this specific Ref as we have actually 4.62% more weeks with sales over quantile 95 than expected.
This is completely normal to have small disparities such as this. If we didn't, we'd probably be overfitting the data.
Regarding the scope of the overall forecast sanity label, it is indeed an average (weighted) concerning only the Ref (not SKU) in the Forecast Sanity table. In details, this is looking only at the history dating of at least 1 year: hence the Items that are more recent are not in the analysis.
Hope it helps!
Hello s40racer,
Indeed if you use Items.AnnouncedSLTValue = same(Suppliers.Leadtime) by [Suppliers.Supplier, Suppliers.Location] at [Items.Supplier, Items.Location], you would get for each item the value corresponding to the pair (Items.Supplier; Items.Location) adding the granularity that you wanted.
However, this implementation implies that all items with the same pair (Supplier; Location) would have the same Lead-Time. If you want to have different Lead-Time for different items provided by the same supplier, you need to add the relevant Reference in the Suppliers table (both for your orignal case at item level and your updated one at SKU level)
Also looking at the original code:
It seems that your table Items has a primary dimension, which is also present in PO, allowing you to have natural aggregation on line 2,3 and 4.
If the primary dimension was previously at the Item level, you might want to change it to the SKU level (Item x Location). This way, Items.SLT_ItemLevel will be the distribution of observed Lead-Time for your specific SKU (versus for your specific Item previously).
Hope it helps!
Formally, $P[X=n/a]$ is not a random variable but a scalar and the corresponding ranvar is not unique.
Could we add more details about how the ranvar returned by transform() is chosen ?
A graphical example might also be a nice addition.
Thanks!