Practical experience of PAW AI forecasting / performance
Posted: Thu Apr 25, 2024 10:12 am
Morning folks!
Does anyone have any actual practical experience of rolling out AI forecasting in PAW and it's limitations?
I've been experimenting with it for the past half a day and I'm wondering whether it's going to be useable for our situation;
The business has a cube storing KPI information and are keen to adopt an AI forecast approach. The cube stores a number of daily KPI metrics which are defined by dimensions such as country, product, billing cycle etc.
Findings (only limited to univariate forecasts so far);
1 - process works quickly when selecting single combinations of n-level elements
2 - works okay when one dimension element is a consolidation with only 8 children. However - I was hopeful that the consolidation would be the aggregation of 8 separate n-level projections. It isn't - the forecast is generated only at the consolidated level and is then spread downwards to the children
3 - attempting to run the forecast across multiple consolidated elements and I'm hitting major issues with the time taken (I'm just attempting to run a forecast for one KPI and one country with remaining dimensions specified as consolidations.
In summary (putting all of the detail above to the side) - has anyone managed to roll this out for anything other than the smallest use cases?
Note - we are running IBM PA on SaaS. I've checked the version of our TM1 server and we need to get IBM to perfrom an upgrade which will enable some additional Forecast Spreading options - hoping that has a positive impact!
Cheers!!!
Martin
Does anyone have any actual practical experience of rolling out AI forecasting in PAW and it's limitations?
I've been experimenting with it for the past half a day and I'm wondering whether it's going to be useable for our situation;
The business has a cube storing KPI information and are keen to adopt an AI forecast approach. The cube stores a number of daily KPI metrics which are defined by dimensions such as country, product, billing cycle etc.
Findings (only limited to univariate forecasts so far);
1 - process works quickly when selecting single combinations of n-level elements
2 - works okay when one dimension element is a consolidation with only 8 children. However - I was hopeful that the consolidation would be the aggregation of 8 separate n-level projections. It isn't - the forecast is generated only at the consolidated level and is then spread downwards to the children
3 - attempting to run the forecast across multiple consolidated elements and I'm hitting major issues with the time taken (I'm just attempting to run a forecast for one KPI and one country with remaining dimensions specified as consolidations.
In summary (putting all of the detail above to the side) - has anyone managed to roll this out for anything other than the smallest use cases?
Note - we are running IBM PA on SaaS. I've checked the version of our TM1 server and we need to get IBM to perfrom an upgrade which will enable some additional Forecast Spreading options - hoping that has a positive impact!
Cheers!!!
Martin