A new visualisation "Impact of Numerical Influencers" was added in the predictive scenario Explanation area to better understand the impact of each influencer and the role they play when computing the predictive forecasts.
This model is forecasting bike hires in London - we can notice different influencing factors playing a role, for instance the maximal outside temperature on the given day.
The new visualization digs deeper for us to understand the impact of the temperature - as you can see the more the temperature increases on a given day (X axis) the more the impact to the predictive forecasts increases too.
A temperature of 31,20°C will increase the number of bikes hired by 14829 while a temperature of 2,1°C will decrease the number of bikes hired by 11905.
Back to the London Bike Hire example, you might have noticed that cycles are also playing a role in the predictive model. Typically the day of the week will influence if people rent more or less bikes.
Now we are curious to understand what's the specific impact of each day. This is what the new visualization "Impact of Cycles" offers.
Basically it says people rent more bikes on Fridays and Sundays compared to the rest of the week.
Wait, what's coming in 2023?
We do not rest on our laurels and have already enhancements planned for Q1 2023.
It is important for some customers to consume not only predictions for future periods (well... predictions!).
They also want to write back the predictions for past periods to compare them to actuals through their own story calculations as a way to gauge the accuracy of the predictive model.
Today it is possible for these customers to do it via the predictive scenario user interface but not through multi actions - we'll be bridging this gap with the new capability.
This capability is planned to be available in the wave 2023.01 and in the Q1 2023 release.
Beyond this, we of course have plans cooking but I now would want to hear from you too!
Vote Existing Predictive Planning Enhancement Requests... or create yours!
When one is defining the base element or combination to forecast on (aka the Entity) the hierarchical selection of the leaves could be eased. This is the focus of this enhancement request, which received 14 votes to date.
Today predictive planning is about doing time series forecasting on top of planning models. What if you could use regression too? This is the focus of this idea, that got 11 votes to date, from kassidyroussel