Skip to Content
Product Information
Author's profile photo Antoine CHABERT

Vote your favourite Predictive Planning enhancement requests…or create new ones to influence the roadmap

Blog updated last: April 12th, 2023

2022 is getting to an end. It’s time to look back for a year that was full of predictive updates!

In Q1 2022 predictive planning was integrated into multi actions, giving the possibility to automate the refresh of predictive forecasts on a regular basis.

Right now a lot of SAP Analytics Cloud customers are using the integration into multi actions to ease the integration of predictions in their stories and their planning

You can learn more to this in the blogs here and there.

Q3 2022 was a significant release with two incremental deliveries, the support of business calendars and increased visibility of the influencers’ impact.

It is now possible to create predictive forecasts when business calendars are used in the underlying planning model. Example of such business calendars include 4-4-5, 13×4, or 5-5-6.

The blog here is making more concrete what the new capability means in particular for retail customers, or in general anyone that’s doing planning on a weekly or periodic level.

This other blog explains the change from a planning standpoint.

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.

In the Q4 2022 release, a new visualization helps understanding the impact of cycles to compute the predictive forecasts.

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!

The enhancement requests have been created in the customer influence portal for SAP Analytics Cloud.

Without further due, here are the existing requests.

The use case that can be tackled out of the box today when automating predictive planning is the rolling forecast one. Some organizations are focused more on the year-end forecast. For instance in July 2022, they want to forecast the remaining months up to December. This enhancement request aims at providing such use case. It was created by Nick Verhoeven and Eric Schuenemann. It got 10 votes to date.

Today one can save these confidence intervals when using datasets as a source but not planning models. Thanks Adam Nekola for raising this. It got 9 votes so far.

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 8 votes to date.

This recent enhancement request focuses on Monte Carlos simulation to determine the sensitivity and risk of certain outcomes. This is not predictive planning per se, yet it’s an interesting idea raised by Jef Baeyens that received 7 votes so far.

Why forecasting just one account / measure at a time when you could do more, Natalio Pluzzer is actually suggesting to do more and got 7 votes.

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 6 votes to date, from Kassidy Roussel.

This one is quite straightforward – today it’s not possible to transport predictive scenarios across SAP Analytics Cloud tenants. Yet this would be useful! Thanks Daniel Weiskircher and your idea got 6 votes so far.

Yes, MAPE is good but there are more statistical indicators out there. Why not having them all? great idea from Ansgar Heidemann with 6 votes so far.

Another idea to ease the selection of an entity by Daniel Weiskircher with 6 votes to date.

Do you want to be able to create a time series forecasting model with more than 1000 entities in one go? Then vote this idea from Anushankari Chandra with 4 votes to date.

Daniel Weiskircher strikes again – this idea is about enriching the filtering possibilities when it comes to the entity definition (3 votes to date).

Roberts Veics raised this idea to ease filtering possibilities when creating forecasting scenarios on aggregated data. The idea has 3 votes so far.

Beyond all these great ideas, there are other tracks that one can think of:

  • Would you want SAP Datasphere and SAC Planning models to be coupled closer together, giving predictive planning additional capabilities when it comes to data management?
  • Do you want every predictive forecast to be explained (what impact is coming from the trend, cycles, influencers etc) leading to this exact prediction?
  • If you are doing “what-if” hypothesis with influencers and generate multiple predictions from this, would you want to automate this logic?
  • when you have many entities, is one indicator enough to assess the accuracy of the model?
  • do you need classification on top of planning models too?

Thanks for your attention, this is #predictiveplanning blog #49 and I am sure you can find interesting information in the other blogs.

Assigned Tags

      Be the first to leave a comment
      You must be Logged on to comment or reply to a post.