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SAP Analytics Cloud Predictive Planning – Frequently Asked Questions


Question: what is Financial Planning and Analysis (FP&A)?

Answer: see here:

Question: What are leading analysts and thought leaders saying about FP&A?

Answer: read these 4 white papers:

All resources can be found on SAP FP&A page.

Question: What is SAP’s strategic direction for Financial Planning & Analysis (FP&A)?

Answer: SAP Analytics Cloud is the recommended solution for organizations looking for a financial planning and analysis solution. It is our one solution for Collaborative Enterprise Planning aligning  finance, HR, marketing, sales, and supply chain plans to respond faster to market changes.

Read the Deep Dive Strategy for Enterprise Planning blog by Matthias Kraemer (Head of SAP Analytics Cloud for Planning) 

Question: What are the major analysts saying about SAP Analytics Cloud FP&A capabilities?

Please see latest insights from IDC, Gartner and BARC.

Question: What is Predictive Planning?

Answer: Smart Predict as part of SAP Analytics Cloud offers time series forecasting, classification & regression scenarios to augment stories with predictions.

Predictive Planning in SAP Analytics Cloud is the ability to run time series forecasting scenarios directly on top of planning-enabled models to offer a smart baseline for the forecasting activities.

Check the intro video below or read more to this here

Question: is Predictive Planning already available in SAP Analytics Cloud?

Answer: yes, it was delivered with SAP Analytics Cloud third quarterly release in 2020.

Since then, a number of incremental improvements have been delivered:

  • In 2020.Q4, local currencies are now supported and user-friendly descriptions are supported to display entity names. Read more about the currency support here.
  • In 2021.01 and 2021.Q1 QRC time series models have been improved to better leverage the most recent data points.
  • In 2021.04, data smoothing techniques are made “first-class citizens” to increase the overall model accuracy, provide simpler models and better handle disrupted data conditions.
  • More improvements are planned in 2021, along the lines of being smart, self-service & trusted. Stay tuned and you can refer to SAP Analytics Cloud roadmap explorer to know more.

Question: Shall we treat Predictive Planning as a new module when it comes to planning?

Answer: no, the Predictive Planning experience is as closely integrated as possible to the planning experience in SAP Analytics Cloud as it can use planning models as a source, and write back to private versions making this the shortest possible loop.

Question: How much of statistics skills should be known to utilize this tool effectively?

Answer: You do not need advanced knowledge to use and benefit from Predictive Planning.

Complexity is hidden so business users can create prediction without the support of a data scientist. The way we surfaced the result make it understandable and trustable.

The only concept that needs to be understood is the HW-MAPE (Horizon-Wide Mean Absolute Percentage Error), which measures the accuracy of a time series model and represents the average error that the predictive model is likely to commit when used in the future.

Question: I am a SAP Analytics Cloud customer. Do I need a specific license to leverage Smart Predict or Predictive Planning?

Answer: Smart Predict and Predictive Planning capabilities are available on SAP Analytics Cloud Cloud Foundry deployments. The official note is here.

The capabilities are available to every BI or Planning license in SAP Analytics Cloud, on the Cloud Foundry deployments. Please refer to the Pricing page to know more.

Predictive Planning time series forecasting are available for SAP Analytics Cloud planning-enabled models. Planning models can only be created by end-users having access to Planning Professional Licenses.

Actuals and predictive forecasts can be reported on using any type of SAC BI / Planning license.

You can refer to the different features available per license type for planning models here.

Question: I have a specific need that’s not served by Predictive Planning existing capabilities. How can I ask enhancement requests to SAP?

Answer: any SAP Analytics Cloud enhancement request should be raised via our Influence portal. Please search for what others have already entered, up-vote enhancement requests and enter your very own enhancement requests if they do not exist today. SAP Analytics Cloud product management teams welcome your ideas and thoughts! Read more from my colleague Christian Happel here

Question: I want to learn more about Predictive Planning. Where I can find more information?


Question: It is possible to access Predictive Planning online demos?

Answer: Yes, please see:


Also this one from Xavier Hacking (SAP Partner Interdobs)

Question: what are the major Predictive Planning use cases?

Answer: The use cases most often seen with Predictive Planning so far are

  • Expense & Cost Planning (see more to this here)
  • Revenue and Sales Planning
  • Headcount planning

Other less recurrent use cases include resource management, cash flow forecasting, volume forecasting… basically whenever you have sufficient historical data and want to use predictive (time series) to support your planning activities.

Question: are there examples of customers using Predictive Planning today?

Answer: Yes.

You can refer to the SAP Corporate Controlling testimony here.

You can also read more to how Roche is using Predictive Planning here.

(SAP Innovation Awards 2021 submission)

Question: are there any known restrictions? How much historical data is required for Predictive Planning to be effective?

Answer: Please see the detailed documentation here.

Some important points:

  • You can generate your forecasting entities including a maximum of 5 dimensions and/or attributes at a given time.
  • You can create up to 1000 time series models in one go and a maximum of 500 forecasts.
  • To be in the sweet spot, you need 5 times more actuals than forecasts you need. If I want to forecast January to December 2021, ideally I should provide actuals from January 2016 to December 2020 as one example. In case you have less history, predictive forecasts will still be generated but you will receive warnings as you can be less confident in some of the predictive forecasts. For instance you have 40 months of history and you want to predict 12 months, then the confidence you can place in month 11 and month 12 predictions will be less.

Please refer to the best practice here to handle the cases where you need to generate 1000+ entities.

Question: can you please tell more about the automated time series forecasting used behind the scenes? Is it possible to understand the  algorithm used?

Answer: The logic of the algorithm used for automated time series is described in this blog.

It has been crafted & tuned to be robust while accurate and yielding results that are explainable to business users and result for 20+ years of product investment, and proven by customer success.

Similar to the modern car industry , our focus is not so much of explaining the logic of the engine to the end-user but rather focusing on the driving pleasure – here giving a user-friendly and simple user interface and providing the full transparency on the predictive models that have been detected (trends, cycles etc) so that more end-users can spend more time experimenting, validating the business value and using to serve the use cases.

The logic we use behind the scenes is constantly evolving and is being improved over time as evidenced by two recently delivered user stories – which form part of an ongoing product investment plan to mitigate the disruption caused by the COVID-19 pandemic to the ability to plan & predict.

  • In 2021.01 and 2021.Q1 QRC time series models have been improved to better leverage the most recent data points.
  • In 2021.04, data smoothing techniques are made “first-class citizens” to increase the overall model accuracy, provide simpler models and better handle disrupted data conditions.

Customers cannot replace the automated time series forecasting logic with custom algorithms (R or Python). This is actually on purpose since Predictive Planning is geared towards business users.

Such users will typically not have the skill set to delve into data science details & algorithms, but are happy to be able to solve complex and labor-intensive forecasting problems by themselves without requiring help by data scientists who are themselves scarce in most organizations.

There are other SAP solutions like SAP Data Intelligence or SAP HANA supporting custom algorithms & approaches to time series. You can read more to the overall SAP AI landscape in Vriddhi Shetty excellent blog.

Question: which data sources can be used in context of SAP Analytics Cloud Planning?

Answer: SAP Analytics Cloud can connect to various on-premise and cloud data sources including SAP HANA, SAP BW, SAP S/4HANA, SAP BPCOData, Google BigQuerySQL and more.

Since Predictive Planning is based on SAC planning models, all data sources that can be leveraged by SAC planning models are automatically supported, with the exception of BPC. 

This includes data from many on-premise data sources (SAP BW, SAP HANA, S/4, Universes, SQL databases, file servers and Odata services) as well as cloud data sources from SAP (like SuccessFactors, Fieldglass, Concur, Hybris, ByDesign) as well as from external provides (like, Google Drive or Google Big Query).

Check out our connecting to data page for more information. Find help on setting up your connections in the connection guide.

Question: I would like to write-back data from SAC / Planning to source systems. On which systems is this possible? 

Answer: please refer to the main page “Exporting Data” in the official product documentation.

Main SAP note:

SAP Business Warehouse (BW) 7.5. See

SAP BW/4HANA 2.0. See (part 1) (part 2)

As a take-away all these data sources can be used in combination with SAC / Planning and Predictive Planning:

  • data can be imported and scheduled refreshes can be put in place
  • it’s then possible to plan, predict and visualize all in SAP Analytics Cloud
  • it’s possible to write back to the underlying source systems

As mentioned in an earlier question, we have proven examples and success stories of customers doing this on top of SAP BW, as one example.

Question: is data being replicated into SAP Analytics Cloud (aka acquired) or live?

Answer: All planning models in SAP Analytics Cloud are based on acquired/replicated data with the exception of SAP BPC Embedded (Business Planning and Consolidation). This means Predictive Planning also deals with acquired/replicated data.

Scheduled data refresh is possible for many of these data sources. Refer here.

Please note that Smart Predict offers a live integration with SAP HANA on-premise since Q4 2019. This can be beneficial to support complementary predictive and planning scenarios. Refer here and  there.

Please also note that SAP Analytics Cloud planning models based on BPC are not supported by Predictive Planning, regardless of the BPC version.

Question: I am a SAP BPC customer, why would I want to extend my investment with SAP Analytics Cloud Planning?

Answer: please read more here and see the top 10 reasons here

Question: What are the data acquisition and data preparation limits when it comes to datasets, models and stories?

Answer: please refer to the official help page here.

Question: how to integrate outcomes from Smart Predict classification and regression models into the planning process?

Answer: there are multiple ways this can be done.

Question: can I run Predictive Planning on SAC / Planning models that use fiscal year concepts?

Answer: Yes.

Question: does Predictive Planning support local currencies? is it possible to predict and plan using local currencies?

Answer: Yes. Please see the detailed blog here:

Question: How to influence the time series predictive scenario with other datasets say Sales data + Temperature datasets from other file?

Answer: Please see the detailed blog here:

Here is the suggested approach to validate the business benefits of using influencers when creating forecasts:

Based on this comparison, determine if it’s worth it to include influencers as data models are by nature more complex to maintain over time.

We do plan to offer support of influencers in Predictive Planning in the second half of 2021.

Question: Will it be possible to use influencers for predictive planning in later releases?

Answer: When Smart Predict time series is using datasets as an underlying data foundation, additional drivers (aka influencers) can be used and might bring a benefit to the predictive forecasting activity. We do plan to add this capability to Predictive Planning in the course of 2021.

Question: On relevant data I rather run quickly into the 1000 entity limit. Customer/Product combinations are common, but also a limit for predictive. Why?

Answer: The limit of 1000 has been defined to avoid consuming too much processing power of SAP Analytics Cloud for one given predictive run and keep the ability to display per-entity results in the user interface. If you face this limit in context of your use cases, you can use the best practice to proceed with multiple, independent runs that you can then combine in a unique private version.

Question: Do you consider making the meaning of Trend, Cycles etc even more clear in Predictive Planning?

Answer: Short answer is yes :-). Long answer is we feel Predictive Planning is about combining and not compromising on three pillars: Smart, Self-Service and Trust:

  • Smart means being accurate and we invest in the first half of this year 2021.
  • Self-service means being intimately integrated in the end-to-end planning experience. While we have an excellent support of all core concepts today, we will continue with for instance support of parent-child hierarchies planned in 2021.
  • To your question, the trust arises when models are clearly understood and acted upon. We will continue to bring the maximum transparency. For instance in Q2 2021 QRC release, we’ll let planners publish predictive forecasts for past period to stories, in order to compare the predictions to the actuals more easily.

(note: find the official roadmap for the entire SAP Analytics Cloud capabilities here).

Question: Is there is any info about the accuracy of the predictive model?

Answer: yes, we do offer a simple measure of model accuracy named the HW-MAPE (standing for Horizon-Wide Mean Absolute Percentage Error), which measures the accuracy of a time series model and represents the average error that the predictive model is likely to commit when used in the future.

In addition it’s possible to deliver predictive forecasts into stories where they can be freely compared with actuals, plans and budget using the strong calculation capabilities of SAP Analytics Cloud.

Question: Besides Horizon-Wide MAPE, are other indicators of predictions validation available, e.g. R Square (R2), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), mean absolute percentage error (MAPE), others?

Answer: Other indicators are not available natively in Predictive Planning. The guidance here would be to use story capabilities and create custom indicators as appropriate, this can help with post-validation of the predictive forecasts.

Question: Let’s say I am too scarce in terms of historical data I can provide. For instance I want to predict the whole of 2021 and 2022 at monthly level but I only have 5 years history (60 months) from 2016 to 2020. Should I predict 2021 first, then predict 2022 combining actuals from 2016 to 2020 and predictions from 2021?

Answer: while this may sound a workable workaround I would not suggest to engage in this route. The 2022 forecasts would be based on 2021 predictions and thus uncertainty might amplify in a way that’s difficult to evaluate beforehand. The sweet spot for Predictive Planning monthly forecasting scenarios probably stands between 6 months and 18 months, based on the historical data that’s available and respecting the 5 to 1 ratio (between actuals and predictions) that was explained earlier on.

Question: How does the machine learning engine use the historical data to predict future forecasts? Does this continue to learn, if it predicts for a few periods and then gets actual data, does it use that new data to make better new forecasts?

Answer: The overall mechanism is detailed in the blog here: To your point, the idea is each time there are new actuals, ideally you should refresh your predictive model and thus the forecasts. In the case of monthly predictions the predictive model should be refreshed every month.

Question: How much Data is needed to predict e.g. 3 month and how good will that prediction compared to HI (human intelligence)?

Answer: Typically the ideal ratio (historical data to predictive forecasts) is 5 to 1. In my view we humans should always make sure that human intelligence rules over the machine 🙂 Pun apart, it’s about comparing the Predictive Planning to the current planning processes in place,  their accuracy, speed and efficiency and compare this to a new process “augmented” with the use of predictive. Good thing is that SAP Analytics Cloud stories make it easy to perform a data-driven comparison.

Question: What are the differences between SAP Analytics Cloud Predictive Planning (aka Smart Predict on planning models) and and SAP Analytics Cloud Smart Predict using datasets?

Answer: SAC Smart Predict has initially been part of SAP Analytics Cloud (SAC),  integrating with SAC‘s datasets which are themselves flat, tabular data structures unlike the more OLAP-oriented SAC BI models or SAC planning models.

With datasets, SAC Smart Predict supports regression, classification and time-series forecasting scenarios. For time-series, candidate influencers are supported. For datasets, only a subset of data sources is supported as compared to the list of data sources for creating BI or planning models. For a detailed list of restrictions, check the documentation.

For Predictive Planning, i.e. the integration of SAC Smart Predict with SAC planning models, only time-series forecasting is supported. This makes ample sense semantically since planning is all about the development of KPIs over time time, i.e. time series. In Predictive Planning, candidate influencers are not yet supported, but their support is part of the roadmap. There are ways forecasts including influencers can be reported on in context of stories – see

Question: How does Smart Predict deal with top-down and bottom-up approaches for budgeting?  

Answer: You are free to do both since predictions can either be done on a leaf-level and aggregated up; alternatively customers are free to forecast on intermediate or top-level of the dimension hierarchy by leveraging dimension attributes for defining the forecasting entity. Forecast results would then be spread down to leaf members through disaggregation that itself can be influenced by the customer. The video Using Entities in the advanced knowledge learning track goes into very much detail on the different possibilities.

Question: Is there a way to simulate different future scenarios with Predictive Planning?  

Answer: First of all, simulation and forecasting are really two different cups of tea and should not be mixed. Therefore the straight answer is that this is only possible indirectly and should be used with caution. If you still want to do this, you have two options:

  1. within Predictive Planning, you can consider preparing a private version of the planning model that describes your scenario consistently in the past. Predictive Planning can use this base data for training and hence generate the respective forecast for it
  2. If your scenario involves a second variable (how much ice cream will I sell if this summar is hot? How much if it’s cold?), you are required to go back to time-series based on datasets. This is necessary in order to leverage that second variable (warm/cold weather) as a “candidate influencer“. You could therefore prepare several datasets with different values of the candidate influencer to represent the different scenarios. After saving the forecasts back to a dataset, you can transfer the predictions back to the planning model by virtue of data actions with a copy step as described in this blog. Another way of doing this is this:

Question: How can I refresh my predictions at a later time?  What automation possibilities exist? 

Answer: Once new data is in your planning model, you can always go back to your predictive scenario and retrain the predictive model on the latest data. Since all the configurations are preserved, this boils down to just pressing a button to retrain the model and save the predictive forecasts.

It is currently not possible to fully automate the end-to-end process through e.g. scheduling. This is not necessarily a big problem, since

  • Predictive forecasts are typically not generated every day but rather every other month according to the schedule of the controlling department. Since there is already a high-level of forecasting automation by generating many forecasts at the same time along the chosen forecasting dimensions, the pressing of single button every other month should not be an issue
  • Controllers would typically want to double-check the output of the predictive planning process in detail before saving results out the planning model. Fully automating the process without any user intervention is not always desirable.

Question: what is the time granularity for which predictive forecasts get generated? 

Answer: Let’s consider the most simple cases first.

I create a planning model and the granularity of the date dimension can be either Year, Quarter, Month or Day (read more about the Date Dimension here).

Let’s also assume the data is being stored at the same level. In some cases I could define the Date Dimension on a certain level but the data is being stored at a different level (example a daily model as per the date dimension but the data is stored on a weekly basis).

In this case Predictive Planning will display the proper time granularity and produce forecasts with the corresponding granularity.

Again let’s continue on the same simple example:

  • The granularity of the date dimension of my planning model is defined as monthly.
  • I do have data for months going from January 2016 to December 2020, 60 months
  • I ask 12 forecasts to Predictive Planning

In this case Predictive Planning will generate forecasts from January 2021 to December 2021.

Similarly if I have a date dimension at daily level, I have data from January 1st 2016 to December 31st, 2020 and ask for 365 forecasts ahead, I will then get predictive forecasts from January 1st, 2021 to December 31st, 2021.

The description I did is nicely summarized in the official help here: Time granularity: The time series predictive model is trained and applied based on the level of time granularity available in the planning model data source. If the planning model lowest level is daily, then Smart Predict will create daily predictive forecasts.”

Let’s now move to somewhat more complex cases where the time granularity in the date dimension and the effective time granularity of the data differs.

One example could be a model with a daily time granularity defined in the date dimension but the data is effectively stored every month or every week for instance. In this scenario Predictive Planning will give priority to the effective granularity of the data.

Let’s take two concrete examples there:

Example 1: I have a model with daily granularity in the Date dimension – from January 1st, 2016 to December 31st, 2021. In practice though the data is stored at monthly level – I do have one row of data from January 2016, one for February 2016 etc. if I ask for 12 forecasts, they will be generated for January 2021 to December 2021, not for January 1st, 2021 to January 12nd, 2021.

Example 2: I have a model with daily granularity in the Date dimension – from January 1st, 2016 to December 31st, 2021. In practice though the data is stored at weekly level – I do have one row of data from January, 1st 2016, one for January 8th 2016 etc. if I ask for 12 forecasts, they will be generated for the first 12 weeks of 2021 (depending when the end of the last week of 2020 falls!)

Now in the most complex scenarios it could be that the data granularity is different from regular patterns. For instance I might have data stored every 10 days. Again Predictive Planning will identify the data granularity and reproduce it for the future, generating one predictive forecast every 10 days.

Question: is it possible to write back predictive forecasts for past periods? 

Answer: in Q2 2021 QRC release, we plan to provide planners with the ability to publish predictive forecasts for past period to stories, in order to compare the predictions to the actuals more easily.

Question: is it possible to exclude certain entities with data issues, be it for lack of information, low number of training records, or large gaps in time series?

Answer: exclusion can be handled in multiple ways:

  • Using specific attributes to remove specific dimension members. In 2021.H2 we plan to offer hierarchy support when defining the entity to facilitate this exclusion / filtering.
  • The source version in the planning model used to train the predictive model can be pre-processed to filter out some parts of the data and avoid the corresponding entities to be created.

Question: what do you suggest as a way to control the quality of the data, before using it to forecats, as the quality of the data may strongly vary accross entities?

Answer:  The suggested best practice is to make ample use of stories before forecasts are generated to pre-validate the quality of the data and exclude what should be excluded from the predictive forecasting scope up-front (pre-processing).

In post-processing there are different ways the quality of the predictions should be gauged before they are effectively exposed to planners’ attention – the Horizon-Wide MAPE indicator in the predictive scenario directly measures the expected error. This can be complemented, creating  custom indicators, calculations or variance analysis in stories.

Question: when writing back the predictive forecasts to the private version, I received an error that not all forecasts could be written back and that the planning model time range should be extended. I am not sure why I receive this error and what to do. Can you please help?

Answer: irregular time series might cause the predictive forecasts to extend beyond what the end-user expects as a range. If I have only one data point filled every two years, Predictive Planning will reproduce this similar pattern in the future. The typical way to prevent this message from appearing is to exclude the entities causing this problem up-front or fixing the fact that data is too sparse.



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