Understand Restrictions with Predictive Planning

The following restrictions currently apply to Predictive Planning in Smart Predict.

Restrictions Using Planning Model as Data Source for Smart Predict

Restrictions on	Information on Restrictions
Type of predictive models	For Smart Predict - Predictive Planning, i.e. the integration of SAP Analytics Cloud Smart Predict with SAP Analytics Cloud planning models, only time series forecasting is supported.
Input planning models	Type: Smart Predict - Predictive Planning only supports standalone planning models (both new model types and classic account models). All data sources that can be leveraged by SAP Analytics Cloud planning models are automatically supported, with the exception of SAP BPC. Neither live nor acquired SAP Business Planning and Consolidation (SAP BPC) planning models are supported. Version: You can use public or private versions. The input version must be a public version, not in edit mode, or a private version. You have at least a read access to it. Note Smart Predict doesn't support predictive forecasting on calculated measures, including currency conversion measures, when your planning model is a new model type. For classic account models, there are specificities while using public or private versions when currency conversion is enabled. For more information, see Use Currencies with Predictive Planning. Prompt: Planning models with prompts are supported. If prompts are present, default prompt values (set by the user or SAP Analytics Cloud defaults) are used to query the data. There is no way to set or change values.
Entities (crossing of multiple dimensions) For more information on entities, see also Get Distinct Predictive Forecasts per Entities For your Planning Model	You cannot select more than 5 dimensions or attributes to create a time series predictive model generating predictive forecast value per entity. The maximum number of entities supported is 1000. Attributes and Hierarchies: You can select the attributes (custom properties) that form part of level-based hierarchies or that can be freely defined. You can select system properties like the currency for instance. The levels can be selected indirectly by selecting the custom properties that form part of the level-based hierarchy, whereas for parent child hierarchies, you need to create them as Smart Predict can generate one segment for each leaf only. Tip It's possible to add custom properties to group members in custom ways: you can use this mechanism to keep the number of entities under 1000 and perform an intermediate forecasting approach where predictive forecast is run on intermediate nodes: For nodes above, predictive forecasts are spread and for nodes below, they will be summed. One entity can combine one or multiple dimension members.
Entity Filters	(Null) and (No Value) values are not supported for attributes and hence not listed for member selection in Available Members. When defining a filter, a maximum of 1000 values are displayed under the list of Available Members for selection. If the selection dimension has more members, the user has to make a search limited to the list of Available Members and the search displays a max of 1000 results.
Predictive Goal	These are Smart Predict - Predictive Planning settings available: Target: a valid target is a numeric value that is data entry enabled. A numeric value which involves formulas, or with aggregation type LABEL or NONE is not a valid target. Supported numeric values are leaf members in the account dimension hierarchy, with no formula, or a parent numeric value with aggregation type SUM, or no aggregation defined (defaults to SUM) provided that none of its descendants is a member which involves a formula, or with aggregation type LABEL or NONE. Smart Predict doesn't support calculated measures when using a planning model, even if an inverse formula is provided. For more information on inverse formulas, you can refer to the chapter Inverse Formulas. Time granularity: indicates the time granularity inherited from the planning model data source. The planning model time granularity is used to infer the forecast dates. For more detail you can refer to the section below called Time Aggregation. Date: the date dimension used to create the time series predictive model. Dimensions or Attributes: you can combine multiple dimensions and attributes to use as source for the entities to get distinct predictive forecasts per entities. Number of Forecasts periods: number of forecast values you want to get. The number of historical data points in the planning model conditions the number of confident predictive forecasts you can get. The current ratio is 5 to 1. For example, if you want one confident forecast, you need at least five historical data points in your planning model. For more information, see How Many Forecasts can be Requested?.
Outputs	Output versions: You can write-back the predictions only on private versions of planning models. The private versions have to be created beforehand in stories. For more information, see About Version Management. You cannot write-back the predictive forecasts in a dataset.
Time aggregation	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. When creating a planning model, the time granularity of the date dimension can be either Year, Quarter, Month or Day. So, as a simple example, if the planning model's lowest level of time granularity is monthly, then Smart Predict creates monthly predictive forecasts. Example The granularity of the date dimension of your planning model is defined as monthly. You have data for the months from January 2016 to December 2020 - 60 months. You ask Smart Predict for 12 forecasts. In this case, Smart Predict generates forecasts from January 2021 to December 2021.
Spreading	The spreading policy is the default policy available in the planning model data source. It depends on how the dimension is used in the model: Date Dimension: no spreading occurs on date dimension as predictive forecasts are generated using data at the lowest level of granularity for the date dimension. Dimension used as part of the entity definition: no spreading occurs. Dimension attribute used as part of the entity definition: spreading occurs no matter if this attribute is used as a level-based hierarchy or not. Dimension not used as part of the entity definition: spreading occurs. If the target cell (in the horizon) is unbooked, the value is allocated to the Unassigned member of this dimension. Whereas if the target cell is booked, the value will be split according to existing weights. For example, if your planning model data source has defined a spreading and if you have run the predictive forecasts on a parent node (for example, `<All Regions>`), results are automatically spread across all levels below (for example, `<North America>`, `<EMEA>`, and then `<all countries>` below, then `<all cities>`, etc..). For more information, you can refer to Spreading a Value, Entering Values in a Table, and Disaggregation of Values during Data Entry.
Influencer Restriction	How Adding Influencers to Your Planning Model Can Potentially Increase the Accuracy of Your Predictive Model?
Support of account dimension with multiple account hierarchies	Predictive Planning supports planning models where multiple hierarchies are defined in the account dimension. It is not possible to select the account hierarchy that should be used to select the target variable for the predictive scenario. Only the accounts that are part of of the default account hierarchy can be selected in the settings of the predictive scenario.
Data volume	The SAP Analytics Cloud does not allow retrieving more than 1.000.000 cells per query. You can try the following solutions to reduce the number of cells to retrieve: Reduce the number of influencers used in the predictive model Reduce the number of entities Reduce the range of observations user to train the predictive model
User Managed Date Members	Date dimensions with User Managed members are supported by Predictive Planning. Nevertheless, you must ensure that no adjustment period exists in the training data. As adjustment periods are meant to collect values that should have been associated to anterior periods, the existence of adjustment periods in the training data would bias the predictive model and the forecast would therefore not be reliable.