SAP C_SAC_2501 Practice Test - Questions Answers, Page 2

SAP Analytics Cloud Help Documentation: Hierarchies in Planning

SAP Analytics Cloud User Guide: Managing Hierarchies for Planning

When you have a dimension with members for product groups and associated products and want to plan by product group without disaggregating into the individual products, using two hierarchies is the best approach. One hierarchy can represent the product groups at a higher level, allowing for planning and analysis at the group level. The second hierarchy can include both the product groups and their associated individual products for more detailed analysis when needed. This approach provides flexibility in planning and analyzing data at different levels of detail without the necessity of disaggregating data at the product group level.

SAP Analytics Cloud Help Documentation: Allocation Steps in Planning

SAP Analytics Cloud User Guide: Using Drivers for Allocation

In the context of creating an allocation step to distribute expenses from the HR cost center to operating cost centers in SAP Analytics Cloud, the 'Driver' dimension setting is crucial. This setting determines the basis or criteria on which the distribution is calculated and applied to each operating cost center. For instance, the driver could be the number of employees, square footage, or any other relevant metric that justifies the distribution of costs. By defining a driver, you ensure that the allocation of expenses is proportional and fair based on the selected criteria.

SAP Analytics Cloud Help Documentation: Predictive Scenarios and Planning

SAP Analytics Cloud User Guide: Time Series Forecasting in Planning Models

In SAP Analytics Cloud, a Time Series Forecast predictive scenario can write back to a planning model. Time Series Forecasting leverages historical data to predict future values over a specified time horizon, using statistical or machine learning methods. This feature is particularly useful in planning and forecasting processes, where future values are predicted based on past trends and seasonality. The ability to write these forecasts back into a planning model allows for the integration of predictive insights into the planning process, enhancing decision-making and strategic planning.