SAP IBP for Demand

Objective

After completing this lesson, you will be able to explain what's new for demand

IBP Demand Forecasting 2411

The image explains how to display results from curve-based forecasting, highlighting key metrics such as the number of planning objects, clusters created, objects per cluster, and centroid curve shapes. It uses an example with planning objects of varying lengths grouped into clusters, indicating specific clusters each planning object belongs to.

Now you can display the results of curve clustering including:

  • The number of planning objects analyzed in each length class
  • The number of clusters created
  • The number of planning objects in each cluster
  • The shape of the centroid curve of each cluster
The image shows the results of curve-based forecasting using the SAP system. It features tables summarizing the number of planning objects in each cluster and a line chart plotting the top 10 most populous clusters.
The image explains that intermittent time series with at least 30% NULL periods should preserve their zero values during outlier correction, as opposed to previous methods where zeros were skipped. It includes a graph showing Preserve these for zero-valued periods and Correct that for identified outliers.

To preserve the characteristics of intermittent time series, zeros must not be corrected.

Before IBP 2411, intermittent time series were skipped by Automatic outlier correction.

From IBP 2411, demand-less time periods are preserved during outlier correction.

The image discusses Automated Exponential Smoothing’s special handling for zeros, explaining that IBP 2411 checks for zeros in time series data before starting and disables multiplicative seasonality if any zeros are found to prevent extreme seasonal peaks. It covers context, root cause, and solution.

Log in to track your progress & complete quizzes

Learning

SAP FacebookSAP YoutubeSAP LinkedIn