How To Fix /SAPAPO/ESPP_PR513 - Q statistic: &1, degrees of freedom: &2, threshold &3

SAP Error Message - Details

• Message type: E = Error

• Message class: /SAPAPO/ESPP_PR -

• Message number: 513

• Message text: Q statistic: &1, degrees of freedom: &2, threshold &3

Self-Explanatory Message

Since SAP believes that this specific error message is 'self-explanatory,' no more information has been given.The majority of messages in the SAP system have a message text, however this is frequently insufficient to comprehend or resolve the problem.

To make things easier, more detailed information is frequently added to describe the issue, how to fix it, and the necessary steps or configuration modifications.

Unfortunately, there isn't any extra information in this error notice.

What else can you do?

First, use our AnswerBot below to get a possible cause and solution (requires a premium subscription).

Also, review the in-depth Common Questions & Answers listed below; you could discover a solution there or be able to connect with others who have faced similar challenges.

You can also try searching the SAP support portal (support.sap.com) but you need a special user ID to access it. It is possible that an SAP support note exists that provides additional details about the mistake or even steps for fixing it.

Smart SAP Assistant

• What is the cause and solution for SAP error message /SAPAPO/ESPP_PR513 - Q statistic: &1, degrees of freedom: &2, threshold &3 ?

  The SAP error message /SAPAPO/ESPP_PR513 is related to the statistical analysis performed in the SAP Advanced Planning and Optimization (APO) module, specifically in the context of demand planning or forecasting. This error message indicates an issue with the statistical calculations, particularly concerning the Q statistic, which is used to assess the quality of the forecast.
  Cause:
  The error message typically arises due to one of the following reasons:
  
  
  Insufficient Data: The dataset being analyzed may not have enough historical data points to perform a reliable statistical analysis. This can lead to an invalid Q statistic calculation.
  
  
  Data Quality Issues: There may be anomalies or outliers in the data that affect the statistical calculations, leading to unexpected results.
  
  
  Model Configuration: The forecasting model or parameters may not be set up correctly, which can lead to issues in the statistical evaluation.
  
  
  Threshold Settings: The threshold value set for the Q statistic may be too low or inappropriate for the dataset being analyzed.
  
  
  Solution:
  To resolve the error, consider the following steps:
  
  
  Check Data Volume: Ensure that there is sufficient historical data for the forecast. If the dataset is too small, try to include more data points.
  
  
  Data Cleansing: Review the data for any anomalies, outliers, or missing values. Clean the data to ensure it is suitable for statistical analysis.
  
  
  Review Model Settings: Check the configuration of the forecasting model being used. Ensure that the parameters are set correctly and are appropriate for the data.
  
  
  Adjust Thresholds: Review the threshold settings for the Q statistic. If necessary, adjust the threshold to a more appropriate value based on the characteristics of the data.
  
  
  Consult Documentation: Refer to SAP documentation or help resources for specific guidance on the Q statistic and its implications in the context of your forecasting model.
  
  
  Seek Expert Help: If the issue persists, consider reaching out to SAP support or consulting with an SAP APO expert who can provide more tailored assistance.
  
  
  Related Information:
  
  
  Q Statistic: The Q statistic is a measure used in time series analysis to evaluate the goodness of fit of a forecasting model. It helps in identifying whether the forecast errors are random or if there are patterns that need to be addressed.
  
  
  SAP APO: SAP Advanced Planning and Optimization is a suite of tools designed for supply chain management, including demand planning, supply network planning, and production planning.
  
  
  Statistical Forecasting: Understanding the principles of statistical forecasting and the various models available in SAP APO can help in better configuring and troubleshooting forecasting issues.
  
  
  By following these steps and understanding the underlying concepts, you should be able to address the error message effectively.