How To Fix /SAPAPO/MA724 - Durbin-Watson is too high

SAP Error Message - Details

• Message type: E = Error

• Message class: /SAPAPO/MA -

• Message number: 724

• Message text: Durbin-Watson is too high

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/MA724 - Durbin-Watson is too high ?

  The SAP error message /SAPAPO/MA724 Durbin-Watson is too high is related to the statistical analysis performed in the context of demand planning or forecasting within the SAP Advanced Planning and Optimization (APO) module. The Durbin-Watson statistic is a measure used to detect the presence of autocorrelation in the residuals from a regression analysis. A value that is too high indicates that there may be a problem with the model, specifically that the residuals are not behaving as expected.
  Cause:
  
  Model Specification Issues: The forecasting model may not be correctly specified, leading to inappropriate relationships between the variables.
  Data Quality: Poor quality data, such as outliers or incorrect values, can skew the results and lead to a high Durbin-Watson statistic.
  Overfitting: The model may be too complex, capturing noise rather than the underlying trend, which can result in high autocorrelation in the residuals.
  Seasonality and Trends: If the data has strong seasonal patterns or trends that are not adequately modeled, it can lead to high Durbin-Watson values.
  
  Solution:
  
  Review the Forecasting Model: Check the model parameters and ensure that the model is appropriate for the data being analyzed. Consider simplifying the model if it is too complex.
  Data Cleaning: Inspect the input data for outliers or anomalies. Clean the data to ensure that it accurately reflects the underlying trends.
  Adjust for Seasonality: If the data exhibits seasonality, ensure that the model accounts for this. You may need to use seasonal decomposition or other techniques to adjust for seasonal effects.
  Re-evaluate the Variables: Consider whether all the variables included in the model are necessary and relevant. Remove any that do not contribute meaningfully to the model.
  Consult Documentation: Refer to SAP documentation or support for specific guidelines on addressing this error in the context of your particular implementation.
  
  Related Information:
  
  Durbin-Watson Statistic: The statistic ranges from 0 to 4, where a value around 2 indicates no autocorrelation. Values below 1 or above 3 indicate potential issues.
  SAP APO: This module is used for advanced planning and optimization in supply chain management, and understanding statistical measures is crucial for effective demand planning.
  Statistical Analysis: Familiarity with regression analysis and time series forecasting can help in diagnosing and resolving issues related to the Durbin-Watson statistic.
  
  If the problem persists after trying the above solutions, it may be beneficial to consult with a data analyst or an SAP expert who can provide more tailored assistance based on the specific context of your data and forecasting model.