How To Fix /SAPAPO/AMON_LIST074 - MLR: Durbin-h upper limit exceeded by &UNI_PERCENT %

SAP Error Message - Details

• Message type: E = Error

• Message class: /SAPAPO/AMON_LIST -

• Message number: 074

• Message text: MLR: Durbin-h upper limit exceeded by &UNI_PERCENT %

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/AMON_LIST074 - MLR: Durbin-h upper limit exceeded by &UNI_PERCENT % ?

  The SAP error message /SAPAPO/AMON_LIST074 MLR: Durbin-h upper limit exceeded by &UNI_PERCENT % is related to the Advanced Planning and Optimization (APO) module in SAP, specifically in the context of statistical forecasting. This error indicates that the Durbin-H statistic, which is used to test for autocorrelation in the residuals of a time series model, has exceeded a predefined upper limit. This can suggest that the model may not be adequately capturing the underlying patterns in the data, leading to unreliable forecasts.
  Cause:
  
  Autocorrelation: The primary cause of this error is the presence of autocorrelation in the time series data. This means that the residuals (the differences between the observed and predicted values) are correlated with each other, which violates the assumption of independence in many statistical models.
  Model Specification: The forecasting model may not be correctly specified. This could be due to missing variables, incorrect functional forms, or inappropriate model selection.
  Data Quality: Poor data quality, such as outliers, missing values, or incorrect data entries, can also lead to this issue.
  Seasonality or Trend: If the data exhibits strong seasonal patterns or trends that are not accounted for in the model, it can lead to autocorrelation in the residuals.
  
  Solution:
  
  Review the Model: Check the forecasting model being used. Consider whether it is appropriate for the data. You may need to switch to a different model that better captures the underlying patterns.
  Check for Autocorrelation: Use statistical tests (like the Durbin-Watson test) to check for autocorrelation in the residuals. If autocorrelation is present, consider using models that account for it, such as ARIMA (AutoRegressive Integrated Moving Average) models.
  Data Cleaning: Ensure that the data is clean and free from outliers or errors. Address any missing values appropriately.
  Incorporate Seasonality/Trend: If the data has seasonal or trend components, ensure that these are included in the model. This may involve using seasonal decomposition or adding trend variables.
  Adjust Parameters: If applicable, adjust the parameters of the forecasting model to better fit the data.
  Consult Documentation: Refer to SAP documentation or support for specific guidelines related to the error message and recommended practices for statistical forecasting in APO.
  
  Related Information:
  
  Durbin-H Statistic: This statistic is used to test for the presence of autocorrelation in the residuals of a regression model. A high value indicates that the residuals are not independent.
  Statistical Forecasting: In SAP APO, various statistical forecasting methods are available, including exponential smoothing, moving averages, and regression models. Understanding the strengths and weaknesses of these methods is crucial for effective forecasting.
  SAP Notes and Support: Check SAP Notes for any known issues or patches related to this error message. SAP support can also provide assistance if the problem persists.
  
  By addressing the underlying causes and implementing the suggested solutions, you should be able to resolve the error and improve the reliability of your forecasts in SAP APO.