How To Fix /SAPAPO/AMON_LIST069 - MLR: R**2 lower limit not reached by &UNI_PERCENT %

SAP Error Message - Details

• Message type: E = Error

• Message class: /SAPAPO/AMON_LIST -

• Message number: 069

• Message text: MLR: R**2 lower limit not reached by &UNI_PERCENT %

Self-Explanatory Message

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Smart SAP Assistant

• What is the cause and solution for SAP error message /SAPAPO/AMON_LIST069 - MLR: R**2 lower limit not reached by &UNI_PERCENT % ?

  The SAP error message /SAPAPO/AMON_LIST069 MLR: R**2 lower limit not reached by &UNI_PERCENT % is related to the Advanced Planning and Optimization (APO) module in SAP, specifically in the context of statistical forecasting and model evaluation.
  Cause:
  This error indicates that the R-squared (R²) value of a statistical model used for forecasting is below an acceptable threshold. R² is a statistical measure that represents the proportion of variance for a dependent variable that's explained by an independent variable or variables in a regression model. A low R² value suggests that the model does not explain the variability of the data well, which can lead to unreliable forecasts.
  The message indicates that the R² value is lower than the defined lower limit by a certain percentage (&UNI_PERCENT), which means the model's performance is not satisfactory.
  Solution:
  To address this error, consider the following steps:
  
  
  Review the Forecasting Model:
  
  Check the statistical model being used for forecasting. Ensure that the model is appropriate for the data being analyzed.
  Consider using different forecasting methods or adjusting the parameters of the current model.
  
  
  
  Data Quality:
  
  Ensure that the data used for forecasting is of high quality. Look for missing values, outliers, or anomalies that could affect the model's performance.
  Clean and preprocess the data as necessary.
  
  
  
  Adjust Model Parameters:
  
  If applicable, adjust the parameters of the forecasting model to improve its fit to the historical data.
  Consider using more relevant independent variables that may better explain the dependent variable.
  
  
  
  Increase Historical Data:
  
  If possible, use a larger dataset for training the model. More data can help improve the model's accuracy and R² value.
  
  
  
  Consult Documentation:
  
  Review SAP documentation or help resources for specific guidelines on improving model performance in APO.
  
  
  
  Seek Expert Advice:
  
  If the issue persists, consider consulting with a data analyst or an SAP APO expert who can provide insights into improving the forecasting model.
  
  
  
  Related Information:
  
  R-squared Interpretation: R² values range from 0 to 1, where 0 indicates that the model does not explain any variability and 1 indicates perfect explanation. Generally, an R² value above 0.7 is considered acceptable for many forecasting applications, but this can vary based on the context.
  Statistical Forecasting Techniques: Familiarize yourself with various statistical forecasting techniques such as moving averages, exponential smoothing, and regression analysis to find the best fit for your data.
  SAP APO Documentation: Refer to the official SAP documentation for Advanced Planning and Optimization for more detailed information on statistical forecasting and model evaluation.
  
  By following these steps, you should be able to address the error and improve the forecasting model's performance in SAP APO.