How To Fix /SAPAPO/AMON_LIST070 - MLR: Adjusted R**2 lower limit not reached by &UNI_PERCENT %

SAP Error Message - Details

• Message type: E = Error

• Message class: /SAPAPO/AMON_LIST -

• Message number: 070

• Message text: MLR: Adjusted 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_LIST070 - MLR: Adjusted R**2 lower limit not reached by &UNI_PERCENT % ?

  The SAP error message /SAPAPO/AMON_LIST070 MLR: Adjusted R**2 lower limit not reached by &UNI_PERCENT % 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.
  Cause:
  This error indicates that the Adjusted R-squared value, which 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, is below an acceptable threshold. The message specifies that the Adjusted R-squared value is lower than the defined limit by a certain percentage (&UNI_PERCENT %). This typically suggests that the model used for forecasting is not adequately capturing the underlying patterns in the data.
  Possible Causes:
  
  Poor Data Quality: The input data may contain outliers, missing values, or inaccuracies that affect the model's performance.
  Inappropriate Model Selection: The forecasting model chosen may not be suitable for the data characteristics (e.g., seasonality, trends).
  Insufficient Data: There may not be enough historical data to build a reliable model.
  Overfitting or Underfitting: The model may be too complex or too simple for the data, leading to poor predictive performance.
  
  Solution:
  To address this error, consider the following steps:
  
  
  Data Review:
  
  Check the quality of the historical data used for forecasting. Look for missing values, outliers, or inconsistencies.
  Ensure that the data is clean and pre-processed appropriately.
  
  
  
  Model Evaluation:
  
  Review the forecasting model being used. Consider whether it is appropriate for the data characteristics (e.g., seasonal patterns, trends).
  Experiment with different forecasting models available in SAP APO to see if a different model yields a better Adjusted R-squared value.
  
  
  
  Increase Data Volume:
  
  If possible, increase the amount of historical data used for the model. More data can help improve the model's accuracy.
  
  
  
  Parameter Tuning:
  
  Adjust the parameters of the forecasting model to see if performance improves. This may involve changing settings related to seasonality, trend adjustments, etc.
  
  
  
  Consult Documentation:
  
  Review SAP documentation or help resources for specific guidelines on improving model performance and understanding the implications of the Adjusted R-squared value.
  
  
  
  Seek Expert Help:
  
  If the issue persists, consider consulting with a data analyst or an SAP APO expert who can provide insights based on the specific context of your data and forecasting needs.
  
  
  
  Related Information:
  
  Adjusted R-squared: A statistical measure that adjusts the R-squared value based on the number of predictors in the model. It is used to determine how well the model explains the variability of the dependent variable.
  Forecasting Models in SAP APO: SAP APO offers various forecasting models, including exponential smoothing, moving averages, and regression models. Each has its strengths and weaknesses depending on the data characteristics.
  SAP Notes and Support: Check SAP Notes for any known issues or updates related to this error message, as well as best practices for forecasting in SAP APO.
  
  By following these steps, you should be able to identify the root cause of the error and implement a solution to improve the forecasting model's performance.