How To Fix /SAPAPO/AMON_LIST066 - Upper MSE limit exceeded by &UNI_PERCENT %

SAP Error Message - Details

• Message type: E = Error

• Message class: /SAPAPO/AMON_LIST -

• Message number: 066

• Message text: Upper MSE limit exceeded 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_LIST066 - Upper MSE limit exceeded by &UNI_PERCENT % ?

  The SAP error message /SAPAPO/AMON_LIST066 Upper MSE limit exceeded by &UNI_PERCENT % typically occurs in the context of SAP Advanced Planning and Optimization (APO), particularly in the context of demand planning or supply network planning. This error indicates that the upper limit for the Mean Squared Error (MSE) has been exceeded by a specified percentage (&UNI_PERCENT).
  Cause:
  
  Modeling Issues: The forecasting model may not be appropriate for the data being analyzed. This can happen if the data has high variability or if the model parameters are not well-tuned.
  Data Quality: Poor data quality, such as missing values, outliers, or incorrect historical data, can lead to inaccurate forecasts and high MSE.
  Seasonality and Trends: If the data has strong seasonal patterns or trends that are not accounted for in the forecasting model, it can lead to high errors.
  Parameter Settings: The settings for the forecasting model may not be optimal, leading to poor performance.
  
  Solution:
  
  Review the Forecasting Model: Analyze the forecasting model being used. Consider switching to a different model that may better fit the data characteristics.
  Data Cleansing: Ensure that the historical data used for forecasting is clean and accurate. Remove outliers and fill in missing values where necessary.
  Adjust Model Parameters: Fine-tune the parameters of the forecasting model to improve its performance. This may involve adjusting seasonal factors, trend settings, or other model-specific parameters.
  Analyze Data Patterns: Use statistical analysis to understand the underlying patterns in the data. If there are seasonal or trend components, ensure that the model accounts for them.
  Consult Documentation: Refer to SAP documentation for specific guidelines on the forecasting models and their configurations.
  Run Diagnostics: Use the diagnostic tools available in SAP APO to analyze the performance of the forecasting models and identify potential issues.
  
  Related Information:
  
  Mean Squared Error (MSE): MSE is a common measure of forecast accuracy. It calculates the average of the squares of the errors—that is, the average squared difference between the estimated values and the actual value.
  SAP APO: Advanced Planning and Optimization is a component of SAP that helps organizations with supply chain planning, including demand planning, supply network planning, and production planning.
  Forecasting Models: SAP APO supports various forecasting models, including exponential smoothing, moving averages, and regression models. Each model has its strengths and weaknesses depending on the data characteristics.
  
  If the issue persists after trying the above solutions, it may be beneficial to consult with an SAP expert or reach out to SAP support for further assistance.