How To Fix /SAPAPO/AMON_LIST414 - MLR using POS: Calculated MAPE greater than statistical forecast MAPE

SAP Error Message - Details

• Message type: E = Error

• Message class: /SAPAPO/AMON_LIST -

• Message number: 414

• Message text: MLR using POS: Calculated MAPE greater than statistical forecast MAPE

Self-Explanatory Message

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• What is the cause and solution for SAP error message /SAPAPO/AMON_LIST414 - MLR using POS: Calculated MAPE greater than statistical forecast MAPE ?

  The SAP error message /SAPAPO/AMON_LIST414 MLR indicates that there is an issue with the calculated Mean Absolute Percentage Error (MAPE) for a forecast generated using the Multi-Level Regression (MLR) method in SAP Advanced Planning and Optimization (APO). Specifically, it states that the calculated MAPE is greater than the statistical forecast MAPE, which is not expected behavior.
  Cause:
  
  Data Quality Issues: Poor quality or insufficient historical data can lead to inaccurate forecasts. If the data used for regression analysis is not representative or has outliers, it can skew the results.
  Model Specification: The MLR model may not be correctly specified. This could include missing important variables, incorrect functional forms, or multicollinearity among predictors.
  Seasonality and Trends: If the data has strong seasonal patterns or trends that are not accounted for in the MLR model, it can lead to poor forecasting performance.
  Overfitting: The model may be overfitted to the historical data, capturing noise rather than the underlying trend, which can result in a higher MAPE.
  Parameter Settings: Incorrect settings in the forecasting parameters or algorithms can also lead to discrepancies in the MAPE calculations.
  
  Solution:
  
  Data Review: Check the historical data for quality issues. Look for missing values, outliers, or anomalies that could affect the forecast.
  Model Adjustment: Review the MLR model specification. Ensure that all relevant variables are included and that the model is appropriately capturing the relationships in the data.
  Incorporate Seasonality: If applicable, consider incorporating seasonal factors or using a different forecasting method that accounts for seasonality and trends.
  Cross-Validation: Use cross-validation techniques to assess the model's performance on different subsets of the data to ensure it generalizes well.
  Parameter Tuning: Review and adjust the forecasting parameters in SAP APO to ensure they are set correctly for your data and forecasting needs.
  Consult Documentation: Refer to SAP documentation or support for specific guidelines on MLR and MAPE calculations to ensure compliance with best practices.
  
  Related Information:
  
  MAPE: Mean Absolute Percentage Error is a measure of prediction accuracy in a forecasting method. It is calculated as the average of the absolute percentage errors.
  MLR: Multi-Level Regression is a statistical technique used to model the relationship between a dependent variable and multiple independent variables.
  SAP APO: Advanced Planning and Optimization is a tool used for supply chain management, including demand planning and forecasting.
  
  If the issue persists after trying the above solutions, it may be beneficial to consult with SAP support or a specialist in SAP APO for further assistance.