How To Fix /SAPAPO/AMON_ALERTS450 - MLR using POS: Calculated MAPE greater than statistical forecast MAPE

SAP Error Message - Details

• Message type: E = Error

• Message class: /SAPAPO/AMON_ALERTS -

• Message number: 450

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

Self-Explanatory Message

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Smart SAP Assistant

• What is the cause and solution for SAP error message /SAPAPO/AMON_ALERTS450 - MLR using POS: Calculated MAPE greater than statistical forecast MAPE ?

  The SAP error message /SAPAPO/AMON_ALERTS450 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 suggests that the calculated MAPE is greater than the MAPE derived from the statistical forecast, which is typically a sign of poor forecast accuracy.
  Cause:
  
  Data Quality Issues: The underlying data used for the MLR forecast may have inconsistencies, missing values, or outliers that can negatively impact the accuracy of the forecast.
  Model Specification: The MLR model may not be correctly specified. This could include inappropriate selection of independent variables or incorrect functional forms.
  Insufficient Historical Data: If there is not enough historical data to generate a reliable forecast, the MLR model may produce inaccurate results.
  Seasonality and Trends: If the data exhibits strong seasonal patterns or trends that are not adequately captured by the MLR model, it can lead to poor forecasting performance.
  Changes in Demand Patterns: Sudden changes in demand patterns (e.g., due to market changes, promotions, or external factors) that are not reflected in the historical data can lead to inaccuracies.
  
  Solution:
  
  Data Review: Check the quality of the historical data used for forecasting. Look for missing values, outliers, and ensure that the data is clean and consistent.
  Model Adjustment: Review the MLR model specification. Ensure that the independent variables included in the model are relevant and that the model is appropriately capturing the relationships in the data.
  Increase Historical Data: If possible, extend the historical data period to provide the model with more information to work with.
  Incorporate Seasonality: If the data has seasonal patterns, consider using seasonal adjustments or alternative forecasting methods that can better capture these patterns.
  Monitor and Adjust: Continuously monitor the forecast performance and adjust the model as necessary. Use performance metrics to evaluate the accuracy of the forecasts and make iterative improvements.
  Use Alternative Forecasting Methods: If MLR consistently underperforms, consider using other forecasting methods (e.g., exponential smoothing, ARIMA, or machine learning approaches) that may better suit the data characteristics.
  
  Related Information:
  
  MAPE Calculation: MAPE is a measure of forecast accuracy that expresses the error as a percentage of the actual values. It is calculated as the average of the absolute percentage errors.
  SAP APO Forecasting: SAP APO provides various forecasting methods, including statistical methods and advanced techniques like MLR. Understanding the strengths and weaknesses of each method is crucial for effective forecasting.
  Documentation and Support: Refer to SAP documentation for detailed guidance on configuring and using forecasting methods in APO. Additionally, consider reaching out to SAP support for assistance if the issue persists.
  
  By addressing the underlying causes and implementing the suggested solutions, you can improve the accuracy of your forecasts and resolve the error message.