How To Fix /SAPAPO/AMON_LIST068 - MLR: Upper MAPE limit exceeded by &UNI_PERCENT %

SAP Error Message - Details

• Message type: E = Error

• Message class: /SAPAPO/AMON_LIST -

• Message number: 068

• Message text: MLR: Upper MAPE 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_LIST068 - MLR: Upper MAPE limit exceeded by &UNI_PERCENT % ?

  The SAP error message /SAPAPO/AMON_LIST068 MLR: Upper MAPE limit exceeded by &UNI_PERCENT % is related to the Advanced Planning and Optimization (APO) module in SAP, specifically in the context of demand planning and forecasting. MAPE stands for Mean Absolute Percentage Error, which is a measure used to assess the accuracy of a forecasting model.
  Cause:
  This error indicates that the forecast accuracy, as measured by MAPE, has exceeded the upper limit set in the system. The &UNI_PERCENT placeholder represents the percentage by which the MAPE limit has been exceeded. This can occur due to several reasons:
  
  Inaccurate Historical Data: If the historical data used for forecasting is inaccurate or contains outliers, it can lead to poor forecast performance.
  Model Selection: The forecasting model being used may not be suitable for the data characteristics, leading to high forecast errors.
  Parameter Settings: The parameters set for the forecasting model may not be optimized, resulting in suboptimal forecasts.
  Seasonality and Trends: If the data has strong seasonal patterns or trends that are not being captured correctly by the model, it can lead to high MAPE values.
  Changes in Demand Patterns: Sudden changes in demand patterns due to market conditions, promotions, or other factors can lead to increased forecast errors.
  
  Solution:
  To address the issue of exceeding the upper MAPE limit, consider the following steps:
  
  Review Historical Data: Check the historical data for accuracy and completeness. Remove any outliers or incorrect entries that may skew the results.
  Adjust Forecasting Model: Evaluate the forecasting model being used. Consider switching to a different model that may better capture the characteristics of the data.
  Optimize Parameters: Review and optimize the parameters of the current forecasting model. This may involve adjusting settings related to seasonality, trend, and other factors.
  Analyze Demand Patterns: Conduct a thorough analysis of demand patterns to identify any changes that may have occurred. Adjust the forecasting approach accordingly.
  Set Realistic Limits: If the upper MAPE limit is set too low for the specific context, consider adjusting it to a more realistic value based on historical performance.
  Use Additional Data: Incorporate additional data sources or variables that may help improve the accuracy of the forecasts, such as market trends, promotions, or economic indicators.
  
  Related Information:
  
  Monitoring and Reporting: Use the monitoring tools in SAP APO to regularly check forecast accuracy and identify trends over time.
  Training and Documentation: Ensure that users involved in demand planning are trained on best practices for data management and forecasting techniques.
  Collaboration: Work closely with stakeholders from sales, marketing, and supply chain to gather insights that can improve forecasting accuracy.
  
  By following these steps, you can work towards resolving the MAPE limit issue and improving the overall accuracy of your demand forecasts in SAP APO.