How To Fix /SAPAPO/AMON_LIST062 - MAD upper limit exceeded by &UNI_PERCENT %

SAP Error Message - Details

• Message type: E = Error

• Message class: /SAPAPO/AMON_LIST -

• Message number: 062

• Message text: MAD upper limit exceeded by &UNI_PERCENT %

Self-Explanatory Message

Since SAP believes that this specific error message is 'self-explanatory,' no more information has been given.The majority of messages in the SAP system have a message text, however this is frequently insufficient to comprehend or resolve the problem.

To make things easier, more detailed information is frequently added to describe the issue, how to fix it, and the necessary steps or configuration modifications.

Unfortunately, there isn't any extra information in this error notice.

What else can you do?

First, use our AnswerBot below to get a possible cause and solution (requires a premium subscription).

Also, review the in-depth Common Questions & Answers listed below; you could discover a solution there or be able to connect with others who have faced similar challenges.

You can also try searching the SAP support portal (support.sap.com) but you need a special user ID to access it. It is possible that an SAP support note exists that provides additional details about the mistake or even steps for fixing it.

Smart SAP Assistant

• What is the cause and solution for SAP error message /SAPAPO/AMON_LIST062 - MAD upper limit exceeded by &UNI_PERCENT % ?

  The SAP error message /SAPAPO/AMON_LIST062 MAD upper limit exceeded by &UNI_PERCENT % typically occurs in the context of SAP Advanced Planning and Optimization (APO) when the Mean Absolute Deviation (MAD) for a particular planning object exceeds a predefined upper limit. This can indicate that the forecast accuracy is poor, which may lead to issues in supply chain planning and inventory management.
  Cause:
  
  Poor Forecasting Accuracy: The MAD value is a measure of forecast error. If the MAD exceeds the upper limit, it suggests that the forecast is not aligning well with actual demand.
  Data Quality Issues: Inaccurate or incomplete historical data can lead to unreliable forecasts.
  Seasonality or Trends: If the product has seasonal demand or trends that are not accounted for in the forecasting model, it can lead to high MAD values.
  Model Selection: The forecasting model being used may not be suitable for the data characteristics of the product.
  
  Solution:
  
  Review and Adjust Forecasting Models: Analyze the forecasting model being used and consider switching to a more appropriate model that better captures the demand patterns.
  Data Cleansing: Ensure that the historical data used for forecasting is accurate and complete. Remove outliers or erroneous data points that could skew the results.
  Parameter Tuning: Adjust the parameters of the forecasting model to better fit the historical data.
  Incorporate Seasonality and Trends: If applicable, use models that can account for seasonal variations and trends in demand.
  Monitor and Analyze: Continuously monitor the MAD values and analyze the reasons for deviations. Implement corrective actions as necessary.
  Consult Documentation: Refer to SAP documentation for specific guidelines on handling MAD limits and improving forecasting accuracy.
  
  Related Information:
  
  Mean Absolute Deviation (MAD): A statistical measure used to assess the accuracy of a forecasting model. It calculates the average absolute differences between forecasted and actual values.
  SAP APO: A component of SAP that provides advanced planning capabilities for supply chain management, including demand planning, supply network planning, and production planning.
  Forecasting Techniques: Familiarize yourself with various forecasting techniques such as moving averages, exponential smoothing, and regression analysis to improve forecast accuracy.
  SAP Notes and Support: Check SAP Notes for any known issues or patches related to this error message, as well as community forums for user experiences and solutions.
  
  By addressing the underlying causes and implementing the suggested solutions, you can reduce the MAD and improve the overall forecasting accuracy in your SAP APO environment.