How To Fix PR429 - Overflow when calculating a seasonal index; 0 or 99.99 is set

PR429 - Overview

• Message type: E = Error

• Message class: PR - Forecast: Help messages

• Message number: 429

• Message text: Overflow when calculating a seasonal index; 0 or 99.99 is set

What causes this issue?

With the calculation of a seasonal index, either the maximum value
of 99.99 was exceeded, or it fell short of the minimum value of 0
(negative seasonal index). Both cases can also occur repeatedly.
In addition, it is possible that both excesses and shortfalls were
found. In these cases, the values 0 or 99.99 are set by the system.

System Response

The system issues an error message and will not allow you to continue with this transaction until the error is resolved.

How to fix this error?

Check the choice of forecast model. If sporadic past development
exists, an average model may be more suited.

Error message extract from SAP system. Copyright SAP SE.

Smart SAP Assistant

• What is the cause and solution for SAP error message PR429 - Overflow when calculating a seasonal index; 0 or 99.99 is set ?

  The SAP error message PR429, which indicates an "Overflow when calculating a seasonal index; 0 or 99.99 is set," typically occurs in the context of demand planning or forecasting within the SAP system. This error suggests that there is an issue with the seasonal index calculation, which is crucial for accurate forecasting.
  
  Cause:
  
  
  Data Issues: The error often arises when the data used for calculating the seasonal index is not appropriate. This could include:
  
  Missing or incomplete historical data.
  Data that is not properly formatted or contains invalid values.
  Seasonal patterns that are not well-defined due to insufficient data points.
  
  
  
  
  Configuration Issues: Incorrect settings in the forecasting model or parameters can lead to this error. For example, if the seasonal index is set to 0 or an unrealistic value like 99.99, it can trigger an overflow during calculations.
  
  
  Model Limitations: The forecasting model being used may not be suitable for the data set, leading to extreme values