How To Fix /SAPAPO/AMON_ALERTS443 - MLR: Shortfall below Durbin Watson lower limit

/SAPAPO/AMON_ALERTS443 - Overview

• Message type: E = Error

• Message class: /SAPAPO/AMON_ALERTS -

• Message number: 443

• Message text: MLR: Shortfall below Durbin Watson lower limit

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_ALERTS443 - MLR: Shortfall below Durbin Watson lower limit ?

  The SAP error message /SAPAPO/AMON_ALERTS443 MLR: Shortfall below Durbin Watson lower limit is related to the Advanced Planning and Optimization (APO) module in SAP, specifically in the context of demand planning and forecasting. This error indicates that the model's residuals (the differences between the observed and predicted values) are not behaving as expected, which can lead to unreliable forecasts.
  
  Cause:
  The Durbin-Watson statistic is a test for autocorrelation in the residuals from a regression analysis. A value below the lower limit indicates that there is significant positive autocorrelation in the residuals, which suggests that the model may not be capturing the underlying patterns in the data effectively. This can occur due to several reasons:
  
  Model Specification Issues: The forecasting model may not be appropriate for the data, leading to poor fit.
  Data Quality Issues: The input data may contain outliers, missing values, or other anomalies that affect the model's performance.
  Seasonality or Trend: The model