How To Fix /SAPAPO/TSM027 - R**2 = &1 ; adjusted R**2 = &2 ; Durbin-Watson = &3 ; MAPE = &4

SAP Error Message - Details

• Message type: E = Error

• Message class: /SAPAPO/TSM -

• Message number: 027

• Message text: R**2 = &1 ; adjusted R**2 = &2 ; Durbin-Watson = &3 ; MAPE = &4

Self-Explanatory Message

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Smart SAP Assistant

• What is the cause and solution for SAP error message /SAPAPO/TSM027 - R**2 = &1 ; adjusted R**2 = &2 ; Durbin-Watson = &3 ; MAPE = &4 ?

  The SAP error message /SAPAPO/TSM027 is related to the time series forecasting functionality in SAP Advanced Planning and Optimization (APO). This message typically indicates that there are issues with the statistical measures used in the forecasting model, specifically concerning the goodness of fit and the reliability of the forecast.
  Cause:
  The error message provides the following statistical metrics:
  
  R² (R-squared): Indicates the proportion of variance in the dependent variable that can be explained by the independent variable(s). A low R² value suggests that the model does not explain much of the variability in the data.
  Adjusted R²: Adjusts the R² value based on the number of predictors in the model. It can decrease if the added predictors do not improve the model.
  Durbin-Watson statistic: Tests for autocorrelation in the residuals from a regression analysis. Values close to 2 suggest no autocorrelation, while values significantly lower than 2 indicate positive autocorrelation.
  MAPE (Mean Absolute Percentage Error): Measures the accuracy of a forecasting method. A high MAPE indicates poor forecasting performance.
  
  The error message indicates that the statistical measures are not within acceptable limits, which can be caused by:
  
  Poor data quality (e.g., missing values, outliers).
  Inappropriate model selection for the data.
  Insufficient historical data for the forecasting model.
  Seasonal patterns not being accounted for properly.
  
  Solution:
  To resolve this error, consider the following steps:
  
  
  Data Quality Check:
  
  Ensure that the historical data used for forecasting is complete and clean. Look for missing values or outliers that could skew the results.
  
  
  
  Model Review:
  
  Review the forecasting model being used. Ensure that it is appropriate for the data characteristics (e.g., trend, seasonality).
  Consider using different forecasting models or adjusting the parameters of the current model.
  
  
  
  Increase Data Volume:
  
  If possible, increase the amount of historical data used for forecasting. More data can help improve the model's accuracy.
  
  
  
  Check for Seasonality:
  
  If the data exhibits seasonal patterns, ensure that the model accounts for these patterns appropriately.
  
  
  
  Statistical Analysis:
  
  Conduct a statistical analysis of the residuals to check for patterns that may indicate model inadequacies.
  
  
  
  Consult Documentation:
  
  Refer to SAP documentation or help resources for specific guidelines on interpreting these statistics and improving forecasting models.
  
  
  
  Seek Expert Help:
  
  If the issue persists, consider consulting with a data analyst or an SAP expert who specializes in forecasting and statistical modeling.
  
  
  
  Related Information:
  
  Familiarize yourself with the statistical concepts behind R², adjusted R², Durbin-Watson, and MAPE to better understand their implications on forecasting.
  Review SAP's documentation on time series forecasting and statistical analysis within the APO module for best practices and troubleshooting tips.
  Consider training or workshops on SAP APO and statistical forecasting methods to enhance your understanding and skills in this area. 
  
  By addressing the underlying issues indicated by the error message, you can improve the forecasting accuracy and reliability in your SAP APO system.