How To Fix /SAPAPO/FCST155 - MAPE for MLR forecasting using POS bigger than MAPE for stat. forecast

SAP Error Message - Details

• Message type: E = Error

• Message class: /SAPAPO/FCST -

• Message number: 155

• Message text: MAPE for MLR forecasting using POS bigger than MAPE for stat. forecast

Self-Explanatory Message

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• What is the cause and solution for SAP error message /SAPAPO/FCST155 - MAPE for MLR forecasting using POS bigger than MAPE for stat. forecast ?

  The SAP error message /SAPAPO/FCST155 with the description "MAPE for MLR forecasting using POS bigger than MAPE for stat. forecast" typically indicates that the Mean Absolute Percentage Error (MAPE) for the Multiple Linear Regression (MLR) forecasting method using Point of Sale (POS) data is greater than the MAPE for the statistical forecast. This situation can arise due to several reasons, and addressing it involves understanding the underlying data and the forecasting methods used.
  Causes:
  
  Data Quality Issues: The POS data may contain outliers, missing values, or inaccuracies that can skew the MLR results.
  Model Specification: The MLR model may not be correctly specified, meaning that the independent variables used in the model do not adequately explain the variability in the dependent variable.
  Insufficient Data: There may not be enough historical data to support a reliable MLR forecast, leading to poor performance compared to simpler statistical methods.
  Seasonality and Trends: If the data exhibits strong seasonal patterns or trends that are not captured by the MLR model, it can lead to higher MAPE values.
  Overfitting: The MLR model may be overfitted to the training data, resulting in poor generalization to new data.
  
  Solutions:
  
  Data Cleaning: Review and clean the POS data to remove outliers and fill in missing values. Ensure that the data is accurate and representative.
  Model Review: Re-evaluate the independent variables used in the MLR model. Consider adding or removing variables based on their relevance and impact on the forecast.
  Increase Data Volume: If possible, gather more historical data to improve the robustness of the MLR model.
  Incorporate Seasonality: If seasonality is present, consider using seasonal decomposition techniques or adding seasonal indicators to the MLR model.
  Compare Models: Conduct a thorough comparison of the MLR model against other forecasting methods (e.g., exponential smoothing, ARIMA) to determine which model performs best for your data.
  Parameter Tuning: Adjust the parameters of the MLR model to improve its fit to the data. This may involve regularization techniques to prevent overfitting.
  
  Related Information:
  
  MAPE Calculation: MAPE is calculated as the average of the absolute percentage errors between the forecasted and actual values. A lower MAPE indicates a better forecasting model.
  Forecasting Techniques: Familiarize yourself with various forecasting techniques available in SAP APO, including statistical methods and machine learning approaches.
  SAP Documentation: Refer to SAP's official documentation and support resources for specific guidance on troubleshooting forecasting errors and best practices for model selection.
  
  By addressing the underlying causes and implementing the suggested solutions, you can improve the performance of your MLR forecasting model and reduce the MAPE to a more acceptable level.