What is mean square error in SAP SRD-SCM-DP - SCM-Demand Planning?

SAP Term: mean square error

• Component: SRD-SCM-DP

• Component Name: SCM-Demand Planning

• Description: The difference between forecasted demand and actual demand, squared and then divided by the number of periods covered. The lower the mean square error the more accurate the forecast.

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• Key Concepts: 
  Mean Square Error (MSE) is a statistical measure of the difference between the actual values and the predicted values in a data set. It is used to measure the accuracy of a model in predicting future outcomes. MSE is calculated by taking the sum of the squared differences between the actual and predicted values, and then dividing by the number of observations. 
  
  How to use it: 
  In SAP Demand Planning, MSE is used to measure the accuracy of a forecast model. The lower the MSE, the more accurate the model is in predicting future outcomes. The MSE can be used to compare different models and determine which one is most accurate. 
  
  Tips & Tricks: 
  When using MSE to compare different models, it is important to consider other factors such as bias and variance. Bias measures how far off the predictions are from the actual values, while variance measures how much variation there is in the predictions. Both of these factors can affect the accuracy of a model and should be taken into account when comparing different models. 
  
  Related Information: 
  MSE is closely related to other statistical measures such as Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). These measures are also used to measure the accuracy of a model in predicting future outcomes.