What is classification mean value in SAP SCM-FRE - Forecasting and Replenishment?

SAP Term: classification mean value

• Component: SCM-FRE

• Component Name: Forecasting and Replenishment

• Description: The classification mean value classifies location products according to their average sales per week. The mean value is recalculated with every forecast run using adaptive smoothing procedures and is used to ascertain the selling class. The selling class, together with other characteristics, forms the basis for the automatic selection of the forecasting technique.

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• Key Concepts: 
  Classification mean value is a feature of the SAP SCM-FRE Forecasting and Replenishment component. It is used to calculate the average value of a certain item or product over a period of time. This average value is then used to forecast future demand for the item or product. 
  
  How to use it: 
  The classification mean value can be used to calculate the average value of an item or product over a period of time. This average value can then be used to forecast future demand for the item or product. To calculate the classification mean value, you must first select the item or product you want to calculate the average for. Then, you must enter the start and end dates for the period you want to calculate the average for. Finally, you must enter the number of periods you want to calculate the average for. 
  
  Tips & Tricks: 
  When calculating the classification mean value, it is important to select an appropriate start and end date for the period you are calculating the average for. This will ensure that your forecast is as accurate as possible. Additionally, it is important to select an appropriate number of periods when calculating the average. Too few periods may result in an inaccurate forecast, while too many periods may result in an overly conservative forecast. 
  
  Related Information: 
  The classification mean value can be used in conjunction with other forecasting methods such as exponential smoothing and moving averages. Additionally, it can be used in combination with other data sources such as customer surveys and market research to create more accurate forecasts.